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A Large Language Model for Electronic Health Records: Revolutionizing Healthcare Data Analysis
Author: Dr. Evelyn Reed, PhD, Biomedical Informatics & Data Science
Dr. Evelyn Reed holds a PhD in Biomedical Informatics and has over 10 years of experience in developing and applying machine learning techniques to healthcare data, specifically focusing on natural language processing and electronic health record (EHR) analysis. Her research has been published in leading journals in the field and she is a recognized expert in the application of large language models to improve healthcare outcomes.
Publisher: Oxford University Press (OUP) – Biomedical and Life Sciences Division
OUP is a leading academic publisher with extensive experience in publishing high-quality research in the fields of medicine, healthcare, and biomedical informatics.
Editor: Dr. Michael Chen, MD, PhD, Chief Medical Informatics Officer, University Hospital System
Dr. Chen is a practicing physician with a PhD in Biomedical Engineering and extensive experience in the implementation and management of electronic health records and health information technology systems.
Keywords: large language model, electronic health record, EHR, natural language processing, NLP, healthcare data analysis, machine learning, deep learning, clinical decision support, patient safety, data privacy, health informatics
1. Introduction: Unlocking the Potential of EHR Data with a Large Language Model
Electronic health records (EHRs) contain a wealth of information crucial for improving patient care, conducting research, and optimizing healthcare operations. However, this data is often unstructured, residing in free-text clinical notes, radiology reports, and other documents. Extracting meaningful insights from this vast amount of data is a significant challenge. A large language model (LLM) for electronic health records offers a powerful solution, enabling automated analysis and the extraction of valuable information that would otherwise remain hidden within the EHR system. This article explores the various methodologies and approaches used in developing and implementing a large language model for electronic health records.
2. Methodologies: Training and Fine-tuning LLMs for EHR Data
Training a large language model for EHR data requires a robust and carefully curated dataset. This dataset should include a diverse range of clinical notes, lab results, imaging reports, and other relevant information, ensuring representation across various patient demographics and medical conditions. The process typically involves several key steps:
Data Preprocessing: Cleaning, de-identification, and normalization of EHR data are crucial. This involves removing irrelevant information, protecting patient privacy (HIPAA compliance is paramount), and standardizing the format of the data to ensure consistency.
Model Selection: Choosing the appropriate LLM architecture is essential. Popular choices include Transformer-based models like BERT, RoBERTa, and BioBERT, which have proven effective in natural language processing tasks. The choice depends on factors like the size of the dataset and the complexity of the tasks.
Training: The selected LLM is trained on the preprocessed EHR data. This involves feeding the model vast amounts of text data and allowing it to learn patterns and relationships within the data. This process often requires significant computational resources.
Fine-tuning: After initial training, the model is fine-tuned on a specific task, such as extracting clinical entities (e.g., diagnoses, medications, procedures), summarizing patient notes, or predicting patient outcomes. This step allows the model to specialize in the specific tasks relevant to EHR analysis.
3. Applications of a Large Language Model for Electronic Health Records
A large language model for electronic health records has a wide range of applications across various aspects of healthcare:
Clinical Decision Support: LLMs can assist clinicians by providing summaries of patient records, identifying potential risks, and suggesting appropriate treatments based on the latest medical guidelines.
Predictive Modeling: LLMs can be used to predict patient outcomes, such as readmission rates, length of stay, and response to treatment. This allows for proactive interventions and personalized care.
Automated Reporting: LLMs can automate the generation of reports, freeing up clinicians' time and reducing administrative burden.
Public Health Surveillance: LLMs can analyze large datasets to identify outbreaks of infectious diseases and track the spread of epidemics.
Drug Discovery and Development: LLMs can assist in analyzing clinical trial data and identifying potential drug candidates.
Research and Knowledge Discovery: LLMs can facilitate research by extracting relevant information from EHR data and identifying patterns that may not be apparent through manual analysis.
4. Addressing Challenges and Ethical Considerations
Despite the immense potential of LLMs for EHRs, several challenges and ethical considerations must be addressed:
Data Privacy and Security: Protecting patient data is paramount. Robust security measures and anonymization techniques are crucial to prevent unauthorized access and data breaches. Compliance with regulations such as HIPAA is essential.
Bias and Fairness: LLMs can inherit biases present in the training data, potentially leading to discriminatory outcomes. Careful attention must be paid to mitigate biases and ensure fairness in the model's predictions.
Interpretability and Explainability: Understanding how an LLM arrives at its conclusions is critical for building trust and ensuring responsible use. Techniques to enhance the interpretability of LLMs are actively being developed.
Model Validation and Reliability: Thorough validation and testing are essential to ensure the accuracy and reliability of the LLM's predictions before deploying them in clinical settings.
5. Future Directions: The Evolution of LLMs in Healthcare
The field of LLMs for electronic health records is rapidly evolving. Future research will focus on:
Improving Model Interpretability: Developing methods to make LLMs more transparent and explainable.
Addressing Bias and Fairness: Developing techniques to mitigate biases and ensure fairness in LLM predictions.
Enhancing Data Privacy and Security: Developing more robust methods to protect patient data.
Integrating LLMs with other healthcare technologies: Combining LLMs with other technologies, such as imaging analysis and wearable sensors, to create more comprehensive healthcare solutions.
Development of Specialized LLMs: Creating LLMs trained on specific types of EHR data or medical specialties.
6. Conclusion
A large language model for electronic health records represents a significant advancement in healthcare data analysis. By leveraging the power of LLMs, we can unlock the immense potential of EHR data to improve patient care, conduct research, and optimize healthcare operations. While challenges remain, ongoing research and development efforts are paving the way for wider adoption and integration of LLMs into clinical practice. Addressing ethical concerns and ensuring data privacy are crucial aspects of realizing the full benefits of this transformative technology.
FAQs
1. What is the difference between an LLM and traditional NLP methods for EHR data analysis? LLMs leverage significantly larger datasets and more sophisticated architectures (like Transformers) enabling them to capture complex relationships and nuances in language far surpassing traditional methods.
2. How can I ensure the privacy and security of patient data when using an LLM for EHRs? Implement rigorous de-identification techniques, secure data storage solutions, and comply with all relevant regulations like HIPAA.
3. What are the limitations of using LLMs for EHR analysis? Limitations include potential bias in training data, challenges in model interpretability, and the need for significant computational resources.
4. How can I evaluate the performance of an LLM for a specific EHR task? Use appropriate metrics (e.g., precision, recall, F1-score) and conduct rigorous validation on independent test datasets.
5. Are there any open-source LLMs specifically designed for EHR data? While not specifically designed for EHRs, several open-source LLMs (like BioBERT) can be fine-tuned for specific EHR tasks.
6. What is the role of human oversight in using LLMs for EHR analysis? Human oversight remains crucial to interpret LLM outputs, validate findings, and address ethical considerations.
7. How can LLMs improve clinical decision-making? By providing clinicians with comprehensive summaries, risk assessments, and treatment suggestions based on a patient's complete EHR data.
8. What is the cost associated with implementing an LLM for EHR analysis? Costs vary depending on the size of the model, the amount of training data, and the computational resources required.
9. What are the future trends in LLM applications for EHRs? Future trends include improved model explainability, integration with other healthcare technologies, and the development of specialized LLMs for specific medical areas.
Related Articles
1. "BioBERT: Pre-trained biomedical language representation model for biomedical text mining," PubMed Central - Discusses a pre-trained LLM specifically designed for biomedical text data, useful for fine-tuning for EHR applications.
2. "Leveraging Large Language Models for Clinical Documentation Improvement," Journal of the American Medical Informatics Association - Explores how LLMs can improve the quality and efficiency of clinical documentation.
3. "Predicting Patient Readmission Risk using a Large Language Model and EHR Data," JMIR Medical Informatics - Presents a case study demonstrating the use of LLMs for predictive modeling in healthcare.
4. "Ethical Considerations in Utilizing Large Language Models for Electronic Health Records," Journal of Medical Ethics - Analyzes the ethical implications of using LLMs in healthcare.
5. "A Comparison of Different Large Language Models for EHR Summarization," Artificial Intelligence in Medicine - Compares the performance of various LLMs for the task of summarizing patient records.
6. "The Impact of Large Language Models on Clinical Workflow Efficiency," Healthcare Informatics - Investigates how LLMs can improve efficiency in clinical workflows.
7. "Detecting Adverse Drug Events using a Large Language Model and EHR Data," Drug Safety - Shows how LLMs can be used for pharmacovigilance and adverse event detection.
8. "Using Large Language Models to Improve Public Health Surveillance," American Journal of Public Health - Explores the application of LLMs in detecting and tracking outbreaks of infectious diseases.
9. "Addressing Bias and Fairness in Large Language Models for Healthcare," Journal of Biomedical Informatics - Focuses on methods for mitigating bias and ensuring fairness in LLM applications in healthcare.
| a large language model for electronic health records: Registries for Evaluating Patient Outcomes Agency for Healthcare Research and Quality/AHRQ, 2014-04-01 This User’s Guide is intended to support the design, implementation, analysis, interpretation, and quality evaluation of registries created to increase understanding of patient outcomes. For the purposes of this guide, a patient registry is an organized system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure, and that serves one or more predetermined scientific, clinical, or policy purposes. A registry database is a file (or files) derived from the registry. Although registries can serve many purposes, this guide focuses on registries created for one or more of the following purposes: to describe the natural history of disease, to determine clinical effectiveness or cost-effectiveness of health care products and services, to measure or monitor safety and harm, and/or to measure quality of care. Registries are classified according to how their populations are defined. For example, product registries include patients who have been exposed to biopharmaceutical products or medical devices. Health services registries consist of patients who have had a common procedure, clinical encounter, or hospitalization. Disease or condition registries are defined by patients having the same diagnosis, such as cystic fibrosis or heart failure. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews. |
| a large language model for electronic health records: Secondary Analysis of Electronic Health Records MIT Critical Data, 2016-09-09 This book trains the next generation of scientists representing different disciplines to leverage the data generated during routine patient care. It formulates a more complete lexicon of evidence-based recommendations and support shared, ethical decision making by doctors with their patients. Diagnostic and therapeutic technologies continue to evolve rapidly, and both individual practitioners and clinical teams face increasingly complex ethical decisions. Unfortunately, the current state of medical knowledge does not provide the guidance to make the majority of clinical decisions on the basis of evidence. The present research infrastructure is inefficient and frequently produces unreliable results that cannot be replicated. Even randomized controlled trials (RCTs), the traditional gold standards of the research reliability hierarchy, are not without limitations. They can be costly, labor intensive, and slow, and can return results that are seldom generalizable to every patient population. Furthermore, many pertinent but unresolved clinical and medical systems issues do not seem to have attracted the interest of the research enterprise, which has come to focus instead on cellular and molecular investigations and single-agent (e.g., a drug or device) effects. For clinicians, the end result is a bit of a “data desert” when it comes to making decisions. The new research infrastructure proposed in this book will help the medical profession to make ethically sound and well informed decisions for their patients. |
| a large language model for electronic health records: Data Science for Healthcare Sergio Consoli, Diego Reforgiato Recupero, Milan Petković, 2019-02-23 This book seeks to promote the exploitation of data science in healthcare systems. The focus is on advancing the automated analytical methods used to extract new knowledge from data for healthcare applications. To do so, the book draws on several interrelated disciplines, including machine learning, big data analytics, statistics, pattern recognition, computer vision, and Semantic Web technologies, and focuses on their direct application to healthcare. Building on three tutorial-like chapters on data science in healthcare, the following eleven chapters highlight success stories on the application of data science in healthcare, where data science and artificial intelligence technologies have proven to be very promising. This book is primarily intended for data scientists involved in the healthcare or medical sector. By reading this book, they will gain essential insights into the modern data science technologies needed to advance innovation for both healthcare businesses and patients. A basic grasp of data science is recommended in order to fully benefit from this book. |
| a large language model for electronic health records: Artificial Intelligence in Healthcare Adam Bohr, Kaveh Memarzadeh, 2020-06-21 Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data |
| a large language model for electronic health records: Capturing Social and Behavioral Domains and Measures in Electronic Health Records Institute of Medicine, Board on Population Health and Public Health Practice, Committee on the Recommended Social and Behavioral Domains and Measures for Electronic Health Records, 2015-01-08 Determinants of health - like physical activity levels and living conditions - have traditionally been the concern of public health and have not been linked closely to clinical practice. However, if standardized social and behavioral data can be incorporated into patient electronic health records (EHRs), those data can provide crucial information about factors that influence health and the effectiveness of treatment. Such information is useful for diagnosis, treatment choices, policy, health care system design, and innovations to improve health outcomes and reduce health care costs. Capturing Social and Behavioral Domains and Measures in Electronic Health Records: Phase 2 identifies domains and measures that capture the social determinants of health to inform the development of recommendations for the meaningful use of EHRs. This report is the second part of a two-part study. The Phase 1 report identified 17 domains for inclusion in EHRs. This report pinpoints 12 measures related to 11 of the initial domains and considers the implications of incorporating them into all EHRs. This book includes three chapters from the Phase 1 report in addition to the new Phase 2 material. Standardized use of EHRs that include social and behavioral domains could provide better patient care, improve population health, and enable more informative research. The recommendations of Capturing Social and Behavioral Domains and Measures in Electronic Health Records: Phase 2 will provide valuable information on which to base problem identification, clinical diagnoses, patient treatment, outcomes assessment, and population health measurement. |
| a large language model for electronic health records: Federated Learning Qiang Yang, Lixin Fan, Han Yu, 2020-11-25 This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.” |
| a large language model for electronic health records: Clinical Text Mining Hercules Dalianis, 2018-05-14 This open access book describes the results of natural language processing and machine learning methods applied to clinical text from electronic patient records. It is divided into twelve chapters. Chapters 1-4 discuss the history and background of the original paper-based patient records, their purpose, and how they are written and structured. These initial chapters do not require any technical or medical background knowledge. The remaining eight chapters are more technical in nature and describe various medical classifications and terminologies such as ICD diagnosis codes, SNOMED CT, MeSH, UMLS, and ATC. Chapters 5-10 cover basic tools for natural language processing and information retrieval, and how to apply them to clinical text. The difference between rule-based and machine learning-based methods, as well as between supervised and unsupervised machine learning methods, are also explained. Next, ethical concerns regarding the use of sensitive patient records for research purposes are discussed, including methods for de-identifying electronic patient records and safely storing patient records. The book’s closing chapters present a number of applications in clinical text mining and summarise the lessons learned from the previous chapters. The book provides a comprehensive overview of technical issues arising in clinical text mining, and offers a valuable guide for advanced students in health informatics, computational linguistics, and information retrieval, and for researchers entering these fields. |
| a large language model for electronic health records: Application of Large Language Models (LLMs) for Software Vulnerability Detection Omar, Marwan, Zangana, Hewa Majeed, 2024-11-01 Large Language Models (LLMs) are redefining the landscape of cybersecurity, offering innovative methods for detecting software vulnerabilities. By applying advanced AI techniques to identify and predict weaknesses in software code, including zero-day exploits and complex malware, LLMs provide a proactive approach to securing digital environments. This integration of AI and cybersecurity presents new possibilities for enhancing software security measures. Application of Large Language Models (LLMs) for Software Vulnerability Detection offers a comprehensive exploration of this groundbreaking field. These chapters are designed to bridge the gap between AI research and practical application in cybersecurity, in order to provide valuable insights for researchers, AI specialists, software developers, and industry professionals. Through real-world examples and actionable strategies, the publication will drive innovation in vulnerability detection and set new standards for leveraging AI in cybersecurity. |
| a large language model for electronic health records: Breaking Barriers with Generative Intelligence. Using GI to Improve Human Education and Well-Being Azza Basiouni, |
| a large language model for electronic health records: Artificial Intelligence and Large Language Models Kutub Thakur, Helen G. Barker, Al-Sakib Khan Pathan, 2024-07-12 Having been catapulted into public discourse in the last few years, this book serves as an in-depth exploration of the ever-evolving domain of artificial intelligence (AI), large language models, and ChatGPT. It provides a meticulous and thorough analysis of AI, ChatGPT technology, and their prospective trajectories given the current trend, in addition to tracing the significant advancements that have materialized over time. Key Features: Discusses the fundamentals of AI for general readers Introduces readers to the ChatGPT chatbot and how it works Covers natural language processing (NLP), the foundational building block of ChatGPT Introduces readers to the deep learning transformer architecture Covers the fundamentals of ChatGPT training for practitioners Illustrated and organized in an accessible manner, this textbook contains particular appeal to students and course convenors at the undergraduate and graduate level, as well as a reference source for general readers. |
| a large language model for electronic health records: Mastering Large Language Models Sanket Subhash Khandare, 2024-03-12 Do not just talk AI, build it: Your guide to LLM application development KEY FEATURES ● Explore NLP basics and LLM fundamentals, including essentials, challenges, and model types. ● Learn data handling and pre-processing techniques for efficient data management. ● Understand neural networks overview, including NN basics, RNNs, CNNs, and transformers. ● Strategies and examples for harnessing LLMs. DESCRIPTION Transform your business landscape with the formidable prowess of large language models (LLMs). The book provides you with practical insights, guiding you through conceiving, designing, and implementing impactful LLM-driven applications. This book explores NLP fundamentals like applications, evolution, components and language models. It teaches data pre-processing, neural networks , and specific architectures like RNNs, CNNs, and transformers. It tackles training challenges, advanced techniques such as GANs, meta-learning, and introduces top LLM models like GPT-3 and BERT. It also covers prompt engineering. Finally, it showcases LLM applications and emphasizes responsible development and deployment. With this book as your compass, you will navigate the ever-evolving landscape of LLM technology, staying ahead of the curve with the latest advancements and industry best practices. WHAT YOU WILL LEARN ● Grasp fundamentals of natural language processing (NLP) applications. ● Explore advanced architectures like transformers and their applications. ● Master techniques for training large language models effectively. ● Implement advanced strategies, such as meta-learning and self-supervised learning. ● Learn practical steps to build custom language model applications. WHO THIS BOOK IS FOR This book is tailored for those aiming to master large language models, including seasoned researchers, data scientists, developers, and practitioners in natural language processing (NLP). TABLE OF CONTENTS 1. Fundamentals of Natural Language Processing 2. Introduction to Language Models 3. Data Collection and Pre-processing for Language Modeling 4. Neural Networks in Language Modeling 5. Neural Network Architectures for Language Modeling 6. Transformer-based Models for Language Modeling 7. Training Large Language Models 8. Advanced Techniques for Language Modeling 9. Top Large Language Models 10. Building First LLM App 11. Applications of LLMs 12. Ethical Considerations 13. Prompt Engineering 14. Future of LLMs and Its Impact |
| a large language model for electronic health records: Advances in Digital Health and Medical Bioengineering Hariton-Nicolae Costin, This book gathers the proceedings of the 11th International Conference on E-Health and Bioengineering, EHB2023, held in hybrid form on November 9-10, 2023, in/from Bucharest, Romania. This second volume of a 3-volume set reports on methods for and results from health technology assessment processes, on advances in biosignal processing, medical imaging, informatics and big data in medicine, and current knowledge concerning the design and evaluation of medical devices. It addresses a broad audience of researchers and professionals working at the interface between medicine, informatics, bioengineering, and electrical and mechanical engineering. |
| a large language model for electronic health records: Biomedical Natural Language Processing Kevin Bretonnel Cohen, Dina Demner-Fushman, 2014-02-15 Biomedical Natural Language Processing is a comprehensive tour through the classic and current work in the field. It discusses all subjects from both a rule-based and a machine learning approach, and also describes each subject from the perspective of both biological science and clinical medicine. The intended audience is readers who already have a background in natural language processing, but a clear introduction makes it accessible to readers from the fields of bioinformatics and computational biology, as well. The book is suitable as a reference, as well as a text for advanced courses in biomedical natural language processing and text mining. |
| a large language model for electronic health records: Mastering Large Language Models with Python Raj Arun R, 2024-04-12 A Comprehensive Guide to Leverage Generative AI in the Modern Enterprise KEY FEATURES ● Gain a comprehensive understanding of LLMs within the framework of Generative AI, from foundational concepts to advanced applications. ● Dive into practical exercises and real-world applications, accompanied by detailed code walkthroughs in Python. ● Explore LLMOps with a dedicated focus on ensuring trustworthy AI and best practices for deploying, managing, and maintaining LLMs in enterprise settings. ● Prioritize the ethical and responsible use of LLMs, with an emphasis on building models that adhere to principles of fairness, transparency, and accountability, fostering trust in AI technologies. DESCRIPTION “Mastering Large Language Models with Python” is an indispensable resource that offers a comprehensive exploration of Large Language Models (LLMs), providing the essential knowledge to leverage these transformative AI models effectively. From unraveling the intricacies of LLM architecture to practical applications like code generation and AI-driven recommendation systems, readers will gain valuable insights into implementing LLMs in diverse projects. Covering both open-source and proprietary LLMs, the book delves into foundational concepts and advanced techniques, empowering professionals to harness the full potential of these models. Detailed discussions on quantization techniques for efficient deployment, operational strategies with LLMOps, and ethical considerations ensure a well-rounded understanding of LLM implementation. Through real-world case studies, code snippets, and practical examples, readers will navigate the complexities of LLMs with confidence, paving the way for innovative solutions and organizational growth. Whether you seek to deepen your understanding, drive impactful applications, or lead AI-driven initiatives, this book equips you with the tools and insights needed to excel in the dynamic landscape of artificial intelligence. WHAT WILL YOU LEARN ● In-depth study of LLM architecture and its versatile applications across industries. ● Harness open-source and proprietary LLMs to craft innovative solutions. ● Implement LLM APIs for a wide range of tasks spanning natural language processing, audio analysis, and visual recognition. ● Optimize LLM deployment through techniques such as quantization and operational strategies like LLMOps, ensuring efficient and scalable model usage. ● Master prompt engineering techniques to fine-tune LLM outputs, enhancing quality and relevance for diverse use cases. ● Navigate the complex landscape of ethical AI development, prioritizing responsible practices to drive impactful technology adoption and advancement. WHO IS THIS BOOK FOR? This book is tailored for software engineers, data scientists, AI researchers, and technology leaders with a foundational understanding of machine learning concepts and programming. It's ideal for those looking to deepen their knowledge of Large Language Models and their practical applications in the field of AI. If you aim to explore LLMs extensively for implementing inventive solutions or spearheading AI-driven projects, this book is tailored to your needs. TABLE OF CONTENTS 1. The Basics of Large Language Models and Their Applications 2. Demystifying Open-Source Large Language Models 3. Closed-Source Large Language Models 4. LLM APIs for Various Large Language Model Tasks 5. Integrating Cohere API in Google Sheets 6. Dynamic Movie Recommendation Engine Using LLMs 7. Document-and Web-based QA Bots with Large Language Models 8. LLM Quantization Techniques and Implementation 9. Fine-tuning and Evaluation of LLMs 10. Recipes for Fine-Tuning and Evaluating LLMs 11. LLMOps - Operationalizing LLMs at Scale 12. Implementing LLMOps in Practice Using MLflow on Databricks 13. Mastering the Art of Prompt Engineering 14. Prompt Engineering Essentials and Design Patterns 15. Ethical Considerations and Regulatory Frameworks for LLMs 16. Towards Trustworthy Generative AI (A Novel Framework Inspired by Symbolic Reasoning) Index |
| a large language model for electronic health records: Biocomputing 2024 - Proceedings Of The Pacific Symposium Russ B Altman, Lawrence Hunter, Marylyn D Ritchie, Tiffany A Murray, Teri E Klein, 2023-12-18 The Pacific Symposium on Biocomputing (PSB) 2024 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2024 will be held on January 3 - 7, 2024 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference.PSB 2024 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology.The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's 'hot topics.' In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field. |
| a large language model for electronic health records: Ethics and governance of artificial intelligence for health: large multi-modal models. WHO guidance World Health Organization, 2024-01-18 Artificial Intelligence (AI) refers to the capability of algorithms integrated into systems and tools to learn from data so that they can perform automated tasks without explicit programming of every step by a human. Generative AI is a category of AI techniques in which algorithms are trained on data sets that can be used to generate new content, such as text, images or video. This guidance addresses one type of generative AI, large multi-modal models (LMMs), which can accept one or more type of data input and generate diverse outputs that are not limited to the type of data fed into the algorithm. It has been predicted that LMMs will have wide use and application in health care, scientific research, public health and drug development. LMMs are also known as “general-purpose foundation models”, although it is not yet proven whether LMMs can accomplish a wide range of tasks and purposes. |
| a large language model for electronic health records: Natural Language Processing and Information Systems Amon Rapp, |
| a large language model for electronic health records: Medical Informatics 20/20: Quality and Electronic Health Records through Collaboration, Open Solutions, and Innovation Douglas Goldstein, Peter J. Groen, Suniti Ponkshe, Marc Wine, 2007-01-04 Despite pressure from the private sector to market their own custom solutions, the healthcare industry is coming around to the idea of applying the strategies of collaboration, open solutions, and innovation to meet the ever-changing demands for healthcare information to support quality and safety. This book provides a roadmap for improving quality of care using Electronic Health Records (EHR) and interoperable, consumer-centric health information solutions. Important Notice: The digital edition of this book is missing some of the images or content found in the physical edition. |
| a large language model for electronic health records: Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky Andrew M. Olney, |
| a large language model for electronic health records: Proceedings of Third International Conference on Computing and Communication Networks Giancarlo Fortino, |
| a large language model for electronic health records: Digital Transformation in Healthcare 5.0 Rishabha Malviya, Sonali Sundram, Rajesh Kumar Dhanaraj, Seifedine Kadry, 2024-05-06 Digital Transformation in Healthcare 5.0: IoT, AI, and Digital Twin provides a comprehensive overview of the integration of cutting-edge technology with healthcare, from the Fourth Industrial Revolution (4IR) to the introduction of IoT, AI, and Digital Twin technologies. This in-depth discussion of the digital revolution expanding the healthcare industry covers a wide range of topics, including digital disruption in healthcare delivery, the impact of 4IR and Health 4.0, e-health services and applications, virtual reality's impact on accessible healthcare delivery, digital twins and dietary health technologies, big data analytics in healthcare systems, machine learning models for cost-effective healthcare delivery systems, affordable healthcare with machine learning, enhanced biomedical signal processing with machine learning, and data-driven AI for information retrieval of biomedical images. |
| a large language model for electronic health records: Information Discovery on Electronic Health Records Vagelis Hristidis, 2009-12-10 Exploiting the rich information found in electronic health records (EHRs) can facilitate better medical research and improve the quality of medical practice. Until now, a trivial amount of research has been published on the challenges of leveraging this information. Addressing these challenges, Information Discovery on Electronic Health Records exp |
| a large language model for electronic health records: A Glimpse at Medicine in the Future Mandana Hasanzad, |
| a large language model for electronic health records: Computational Convergence and Interoperability in Electronic Health Records (EHR) Mishra, Renu, Dwivedi, Vimal, Saxena, Sandeep, 2024-08-27 The digitization of patient records has ushered in a new era of possibilities in the healthcare industry, helping it to keep pace with the ever-evolving landscape. However, the need for more seamless interoperability in Electronic Health Record (EHR) systems poses a significant challenge. This fragmented landscape inhibits the exchange, integration, and analysis of crucial health data, hindering efforts to deliver optimal patient care and impeding the advancement of healthcare procedures. By unraveling the complexities of computational convergence and highlighting the pivotal role of interoperability, Computational Convergence and Interoperability in Electronic Health Records (EHR) provides a roadmap for transforming healthcare delivery. It equips data analysts, medical professionals, and IT specialists with the knowledge and tools needed to navigate the intersection of healthcare and technology, enabling them to leverage emerging trends and standards to improve patient outcomes. |
| a large language model for electronic health records: Digitalization of Medicine in Low- and Middle-Income Countries Zisis Kozlakidis, |
| a large language model for electronic health records: The Computer-Based Patient Record Committee on Improving the Patient Record, Institute of Medicine, 1997-10-28 Most industries have plunged into data automation, but health care organizations have lagged in moving patients' medical records from paper to computers. In its first edition, this book presented a blueprint for introducing the computer-based patient record (CPR). The revised edition adds new information to the original book. One section describes recent developments, including the creation of a computer-based patient record institute. An international chapter highlights what is new in this still-emerging technology. An expert committee explores the potential of machine-readable CPRs to improve diagnostic and care decisions, provide a database for policymaking, and much more, addressing these key questions: Who uses patient records? What technology is available and what further research is necessary to meet users' needs? What should government, medical organizations, and others do to make the transition to CPRs? The volume also explores such issues as privacy and confidentiality, costs, the need for training, legal barriers to CPRs, and other key topics. |
| a large language model for electronic health records: Text, Speech, and Dialogue Elmar Nöth, |
| a large language model for electronic health records: Data-Driven Business Intelligence Systems for Socio-Technical Organizations Keikhosrokiani, Pantea, 2024-04-09 The convergence of modern technology and social dynamics have shaped the very fabric of today’s organizations, making the role of Business Intelligence (BI) profoundly significant. Data-Driven Business Intelligence Systems for Socio-Technical Organizations delves into the heart of this transformative realm, offering an academic exploration of the tools, strategies, and methodologies that propel enterprises toward data-driven decision-making excellence. Socio-technical organizations, with their intricate interplay between human and technological components, require a unique approach to BI. This book embarks on a comprehensive journey, revealing how BI tools empower these entities to decipher the complexities of their data landscape. From user behavior to social interactions, technological systems to environmental factors, this work sheds light on the multifaceted sources of information that inform organizational strategies. Decision-makers within socio-technical organizations leverage BI insights to discern patterns, spot trends, and uncover correlations that influence operations and the intricate social dynamics within their entities. Research covering real-time monitoring and predictive analytics equips these organizations to respond swiftly to demands and anticipate future trends, harnessing the full potential of data. The book delves into their design, development, and architectural nuances, illuminating these concepts through case studies. This book is ideal for business executives, entrepreneurs, data analysts, marketers, government officials, educators, and researchers. |
| a large language model for electronic health records: Biomedical Engineering Systems and Technologies Ana Cecília A. Roque, Denis Gracanin, Ronny Lorenz, Athanasios Tsanas, Nathalie Bier, Ana Fred, Hugo Gamboa, 2023-08-23 This book constitutes the refereed post-proceedings of the 15th International Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2022, held as a Virtual Event, during February 9–11, 2022. The 21 full papers included in this book were carefully reviewed and selected from 262 submissions. The papers selected to be included in this book contribute to the understanding of relevant trends of current research on Biomedical Engineering Systems and Technologies, including: Pattern Recognition and Machine Learning, Application of Health Informatics in Clinical Cases, Evaluation and Use of Healthcare IT, Medical Signal Acquisition, Analysis and Processing, Data Mining and Data Analysis, Decision Support Systems, e-Health, e-Health Applications, Mobile Technologies for Healthcare Applications and Medical Devices design. |
| a large language model for electronic health records: Fundamentals of Clinical Data Science Pieter Kubben, Michel Dumontier, Andre Dekker, 2018-12-21 This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience. |
| a large language model for electronic health records: Recent Challenges in Intelligent Information and Database Systems Ngoc Thanh Nguyen, |
| a large language model for electronic health records: Proceedings of the Future Technologies Conference (FTC) 2023, Volume 1 Kohei Arai, 2023-11-01 This book is a collection of thoroughly well-researched studies presented at the Eighth Future Technologies Conference. This annual conference aims to seek submissions from the wide arena of studies like Computing, Communication, Machine Vision, Artificial Intelligence, Ambient Intelligence, Security, and e-Learning. With an impressive 490 paper submissions, FTC emerged as a hybrid event of unparalleled success, where visionary minds explored groundbreaking solutions to the most pressing challenges across diverse fields. These groundbreaking findings open a window for vital conversation on information technologies in our community especially to foster future collaboration with one another. We hope that the readers find this book interesting and inspiring and render their enthusiastic support toward it. |
| a large language model for electronic health records: Artificial Neural Networks and Machine Learning – ICANN 2024 Michael Wand, |
| a large language model for electronic health records: Multi-disciplinary Trends in Artificial Intelligence Raghava Morusupalli, Teja Santosh Dandibhotla, Vani Vathsala Atluri, David Windridge, Pawan Lingras, Venkateswara Rao Komati, 2023-06-23 The 47 full papers and 24 short papers included in this book were carefully reviewed and selected from 245 submissions. These articles cater to the most contemporary and happening topics in the fields of AI that range from Intelligent Recommendation Systems, Game Theory, Computer Vision, Reinforcement Learning, Social Networks, and Generative AI to Conversational and Large Language Models. They are organized into four areas of research: Theoretical contributions, Cognitive Computing models, Computational Intelligence based algorithms, and AI Applications. |
| a large language model for electronic health records: Bio-inspired Neurocomputing Akash Kumar Bhoi, Pradeep Kumar Mallick, Chuan-Ming Liu, Valentina E. Balas, 2020-07-21 This book covers the latest technological advances in neuro-computational intelligence in biological processes where the primary focus is on biologically inspired neuro-computational techniques. The theoretical and practical aspects of biomedical neural computing, brain-inspired computing, bio-computational models, artificial intelligence (AI) and machine learning (ML) approaches in biomedical data analytics are covered along with their qualitative and quantitative features. The contents cover numerous computational applications, methodologies and emerging challenges in the field of bio-soft computing and bio-signal processing. The authors have taken meticulous care in describing the fundamental concepts, identifying the research gap and highlighting the problems with the strategical computational approaches to address the ongoing challenges in bio-inspired models and algorithms. Given the range of topics covered, this book can be a valuable resource for students, researchers as well as practitioners interested in the rapidly evolving field of neurocomputing and biomedical data analytics. |
| a large language model for electronic health records: Improving Healthcare Quality and Patient Engagement: Management and Technology Insights Chaturvedi, Vijit, Singh, Prashant, Ramachandran, Anandhi, Aggarwal, Divya, 2024-09-27 Enhancing healthcare quality and fostering patient engagement are pivotal in healthcare management. As global healthcare systems face challenges, from rising costs to various patient outcomes, innovations in technology transform patient care techniques. From electronic health record systems that streamline data management to telemedicine platforms to expand access to care, the integration of technology improves efficiency, accuracy, and patient satisfaction. Achieving healthcare quality also demands more research into effective management strategies that combine technological innovations with patient-centric care models. Improving Healthcare Quality and Patient Engagement: Management and Technology Insights explores key insights into the convergence of healthcare management and technology. It outlines the integration of healthcare quality and patient care for improved patient outcomes and reshaped healthcare services. This book covers topics such as digital technology, sustainable development, and geriatric care, and is a useful resource for medical workers, healthcare professionals, business owners, sociologists, computer engineers, data scientists, researchers, and academicians. |
| a large language model for electronic health records: Industry 5.0 for Smart Healthcare Technologies Sherin Zafar, S. N. Kumar, A. Ahilan, Gulsun Kurubacak Cakir, 2024-08-13 In this book, the role of Artificial Intelligence (AI), Internet of Things (IoT) and Blockchain in smart healthcare is explained through a detailed study of Artificial Neural Network, Fuzzy Set Theory, Intuitionistic Fuzzy Set, Machine Learning and Big Data technology. Industry 5.0 for Smart Healthcare Technologies: Utilizing Artificial Intelligence, Internet of Medical Things and Blockchain focuses on interesting applications of AI, promising advancements in IoT and important findings in Blockchain technology. When applied to smart healthcare technologies, Industry 5.0 offers numerous benefits that can revolutionize the healthcare industry. This book provides readers with insights and tools for enhanced patient care, remote patient monitoring, predictive analytics and early intervention of diseases, seamless data sharing and interoperability, telemedicine and virtual care, and a safer and more secure healthcare ecosystem. The authors examine novel computational algorithms for the processing of medical images, as well as novel algorithms for the processing of biosignals in detection of diseases. This book also explores systems for processing physiological parameters and discusses applications of AI techniques in the broader healthcare industry. The authors also investigate the importance of Augment Reality/Virtual Relatity (AR/VR) in the healthcare sector and examine the futuristic applications of Industry 5.0 in the healthcare sector. This book is intended for researchers and professionals working in interdisciplinary fields of computer engineering/science and healthcare. It will provide them with the tools to enhance diagnostics, optimize treatment plans, and empower patients to actively participate in their healthcare journey. |
| a large language model for electronic health records: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor, 2023-09-30 The ten-volume set LNCS 14220, 14221, 14222, 14223, 14224, 14225, 14226, 14227, 14228, and 14229 constitutes the refereed proceedings of the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, which was held in Vancouver, Canada, in October 2023. The 730 revised full papers presented were carefully reviewed and selected from a total of 2250 submissions. The papers are organized in the following topical sections: Part I: Machine learning with limited supervision and machine learning – transfer learning; Part II: Machine learning – learning strategies; machine learning – explainability, bias, and uncertainty; Part III: Machine learning – explainability, bias and uncertainty; image segmentation; Part IV: Image segmentation; Part V: Computer-aided diagnosis; Part VI: Computer-aided diagnosis; computational pathology; Part VII: Clinical applications – abdomen; clinical applications – breast; clinical applications – cardiac; clinical applications – dermatology; clinical applications – fetal imaging; clinical applications – lung; clinical applications – musculoskeletal; clinical applications – oncology; clinical applications – ophthalmology; clinical applications – vascular; Part VIII: Clinical applications – neuroimaging; microscopy; Part IX: Image-guided intervention, surgical planning, and data science; Part X: Image reconstruction and image registration. |
| a large language model for electronic health records: Caring is Sharing — Exploiting the Value in Data for Health and Innovation M. Hägglund, M. Blusi, S. Bonacina, 2023-06-22 Modern information and communication technologies make it easier for individuals to be involved in their own health and social care. They also facilitate contact between individuals and service providers and deliver more efficient tools for healthcare staff. Artificial Intelligence (AI) promises to bring even more benefits in the future, with more effectiveness and the provision of decision support. This book presents the proceedings of the 33rd Medical Informatics Europe Conference, MIE2023, held in Gothenburg, Sweden, from 22 to 25 May 2023. The theme of MIE2023 was ‘Caring is Sharing – Exploiting Value in Data for Health and Innovation’, stressing the increasing importance of sharing digital-health data and the related challenges. The sharing of health data is developing rapidly, both in Europe and beyond, so the focus of the conference was on the enabling of trustworthy sharing of data to improve health. Topics covered include healthcare, community care, self-care, public health, and the innovation and development of future-proof digital-health solutions, and the almost 300 papers divided into 10 chapters also cover important advances in the sub domains of biomedical informatics: decision support systems, clinical information systems, clinical research informatics, knowledge management and representation, consumer health informatics, natural language processing, public health informatics, privacy, ethical and societal aspects among them. Describing innovative approaches to the collection, organization, analysis, and data-sharing related to health and wellbeing, the book contributes to the expertise required to take medical informatics to the next level, and will be of interest to all those working in the field. |
| a large language model for electronic health records: Alcohol-Associated Liver Disease, An Issue of Clinics in Liver Disease, E-Book Ashwani K. Singal, 2024-10-07 In this issue of Clinics in Liver Disease, guest editor Dr. Ashwani K. Singal brings his considerable expertise to the topic of Alcohol-Associated Liver Disease. Portal hypertension is often one of the major complications seen in advanced liver disease. Top experts discuss various aspects of alcohol-associated liver disease (ALD), to provide comprehensive coverage on current status, update recent developments especially during the last decade, and highlight clinical and experimental unmet needs for practicing providers and for researchers in the field of ALD. - Contains 18 relevant, practice-oriented topics including microbiome in ALD; diagnosis of alcoholic use disorder and of ALD; non-invasive tests in assessment of ALD patients; treatment of alcoholic use disorder: behavioral and pharmacological therapies; current medical treatment of ALD: pharmacological and nutrition; liver transplantation in ALD; digital technology and AI in management of ALD; and more. - Provides in-depth clinical reviews on alcohol-associated liver disease, offering actionable insights for clinical practice. - Presents the latest information on this timely, focused topic under the leadership of experienced editors in the field. Authors synthesize and distill the latest research and practice guidelines to create clinically significant, topic-based reviews. |
LARGE Definition & Meaning - Merriam-Webster
The meaning of LARGE is exceeding most other things of like kind especially in quantity or size : big. How to use large in a sentence.
LARGE | English meaning - Cambridge Dictionary
LARGE definition: 1. big in size or amount: 2. enjoying yourself very much by dancing and drinking alcohol: 3. big…. Learn more.
large adjective - Definition, pictures, pronunciation and usage notes ...
Definition of large adjective in Oxford Advanced American Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.
LARGE definition and meaning | Collins English Dictionary
A large amount or number of people or things is more than the average amount or number.
Large - definition of large by The Free Dictionary
Of greater than average size, extent, quantity, or amount; big. 2. Of greater than average scope, breadth, or capacity; comprehensive. 3. Important; significant: had a large role in the negotiations; a large producer of …
LARGE Definition & Meaning - Merriam-Webster
The meaning of LARGE is exceeding most other things of like kind especially in quantity or size : big. How to use large in a sentence.
LARGE | English meaning - Cambridge Dictionary
LARGE definition: 1. big in size or amount: 2. enjoying yourself very much by dancing and drinking alcohol: 3. big…. Learn more.
large adjective - Definition, pictures, pronunciation and usage …
Definition of large adjective in Oxford Advanced American Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.
LARGE definition and meaning | Collins English Dictionary
A large amount or number of people or things is more than the average amount or number.
Large - definition of large by The Free Dictionary
Of greater than average size, extent, quantity, or amount; big. 2. Of greater than average scope, breadth, or capacity; comprehensive. 3. Important; significant: had a large role in the …
large, adj., adv., & n. meanings, etymology and more | Oxford …
There are 58 meanings listed in OED's entry for the word large, 18 of which are labelled obsolete. See ‘Meaning & use’ for definitions, usage, and quotation evidence.
Large Definition & Meaning - YourDictionary
Large definition: Of greater than average size, extent, quantity, or amount; big.
LARGE Synonyms: 238 Similar and Opposite Words - Merriam-Webster
Synonyms for LARGE: sizable, substantial, considerable, big, huge, handsome, great, oversized; Antonyms of LARGE: small, smallish, little, puny, dwarf, dinky, undersized, tiny
Meaning of large – Learner’s Dictionary - Cambridge Dictionary
LARGE definition: 1. big in size or amount: 2. If someone dangerous is at large, they are not in prison. 3. people…. Learn more.
LARGE | definition in the Cambridge English Dictionary
LARGE meaning: 1. big in size or amount: 2. enjoying yourself very much by dancing and drinking alcohol: 3. big…. Learn more.
