Medical Information Extraction And Analysis PDF Download
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| Author | : Alexander Scarlat |
| Publisher | : Createspace Independent Publishing Platform |
| Total Pages | : 400 |
| Release | : 2018-03-19 |
| Genre | : |
| ISBN | : 9781544093376 |
Download Medical Information Extraction and Analysis Book in PDF, ePub and Kindle
Book introduces clinicians to SQL and using hands on examples on MIMIC2 - a publicly available, de-identified ICU database from Beth Israel Deaconess Medical Center and MIT Physionet - it enables care providers to query any database of an Electronic Health Record (EHR) and create meaningful reports to support their quality initiatives. IT professionals will benefit from a structured analysis of the main parameters that interest clinicians: diagnosis, procedure, lab, meds, imaging reports, nursing assessments and interventions, scoring systems, mortality, length of stay, readmissions' rate, costs, etc. Using this book, both clinicians and IT professionals can easily retrieve any information from the data in their EHR and discover new clinical insights hidden in their database. 1. A Brief Database Primer 1 2. MIMIC2 Clinical Database 9 3. MySQL Workbench 12 4. Introduction to the Mighty SELECT 21 5. Aggregate / Summary Functions 48 6. Querying Multiple Tables 63 7. Entity Relationship Diagram - ERD 76 8. Systematic information extraction 91 9. Patient 102 10. Mortality 125 11. Length of stay (LOS) 139 12. Readmissions 145 13. Diagnosis 153 14. Sepsis profile 174 15. Lab 200 16. Drug 217 17. Procedure 230 18. Chart, note and much more 239 19. Provider and care unit 248 20. Nursing 259 21. Fluid 267 22. Score and scale 274 Appendix Answers to Questions
| Author | : Agency for Healthcare Research and Quality/AHRQ |
| Publisher | : Government Printing Office |
| Total Pages | : 396 |
| Release | : 2014-04-01 |
| Genre | : Medical |
| ISBN | : 1587634333 |
Download Registries for Evaluating Patient Outcomes Book in PDF, ePub and Kindle
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.
| Author | : Hercules Dalianis |
| Publisher | : Springer |
| Total Pages | : 192 |
| Release | : 2018-05-14 |
| Genre | : Computers |
| ISBN | : 3319785036 |
Download Clinical Text Mining Book in PDF, ePub and Kindle
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.
| Author | : 王子軍 |
| Publisher | : |
| Total Pages | : |
| Release | : 2010 |
| Genre | : |
| ISBN | : |
Download Methodology of Facilitating Data Analysis in Medical Informatics Book in PDF, ePub and Kindle
| Author | : Hulin Wu |
| Publisher | : CRC Press |
| Total Pages | : 329 |
| Release | : 2020-12-09 |
| Genre | : Business & Economics |
| ISBN | : 1000260941 |
Download Statistics and Machine Learning Methods for EHR Data Book in PDF, ePub and Kindle
The use of Electronic Health Records (EHR)/Electronic Medical Records (EMR) data is becoming more prevalent for research. However, analysis of this type of data has many unique complications due to how they are collected, processed and types of questions that can be answered. This book covers many important topics related to using EHR/EMR data for research including data extraction, cleaning, processing, analysis, inference, and predictions based on many years of practical experience of the authors. The book carefully evaluates and compares the standard statistical models and approaches with those of machine learning and deep learning methods and reports the unbiased comparison results for these methods in predicting clinical outcomes based on the EHR data. Key Features: Written based on hands-on experience of contributors from multidisciplinary EHR research projects, which include methods and approaches from statistics, computing, informatics, data science and clinical/epidemiological domains. Documents the detailed experience on EHR data extraction, cleaning and preparation Provides a broad view of statistical approaches and machine learning prediction models to deal with the challenges and limitations of EHR data. Considers the complete cycle of EHR data analysis. The use of EHR/EMR analysis requires close collaborations between statisticians, informaticians, data scientists and clinical/epidemiological investigators. This book reflects that multidisciplinary perspective.
| Author | : Om Prakash Jena |
| Publisher | : CRC Press |
| Total Pages | : 269 |
| Release | : 2023-11-06 |
| Genre | : Technology & Engineering |
| ISBN | : 1000983609 |
Download Medical Data Analysis and Processing using Explainable Artificial Intelligence Book in PDF, ePub and Kindle
The text presents concepts of explainable artificial intelligence (XAI) in solving real world biomedical and healthcare problems. It will serve as an ideal reference text for graduate students and academic researchers in diverse fields of engineering including electrical, electronics and communication, computer, and biomedical Presents explainable artificial intelligence (XAI) based machine analytics and deep learning in medical science Discusses explainable artificial intelligence (XA)I with the Internet of Medical Things (IoMT) for healthcare applications Covers algorithms, tools, and frameworks for explainable artificial intelligence on medical data Explores the concepts of natural language processing and explainable artificial intelligence (XAI) on medical data processing Discusses machine learning and deep learning scalability models in healthcare systems This text focuses on data driven analysis and processing of advanced methods and techniques with the help of explainable artificial intelligence (XAI) algorithms. It covers machine learning, Internet of Things (IoT), and deep learning algorithms based on XAI techniques for medical data analysis and processing. The text will present different dimensions of XAI based computational intelligence applications. It will serve as an ideal reference text for graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and biomedical engineering.
| Author | : Amy Neustein |
| Publisher | : Walter de Gruyter GmbH & Co KG |
| Total Pages | : 286 |
| Release | : 2014-10-09 |
| Genre | : Computers |
| ISBN | : 1614513902 |
Download Text Mining of Web-Based Medical Content Book in PDF, ePub and Kindle
• Includes Text Mining and Natural Language Processing Methods for extracting information from electronic health records and biomedical literature. • Analyzes text analytic tools for new media such as online forums, social media posts, tweets and video sharing. • Demonstrates how to use speech and audio technologies for improving access to online content for the visually impaired. Text Mining of Web-Based Medical Content examines various approaches to deriving high quality information from online biomedical literature, electronic health records, query search terms, social media posts and tweets. Using some of the latest empirical methods of knowledge extraction, the authors show how online content, generated by both professionals and laypersons, can be mined for valuable information about disease processes, adverse drug reactions not captured during clinical trials, and tropical fever outbreaks. Additionally, the authors show how to perform infromation extraction on a hospital intranet, how to build a social media search engine to glean information about patients' own experiences interacting with healthcare professionals, and how to improve access to online health information. This volume provides a wealth of timely material for health informatic professionals and machine learning, data mining, and natural language researchers. Topics in this book include: • Mining Biomedical Literature and Clinical Narratives • Medication Information Extraction • Machine Learning Techniques for Mining Medical Search Queries • Detecting the Level of Personal Health Information Revealed in Social Media • Curating Layperson’s Personal Experiences with Health Care from Social Media and Twitter • Health Dialogue Systems for Improving Access to Online Content • Crowd-based Audio Clips to Improve Online Video Access for the Visually Impaired • Semantic-based Visual Information Retrieval for Mining Radiographic Image Data • Evaluating the Importance of Medical Terminology in YouTube Video Titles and Descriptions
| Author | : Kerstin Denecke |
| Publisher | : Springer Nature |
| Total Pages | : 151 |
| Release | : 2023-05-24 |
| Genre | : Medical |
| ISBN | : 3031301870 |
Download Sentiment Analysis in the Medical Domain Book in PDF, ePub and Kindle
Sentiment analysis deals with extracting information about opinions, sentiments, and even emotions conveyed by writers towards topics of interest. Medical sentiment analysis refers to the identification and analysis of sentiments or emotions expressed in free-textual documents with a scope on healthcare and medicine. This fascinating problem offers numerous application areas in the domain of medicine, but also research challenges. The book provides a comprehensive introduction to the topic. The primary purpose is to provide the necessary background on medical sentiment analysis, ranging from a description of the notions of medical sentiment to use cases that have been considered already and application areas of relevance. Medical sentiment analysis uses natural language processing (NLP), text analysis and machine learning to realise the process of extracting and classifying statements regarding expressed opinion and sentiment. The book offers a comprehensive overview on existing methods of sentiment analysis applied to healthcare resources or health-related documents. It concludes with open research avenues providing researchers indications which topics still have to be developed in more depth.
| Author | : Yoav Goldberg |
| Publisher | : Springer Nature |
| Total Pages | : 20 |
| Release | : 2022-06-01 |
| Genre | : Computers |
| ISBN | : 3031021657 |
Download Neural Network Methods for Natural Language Processing Book in PDF, ePub and Kindle
Neural networks are a family of powerful machine learning models. This book focuses on the application of neural network models to natural language data. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries. The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning.
| Author | : Chandan K. Reddy |
| Publisher | : CRC Press |
| Total Pages | : 756 |
| Release | : 2015-06-23 |
| Genre | : Business & Economics |
| ISBN | : 148223212X |
Download Healthcare Data Analytics Book in PDF, ePub and Kindle
At the intersection of computer science and healthcare, data analytics has emerged as a promising tool for solving problems across many healthcare-related disciplines. Supplying a comprehensive overview of recent healthcare analytics research, Healthcare Data Analytics provides a clear understanding of the analytical techniques currently available
