Secondary Analysis of Electronic Health Records
(Sprache: Englisch)
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...
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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 generalizableto 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.
Inhaltsverzeichnis zu „Secondary Analysis of Electronic Health Records “
Introduction to the Book.- Objectives of secondary analysis of EHR data.- Review of clinical database.- Challenges and opportunities.- Secondary Analysis of EHR Data Cookbook.- Overview.- Step 1: Formulate research question.- Step 2: Data extraction and preprocessing.- Step 3: Exploratory Analysis.- Step 4: Data analysis.- Step 5: Validation and sensitivity analysis.- Missing Data.- Noise vs. Outliers.- Case Studies.- Introduction.- Predictive Modeling: outcome prediction (discrete).- Predictive Modeling: dose optimization (regression).- Pharmacovigilance (classification).- Comparative effectiveness: propensity score analysis.- Comparative effectiveness: instrumental variable analysis.- Decision and Cost Effectiveness Analysis: Hidden Markov models and Monte Carlo simulation.- Time series analysis: Gaussian processes (ICP modelling).- Time series analysis: Bayesian inference(Motif discovery in numerical signals).- Time Series analysis: Optimization techniques for hyperparameter selection.- Signal processing: analysis of waveform data.- Signal processing: False alarm reduction.
Autoren-Porträt von MIT Critical Data
MIT Critical DataMIT Critical Data consists of data scientists and clinicians from around the globe brought together by a vision to engender a data-driven healthcare system supported by clinical informatics without walls. In this ecosystem, the creation of evidence and clinical decision support tools is initiated, updated, honed and enhanced by scaling the access to and meaningful use of clinical data.
Leo Anthony Celi
Leo has practiced medicine in three continents, giving him broad perspectives in healthcare delivery. His research is on secondary analysis of electronic health records and global health informatics. He founded and co-directs Sana at the Institute for Medical Engineering and Science at the Massachusetts Institute of Technology. He also holds a faculty position at Harvard Medical School as an intensivist at the Beth Israel Deaconess Medical Center and is the clinical research director for the Laboratory of Computational Physiology at MIT.Finally, he is one of the course directors for HST.936 at MIT - innovations in global health informatics and HST.953 - secondary analysis of electronic health records.
Peter Charlton
Peter gained the degree of MEng in Engineering Science in 2010 from the University of Oxford. Since then he held a research position, working jointly with Guy's and St Thomas' NHS Foundation Trust, and King's College London. Peter's research focuses on physiological monitoring of hospital patients, divided into three areas. The first area concerns the development of signal processing techniques to estimate clinical parameters from physiological signals. He has focused on unobtrusive estimation of respiratory rate for use in ambulatory settings, invasive estimation of cardiac output for use in critical care, and novel techniques for analysis of the pulse oximetry (photoplethysmogram) signal. Secondly, he is investigating the effectiveness of technologies for the acquisition
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of continuous and intermittent physiological measurements in ambulatory and intensive care settings. Thirdly, he is developing techniques to transform continuous monitoring data into measurements that are appropriate for real-time alerting of patient deteriorations.Mohammad Ghassemi
Mohammad is a doctoral candidate at the Massachusetts Institute of Technology. As an undergraduate, he studied Electrical Engineering and graduated as both a Goldwater scholar and the University's "Outstanding Engineer". In 2011, Mohammad received an MPhil in Information Engineering from the University of Cambridge where he was also a recipient of the Gates-Cambridge Scholarship. Since arriving at MIT, he has perused research at the interface of machine learning and medical informatics. Mohammad's doctoral focus is on signal processing and machine learning techniques in the context of multi-modal, multi-scale datasets. He has helped put together the largest collection of post-anoxic coma EEGs inthe world. In addition to his thesis work, Mohammad has worked with the Samsung corporation, and several entities across campus building "smart devices" including: a multi-sensor wearable that passively monitors the physiological, audio and video activity of a user to estimate a latent emotional state.
Alistair Johnson
Alistair joined the Laboratory for Computational Physiology as a postdoctoral associate in 2015. He received his B.Eng in Biomedical and Electrical Engineering at McMaster University, Canada, and subsequently read for a D.Phil in Healthcare Innovation at the University of Oxford. His thesis was titled "Mortality and acuity assessment in critical care", and its focus included using machine learning techniques to predict mortality and develop new severity of illness scores for patients admitted to intensive care units. Before joining the LCP, Alistair spent a year as a research assistant at the John Radcliffe hospital in Oxford, where he worked on build
Mohammad is a doctoral candidate at the Massachusetts Institute of Technology. As an undergraduate, he studied Electrical Engineering and graduated as both a Goldwater scholar and the University's "Outstanding Engineer". In 2011, Mohammad received an MPhil in Information Engineering from the University of Cambridge where he was also a recipient of the Gates-Cambridge Scholarship. Since arriving at MIT, he has perused research at the interface of machine learning and medical informatics. Mohammad's doctoral focus is on signal processing and machine learning techniques in the context of multi-modal, multi-scale datasets. He has helped put together the largest collection of post-anoxic coma EEGs inthe world. In addition to his thesis work, Mohammad has worked with the Samsung corporation, and several entities across campus building "smart devices" including: a multi-sensor wearable that passively monitors the physiological, audio and video activity of a user to estimate a latent emotional state.
Alistair Johnson
Alistair joined the Laboratory for Computational Physiology as a postdoctoral associate in 2015. He received his B.Eng in Biomedical and Electrical Engineering at McMaster University, Canada, and subsequently read for a D.Phil in Healthcare Innovation at the University of Oxford. His thesis was titled "Mortality and acuity assessment in critical care", and its focus included using machine learning techniques to predict mortality and develop new severity of illness scores for patients admitted to intensive care units. Before joining the LCP, Alistair spent a year as a research assistant at the John Radcliffe hospital in Oxford, where he worked on build
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Bibliographische Angaben
- Autor: MIT Critical Data
- 2018, Softcover reprint of the original 1st ed. 2016, XXI, 427 Seiten, 100 farbige Abbildungen, Maße: 16,2 x 23,7 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 3319828991
- ISBN-13: 9783319828992
Sprache:
Englisch
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