Literature DB >> 19449621

Machine learning techniques to examine large patient databases.

Geert Meyfroidt1, Fabian Güiza, Jan Ramon, Maurice Bruynooghe.   

Abstract

Computerization in healthcare in general, and in the operating room (OR) and intensive care unit (ICU) in particular, is on the rise. This leads to large patient databases, with specific properties. Machine learning techniques are able to examine and to extract knowledge from large databases in an automatic way. Although the number of potential applications for these techniques in medicine is large, few medical doctors are familiar with their methodology, advantages and pitfalls. A general overview of machine learning techniques, with a more detailed discussion of some of these algorithms, is presented in this review.

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Year:  2009        PMID: 19449621     DOI: 10.1016/j.bpa.2008.09.003

Source DB:  PubMed          Journal:  Best Pract Res Clin Anaesthesiol        ISSN: 1521-6896


  16 in total

Review 1.  Surgical robotics beyond enhanced dexterity instrumentation: a survey of machine learning techniques and their role in intelligent and autonomous surgical actions.

Authors:  Yohannes Kassahun; Bingbin Yu; Abraham Temesgen Tibebu; Danail Stoyanov; Stamatia Giannarou; Jan Hendrik Metzen; Emmanuel Vander Poorten
Journal:  Int J Comput Assist Radiol Surg       Date:  2015-10-08       Impact factor: 2.924

2.  Automated annotation and classification of BI-RADS assessment from radiology reports.

Authors:  Sergio M Castro; Eugene Tseytlin; Olga Medvedeva; Kevin Mitchell; Shyam Visweswaran; Tanja Bekhuis; Rebecca S Jacobson
Journal:  J Biomed Inform       Date:  2017-04-18       Impact factor: 6.317

3.  Comparison of algorithm-based estimates of occupational diesel exhaust exposure to those of multiple independent raters in a population-based case-control study.

Authors:  Melissa C Friesen; Anjoeka Pronk; David C Wheeler; Yu-Cheng Chen; Sarah J Locke; Dennis D Zaebst; Molly Schwenn; Alison Johnson; Richard Waddell; Dalsu Baris; Joanne S Colt; Debra T Silverman; Patricia A Stewart; Hormuzd A Katki
Journal:  Ann Occup Hyg       Date:  2012-11-25

4.  Predictive data mining on monitoring data from the intensive care unit.

Authors:  Fabian Güiza; Jelle Van Eyck; Geert Meyfroidt
Journal:  J Clin Monit Comput       Date:  2012-11-24       Impact factor: 2.502

Review 5.  Predictive classification of individual magnetic resonance imaging scans from children and adolescents.

Authors:  B A Johnston; B Mwangi; K Matthews; D Coghill; J D Steele
Journal:  Eur Child Adolesc Psychiatry       Date:  2012-08-29       Impact factor: 4.785

6.  Inside the black box: starting to uncover the underlying decision rules used in a one-by-one expert assessment of occupational exposure in case-control studies.

Authors:  David C Wheeler; Igor Burstyn; Roel Vermeulen; Kai Yu; Susan M Shortreed; Anjoeka Pronk; Patricia A Stewart; Joanne S Colt; Dalsu Baris; Margaret R Karagas; Molly Schwenn; Alison Johnson; Debra T Silverman; Melissa C Friesen
Journal:  Occup Environ Med       Date:  2012-11-15       Impact factor: 4.402

7.  Optimization of Electronic Medical Records for Data Mining Using a Common Data Model.

Authors:  Manlik Kwong; Heather L Gardner; Neil Dieterle; Virginia Rentko
Journal:  Top Companion Anim Med       Date:  2019-09-26

8.  IRIS: A Modular Platform for Continuous Monitoring and Caretaker Notification in the Intensive Care Unit.

Authors:  Steven N Baldassano; Shawniqua Williams Roberson; Ramani Balu; Brittany Scheid; John M Bernabei; Jay Pathmanathan; Brian Oommen; Damien Leri; Javier Echauz; Michael Gelfand; Paulomi Kadakia Bhalla; Chloe E Hill; Amanda Christini; Joost B Wagenaar; Brian Litt
Journal:  IEEE J Biomed Health Inform       Date:  2020-01-13       Impact factor: 5.772

9.  Detecting chronic kidney disease in population-based administrative databases using an algorithm of hospital encounter and physician claim codes.

Authors:  Jamie L Fleet; Stephanie N Dixon; Salimah Z Shariff; Robert R Quinn; Danielle M Nash; Ziv Harel; Amit X Garg
Journal:  BMC Nephrol       Date:  2013-04-05       Impact factor: 2.388

10.  Validity of physician billing claims to identify deceased organ donors in large healthcare databases.

Authors:  Alvin Ho-ting Li; S Joseph Kim; Jagadish Rangrej; Damon C Scales; Salimah Shariff; Donald A Redelmeier; Greg Knoll; Ann Young; Amit X Garg
Journal:  PLoS One       Date:  2013-08-14       Impact factor: 3.240

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