AIMC Topic: Electronic Health Records
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Prediction of In-hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data-Driven, Machine Learning Approach.
Academic emergency medicine : official journal of the Society for Academic Emergency Medicine
Feb 13, 2016
OBJECTIVES: Predictive analytics in emergency care has mostly been limited to the use of clinical decision rules (CDRs) in the form of simple heuristics and scoring systems. In the development of CDRs, limitations in analytic methods and concerns wit...
Toward rapid learning in cancer treatment selection: An analytical engine for practice-based clinical data.
Journal of biomedical informatics
Feb 2, 2016
OBJECTIVE: Wide-scale adoption of electronic medical records (EMRs) has created an unprecedented opportunity for the implementation of Rapid Learning Systems (RLSs) that leverage primary clinical data for real-time decision support. In cancer, where ...
Digital Family History Data Mining with Neural Networks: A Pilot Study.
Perspectives in health information management
Jan 1, 2016
Following the passage of the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009, electronic health records were widely adopted by eligible physicians and hospitals in the United States. Stage 2 meaningful use menu obj...
Reverse Engineering and Evaluation of Prediction Models for Progression to Type 2 Diabetes: An Application of Machine Learning Using Electronic Health Records.
Journal of diabetes science and technology
Dec 20, 2015
BACKGROUND: Application of novel machine learning approaches to electronic health record (EHR) data could provide valuable insights into disease processes. We utilized this approach to build predictive models for progression to prediabetes and type 2...
A numerical similarity approach for using retired Current Procedural Terminology (CPT) codes for electronic phenotyping in the Scalable Collaborative Infrastructure for a Learning Health System (SCILHS).
BMC medical informatics and decision making
Dec 11, 2015
BACKGROUND: Interoperable phenotyping algorithms, needed to identify patient cohorts meeting eligibility criteria for observational studies or clinical trials, require medical data in a consistent structured, coded format. Data heterogeneity limits s...
Stabilizing l1-norm prediction models by supervised feature grouping.
Journal of biomedical informatics
Dec 9, 2015
Emerging Electronic Medical Records (EMRs) have reformed the modern healthcare. These records have great potential to be used for building clinical prediction models. However, a problem in using them is their high dimensionality. Since a lot of infor...
Lynx: Automatic Elderly Behavior Prediction in Home Telecare.
BioMed research international
Dec 9, 2015
This paper introduces Lynx, an intelligent system for personal safety at home environments, oriented to elderly people living independently, which encompasses a decision support machine for automatic home risk prevention, tested in real-life environm...
EHR based Genetic Testing Knowledge Base (iGTKB) Development.
BMC medical informatics and decision making
Nov 25, 2015
BACKGROUND: The gap between a large growing number of genetic tests and a suboptimal clinical workflow of incorporating these tests into regular clinical practice poses barriers to effective reliance on advanced genetic technologies to improve qualit...
Predictive modeling of structured electronic health records for adverse drug event detection.
BMC medical informatics and decision making
Nov 25, 2015
BACKGROUND: The digitization of healthcare data, resulting from the increasingly widespread adoption of electronic health records, has greatly facilitated its analysis by computational methods and thereby enabled large-scale secondary use thereof. Th...
Classification of radiology reports for falls in an HIV study cohort.
Journal of the American Medical Informatics Association : JAMIA
Nov 13, 2015
OBJECTIVE: To identify patients in a human immunodeficiency virus (HIV) study cohort who have fallen by applying supervised machine learning methods to radiology reports of the cohort.
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