AIMC Topic: Electronic Health Records

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Predicting early psychiatric readmission with natural language processing of narrative discharge summaries.

Translational psychiatry
The ability to predict psychiatric readmission would facilitate the development of interventions to reduce this risk, a major driver of psychiatric health-care costs. The symptoms or characteristics of illness course necessary to develop reliable pre...

Semi-supervised learning of the electronic health record for phenotype stratification.

Journal of biomedical informatics
Patient interactions with health care providers result in entries to electronic health records (EHRs). EHRs were built for clinical and billing purposes but contain many data points about an individual. Mining these records provides opportunities to ...

A Concept-Wide Association Study of Clinical Notes to Discover New Predictors of Kidney Failure.

Clinical journal of the American Society of Nephrology : CJASN
BACKGROUND AND OBJECTIVES: Identifying predictors of kidney disease progression is critical toward the development of strategies to prevent kidney failure. Clinical notes provide a unique opportunity for big data approaches to identify novel risk fac...

Tumor reference resolution and characteristic extraction in radiology reports for liver cancer stage prediction.

Journal of biomedical informatics
BACKGROUND: Anaphoric references occur ubiquitously in clinical narrative text. However, the problem, still very much an open challenge, is typically less aggressively focused on in clinical text domain applications. Furthermore, existing research on...

A machine learning-based framework to identify type 2 diabetes through electronic health records.

International journal of medical informatics
OBJECTIVE: To discover diverse genotype-phenotype associations affiliated with Type 2 Diabetes Mellitus (T2DM) via genome-wide association study (GWAS) and phenome-wide association study (PheWAS), more cases (T2DM subjects) and controls (subjects wit...

Mapping Phenotypic Information in Heterogeneous Textual Sources to a Domain-Specific Terminological Resource.

PloS one
Biomedical literature articles and narrative content from Electronic Health Records (EHRs) both constitute rich sources of disease-phenotype information. Phenotype concepts may be mentioned in text in multiple ways, using phrases with a variety of st...

Diagnosis, misdiagnosis, lucky guess, hearsay, and more: an ontological analysis.

Journal of biomedical semantics
BACKGROUND: Disease and diagnosis have been the subject of much ontological inquiry. However, the insights gained therein have not yet been well enough applied to the study, management, and improvement of data quality in electronic health records (EH...

Machine learning approaches to personalize early prediction of asthma exacerbations.

Annals of the New York Academy of Sciences
Patient telemonitoring results in an aggregation of significant amounts of information about patient disease trajectory. However, the potential use of this information for early prediction of exacerbations in adult asthma patients has not been system...

A Part-Of-Speech term weighting scheme for biomedical information retrieval.

Journal of biomedical informatics
In the era of digitalization, information retrieval (IR), which retrieves and ranks documents from large collections according to users' search queries, has been popularly applied in the biomedical domain. Building patient cohorts using electronic he...