AIMC Topic: Quality Improvement

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Using machine learning to examine medication adherence thresholds and risk of hospitalization.

Medical care Aug 1, 2015
BACKGROUND: Quality improvement efforts are frequently tied to patients achieving ≥80% medication adherence. However, there is little empirical evidence that this threshold optimally predicts important health outcomes.
Humans Artificial Intelligence Retrospective Studies Female Hospitalization Aged Aged, 80 and over Adult Male Middle Aged Risk Assessment Cohort Studies Diabetes Mellitus, Type 2 United States Hypoglycemic Agents Patient Compliance Quality Improvement Multivariate Analysis Assessment of Medication Adherence Medicaid
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Enhancing Patient Safety Event Reporting by K-nearest Neighbor Classifier.

Studies in health technology and informatics Jan 1, 2015
Data quality was placed as a major reason for the low utility of patient safety event reporting systems. A pressing need in improving data quality has advanced recent research focus in data entry associated with human factors. The debate on structure...
Machine Learning Patient Safety Decision Support Systems, Clinical United States Risk Management Data Mining Data Accuracy Quality Improvement Pattern Recognition, Automated Medical Errors Meaningful Use
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Improving Hospital-Wide Early Resource Allocation through Machine Learning.

Studies in health technology and informatics Jan 1, 2015
The objective of this paper is to evaluate the extent to which early determination of diagnosis-related groups (DRGs) can be used for better allocation of scarce hospital resources. When elective patients seek admission, the true DRG, currently deter...
Machine Learning Natural Language Processing Data Mining Quality Improvement Hospital Information Systems Needs Assessment Diagnosis-Related Groups Health Care Rationing Hospital Administration
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Identifying redundant and missing relations in the gene ontology.

Studies in health technology and informatics Jan 1, 2015
Significant efforts have been undertaken for providing the Gene Ontology (GO) in a computable format as well as for enriching it with logical definitions. Automated approaches can thus be applied to GO for assisting its maintenance and for checking i...
Natural Language Processing Data Accuracy Quality Improvement Pattern Recognition, Automated Terminology as Topic Vocabulary, Controlled Gene Ontology Genes
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