Artificial Intelligence in Medicine
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Highlights • We present DANIEL, a multilingual system for tele-epidemiology. • Classical approaches use language-dependent resources, which limits covera...
Objective The ability to predict patient readmission risk is extremely valuable for hospitals, especially under the Hospital Readmission Reduction Program of the Center for Medicare and Medicaid Services which went into effect starting October 1, 2012. There is a plethora of ...
Objective Elderly people who live alone can be assisted by home monitoring systems that identify risk scenarios such as falls, fatigue symptoms or burglary. Given that these systems have to manage spatiotemporal data, human intervention is required to validate automatic alarm...
Highlights • We investigate drug NER using limited or no manually annotated data. • We propose an algorithm for combining methods based on annotations an...
Objectives Confounding factors in unsupervised data can lead to undesirable clustering results. For example in medical datasets, age is often a confounding factor in tests designed to judge the severity of a patient's disease through measures of mobility, eyesight and hearing...
Objective Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision m...
• We propose a method for detecting lexical regularities in ontology labels. • We present a metric that measures how to decompose classes that exhibit regularity. ...