Healthcare decision support system for administration of chronic diseases.

Objectives: A healthcare decision-making support model and rule management system is proposed based on a personalized rule-based intelligent concept, to effectively manage chronic diseases.

Methods: A Web service was built using a standard message transfer protocol for interoperability of personal health records among healthcare institutions. An intelligent decision service is provided that analyzes data using a service-oriented healthcare rule inference function and machine-learning platform; the rules are extensively compiled by physicians through a developmental user interface that enables knowledge base construction, modification, and integration.

Comparison of machine learning algorithms for classification of the sentences in three clinical practice guidelines.

Objectives: Clinical Practice Guidelines (CPGs) are an effective tool for minimizing the gap between a physician's clinical decision and medical evidence and for modeling the systematic and standardized pathway used to provide better medical treatment to patients.

Methods: In this study, sentences within the clinical guidelines are categorized according to a classification system. We used three clinical guidelines that incorporated knowledge from medical experts in the field of family medicine.

Retrospective evaluation of the prescribing behavior of residents with respect to nephrotoxic drugs.

This study is a preliminary analysis of the prescription behavior of residents in a teaching hospital, with respect to nephrotoxic drugs during a 2-month period. The overdose rate was 3%. Only 5.