Work

Mining Clinical Data using Minimal Predictive Rules.

Year: 2010

Type: article

Abstract: Modern hospitals and health-care institutes collect huge amounts of clinical data. Those who deal with such data know that there is a widening gap between data collection and data comprehension. Thus, it is very important to develop data mining techniques capable of automatically extracting useful knowledge to support clinical decision-making in various diagnostic and patient-management tasks. In this paper, we develop a new framework for rule mining based on minimal predictive rules (MPR). Our goal is to minimize the number of rules in order to reduce the information overhead, while preserving and concisely describing the important underlying patterns. We develop an algorithm to efficiently mine these MPRs and apply it to predict Heparin Platelet Factor 4 antibody (HPF4) test orders from electronic health records.

Source: PubMed Central

Authors: Iyad Batal, Milos Hauskrecht

Institution: University of Pittsburgh

Language: English

Topic: Data Mining Algorithms and Applications

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institute of General Medical Sciences, U.S. National Library of Medicine

Awards: Real-time detection of deviations in clinical care in ICU data streams, Using medical records repositories to improve the alert system design, Evidence based anomaly detection in clinical databases.