Clinical Decision Support Systems: Decision-Making System for Clinical Data

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Author(s): Diviya Prabha V. (Periyar University, India)and Rathipriya R. (Periyar University, India)
Copyright: 2021
Pages: 13
Source title: Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning
Source Author(s)/Editor(s): Geeta Rani (Manipal University Jaipur, India)and Pradeep Kumar Tiwari (Manipal University Jaipur, India)
DOI: 10.4018/978-1-7998-2742-9.ch014

Keywords: Knowledge Management and Library Science / Machine Learning / Medical Diagnosis and Practice / Medical Information Science Reference

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Abstract

Clinical data is increasing day-by-day mainly in hospitals by an ageing of the human population. Patients discharged from hospitals are readmitted due to health issues. As the number of patients increases, there are a smaller number of hospitals and an increase in healthcare costs. This results in ineffective decision making that minimizes the healthcare. Machine learning techniques score better for solving this kind of problem. The proposed work, minimum entropy feature selection with logistic regression (MELR), is performing better for the readmission rates. Decision cannot be based on the clinical knowledge and personal data about the patient. It must be precise in choosing the future patient outcomes. This chapter produces promising results for clinical data.

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Clinical Decision Support Systems: Decision-Making System for Clinical Data

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