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Application of Data Mining Techniques in Clinical Decision Making: A Literature Review and Classification
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Author(s): Hakimeh Ameri (K. N. Toosi University of Technology, Iran), Somayeh Alizadeh (K. N. Toosi University of Technology, Iran)and Elham Akhond Zadeh Noughabi (University of Calgary, Canada)
Copyright: 2017
Pages: 39
Source title:
Handbook of Research on Data Science for Effective Healthcare Practice and Administration
Source Author(s)/Editor(s): Elham Akhond Zadeh Noughabi (University of Calgary, Canada), Bijan Raahemi (University of Ottawa, Canada), Amir Albadvi (Tarbiat Modares University, Iran)and Behrouz H. Far (University of Calgary, Canada)
DOI: 10.4018/978-1-5225-2515-8.ch012
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Abstract
Data mining techniques are increasingly used in clinical decision making and help the physicians to make more accurate and effective decisions. In this chapter, a classification of data mining applications in clinical decision making is presented through a systematic review. The applications of data mining techniques in clinical decision making are divided into two main categories: diagnosis and treatment. Early prediction of medical conditions, detecting multi-morbidity and complications of diseases, identifying and predicting the chronic diseases and medical imaging are the subcategories which are defined in the diagnosis part. The Treatment category is composed of treatment effectiveness and predicting the average length of stay in hospital. The majority of the reviewed articles are related to diagnosis and there is only one article which discusses the determination of drug dosage in successful treatment. The classification model is the most commonly practical model in the clinical decision making.
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