4.6 Review

A Comprehensive Review on Smart Decision Support Systems for Health Care

Journal

IEEE SYSTEMS JOURNAL
Volume 13, Issue 3, Pages 3536-3545

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSYST.2018.2890121

Keywords

Applications; data mining (DM); decision-making; health care; smart decision support systems (DSSs) technologies

Funding

  1. National Funding from the FCT - Fundacao para a Ciencia e a Tecnologia [UID/EEA/50008/2013]
  2. Government of the Russian Federation [08-08]
  3. Finep [01.14.0231.00]
  4. Centro de Referencia em Radiocomunicacoes - CRR project of the Instituto Nacional de Telecomunicacoes
  5. Brazilian National Council for Scientific and Technological Development (CNPq) [309335/2017-5]
  6. CNPq, Brazil [207706/2014-0]
  7. International Scientific Partnership Program ISPP at King Saud University [0129]

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Medical activity requires responsibility not only based on knowledge and clinical skills, but also in managing a vast amount of information related to patient care. It is through the appropriate treatment of information that experts can consistently build a strong policy of welfare. The primary goal of decision support systems (DSSs) is to give information to the experts where and when it is needed. These systems provide knowledge, models, and data processing tools to help the experts make better decisions in several situations. They aim to resolve several problems in health services to help patients and their families manage their health care by providing better access to these services. This paper presents a deep review of the state of the art of smart DSSs. It also elaborates on the latest developments in intelligent systems to support decision-makers in health care. The most promising findings brought in literature are analyzed and summarized according to their taxonomy, application area, year of publication, and the approaches and technologies used. Smart systems can assist decisionmakers to improve the effectiveness of their decisions using the integration of data mining techniques and model-based systems. It significantly improves the current approaches, enabling the combination of knowledge from experts and knowledge extracted from data.

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