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Cognitive computing-based clinical decision support systems (CDSS) offer probabilistic support by learning from medical big data. These systems manage multimodal data and computerize knowledge, improving healthcare efficiency and reducing costs.

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Health Services Research

Background:

  • Cognitive computing has advanced significantly in medical studies over the last decade.
  • It integrates data-driven and knowledge-driven machine intelligence for clinical decision-making.
  • Cognitive computing-based clinical decision support systems (CDSS) assist healthcare professionals.

Purpose of the Study:

  • To provide a comprehensive review of cognitive computing-based CDSS from research and industrial perspectives over the past decade.
  • To identify the necessity, characteristics, and general framework for constructing these systems.
  • To discuss limitations and future directions.

Main Methods:

  • Holistic review of research papers and industrial practices concerning cognitive computing-based CDSS.
  • Detailed introduction of typical applications and existing real-world systems.
  • Analysis of current limitations and future research avenues.

Main Results:

  • Cognitive computing-based CDSS provide probabilistic clinical decision support through learning and inference from medical big data.
  • These systems are distinguished by their ability to manage multimodal data and computerize medical knowledge.
  • Typical applications and existing systems within a defined framework are presented.

Conclusions:

  • Cognitive computing-based CDSS offer a distinct approach compared to traditional medical content providers.
  • They present a viable solution to challenges in primary healthcare, such as high diagnostic error rates and resource shortages.
  • The medical community is encouraged to adopt these systems for their convenience, low cost, and high efficiency.