Cognitive Computing-Based CDSS in Medical Practice

Abstract

Importance. The last decade has witnessed the advances of cognitive computing technologies that learn at scale and reason with purpose in medicine studies. From the diagnosis of diseases till the generation of treatment plans, cognitive computing encompasses both data-driven and knowledge-driven machine intelligence to assist health care roles in clinical decision-making. This review provides a comprehensive perspective from both research and industrial efforts on cognitive computing-based CDSS over the last decade. Highlights. (1) A holistic review of both research papers and industrial practice about cognitive computing-based CDSS is conducted to identify the necessity and the characteristics as well as the general framework of constructing the system. (2) Several of the typical applications of cognitive computing-based CDSS as well as the existing systems in real medical practice are introduced in detail under the general framework. (3) The limitations of the current cognitive computing-based CDSS is discussed that sheds light on the future work in this direction. Conclusion. Different from medical content providers, cognitive computing-based CDSS provides probabilistic clinical decision support by automatically learning and inferencing from medical big data. The characteristics of managing multimodal data and computerizing medical knowledge distinguish cognitive computing-based CDSS from other categories. Given the current status of primary health care like high diagnostic error rate and shortage of medical resources, it is time to introduce cognitive computing-based CDSS to the medical community which is supposed to be more open-minded and embrace the convenience and low cost but high efficiency brought by cognitive computing-based CDSS.

Exploring the KISS principle (Keep It Simple) for decision support systems

“…As already mentioned in this section, this is an aspect that brings benefits (direct involvement of healthcare professionals throughout the implementation of the DSS), but also limits the implementation of highly complex systems, encompassing too heterogeneous, ambiguous and large-volume information contents. In these situations (which are beyond the scope of the model proposed in the present work), more complex systems, specially involving artificial intelligence algorithms, may be more indicated, inevitably (see (8) and (12)).…”

Mapping Chinese Medical Entities to the Unified Medical Language System

“…Well-developed medical terminology systems, such as the Unified Medical Language System (UMLS), are the cornerstone of medical informatics research and health informatics technology for facilitating fine-grained medical knowledge representation and other high-level intelligent applications for medicines [ 1 – 3 ]. A large number of unorganized Chinese medical terms remain challenges for Chinese medical informatics development and applications [ 4 ].…”