A Clinical Decision Support System for Metabolism Synthesis

Zhou, Qunyi

doi:10.1109/cinc.2009.89

Cited by 7 publications

(2 citation statements)

References 3 publications

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“…Figure 5 shows a word cloud image generated from ATLAS.ti based on the codes used in all included publications. In the publications, we also found many large international collaborations [94,101,110] and solo author publications [57,64,105,106]. We also noticed that some publications were missing necessary information for explaining the mechanisms of the systems that can be critical for reproducibility.…”

Section: Other Resultsmentioning

confidence: 89%

A systematic review of ontology-based clinical decision support system rules: usage, management, and interoperability

Jing

Min

Gong

et al. 2022

Preprint

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ObjectiveClinical decision support systems (CDSS) have a critical role in improving the quality and safety of health care delivery. CDSS rules direct the behavior of CDSS. However, the CDSS rules have not been routinely shared and reused, and ontology can promote the reusing of CDSS rules. We systematically screened literature to elaborate on the current status of ontology applied in CDSS rule management.MethodsWe searched PubMed, the Association for Computing Machinery (ACM) Digital Library, and the Nursing & Allied Health Database for publications focusing on ontology, clinical decision support, and rules. Grounded theory and PRISMA 2020 guidelines were followed. One author started the screening and literature analysis, and two authors validated the processes and results. Inclusion and exclusion criteria were developed and refined iteratively.ResultsAmong 81 included publications, the identified CDSS were mainly applied to managing chronic conditions, alerts for medication prescriptions, reminders for immunizations and preventive services, diagnoses, and treatment recommendations. The CDSS rules were presented in Semantic Web Rule Language, Jess, or Jena formats. Despite ontology was used to supply medical knowledge, CDSS rules, and terminologies to CDSS, ontology has not been used in CDSS rule management.ConclusionsAlthough ontology can facilitate the reuse, management, and maintenance of CDSS rules, CDSS ontology remains unavailable indicating that more efforts are needed to improve the reusability and interoperability of CDSS rules.

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Section: Other Resultsmentioning

confidence: 89%

A systematic review of ontology-based clinical decision support system rules: usage, management, and interoperability

Jing

Min

Gong

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…The authors of [12] proposed independent inference engine which separated the knowledge reasoning from knowledge representation. Inference engine separation made it superior then other approaches.…”

Section: Related Workmentioning

confidence: 99%

Socially interactive CDSS for u-life care

Fatima

Fahim

Guan

et al. 2011

Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication

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Clinical decision support system (CDSS) is an interactive decision support system computer software, which is designed to assist physicians and other health professionals with decision making tasks, such as determining diagnosis of patient data, disease prevention, and alerting adverse drug events. It links health observations with health knowledge to influence health choices by clinicians for improved health care. Different from conventional CDSSs which focus on diagnosis assistance, the focus of our CDSS is to provide recommendations and healthcare services for chronic disease patients by long term monitoring. To make our CDSS more intelligent, it induces the patients to interact with the system. By continuously learning and digesting patients' experience and knowledge, the knowledge base of our CDSS is self-evolutionary and dynamically enhanced. We mainly develop two modules to achieve the function of social interaction. Firstly, Knowledge Authority Module (KAM) is developed which is capable of manipulating and preprocessing social data. Secondly, to support self-evolutionary and dynamical learning, we designed the rough set based inference engine. Through social interaction, the patients can get continuous relevant medical recommendations from the system, so they can get a chance to improve their health conditions which in turns keeping on their quality of life.

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