Clinical decision-support for diagnosing stress-related disorders by applying psychophysiological medical knowledge to an instance-based learning system

2006 ◽  
Vol 36 (2) ◽  
pp. 159-176 ◽  
Author(s):  
Markus Nilsson ◽  
Peter Funk ◽  
Erik M.G. Olsson ◽  
Bo von Schéele ◽  
Ning Xiong
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Medical Knowledge ◽  
Clinical Decision ◽  
Learning System ◽  
Instance Based Learning

Clinical Decision Support Systems

2021 ◽  
pp. 1-7
Author(s):  
Andreas Teufel ◽  
Harald Binder
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Health Care Providers ◽  
Support Systems ◽  
Medical Knowledge ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Current Status ◽  
Care Providers

<b><i>Background:</i></b> By combining up-to-date medical knowledge and steadily increasing patient data, a new level of medical care can emerge. <b><i>Summary and Key Messages:</i></b> Clinical decision support systems (CDSSs) are an arising solution to handling rich data and providing them to health care providers in order to improve diagnosis and treatment. However, despite promising examples in many areas, substantial evidence for a thorough benefit of these support solutions is lacking. This may be due to a lack of general frameworks and diverse health systems around the globe. We therefore summarize the current status of CDSSs in medicine but also discuss potential limitations that need to be overcome in order to further foster future development and acceptance.

scholarly journals Mobilising computable biomedical knowledge: challenges for clinical decision support from a medical knowledge provider

2020 ◽  
Vol 27 (2) ◽  
pp. e100121 ◽  
Author(s):  
Kieran Walsh ◽  
Chris Wroe
Keyword(s):  
Decision Support ◽  
Problem Solving ◽  
Patient Outcomes ◽  
Clinical Decision Support ◽  
Healthcare Professionals ◽  
Medical Knowledge ◽  
Clinical Decision ◽  
Biomedical Knowledge ◽  
Procedural Skills ◽  
Safety Standards

IntroductionThis paper summarises a talk given at the first UK workshop on mobilising computable biomedical knowledge on 29 October 2019 in London. It examines challenges in mobilising computable biomedical knowledge for clinical decision support from the perspective of a medical knowledge provider.MethodsWe developed the themes outlined below after personally reflecting on the challenges that we have encountered in this field and after considering the barriers that knowledge providers face in ensuring that their content is accessed and used by healthcare professionals. We further developed the themes after discussing them with delegates at the workshop and listening to their feedback.DiscussionThere are many challenges in mobilising computable knowledge for clinical decision support from the perspective of a medical knowledge provider. These include the size of the task at hand, the challenge of creating machine interpretable content, the issue of standards, the need to do better in tracing how computable medical knowledge that is part of clinical decision support impacts patient outcomes, the challenge of comorbidities, the problem of adhering to safety standards and finally the challenge of integrating knowledge with problem solving and procedural skills, healthy attitudes and professional behaviours. Partnership is likely to be essential if we are to make progress in this field. The problems are too complex and interrelated to be solved by any one institution alone.

scholarly journals Best Paper Selection

2020 ◽  
Vol 29 (01) ◽  
pp. 158-158
Keyword(s):  
Breast Cancer ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Knowledge Engineering ◽  
Medical Knowledge ◽  
Clinical Decision ◽  
Data Driven ◽  
Evidence Based ◽  
National Breast ◽  
Multi Level

Hendriks MP, Verbeek XAAM, van Vegchel T, van der Sangen MJC, Strobbe LJA, Merkus JWS, Zonderland HM, Smorenburg CH, Jager A, Siesling S. Transformation of the National Breast Cancer Guideline into data-driven clinical decision trees. JCO Clin Cancer Inform 2019 May;3:1-14 https://ascopubs.org/doi/full/10.1200/CCI.18.00150 Kamišalić A, Riaño D, Kert S, Welzer T, Nemec Zlatolas L. Multi-level medical knowledge formalization to support medical practice for chronic diseases. Data & Knowledge Engineering 2019; 119:36–57 https://www.sciencedirect.com/science/article/abs/pii/S0169023X16303937 Khalifa M, Magrabi F, Gallego B. Developing a framework for evidence-based grading and assessment of predictive tools for clinical decision support. BMC Med Inform Decis Mak 2019 Oct 29;19(1):207 https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-019-0940-7

scholarly journals Using clinical reasoning ontologies to make smarter clinical decision support systems: a systematic review and data synthesis

2019 ◽  
Vol 27 (1) ◽  
pp. 159-174 ◽  
Author(s):  
Pavithra I Dissanayake ◽  
Tiago K Colicchio ◽  
James J Cimino
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Reasoning ◽  
Support Systems ◽  
Medical Knowledge ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
High Quality ◽  
Current Practices

Abstract Objective The study sought to describe the literature describing clinical reasoning ontology (CRO)–based clinical decision support systems (CDSSs) and identify and classify the medical knowledge and reasoning concepts and their properties within these ontologies to guide future research. Methods MEDLINE, Scopus, and Google Scholar were searched through January 30, 2019, for studies describing CRO-based CDSSs. Articles that explored the development or application of CROs or terminology were selected. Eligible articles were assessed for quality features of both CDSSs and CROs to determine the current practices. We then compiled concepts and properties used within the articles. Results We included 38 CRO-based CDSSs for the analysis. Diversity of the purpose and scope of their ontologies was seen, with a variety of knowledge sources were used for ontology development. We found 126 unique medical knowledge concepts, 38 unique reasoning concepts, and 240 unique properties (137 relationships and 103 attributes). Although there is a great diversity among the terms used across CROs, there is a significant overlap based on their descriptions. Only 5 studies described high quality assessment. Conclusion We identified current practices used in CRO development and provided lists of medical knowledge concepts, reasoning concepts, and properties (relationships and attributes) used by CRO-based CDSSs. CRO developers reason that the inclusion of concepts used by clinicians’ during medical decision making has the potential to improve CDSS performance. However, at present, few CROs have been used for CDSSs, and high-quality studies describing CROs are sparse. Further research is required in developing high-quality CDSSs based on CROs.

Lessons learned from the development of the CancerLinQ prototype: Clinical decision support.

2013 ◽  
Vol 31 (31_suppl) ◽  
pp. 237-237
Author(s):  
Richard L. Schilsky ◽  
Sandra M. Swain ◽  
Robert Hauser ◽  
Joshua Mann ◽  
George W. Sledge ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Point Of Care ◽  
Clinical Decision ◽  
Lessons Learned ◽  
Learning System ◽  
Unified Medical Language System ◽  
Platform Process ◽  
Share Data ◽  
Rules Engine

237 Background: CancerLinQ (CLQ) is a rapid learning system (RLS) for oncology in development by ASCO. CLQ is based on the transfer of electronic health records (EHR) from participating oncology practices to a data warehouse where data aggregation and de-identification occurs. A prototype was built using open source software and has collected de-identified data on 170,000+ pts with breast cancer (BC) from 31 community oncology practices using 4 different EHRs. The primary goals for the prototype were 1. Aggregate patient data from any EHR platform, process it and create a longitudinal record; 2. Develop quality reports from EHRs; 3. Point of care Clinical Decision Support (CDS) from ASCO guidelines; 4. Data visualization for hypothesis generation; 5. Demonstrate desire to share data for quality improvement; 6. Describe lessons learned (LL). This report focuses on LL about CDS. Methods: Physician experts identified specific elements from each ASCO BC guideline to make machine readable (MR). Abstractors then GEM-cut the elements using the GEM Abstraction Manual and Style Guide. The output reports were reviewed for comprehensiveness, accuracy, and style. Following verification of the GEM-cut content, reports were sent for meta-tagging, done by selecting widely used EHR vocabulary from the Unified Medical Language System (UMLS). The GEM-cut output and meta-tags were converted to DROOLS syntax and the resulting coded files were inserted into the DROOLS rules engine. When the rules engine encounters a combination of facts that match a rule, that rule is presented to the user. The enduring responses are collected using ‘queries’ and the CDS results are delivered to the EHR. Results: Guidelines are often not written as “if”/“then” statements which is key for computer-based CDS. Any unintentional ambiguity must be removed for machine MR CDS. Using new methodologies, we have been able to convert narrative guidelines into MR CDS. Conclusions: Conversion of ASCO’s clinical guidelines into a MR format is possible. New and emerging methods such as GLIDES, BRIDGE-Wiz, and GEM-cutting provide excellent tools to migrate existing narrative recommendations into MR format that can populate CDS tools, such as those provided by CancerLinQ.

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