Multi-model-based interactive authoring environment for creating shareable medical knowledge

Ali, Taqdir; Hussain, Maqbool; Khan, Wajahat Ali; Afzal, Muhammad; Hussain, Jamil; Ali, Rahman; Hassan, Waseem; Jamshed, Arif; Kang, Byeong Ho; Lee, Sungyoung

doi:10.1016/j.cmpb.2017.07.010

Cited by 17 publications

(11 citation statements)

References 27 publications

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“…Finally, we developed a web-based application in the form of cardiovascular AI-CDSS for use of physicians in real clinical practice. For this purpose, the R-CKM knowledge was transformed into MLM for knowledge shareability and computer-executable format using I-KAT, which had been developed by our team 7 . The resultant knowledge can be easily shared and integrated into various formats of HF diagnosis systems, because the resultant knowledge was built with consolidation of the standard data model vMR (Virtual Medical Record) and the standard terminology SNOMED CT (Systematized Nomenclature of Medicine-Clinical Terms).…”

Section: Discussionmentioning

confidence: 99%

“…Two types of AI decision systems are available: a white-boxbased and a black-box-based one. A white-box AI-based decision system involves correlations and transparency among rules for the analysis of accumulated data, and is mainly constructed using supervised algorithms such as decision tree algorithm 7 . On the contrary, a black-box-based AI has opaque algorithms and its process and reasoning applied in providing the respective conclusions are difficult to clarify.…”

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence for the diagnosis of heart failure

et al. 2020

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The diagnosis of heart failure can be difficult, even for heart failure specialists. Artificial Intelligence-Clinical Decision Support System (AI-CDSS) has the potential to assist physicians in heart failure diagnosis. The aim of this work was to evaluate the diagnostic accuracy of an AI-CDSS for heart failure. AI-CDSS for cardiology was developed with a hybrid (expert-driven and machine-learningdriven) approach of knowledge acquisition to evolve the knowledge base with heart failure diagnosis. A retrospective cohort of 1198 patients with and without heart failure was used for the development of AI-CDSS (training dataset, n = 600) and to test the performance (test dataset, n = 598). A prospective clinical pilot study of 97 patients with dyspnea was used to assess the diagnostic accuracy of AI-CDSS compared with that of non-heart failure specialists. The concordance rate between AI-CDSS and heart failure specialists was evaluated. In retrospective cohort, the concordance rate was 98.3% in the test dataset. The concordance rate for patients with heart failure with reduced ejection fraction, heart failure with mid-range ejection fraction, heart failure with preserved ejection fraction, and no heart failure was 100%, 100%, 99.6%, and 91.7%, respectively. In a prospective pilot study of 97 patients presenting with dyspnea to the outpatient clinic, 44% had heart failure. The concordance rate between AI-CDSS and heart failure specialists was 98%, whereas that between non-heart failure specialists and heart failure specialists was 76%. In conclusion, AI-CDSS showed a high diagnostic accuracy for heart failure. Therefore, AI-CDSS may be useful for the diagnosis of heart failure, especially when heart failure specialists are not available.npj Digital Medicine (2020) 3:54 ; https://doi.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial intelligence for the diagnosis of heart failure

et al. 2020

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“…Other technical factors of the SOA architecture are concerns, such as the delay in response time due to the excessive time required for data retrieval, or the balance between availability and security requirements for ensuring protection of potentially sensitive patient data. Requirements for hosting SOAs on commercially-owned cloud computing environments that allow for scalability and rapid deployment are being proposed and evaluated but may not yet be cost effective until they demonstrate substantial shared usage 19 20 . Work reporting a privacy-preserving framework for implementing real-time monitoring proposed new data protocols that support patient health status monitoring without privacy leakage in simulation settings 21 .…”

Section: Resultsmentioning

confidence: 99%

Advances in Sharing Multi-sourced Health Data on Decision Support Science 2016-2017

Shankar

Anderson

2018

Yearb Med Inform

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“…The first two phases of the process model dedicated to knowledge acquisition, which covers knowledge model creations from CPGs and patient data and the validation process. The third phase concentrates on the executable knowledge model and the development of associated toolset [23].…”

Section: Hybrid Knowledge Acquisition Approach For Guideline Enabled mentioning

confidence: 99%

“…The hybrid knowledge acquisition method was used in the creation of the knowledge model for Smart CDSS in oral cavity cancer [17,23] with proper validation mechanism. However, it was not formally verified even after using as a core method of knowledge acquisition for Smart CDSS.…”

Section: Figure 3: Validation Process [17]mentioning

confidence: 99%

Guideline-Enabled Data Driven Clinical Knowledge Model for the Treatment of Oral Cavity Cancer Acquired Through a Refined Knowledge Acquisition Method

Hussain¹,

Afzal²,

Malik³

et al. 2019

Preprint

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Validation and verification are the critical requirements in the knowledge acquisition method for the clinical decision support system (CDSS). After acquiring the medical knowledge from diverse sources, the rigorous validation and formal verification process are required before creating the final knowledge model. Previously, we have proposed a hybrid knowledge acquisition method for acquiring medical knowledge from clinical practice guidelines (CPGs) and patient data in the Smart CDSS for treatment of oral cavity cancer. The final knowledge model was created by combining knowledge models obtained from CPGs and patient data after passing through a rigorous validation process. However, detailed analysis shows that due to lack of formal verification process, it involves various inconsistencies in knowledge relevant to the formalism of knowledge, conformance to CPGs, quality of knowledge, and complexities of knowledge acquisition artifacts. Therefore, it is required to enhance a hybrid knowledge acquisition method that thwarts the inconsistencies using formal verification. This paper presents the verification process using the Z formal method and its outcome as an enhanced acquisition method – known as the refined knowledge acquisition (ReKA) method. The ReKA method adopted verification method and explored the mechanism of theorem proving using the Z notation. It enables to identify inconsistencies in the validation process used for hybrid knowledge acquisition. Additionally, it refines the hybrid knowledge acquisition method by discovering the missing steps in the current validation process at the acquisition stage. Consequently, ReKA adds a set of nine additional criteria to be used to have a final valid refined clinical knowledge model. The criteria ensure the validity of final knowledge model concerning formalism of knowledge, conformance to GPGs, quality of the knowledge, usage of stringent conditions and treatment plans, and inconsistencies possibly resulting from the complexities. Evaluation, using four medical knowledge acquisition scenarios, shows that newly added knowledge in CDSS due to the addition of criteria by ReKA method always produces a valid knowledge model. The final knowledge model was also evaluated with 1229 oral cavity patient cases, which outperformed with an accuracy of 72.57\% compared to a similar approach with an accuracy of 69.7\%. Furthermore, ReKA method identified a set of decision paths (about 47.8%) in the existing approach, which results in a final knowledge model with low quality, non-conformed from standard CPGs. In conclusion, ReKA is formally proved method which always yields valid knowledge model having high quality, supporting local practices, and influenced from standard guidelines.

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Multi-model-based interactive authoring environment for creating shareable medical knowledge

Cited by 17 publications

References 27 publications

Artificial intelligence for the diagnosis of heart failure

Artificial intelligence for the diagnosis of heart failure

Advances in Sharing Multi-sourced Health Data on Decision Support Science 2016-2017

Guideline-Enabled Data Driven Clinical Knowledge Model for the Treatment of Oral Cavity Cancer Acquired Through a Refined Knowledge Acquisition Method

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