Abstract 13269: Artificial Intelligence (AI) Clinical Decision-Supporting System (CDSS) for Diagnosis of Heart Failure: Concordance With Expert Decision.

Background: Technologically integrated healthcare environments can be realized if physicians are encouraged to use smart systems for the creation and sharing of knowledge used in clinical decision support systems (CDSS). Physicians are challenged to personalize care with rapidly changing scientific evidence, drug approvals, and guidelines for heart failure (HF). Artificial intelligence (AI) CDSS have the potential to help lessen this challenge. Hypothesis: We report here the results of examining the level of correspondence between diagnostic recommendations made by the AI CDSS and a cardiology board for HF.. Method: AI CDSS system for cardiology (e-Health for cardiology (eHFC)) was developed by using Intelligence Knowledge Authoring Tool (I-KAT) and archiving Arden Syntax MLM (Medical Logic Module) as shareable knowledge rules for intelligent decision-making. A total of 598 patients with suspected HF were available between 2016 and 2017 at Seoul National University Bundang Hospital, South Korea. Both eHFC and HF specialist gave a diagnosis for each patient independently. Concordance rate between eHFC and HF specialist was evaluated. Results: Overall, the concordance rate was 98.3%. The concordance rate for patients with HFrEF, HFmrEF, HFpEF and no-HF was 100%, 100%, 99.56%, and 91.67%, respectively (Table 1): 9 patients without HF were wrongly classified as HFpEF. The concordance was independent of age of the patients. We divided patients according to echocardiographic parameters. Set A (all echocardiography parameters were available) and Set B (only LVEF, LAVI, and LVMI were available). The concordance rate was lower in Set B than in Set A (Figure). Conclusions: eHFC shows high diagnostic accuracy to diagnose HF, independent of HF types. eHFC may be useful for diagnosis of HF, especially at centers without HF specialist. Key words: artificial intelligence, clinical decision-support system, heart failure Table 1: Confusion Matrix of eHFC for HFrEF, HFmrEF, HFpEF, and no-HF Figure 1: Accuracy Comparison of Set A, and Set B [ABSTRACT FROM AUTHOR]