; Smole, Tim ; Žunkovič, Bojan ; Kokalj, Enja
; Robnik-Šikonja, Marko ; Kukar, Matjaž
; Fotiadis, Dimitrios I.
; Pezoulas, Vasileios C.
; Tachos, Nikolaos S.
; Barlocco, Fausto ; Mazzarotto, Francesco
; Popović, Dejana ; Maier, Lars S.
; Velicki, Lazar
; Olivotto, Iacopo ; MacGowan, Guy A. ; Jakovljević, Djordje G.
; Filipović, Nenad ; Bosnić, Zoran | Item type: | Article | ||||
|---|---|---|---|---|---|
| Journal or Publication Title: | JMIR Medical Informatics | ||||
| Publisher: | JMIR PUBLICATIONS, INC | ||||
| Place of Publication: | TORONTO | ||||
| Volume: | 10 | ||||
| Number of Issue or Book Chapter: | 2 | ||||
| Page Range: | e30483 | ||||
| Date: | 2022 | ||||
| Institutions: | Medicine > Lehrstuhl für Innere Medizin II | ||||
| Identification Number: |
| ||||
| Keywords: | RISK STRATIFICATION; ARTIFICIAL-INTELLIGENCE; PREDICTION; CARDIOLOGY; hypertrophic cardiomyopathy; disease progression; machine learning; artificial intelligence; AI; ML; cardiomyopathy; cardiovascular disease; sudden cardiac death; SCD; prediction; prediction model; validation | ||||
| Dewey Decimal Classification: | 600 Technology > 610 Medical sciences Medicine | ||||
| Status: | Published | ||||
| Refereed: | Yes, this version has been refereed | ||||
| Created at the University of Regensburg: | Partially | ||||
| Item ID: | 57622 |

Abstract
Background: Cardiovascular disorders in general are responsible for 30% of deaths worldwide. Among them, hypertrophic cardiomyopathy (HCM) is a genetic cardiac disease that is present in about 1 of 500 young adults and can cause sudden cardiac death (SCD). Objective: Although the current state-of-the-art methods model the risk of SCD for patients, to the best of our knowledge, no methods are ...

Abstract
Background: Cardiovascular disorders in general are responsible for 30% of deaths worldwide. Among them, hypertrophic cardiomyopathy (HCM) is a genetic cardiac disease that is present in about 1 of 500 young adults and can cause sudden cardiac death (SCD). Objective: Although the current state-of-the-art methods model the risk of SCD for patients, to the best of our knowledge, no methods are available for modeling the patient's clinical status up to 10 years ahead. In this paper, we propose a novel machine learning (ML)-based tool for predicting disease progression for patients diagnosed with HCM in terms of adverse remodeling of the heart during a 10-year period. Methods: The method consisted of 6 predictive regression models that independently predict future values of 6 clinical characteristics: left atrial size, left atrial volume, left ventricular ejection fraction, New York Heart Association functional classification, left ventricular internal diastolic diameter, and left ventricular internal systolic diameter. We supplemented each prediction with the explanation that is generated using the Shapley additive explanation method. Results: The final experiments showed that predictive error is lower on 5 of the 6 constructed models in comparison to experts (on average, by 0.34) or a consortium of experts (on average, by 0.22). The experiments revealed that semisupervised learning and the artificial data from virtual patients help improve predictive accuracies. The best-performing random forest model improved R-2 from 0.3 to 0.6. Conclusions: By engaging medical experts to provide interpretation and validation of the results, we determined the models' favorable performance compared to the performance of experts for 5 of 6 targets.
Metadata last modified: 27 Aug 2024 05:33
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