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- 저자 : Bambang B Siswanto (검색결과 2 건)
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Finna E. Indriany,Kemal N. Siregar,Budhi Setianto Purwowiyoto,Bambang Budi Siswanto,Indrajani Sutedja,Hendy R. Wijaya 대한의료정보학회 2024 Healthcare Informatics Research Vol.30 No.3
Objectives: In Indonesia, the poor prognosis and high hospital readmission rates of patients with heart failure (HF) have yet to receive focused attention. However, machine learning (ML) approaches can help to mitigate these problems. We aimed to determine which ML models best predicted HF severity and hospital readmissions and could be used in a patient selfmonitoring mobile application. Methods: In a retrospective cohort study, we collected the data of patients admitted with HF to the Siloam Diagram Heart Center in 2020, 2021, and 2022. Data was analyzed using the Orange data mining classification method. ML support algorithms, including artificial neural network (ANN), random forest, gradient boosting, Naïve Bayes, tree-based models, and logistic regression were used to predict HF severity and hospital readmissions. The performance of these models was evaluated using the area under the curve (AUC), accuracy, and F1-scores. Results: Of the 543 patients with HF, 3 (0.56%) were excluded due to death on admission. Hospital readmission occurred in 138 patients (25.6%). Of the six algorithms tested, ANN showed the best performance in predicting both HF severity (AUC = 1.000, accuracy = 0.998, F1-score = 0.998) and readmission for HF (AUC = 0.998, accuracy = 0.975, F1-score = 0.972). Other studies have shown variable results for the best algorithm to predict hospital readmission in patients with HF. Conclusions: The ANN algorithm performed best in predicting HF severity and hospital readmissions and will be integrated into a mobile application for patient self-monitoring to prevent readmissions.
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Estu Rudiktyo,Amiliana M Soesanto,Maarten J Cramer,Emir Yonas,Arco J Teske,Bambang B Siswanto,Pieter A Doevendans 한국심초음파학회 2023 Journal of Cardiovascular Imaging (J Cardiovasc Im Vol.31 No.4
BACKGROUND: Assessment of left ventricular (LV) function plays a pivotal role in the management of patients with valvular heart disease, including those caused by rheumatic heart disease. Noninvasive LV pressure-strain loop analysis is emerging as a new echocardiographic method to evaluate global LV systolic function, integrating longitudinal strain by speckle-tracking analysis and noninvasively measured blood pressure to estimate myocardial work. The aim of this study was to characterize global LV myocardial work efficiency in patients with severe rheumatic mitral stenosis (MS) with preserved ejection fraction (EF). METHODS: We retrospectively included adult patients with severe rheumatic MS with preserved EF (> 50%) and sinus rhythm. Healthy individuals without structural heart disease were included as a control group. Global LV myocardial work efficiency was estimated with a proprietary algorithm from speckle-tracking strain analyses, as well as noninvasive blood pressure measurements. RESULTS: A total of 45 individuals with isolated severe rheumatic MS with sinus rhythm and 45 healthy individuals were included. In healthy individuals without structural heart disease, the mean global LV myocardial work efficiency was 96% (standard deviation [SD], 2), Compared with healthy individuals, median global LV myocardial work efficiency was significantly worse in MS patients (89%; SD, 4; p < 0.001) although the LVEF was similar. CONCLUSIONS: Individuals with isolated severe rheumatic MS and preserved EF, had global LV myocardial work efficiencies lower than normal controls.
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