دورية أكاديمية
أعرض في EDS

Deep learning for predicting rehospitalization in acute heart failure: Model foundation and external validation.

التفاصيل البيبلوغرافية
العنوان: Deep learning for predicting rehospitalization in acute heart failure: Model foundation and external validation.
المؤلفون: Kim MN; Department of Internal Medicine, Division of Cardiology, Anam Hospital, Korea University Medicine, Seoul, Korea., Lee YS; Data Analytics Group, Samsung SDS, Seoul, Korea., Park Y; Data Analytics Group, Samsung SDS, Seoul, Korea., Jung A; Data Analytics Group, Samsung SDS, Seoul, Korea., So H; Data Analytics Group, Samsung SDS, Seoul, Korea., Park J; Data Analytics Group, Samsung SDS, Seoul, Korea., Park JJ; Department of Internal Medicine, Division of Cardiology, Seoul National University Bundang Hospital, Seongnam, Korea., Choi DJ; Department of Internal Medicine, Division of Cardiology, Seoul National University Bundang Hospital, Seongnam, Korea., Kim SR; Department of Internal Medicine, Division of Cardiology, Anam Hospital, Korea University Medicine, Seoul, Korea., Park SM; Department of Internal Medicine, Division of Cardiology, Anam Hospital, Korea University Medicine, Seoul, Korea.
المصدر: ESC heart failure [ESC Heart Fail] 2024 Jul 09. Date of Electronic Publication: 2024 Jul 09.
Publication Model: Ahead of Print
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: John Wiley & Sons Ltd on behalf of the European Society of Cardiology Country of Publication: England NLM ID: 101669191 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2055-5822 (Electronic) Linking ISSN: 20555822 NLM ISO Abbreviation: ESC Heart Fail Subsets: MEDLINE
أسماء مطبوعة: Original Publication: [Oxford] : John Wiley & Sons Ltd on behalf of the European Society of Cardiology, [2014]-
مستخلص: Aims: Assessing the risk for HF rehospitalization is important for managing and treating patients with HF. To address this need, various risk prediction models have been developed. However, none of them used deep learning methods with real-world data. This study aimed to develop a deep learning-based prediction model for HF rehospitalization within 30, 90, and 365 days after acute HF (AHF) discharge.
Methods and Results: We analysed the data of patients admitted due to AHF between January 2014 and January 2019 in a tertiary hospital. In performing deep learning-based predictive algorithms for HF rehospitalization, we use hyperbolic tangent activation layers followed by recurrent layers with gated recurrent units. To assess the readmission prediction, we used the AUC, precision, recall, specificity, and F1 measure. We applied the Shapley value to identify which features contributed to HF readmission. Twenty-two prognostic features exhibiting statistically significant associations with HF rehospitalization were identified, consisting of 6 time-independent and 16 time-dependent features. The AUC value shows moderate discrimination for predicting readmission within 30, 90, and 365 days of follow-up (FU) (AUC:0.63, 0.74, and 0.76, respectively). The features during the FU have a relatively higher contribution to HF rehospitalization than features from other time points.
Conclusions: Our deep learning-based model using real-world data could provide valid predictions of HF rehospitalization in 1 year follow-up. It can be easily utilized to guide appropriate interventions or care strategies for patients with HF. The closed monitoring and blood test in daily clinics are important for assessing the risk of HF rehospitalization.
(© 2024 The Author(s). ESC Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.)
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معلومات مُعتمدة: Samsung SDS and Novartis Korea; Novartis Korea
فهرسة مساهمة: Keywords: Deep learning; Heart failure; Rehospitalization; Risk assessment
تواريخ الأحداث: Date Created: 20240709 Latest Revision: 20240709
رمز التحديث: 20240710
DOI: 10.1002/ehf2.14918
PMID: 38981003
قاعدة البيانات: MEDLINE
الوصف
تدمد:2055-5822
DOI:10.1002/ehf2.14918