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Deep Learning-Based Dynamic Risk Prediction of Venous Thromboembolism for Patients With Ovarian Cancer in Real-World Settings From Electronic Health Records.
Lee, Dahhay; Kim, Seongyoon; Lee, Sanghee; Kim, Hak Jin; Kim, Ji Hyun; Lim, Myong Cheol; Cho, Hyunsoon.
Afiliação
  • Lee D; Department of Cancer AI and Digital Health, National Cancer Center Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Republic of Korea.
  • Kim S; School of Mathematics and Computing (Computational Science and Engineering), Yonsei University, Seoul, Republic of Korea.
  • Lee S; School of Mathematics and Computing (Computational Science and Engineering), Yonsei University, Seoul, Republic of Korea.
  • Kim HJ; Department of Cancer AI and Digital Health, National Cancer Center Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Republic of Korea.
  • Kim JH; Health Insurance Research Institute, National Health Insurance Service, Wonju, Republic of Korea.
  • Lim MC; Department of Cardiology, Gumdan Top General Hospital, Incheon, Republic of Korea.
  • Cho H; Branch of Cardiology, Department of Internal Medicine, National Cancer Center, Goyang, Republic of Korea.
JCO Clin Cancer Inform ; 8: e2300192, 2024 Jul.
Article em En | MEDLINE | ID: mdl-38996199
ABSTRACT

PURPOSE:

Patients with epithelial ovarian cancer (EOC) have an elevated risk for venous thromboembolism (VTE). To assess the risk of VTE, models were developed by statistical or machine learning algorithms. However, few models have accommodated deep learning (DL) algorithms in realistic clinical settings. We aimed to develop a predictive DL model, exploiting rich information from electronic health records (EHRs), including dynamic clinical features and the presence of competing risks.

METHODS:

We extracted EHRs of 1,268 patients diagnosed with EOC from January 2007 through December 2017 at the National Cancer Center, Korea. DL survival networks using fully connected layers, temporal attention, and recurrent neural networks were adopted and compared with multi-perceptron-based classification models. Prediction accuracy was independently validated in the data set of 423 patients newly diagnosed with EOC from January 2018 to December 2019. Personalized risk plots displaying the individual interval risk were developed.

RESULTS:

DL-based survival networks achieved a superior area under the receiver operating characteristic curve (AUROC) between 0.95 and 0.98 while the AUROC of classification models was between 0.85 and 0.90. As clinical information benefits the prediction accuracy, the proposed dynamic survival network outperformed other survival networks for the test and validation data set with the highest time-dependent concordance index (0.974, 0.975) and lowest Brier score (0.051, 0.049) at 6 months after a cancer diagnosis. Our visualization showed that the interval risk fluctuating along with the changes in longitudinal clinical features.

CONCLUSION:

Adaption of dynamic patient clinical features and accounting for competing risks from EHRs into the DL algorithms demonstrated VTE risk prediction with high accuracy. Our results show that this novel dynamic survival network can provide personalized risk prediction with the potential to assist risk-based clinical intervention to prevent VTE among patients with EOC.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Ovarianas / Tromboembolia Venosa / Registros Eletrônicos de Saúde / Aprendizado Profundo Limite: Adult / Aged / Female / Humans / Middle aged País/Região como assunto: Asia Idioma: En Revista: JCO Clin Cancer Inform Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Ovarianas / Tromboembolia Venosa / Registros Eletrônicos de Saúde / Aprendizado Profundo Limite: Adult / Aged / Female / Humans / Middle aged País/Região como assunto: Asia Idioma: En Revista: JCO Clin Cancer Inform Ano de publicação: 2024 Tipo de documento: Article