Improving cardiovascular risk prediction through machine learning modelling of irregularly repeated electronic health records

Li, Chaiquan; Liu, Xiaofei; Shen, Peng; Sun, Yexiang; Zhou, Tianjing; Chen, Weiye; Chen, Qi; Lin, Hongbo; Tang, Xun; Gao, Pei

doi:10.1093/ehjdh/ztad058

Cited by 4 publications

(2 citation statements)

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“…We adjusted hyperparameters through 1000 iterations of random search and fivefold cross-validation on the training set. The performance of models with different hyperparameter combinations was assessed using the concordance index (C-index).The difference between the two models’ C-index was tested using Kang’s method 17 . Model accuracy was assessed using the C-index, and we applied the Brier score to represent the mean squared difference between the observed patient state and the predicted survival probability.…”

Section: Methodsmentioning

confidence: 99%

Deep learning models for predicting the survival of patients with hepatocellular carcinoma based on a surveillance, epidemiology, and end results (SEER) database analysis

Wang,

Shao,

et al. 2024

Sci Rep

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Hepatocellular carcinoma (HCC) is a common malignancy with poor survival and requires long-term follow-up. Hence, we collected information on patients with Primary Hepatocellular Carcinoma in the United States from the Surveillance, Epidemiology, and EndResults (SEER) database. We used this information to establish a deep learning with a multilayer neural network (the NMTLR model) for predicting the survival rate of patients with Primary Hepatocellular Carcinoma. HCC patients pathologically diagnosed between January 2011 and December 2015 in the SEER (Surveillance, Epidemiology, and End Results) database of the National Cancer Institute of the United States were selected as study subjects. We utilized two deep learning-based algorithms (DeepSurv and Neural Multi-Task Logistic Regression [NMTLR]) and a machine learning-based algorithm (Random Survival Forest [RSF]) for model training. A multivariable Cox Proportional Hazards (CoxPH) model was also constructed for comparison. The dataset was randomly divided into a training set and a test set in a 7:3 ratio. The training dataset underwent hyperparameter tuning through 1000 iterations of random search and fivefold cross-validation. Model performance was assessed using the concordance index (C-index), Brier score, and Integrated Brier Score (IBS). The accuracy of predicting 1-year, 3-year, and 5-year survival rates was evaluated using Receiver Operating Characteristic (ROC) curves, calibration plots, and Area Under the Curve (AUC). The primary outcomes were the 1-year, 3-year, and 5-year overall survival rates. Models were developed using DeepSurv, NMTLR, RSF, and Cox Proportional Hazards regression. Model differentiation was evaluated using the C-index, calibration with concordance plots, and risk stratification capability with the log-rank test. The study included 2197 HCC patients, randomly divided into a training cohort (70%, n = 1537) and a testing cohort (30%, n = 660). Clinical characteristics between the two cohorts showed no significant statistical difference (p > 0.05). The deep learning models outperformed both RSF and CoxPH models, with C-indices of 0.735 (NMTLR) and 0.731 (DeepSurv) in the test dataset. The NMTLR model demonstrated enhanced accuracy and well-calibrated survival estimates, achieving an Area Under the Curve (AUC) of 0.824 for 1-year survival predictions, 0.813 for 3-year, and 0.803 for 5-year survival rates. This model's superior calibration and discriminative ability enhance its utility for clinical prognostication in Primary Hepatocellular Carcinoma. We deployed the NMTLR model as a web application for clinical practice. The NMTLR model have potential advantages over traditional linear models in prognostic assessment and treatment recommendations. This novel analytical approach may provide reliable information on individual survival and treatment recommendations for patients with primary liver cancer.

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Section: Methodsmentioning

confidence: 99%

Deep learning models for predicting the survival of patients with hepatocellular carcinoma based on a surveillance, epidemiology, and end results (SEER) database analysis

Wang,

Shao,

et al. 2024

Sci Rep

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“…In this issue of EHJ Digital Health, Li et al .’s 4 study of more than 200 000 adults (40–79 years) without ASCVD in China illuminate the way forward. The authors followed individuals longitudinally and analysed demographic data, and 25 repeated measures of anthropometric measures and vital signs (e.g.…”

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confidence: 99%

From data to wisdom: harnessing the power of multimodal approach for personalized atherosclerotic cardiovascular risk assessment

Al-Kindi,

Nasir

2023

European Heart Journal - Digital Health

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Continuous patient state attention model for addressing irregularity in electronic health records

Chauhan,

Thakur,

O’Donoghue

et al. 2024

BMC Med Inform Decis Mak

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Background Irregular time series (ITS) are common in healthcare as patient data is recorded in an electronic health record (EHR) system as per clinical guidelines/requirements but not for research and depends on a patient’s health status. Due to irregularity, it is challenging to develop machine learning techniques to uncover vast intelligence hidden in EHR big data, without losing performance on downstream patient outcome prediction tasks. Methods In this paper, we propose Perceiver, a cross-attention-based transformer variant that is computationally efficient and can handle long sequences of time series in healthcare. We further develop continuous patient state attention models, using Perceiver and transformer to deal with ITS in EHR. The continuous patient state models utilise neural ordinary differential equations to learn patient health dynamics, i.e., patient health trajectory from observed irregular time steps, which enables them to sample patient state at any time. Results The proposed models’ performance on in-hospital mortality prediction task on PhysioNet-2012 challenge and MIMIC-III datasets is examined. Perceiver model either outperforms or performs at par with baselines, and reduces computations by about nine times when compared to the transformer model, with no significant loss of performance. Experiments to examine irregularity in healthcare reveal that continuous patient state models outperform baselines. Moreover, the predictive uncertainty of the model is used to refer extremely uncertain cases to clinicians, which enhances the model’s performance. Code is publicly available and verified at https://codeocean.com/capsule/4587224. Conclusions Perceiver presents a computationally efficient potential alternative for processing long sequences of time series in healthcare, and the continuous patient state attention models outperform the traditional and advanced techniques to handle irregularity in the time series. Moreover, the predictive uncertainty of the model helps in the development of transparent and trustworthy systems, which can be utilised as per the availability of clinicians.

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Improving cardiovascular risk prediction through machine learning modelling of irregularly repeated electronic health records

Cited by 4 publications

References 60 publications

Deep learning models for predicting the survival of patients with hepatocellular carcinoma based on a surveillance, epidemiology, and end results (SEER) database analysis

Deep learning models for predicting the survival of patients with hepatocellular carcinoma based on a surveillance, epidemiology, and end results (SEER) database analysis

From data to wisdom: harnessing the power of multimodal approach for personalized atherosclerotic cardiovascular risk assessment

Continuous patient state attention model for addressing irregularity in electronic health records

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