Pre-assessment of Machine Learning Approaches for Patient Length of Stay Prediction

Mahmud, Farhanahani; Khudzari, Ahmad Zahran Md; Pau, C P; Ramli, Mohd Faizal; Jaffar, Norfazlina; Gaaffar, Intan Fariza

doi:10.1007/978-981-16-8903-1_32

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2023

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“…The downloaded data contained a total of 58,976 samples with 28 characteristics. The feature meanings are shown in Table 1 [29].…”

Section: Methodology Figure 1 Flowchart Of the Article Frameworkmentioning

confidence: 99%

Deep Residual Networks and Bayesian Data Priors in the Survival Prediction and Classification

Zhang

2023

HSET

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Sepsis is a highly lethal disease in intensive care units, and patient indicators are constantly changing, making accurate prediction of patient mortality crucial for doctors to develop appropriate treatment plans. While machine learning and deep learning have been applied to sepsis research, model generalization performance can suffer from underfitting or gradient issues. To address these challenges, we propose using a deep residual network and a deep residual network incorporating Bayesian data prior to predict patient mortality using the MIMIC-III dataset. The accuracy of the two models on the validation set reached 0.9392 and 0.9329, respectively.

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“…The downloaded data contained a total of 58,976 samples with 28 characteristics. The feature meanings are shown in Table 1 [29].…”

Section: Methodology Figure 1 Flowchart Of the Article Frameworkmentioning

confidence: 99%

Deep Residual Networks and Bayesian Data Priors in the Survival Prediction and Classification

Zhang

2023

HSET

View full text Add to dashboard Cite

show abstract

Pre-assessment of Machine Learning Approaches for Patient Length of Stay Prediction

Cited by 1 publication

References 12 publications

Deep Residual Networks and Bayesian Data Priors in the Survival Prediction and Classification

Deep Residual Networks and Bayesian Data Priors in the Survival Prediction and Classification

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