Deep learning architecture to predict daily hospital admissions

Navares, Ricardo; Aznarte, José Luis

doi:10.1007/s00521-020-04840-8

Cited by 11 publications

(1 citation statement)

References 38 publications

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“…In [34], by analyzing the effectiveness of various machine learning (ML) models for predicting mortality, critical care outcome, and the need for hospitalization of ED patients reported in 11 studies, and showed that deep neural network (DNN) [35][36][37] and extreme gradient boosting (XGBoost) [37][38][39] achieved the greatest predictive accuracy among the assessed ML models, with an AUC of 0.782-0.92 and 0.922-0.962, respectively. In [40], independent meteorological, pollen, and chemical pollution data were adopted to design predictive models using long short-term memories and CNN (LSTM + CNN) to forecast daily hospital admissions for patients due to respiratory-and circulatory-related disorders, which showed that the models could precisely forecast hospital admissions with a root mean squared error (RMSE) of 11.21 and 11.76 for circulatory and respiratory cases, respectively. Moreover, in [41], a neural network, namely COVID-Net Clinical ICU, was proposed to predict admission to intensive care units (ICU) for COVID patients with an accuracy of 96.9%.…”

Section: Ai Models For Predicting Associated Events Of Hospital Admis...mentioning

confidence: 99%

AI Models for Predicting Readmission of Pneumonia Patients within 30 Days after Discharge

Hsu

Lin

et al. 2022

Electronics

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A model with capability for precisely predicting readmission is a target being pursued worldwide. The objective of this study is to design predictive models using artificial intelligence methods and data retrieved from the National Health Insurance Research Database of Taiwan for identifying high-risk pneumonia patients with 30-day all-cause readmissions. An integrated genetic algorithm (GA) and support vector machine (SVM), namely IGS, were used to design predictive models optimized with three objective functions. In IGS, GA was used for selecting salient features and optimal SVM parameters, while SVM was used for constructing the models. For comparison, logistic regression (LR) and deep neural network (DNN) were also applied for model construction. The IGS model with AUC used as the objective function achieved an accuracy, sensitivity, specificity, and area under ROC curve (AUC) of 70.11%, 73.46%, 69.26%, and 0.7758, respectively, outperforming the models designed with LR (65.77%, 78.44%, 62.54%, and 0.7689, respectively) and DNN (61.50%, 79.34%, 56.95%, and 0.7547, respectively), as well as previously reported models constructed using thedata of electronic health records with an AUC of 0.71–0.74. It can be used for automatically detecting pneumonia patients with a risk of all-cause readmissions within 30 days after discharge so as to administer suitable interventions to reduce readmission and healthcare costs.

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