LoSNet: A Tailored Deep Neural Network Framework for Precise Length of Stay Prediction in Disease-Specific Hospitalization

Abstract

During the COVID-19 pandemic, the healthcare sector faced unprecedented challenges in effectively managing hospital resources. A crucial aspect of resource planning and allocation is the ability to predict the expected length of a patient’s stay. Detecting whether a patient requires extended hospitalization or a shorter stay becomes vital for efficient hospital resource allocation and utilization. This paper aims to build a deep learning-based analytical model named LoSNet that predicts the length of stay for each patient at the time of admission to the Hospital. The early prediction of the length of stay requirement would aid healthcare professionals in optimizing the utility of hospital beds and other resources. In this direction, this paper compares the prediction ability of various machine-learning models including Random Forest, Decision Tree, Logistic Regression, and Naïve Bayes with a customized deep neural network model. The dataset used for this analysis includes ground truth on 3,18,438 patients’ length of stay categorized into eleven classes such as 0-10 days being one class, 11-20 days being another class, and so on to more than 100 days. The methodology employed in this study involves data collection, data transformation, and training LoSNet, a deep neural network with no attention mechanisms. The results indicate impressive performance over other models and a random classifier, with a cross-entropy loss of 1.531 and an accuracy of 0.408 in predicting hospitalization durations in an 11-class classification setup, highlighting the framework’s effectiveness in healthcare management.