Patient Discharge Classification Based on the Hospital Treatment Process

Abstract

Heart failure is one of the leading causes of hospitalization and rehospitalization in American hospitals, leading to high expenditures and increased medical risk for patients. The discharge location has a strong association with the risk of rehospitalization and mortality, which makes determining the most suitable discharge location for a patient a crucial task. So far, work regarding patient discharge classification is limited to the state of the patients at the end of the treatment, including statistical analysis and machine learning. However, the treatment process has not been considered yet. In this contribution, the methods of process outcome prediction are utilized to predict the discharge location for patients with heart failure by incorporating the patient’s department visits and measurements during the treatment process. This paper shows that, with the help of convolutional neural networks, an accuracy of 77% can be achieved for the hospital discharge classification of heart failure patients. The model has been trained and evaluated on the MIMIC-IV real-world dataset on hospitalizations in the US.

Shifting psychometric bipolar scales data towards the normal distribution

“…As shown by [Valera andGhahramani, 2017, Cremerius andWeske, 2022], we can apply simple logic rules to distinguish between categorical and continuous variables. Thus, we apply a check that divides the number of distinct values by the total number of values and compares this fraction against a user-defined threshold.…”