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Programme: Medical Data Science2
Prediction of Intensive Care Unit Length of Stay in the MIMIC-IV Dataset
Prediction model of Length of Stay and probability of rehospitalization

Abstract (Expand)

Accurately estimating the length of stay (LOS) of patients admitted to the intensive care unit (ICU) in relation to their health status helps healthcare management allocate appropriate resources and … resources and better plan for the future. This paper presents predictive models for the LOS of ICU patients from the MIMIC-IV database based on typical demographic and administrative data, as well as early vital signs and laboratory measurements collected on the first day of ICU stay. The goal of this study was to demonstrate a practical, stepwise approach to predicting patient’s LOS in the ICU using machine learning and early available typical clinical data. The results show that this approach significantly improves the performance of models for predicting actual LOS in a pragmatic framework that includes only data with short stays predetermined by a prior classification.

Authors: Lars Hempel, Sina Sadeghi, Toralf Kirsten

Date Published: 1st Jun 2023

Publication Type: Journal article

GAN-Based Approaches for Generating Structured Data in the Medical Domain
Synthetica: generation and evaluation of synthetic data

Abstract (Expand)

Modern machine and deep learning methods require large datasets to achieve reliable and robust results. This requirement is often difficult to meet in the medical field, due to data sharing limitations …ng limitations imposed by privacy regulations or the presence of a small number of patients (e.g., rare diseases). To address this data scarcity and to improve the situation, novel generative models such as Generative Adversarial Networks (GANs) have been widely used to generate synthetic data that mimic real data by representing features that reflect health-related information without reference to real patients. In this paper, we consider several GAN models to generate synthetic data used for training binary (malignant/benign) classifiers, and compare their performances in terms of classification accuracy with cases where only real data are considered. We aim to investigate how synthetic data can improve classification accuracy, especially when a small amount of data is available. To this end, we have developed and implemented an evaluation framework where binary classifiers are trained on extended datasets containing both real and synthetic data. The results show improved accuracy for classifiers trained with generated data from more advanced GAN models, even when limited amounts of original data are available.

Authors: Masoud Abedi, Lars Hempel, Sina Sadeghi, Toralf Kirsten

Date Published: 1st Jul 2022

Publication Type: Journal article

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