On Critical Care Data and Machine Learning Loss Function Landscapes
type: CreativeWork
description: Intensive Care Units (ICUs) are constantly under strain, with many vulnerable people needing urgent critical care. Often doctors do not have the capacity to accommodate everyone, and so it is important that patients are discharged when it is safe to do so. However, discharging a patient at the wrong time may result in the patient being readmitted or simply not surviving; both of these cases are highly undesirable. Therefore, it is useful for there to be an understanding of what factors contribute most to the mortality rate of patients in intensive care units. In the present work, machine learning models are trained to predict the mortality of a given patient with certain measurements. Neural networks with one hidden layer and two outcomes (alive or deceased) are used in these models, and local minima of the neural network loss functions are found using basin-hopping methods implemented with the open source software GMIN. In this way, the landscape of the loss function defined by the neural network model can be explored. Area Under the Curve (AUC) values were used to evaluate these models. Two databases, MIMIC III and Amsterdam UMC db, are compared. There are many possible calculations to perform, and initially time-series for single variables and pairs of variables are used as inputs to the neural network. From MIMIC III, Glasgow Coma Scale (GCS) and Blood Urea Nitrogen (BUN) perform well, with AUCs just below 0.8 on their own, and an AUC above 0.8 together. From Amsterdam UMC db, Blood Pressure (BP) measurements perform well, with AUCs around 0.8. Generally the data from Amsterdam UMC db appears to outperform MIMIC III. The effect of using a model trained on one time window and evaluated on different time windows is also investigated, and we find that the AUC value decreases but not substantially for most clinical variables, suggesting the most recent data is the most useful for mortality prediction. There is a notable exception in Respiration Rate, where it is fou…
abstract: Intensive Care Units (ICUs) are constantly under strain, with many vulnerable people needing urgent critical care. Often doctors do not have the capacity to accommodate everyone, and so it is important that patients are discharged when it is safe to do so. However, discharging a patient at the wrong time may result in the patient being readmitted or simply not surviving; both of these cases are highly undesirable. Therefore, it is useful for there to be an understanding of what factors contribute most to the mortality rate of patients in intensive care units. In the present work, machine learning models are trained to predict the mortality of a given patient with certain measurements. Neural networks with one hidden layer and two outcomes (alive or deceased) are used in these models, and local minima of the neural network loss functions are found using basin-hopping methods implemented with the open source software GMIN. In this way, the landscape of the loss function defined by the neural network model can be explored. Area Under the Curve (AUC) values were used to evaluate these models. Two databases, MIMIC III and Amsterdam UMC db, are compared. There are many possible calculations to perform, and initially time-series for single variables and pairs of variables are used as inputs to the neural network. From MIMIC III, Glasgow Coma Scale (GCS) and Blood Urea Nitrogen (BUN) perform well, with AUCs just below 0.8 on their own, and an AUC above 0.8 together. From Amsterdam UMC db, Blood Pressure (BP) measurements perform well, with AUCs around 0.8. Generally the data from Amsterdam UMC db appears to outperform MIMIC III. The effect of using a model trained on one time window and evaluated on different time windows is also investigated, and we find that the AUC value decreases but not substantially for most clinical variables, suggesting the most recent data is the most useful for mortality prediction. There is a notable exception in Respiration Rate, where it is fou…
author: Cafolla, Conor Thomas
language: eng
keywords: Data Science, Energy Landscapes, Intensive Care Unit, Machine Learning
license: https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/0f26d6e2-7f94-4769-9724-d7f43291e76e/download
license: https://www.rioxx.net/licenses/all-rights-reserved/
date published: 2023-09-29
date modified: 2026-07-22T22:24:11Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.106132