• English
  • Deutsch
  • Log In
    Password Login
    Research Outputs
    Fundings & Projects
    Researchers
    Institutes
    Statistics
Repository logo
Fraunhofer-Gesellschaft
  1. Home
  2. Fraunhofer-Gesellschaft
  3. Konferenzschrift
  4. Patient Mortality prediction Using Clinical Notes
 
  • Details
  • Full
Options
December 3, 2024
Presentation
Title

Patient Mortality prediction Using Clinical Notes

Title Supplement
Paper presented at 31st International Conference on Neural Information Processing, ICONIP 2024, December 2-6, 2024, Auckland, New Zealand
Abstract
Predicting various aspects of medical diagnosis and prognosis is an ever expanding discipline in machine learning. In this paper we present a comparison of the predicting capability of conventional algorithms, traditional deep learning algorithms and fine tuned deep learning models on a prognosis prediction task using clinical reports from various medical professionals. We used the MIMIC III dataset containing various types of medical reports on hospital admissions and predicted the mortality of the patients discharged from the hospital. The objective was to find the best model to predict the mortality of patients being admitted and particularly, how accurately we could do this at the beginning of admission compared to when the patient got discharged. Additionally, with all the hype around deep learning, we also wanted to compare various conventional and the deep learning classifiers in order to determine how well the deep learning ones performed compared to the conventional ones. We found that the the the deep learning model, BlueBert gave the highest accuracies upwards of 80%.
The results showed that mortality prediction with multiple clinical notes at the discharge time was only marginally better then the predictions with only the first clinical note. We also show that the conventional classifiers perform as good as the traditional deep learning algorithms such as LSTM and ConvNet. However, the fine tuned BERT models fine tuned on Pubmed data performed much better on all aspects of the prediction task.
Author(s)
Gana Castillo, Bady Patricio
Pontificia Universidad Católica de Valparaíso
Figueroa Mandujano, Alen David
Allende-Cid, Héctor
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Nand, Parma  
Auckland University of Technology
Zamora, Juan  
Pontificia Universidad Católica de Valparaíso
Ramos, Andrés
University of Valparaíso
Conference
International Conference on Neural Information Processing 2024  
File(s)
Download (408.27 KB)
Rights
Use according to copyright law
DOI
10.24406/publica-4147
Language
English
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Keyword(s)
  • Text Classification

  • Deep Learning

  • Machine Learning

  • Clinical Texts

  • Mortality Prediction

  • Cookie settings
  • Imprint
  • Privacy policy
  • Api
  • Contact
© 2024