Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration

Betty van Aken, Jens-Michalis Papaioannou, Manuel Mayrdorfer, Klemens Budde, Felix Gers, Alexander Loeser


Abstract
Outcome prediction from clinical text can prevent doctors from overlooking possible risks and help hospitals to plan capacities. We simulate patients at admission time, when decision support can be especially valuable, and contribute a novel *admission to discharge* task with four common outcome prediction targets: Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction. The ideal system should infer outcomes based on symptoms, pre-conditions and risk factors of a patient. We evaluate the effectiveness of language models to handle this scenario and propose *clinical outcome pre-training* to integrate knowledge about patient outcomes from multiple public sources. We further present a simple method to incorporate ICD code hierarchy into the models. We show that our approach improves performance on the outcome tasks against several baselines. A detailed analysis reveals further strengths of the model, including transferability, but also weaknesses such as handling of vital values and inconsistencies in the underlying data.
Anthology ID:
2021.eacl-main.75
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Month:
April
Year:
2021
Address:
Online
Editors:
Paola Merlo, Jorg Tiedemann, Reut Tsarfaty
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
881–893
Language:
URL:
https://aclanthology.org/2021.eacl-main.75
DOI:
10.18653/v1/2021.eacl-main.75
Bibkey:
Cite (ACL):
Betty van Aken, Jens-Michalis Papaioannou, Manuel Mayrdorfer, Klemens Budde, Felix Gers, and Alexander Loeser. 2021. Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 881–893, Online. Association for Computational Linguistics.
Cite (Informal):
Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration (van Aken et al., EACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.eacl-main.75.pdf
Code
 bvanaken/clinical-outcome-prediction
Data
Clinical Admission Notes from MIMIC-IIIMIMIC-IIIMedQuAD