Exploring Language-Interfaced Fine-Tuning for COVID-19 Patient Survival Classification
34th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2022
; 2022-October:1449-1454, 2022.
Article
in English
| Scopus | ID: covidwho-2319284
ABSTRACT
We present Language-Interfaced Fine-Tuning (LIFT) in application to COVID-19 patient survival classification. LIFT describes translating tabular Electronic Health Records (EHRs) into text inputs for transformer neural networks. We study LIFT with a dataset of 5,371 COVID-19 patients. We focus on the predictive task of survival classification utilizing demographic and medical history features. We begin by presenting information about our dataset. We preface our investigation in text-based transformers by reporting the performances of conventional machine learning models such as Logistic Regression and Random Forest classifiers. We also present the results of a few configurations of tabular input-based Deep Multilayer Perceptron (MLP) networks. 86% of the patients in our database survived in the measured time window. Thus, predictive models are heavily biased to predict that a patient will survive. We emphasize that this problem of Class Imbalance was a major challenge in developing these models. Our balanced sampling strategy from examples in the majority and minority classes is crucial to achieving even reasonable predictive performance. For this reason, we also report performance based on Precision, Recall, and F-score metrics, in addition to Accuracy. Having established baselines with tabular inputs, we then shift our focus to the prompts for translating from tabular to text inputs. We report the performance of 5 prompts. The LIFT model achieves an F-score on the held-out test set of 0.21, slightly behind the Deep MLP with Tabular Features score of 0.23. Both models outperform the Random Forest with Tabular Features at 0.15. We believe that LIFT is a very exciting direction for machine learning in healthcare applications because text-based inputs enables us to take advantage of recent advances in Transfer Learning and Retrieval-Augmented Learning. This study illustrates the effectiveness of converting tabular EHRs to text inputs and utilizing transformer neural networks for prediction. © 2022 IEEE.
Electronic Health Records; Natural Language Processing; Patient Outcome Classification; Forestry; Learning algorithms; Logistic regression; Machine learning; Natural language processing systems; Records management; Tuning; Electronic health; Electronic health record; Fine tuning; Health records; Language processing; Natural languages; Patient survivals; Text input; COVID-19
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
34th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2022
Year:
2022
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS