Exploring Language-Interfaced Fine-Tuning for COVID-19 Patient Survival Classification

• Published in: 2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) pp. 1449 - 1454
• Main Authors: Shorten, Connor, Cardenas, Erika, Khoshgoftaar, Taghi M., Hashemi, Javad, Dalmida, Safiya George, Newman, David, Datta, Debarshi, Martinez, Laurie, Sareli, Candice, Eckard, Paula
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2022
• Subjects: COVID-19; Electronic Health Records; Multilayer perceptrons; Natural Language Processing; Natural languages; Neural networks; Patient Outcome Classification; Predictive models; Transfer learning; Transformers
• ISSN: 2375-0197
• DOI: 10.1109/ICTAI56018.2022.00219

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.