Relational Learning Improves Prediction of Mortality in COVID-19 in the Intensive Care Unit
| Autor: | Ariful Azad, Benjamin S. Glicksberg, Ying Ding, Jessica K De Freitas, Akhil Vaid, Sulaiman Somani, Tingyi Wanyan, Riccardo Miotto, Girish N. Nadkarni |
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| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
Information Systems and Management Computer science relational learning Statistical relational learning 010501 environmental sciences Machine learning computer.software_genre 01 natural sciences Article Data modeling 03 medical and health sciences Similarity (psychology) Electronic health records Representation (mathematics) 0105 earth and related environmental sciences heterogeneous graph model Modalities Receiver operating characteristic business.industry Deep learning COVID-19 deep learning mortality 030104 developmental biology machine learning Softmax function ICU Artificial intelligence business LSTM computer embeddings Information Systems |
| Zdroj: | IEEE transactions on big data |
| ISSN: | 2332-7790 |
| Popis: | Traditional Machine Learning (ML) models have had limited success in predicting Coronoavirus-19 (COVID-19) outcomes using Electronic Health Record (EHR) data partially due to not effectively capturing the inter-connectivity patterns between various data modalities. In this work, we propose a novel framework that utilizes relational learning based on a heterogeneous graph model (HGM) for predicting mortality at different time windows in COVID-19 patients within the intensive care unit (ICU). We utilize the EHRs of one of the largest and most diverse patient populations across five hospitals in major health system in New York City. In our model, we use an LSTM for processing time varying patient data and apply our proposed relational learning strategy in the final output layer along with other static features. Here, we replace the traditional softmax layer with a Skip-Gram relational learning strategy to compare the similarity between a patient and outcome embedding representation. We demonstrate that the construction of a HGM can robustly learn the patterns classifying patient representations of outcomes through leveraging patterns within the embeddings of similar patients. Our experimental results show that our relational learning-based HGM model achieves higher area under the receiver operating characteristic curve (auROC) than both comparator models in all prediction time windows, with dramatic improvements to recall. |
| Databáze: | OpenAIRE |
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