AI and Machine Learning (ML) offer powerful tools to support clinical decision making in emergency situations such as the COVID-19 pandemic. In this context, the application of ML requires to design predictive systems that have adequate accuracy and can effectively deal with issues concerning data quality, sensitive errors, uncertainty, and interpretability of the predictions. We present a methodology that deals with all these problems and a concrete study of its application to estimate the prognosis of hospitalised patients with COVID-19. In particular, we address the task of predicting the outcome (alive or deceased) of a patient at different times of her/his hospitalisation minimising false negatives (wrong survival predictions). The proposed methodology builds different optimised ML models to select those that perform the best to recognise, at different times of hospitalisation, patients who will have an unfavourable prognosis (decease). These models exploit a new algorithm, presented in the paper, that identifies an uncertainty threshold to rule out uncertain predictions with the purpose of making a ML model both more performing and more reliable. Moreover, we propose a general method for automatically extracting multi-variable prognostic rules from the available data. Such rules can provide possible new useful knowledge on the considered disease. We also show how they can be used effectively to explain the predictions made by the ML models. All proposed methods and techniques are experimentally evaluated in the context of our application task.
Machine Learning Techniques for Prognosis Estimation and Knowledge Discovery from Lab Test Results with Application to the COVID-19 Emergency
Gerevini A. E.;Maroldi R.;Olivato M.;Putelli L.;Serina I.
2023-01-01
Abstract
AI and Machine Learning (ML) offer powerful tools to support clinical decision making in emergency situations such as the COVID-19 pandemic. In this context, the application of ML requires to design predictive systems that have adequate accuracy and can effectively deal with issues concerning data quality, sensitive errors, uncertainty, and interpretability of the predictions. We present a methodology that deals with all these problems and a concrete study of its application to estimate the prognosis of hospitalised patients with COVID-19. In particular, we address the task of predicting the outcome (alive or deceased) of a patient at different times of her/his hospitalisation minimising false negatives (wrong survival predictions). The proposed methodology builds different optimised ML models to select those that perform the best to recognise, at different times of hospitalisation, patients who will have an unfavourable prognosis (decease). These models exploit a new algorithm, presented in the paper, that identifies an uncertainty threshold to rule out uncertain predictions with the purpose of making a ML model both more performing and more reliable. Moreover, we propose a general method for automatically extracting multi-variable prognostic rules from the available data. Such rules can provide possible new useful knowledge on the considered disease. We also show how they can be used effectively to explain the predictions made by the ML models. All proposed methods and techniques are experimentally evaluated in the context of our application task.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
