TEXT SUMMARIZATION AS AN EMPIRICAL LEGAL RESEARCH TOOL BASED ON MACHINE LEARNING ALGORITHMS

This paper aims to explore text summarization techniques as a tool for empirical legal research, creating a summary of the decisions given the phrases predictive power with regard to the decision outcome. A dataset of habeas corpus decisions prompted by innumerable courts in Brazil is used that explicitly cite the COVID pandemic as a reason for requesting the release of the patients. A predictive model is created and through this analysis we propose to find the arguments most correlated with the outcome. © 2022 Universidad Diego Portales. All rights reserved.

COVID-19 therapy optimization by AI-driven biomechanical simulations.

The COVID-19 disease causes pneumonia in many patients that in the most serious cases evolves into the Acute Distress Respiratory Syndrome (ARDS), requiring assisted ventilation and intensive care. In this context, identification of patients at high risk of developing ARDS is a key point for early clinical management, better clinical outcome and optimization in using the limited resources available in the intensive care units. We propose an AI-based prognostic system that makes predictions of oxygen exchange with arterial blood by using as input lung Computed Tomography (CT), the air flux in lungs obtained from biomechanical simulations and Arterial Blood Gas (ABG) analysis. We developed and investigated the feasibility of this system on a small clinical database of proven COVID-19 cases where the initial CT and various ABG reports were available for each patient. We studied the time evolution of the ABG parameters and found correlation with the morphological information extracted from CT scans and disease outcome. Promising results of a preliminary version of the prognostic algorithm are presented. The ability to predict the evolution of patients' respiratory efficiency would be of crucial importance for disease management.