Study of machine learning techniques for outcome assessment of leptospirosis patients.

Leptospirosis is a global disease that impacts people worldwide, particularly in humid and tropical regions, and is associated with significant socio-economic deficiencies. Its symptoms are often confused with other syndromes, which can compromise clinical diagnosis and the failure to carry out specific laboratory tests. In this respect, this paper presents a study of three algorithms (Decision Tree, Random Forest and Adaboost) for predicting the outcome (cure or death) of individuals with leptospirosis. Using the records contained in the government National System of Aggressions and Notification (SINAN, in portuguese) from 2007 to 2017, for the state of Pará, Brazil, where the temporal attributes of health care, symptoms (headache, vomiting, jaundice, calf pain) and clinical evolution (renal failure and respiratory changes) were used. In the performance evaluation of the selected models, it was observed that the Random Forest exhibited an accuracy of 90.81% for the training dataset, considering the attributes of experiment 8, and the Decision Tree presented an accuracy of 74.29 for the validation database. So, this result considers the best attributes pointed out by experiment 10: time first symptoms medical attention, time first symptoms ELISA sample collection, medical attention hospital admission time, headache, calf pain, vomiting, jaundice, renal insufficiency, and respiratory alterations. The contribution of this article is the confirmation that artificial intelligence, using the Decision Tree model algorithm, depicting the best choice as the final model to be used in future data for the prediction of human leptospirosis cases, helping in the diagnosis and course of the disease, aiming to avoid the evolution to death.

Innovative and disruptive technologies to prescribe, encourage, and evaluate physical exercise in healthy adults: a protocol of exploratory study followed by a noninferiority, investigator-blinded randomized clinical trial.

BACKGROUND: Cardiovascular diseases are a leading cause of mortality worldwide. A significant contributing factor to this mortality is the lack of engagement in preventive activities. Consequently, strategies for enhancing adherence to and duration of physical activity (PA) have become pivotal. This project aims to create and validate innovative, disruptive, and secure technologies that ensure appropriate exercise intensity, bolster adherence to PA, and monitor health biomarker responses pre-, during, and post-physical activity. METHODS: This exploratory study, followed by a noninferiority, investigator-blinded randomized clinical trial, will be divided into three phases: (1) development and validation of a sensor for real-time biofeedback during a functional assessment test; (2) integration of biofeedback and gamification into an app for the structured prescription of physical training within a controlled setting; and (3) implementation of biofeedback and gamification into an app for the prescription and monitoring of physical training in an uncontrolled setting. Phase 1 entails a validation test of a biosensor-monitoring heart rate (HR) and steps-during a modified shuttle walk test. In phase 2, the biosensor interfaces with a gamified smartphone application. The training regimen spans 6 weeks, 5 days weekly, with each session lasting 60 min: a five-min warm-up involving stationary gait, followed by 50 min of training at the target HR on the step and concluding with a five-min cool-down at a stationary pace. After 6 weeks of training, a new functional capacity test is conducted. Phase 3 involves an investigator-blinded, randomized clinical trial to demonstrate noninferiority. Participants are randomly assigned to either the intervention group (IG) or the control group (CG). IG participants practice exercise using the gamified application in an uncontrolled environment according to the prescribed method outlined in phase 2. CG participants receive PA practice guidelines exclusively. DISCUSSION: Anticipated outcomes include improved exercise adherence through the gamified application, better maintenance of prescribed exercise intensity, and enhanced health biomarkers. The results of this study will inform health-related decision-making. TRIAL REGISTRATION: The study protocol received approval from the Ethics Committee of Universidade Federal de Ciências da Saúde de Porto Alegre (54,492,221.80000.5345) and has been registered with the Brazilian Registry of Clinical Trials (ReBEC, RBR-359p69v).