View clinical trials related to Machine Learning.
Filter by:To study the effects of the use of a mobile application plus recommendations based on a Mediterranean diet on the intake of micronutrients from natural sources (not drugs) on health indicators, cardiovascular parameters (blood pressure...), physical condition and body composition in a Spanish adult population.
Background: Insufficient physical activity is one of the leading risk factors of death worldwide. Behavioral treatments delivered via smartphone apps, hold great promise for helping people engage in healthy behaviors including becoming more physically active. However, similar to 'face-to-face' treatments, effects typically do not seem to be sustained over longer periods of time. Methods: the investigators developed a smartphone application that uses different types of motivational and feedback text-messaging to motivate individuals to increase physical activity. Here, participants are randomized to either receive messages by a uniform random distribution (n=50), or chosen by a reinforcement learning algorithm (n=50), which learns from daily participant data to personalize the frequency and type of motivation of messages. Objectives: In the current study, the investigators examine this application in undergraduate and graduate students at the University of California, Berkeley. The investigators compare whether participants in the uniform random or adaptive group have higher increases in steps during the study. The investigators also examine the effect of the different types of messages on step counts. Further the investigators assess the influence of patient characteristics, such as socio-demographic, psychological questionnaire scores and baseline physical activity on the effect of the adaptive arm and effectiveness of the messages. Finally, the investigators assess participant qualitative feedback on the text-messaging program, through feedback provided via questionnaires, text-message and phone interviews.
To develop and validate a machine-learning model based on clinical, laboratory, and radiological characteristics alone or combination of COVID-19 patients to facilitate risk Assessment before and after symptoms and triage (home, hospitalization inward or ICU).
Intraoperative hypotension occurs often and is associated with adverse patient outcomes such as stroke, myocardial infarction and renal injury. The aim of this study was to test the accuracy of a physiology-based machine-learning algorithm using continuous non-invasive measurement of the blood pressure waveform with the Nexfin® finger cuff during surgery.