Predictive health monitoring: Leveraging artificial intelligence for early detection of infectious diseases in nursing home residents through discontinuous vital signs analysis.

This research addresses the problem of detecting acute respiratory, urinary tract, and other infectious diseases in elderly nursing home residents using machine learning algorithms. The study analyzes data extracted from multiple vital signs and other contextual information for diagnostic purposes. The daily data collection process encounters sampling constraints due to weekends, holidays, shift changes, staff turnover, and equipment breakdowns, resulting in numerous nulls, repeated readings, outliers, and meaningless values.

Medical Prognosis of Infectious Diseases in Nursing Homes by Applying Machine Learning on Clinical Data Collected in Cloud Microservices.

Background: treating infectious diseases in elderly individuals is difficult; patient referral to emergency services often occurs, since the elderly tend to arrive at consultations with advanced, serious symptoms.

Aim: it was hypothesized that anticipating an infectious disease diagnosis by a few days could significantly improve a patient's well-being and reduce the burden on emergency health system services.

Methods: vital signs from residents were taken daily and transferred to a database in the cloud.

Predicting the Appearance of Hypotension During Hemodialysis Sessions Using Machine Learning Classifiers.

A patient suffering from advanced chronic renal disease undergoes several dialysis sessions on different dates. Several clinical parameters are monitored during the different hours of any of these sessions. These parameters, together with the information provided by other parameters of analytical nature, can be very useful to determine the probability that a patient may suffer from hypotension during the session, which should be specially watched since it represents a proven factor of possible mortality.