Wheeze and Crackle Discrimination Algorithm in Pneumonia Respiratory Signals.

A new pneumonia detection method is proposed to provide both pneumonia detection in respiratory sound signals and wheeze and crackle discrimination when pneumonia episodes are detected. In the proposed method, two-step hierarchy, classifying pneumonia in the first step and discriminating wheezing and crackling in the second step, is considered; the conventional pneumonia detection method is modified to improve pneumonia detection performance, while wheezing and crackling discrimination functionality is added to facilitate the application of appropriate remedies for each case. We used resampling techniques to address the imbalance in the ICBHI pneumonia dataset.

A Novel Prediction Method for Glaucoma Detection Using Retinographies.

Glaucoma, characterized by its damage to the retinal nerve, is one of the most statistically dominant eye diseases in the U.S. It can cause vision loss and blindness by affecting the optic nerve.

Data Preprocessing Techniques for AI and Machine Learning Readiness: Scoping Review of Wearable Sensor Data in Cancer Care.

Background: Wearable sensors are increasingly being explored in health care, including in cancer care, for their potential in continuously monitoring patients. Despite their growing adoption, significant challenges remain in the quality and consistency of data collected from wearable sensors. Moreover, preprocessing pipelines to clean, transform, normalize, and standardize raw data have not yet been fully optimized.

Dyadic and Individual Variation in 24-Hour Heart Rates of Cancer Patients and Their Caregivers.

Background: Twenty-four-hour heart rate (HR) integrates multiple physiological and psychological systems related to health and well-being, and can be continuously monitored in high temporal resolution over several days with wearable HR monitors. Using HR data from two independent datasets of cancer patients and their caregivers, we aimed to identify dyadic and individual patterns of 24 h HR variation and assess their relationship to demographic, environmental, psychological, and clinical variables of interest.

Methods: a novel regularized approach to high-dimensional canonical correlation analysis (CCA) was used to identify factors reflecting dyadic and individual variation in the 24 h (circadian) HR trajectories of 430 people in 215 dyads, then regression analysis was used to relate these patterns to explanatory variables.