Impact of body mass index and examination type on utilization of screening programs: A big data study.

Objective: To investigate the relationship between Body Mass Index (BMI) and adherence to recommended screening tests, addressing gaps in previous literature by utilizing a large cohort, while considering longitudinal changes in weight and the type of screening.

Methods: Data from Clalit Health Services in Israel were retrospectively analyzed, including participants aged 50 and above from 2002 to 2021. BMI measurements and various screening test records were examined.

Impact of Long-COVID in children: a large cohort study.

Background: The impact of long-term Coronavirus disease 2019 (COVID-19) on the pediatric population is still not well understood. This study was designed to estimate the magnitude of COVID-19 long-term morbidity 3-6 months after the date of diagnosis.

Methods: A retrospective study of all Clalit Health Services members in Israel aged 1-16 years who tested positive for SARS-CoV-2 between April 1, 2020 and March 31, 2021.

Marginal Contribution Feature Importance - an Axiomatic Approach for Explaining Data.

In recent years, methods were proposed for assigning feature importance scores to measure the contribution of individual features. While in some cases the goal is to understand a specific model, in many cases the goal is to understand the contribution of certain properties (features) to a real-world phenomenon. Thus, a distinction has been made between feature importance scores that explain a and scores that explain the .

Detecting Impending Stroke From Cognitive Traits Evident in Internet Searches: Analysis of Archival Data.

Background: Cerebrovascular disease is a leading cause of mortality and disability. Common risk assessment tools for stroke are based on the Framingham equation, which relies on traditional cardiovascular risk factors to predict an acute event in the near decade. However, no tools are currently available to predict a near/impending stroke, which might alert patients at risk to seek immediate preventive action (eg, anticoagulants for atrial fibrillation, control of hypertension).

Logistic regression over encrypted data from fully homomorphic encryption.

Background: One of the tasks in the 2017 iDASH secure genome analysis competition was to enable training of logistic regression models over encrypted genomic data. More precisely, given a list of approximately 1500 patient records, each with 18 binary features containing information on specific mutations, the idea was for the data holder to encrypt the records using homomorphic encryption, and send them to an untrusted cloud for storage. The cloud could then homomorphically apply a training algorithm on the encrypted data to obtain an encrypted logistic regression model, which can be sent to the data holder for decryption.

Full body gait analysis with Kinect.

Human gait is an important indicator of health, with applications ranging from diagnosis, monitoring, and rehabilitation. In practice, the use of gait analysis has been limited. Existing gait analysis systems are either expensive, intrusive, or require well-controlled environments such as a clinic or a laboratory.