Use of natural language processing to uncover racial bias in obstetrical documentation.

Natural Language Processing (NLP), a form of Artificial Intelligence, allows free-text based clinical documentation to be integrated in ways that facilitate data analysis, data interpretation and formation of individualized medical and obstetrical care. In this cross-sectional study, we identified all births during the study period carrying the radiology-confirmed diagnosis of fibroid uterus in pregnancy (defined as size of largest diameter of >5 cm) by using an NLP platform and compared it to non-NLP derived data using ICD10 codes of the same diagnosis. We then compared the two sets of data and stratified documentation gaps by race.

Use of Machine Learning to Identify Clinical Variables in Pregnant and Non-Pregnant Women with SARS-CoV-2 Infection.

Objective: The aim of the study is to identify the important clinical variables found in both pregnant and non-pregnant women who tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, using an artificial intelligence (AI) platform.

Methods: This was a retrospective cohort study of all women between the ages of 18 to 45, who were admitted to Maimonides Medical Center between March 10, 2020 and December 20, 2021. Patients were included if they had nasopharyngeal PCR swab positive for SARS-CoV-2.

A Novel and Precise Profiling Tool to Predict Gestational Diabetes.

Background: There is a trend in healthcare for developing models for predictions of disease to enable early intervention and improve outcome.

Instrument: We present the use of artificial intelligence algorithms that were developed by Gynisus Ltd. using mathematical algorithms.

A COMPARISON OF RADON INDOOR MEASUREMENTS WITH INTERPOLATED RADON SOIL GAS VALUES USING THE INVERSE WEIGHTING METHOD ON MEASURED RESULTS.

The European Basic Safety Standards demand the prediction of areas where a significant number of households exceed the reference level for the radon activity concentration. Therefore, radon maps are established which are based on indoor and soil gas measurements. In this study results of soil gas measurements are interpolated to get a value for the radon activity concentration in the soil gas at the coordinates of an indoor measurement and enable a direct comparison of both results.