Combination of machine learning algorithms with natural language processing may increase the probability of bacteremia detection in the emergency department: A retrospective, big-data analysis of 94,482 patients.

Background: Prompt diagnosis of bacteremia in the emergency department (ED) is of utmost importance. Nevertheless, the average time to first clinical laboratory finding range from 1 to 3 days. Alongside a myriad of scoring systems for occult bacteremia prediction, efforts for applying artificial intelligence (AI) in this realm are still preliminary.

Prevalence of common and rare ophthalmic findings in adults attending a medical survey institute.

Purpose: To examine the ophthalmic data from a large database of people attending a general medical survey institute, and to investigate ophthalmic findings of the eye and its adnexa, including differences in age and sex.

Methods: Retrospective analysis including medical data of all consecutive individuals whose ophthalmic data and the prevalences of ocular pathologies were extracted from a very large database of subjects examined at a single general medical survey institute.

Results: Data were derived from 184,589 visits of 3676 patients (mean age 52 years, 68% males).

Glaucoma Prediction Models Based on Ocular and Systemic Findings.

Introduction: Our aim was to explore the impact of various systemic and ocular findings on predicting the development of glaucoma.

Methods: Medical records of 37,692 consecutive patients examined at a single medical center between 2001 and 2020 were analyzed using machine learning algorithms. Systemic and ocular features were included.

Big Data Analysis of Glaucoma Prevalence in Israel.

Prcis: The prevalence of glaucoma in the adult population included in this study was 2.3%. Normal values of routine eye examinations are provided including age and sex variations.

A Novel Prediction Tool for Endoscopic Intervention in Patients with Acute Upper Gastro-Intestinal Bleeding.

(1) Background: Predicting which patients with upper gastro-intestinal bleeding (UGIB) will receive intervention during urgent endoscopy can allow for better triaging and resource utilization but remains sub-optimal. Using machine learning modelling we aimed to devise an improved endoscopic intervention predicting tool. (2) Methods: A retrospective cohort study of adult patients diagnosed with UGIB between 2012−2018 who underwent esophagogastroduodenoscopy (EGD) during hospitalization.