Epidemiological breast cancer prediction by country: A novel machine learning approach.

Breast cancer remains a significant contributor to cancer-related deaths among women globally. We seek for this study to examine the correlation between the incidence rates of breast cancer and newly identified risk factors. Additionally, we aim to utilize machine learning models to predict breast cancer incidence at a country level.

Evaluation of the use of methylprednisolone and dexamethasone in asthma critically ill patients with COVID-19: a multicenter cohort study.

Background: Previous studies have shown mortality benefits with corticosteroids in Coronavirus disease-19 (COVID-19). However, there is inconsistency regarding the use of methylprednisolone over dexamethasone in COVID-19, and this has not been extensively evaluated in patients with a history of asthma. This study aims to investigate and compare the effectiveness and safety of methylprednisolone and dexamethasone in critically ill patients with asthma and COVID-19.

Evolution of Breast Cancer Recurrence Risk Prediction: A Systematic Review of Statistical and Machine Learning-Based Models.

Purpose: Selection of appropriate adjuvant therapy to ultimately reduce the risk of breast cancer (BC) recurrence is a challenge for medical oncologists. Several automated risk prediction models have been developed using retrospective clinical data and have evolved significantly over the years in terms of predictors of recurrence, data usage, and predictive techniques (statistical/machine learning [ML]).

Methods: Following PRISMA guidelines, we performed a systematic literature review of the aforementioned statistical and ML models published between January 2008 and December 2022 through searching five digital databases-PubMed, ScienceDirect, Scopus, Cochrane, and Web of Science.