Impact of COVID-19 pandemic on traumatic brain injury emergency department visits, interfacility transfer and mortality in the United States, 2016-2020: a cross-sectional study.

Objective: The aim of this study was to determine how the COVID-19 pandemic affected patient demographics, injury mechanisms, interhospital transfers and mortality of patients with traumatic brain injuries (TBIs) treated in US emergency departments (EDs).

Design: This cross-sectional study analysed 2016-2020 Nationwide Emergency Department Sample (NEDS) data.

Setting: US EDs contained in the NEDS.

The value of PROMs for predicting erectile dysfunction in prostate cancer patients with Bayesian network.

Purpose: This study aims to develop and externally validate a clinically plausible Bayesian network structure to predict one-year erectile dysfunction in prostate cancer patients by combining expert knowledge with evidence from data using clinical and Patient-reported outcome measures (PROMs) data. In addition, compare and contrast structures that stem from PROM information and routine clinical data.

Summary Of Background: For men with localized prostate cancer, choosing the optimal treatment can be challenging since each option comes with different side effects, such as erectile dysfunction, which negatively impacts their quality of life.

Emerging role of quantitative imaging (radiomics) and artificial intelligence in precision oncology.

Cancer is a fatal disease and the second most cause of death worldwide. Treatment of cancer is a complex process and requires a multi-modality-based approach. Cancer detection and treatment starts with screening/diagnosis and continues till the patient is alive.

A comparison of machine learning models for predicting urinary incontinence in men with localized prostate cancer.

Introduction: Urinary incontinence (UI) is a common side effect of prostate cancer treatment, but in clinical practice, it is difficult to predict. Machine learning (ML) models have shown promising results in predicting outcomes, yet the lack of transparency in complex models known as "black-box" has made clinicians wary of relying on them in sensitive decisions. Therefore, finding a balance between accuracy and explainability is crucial for the implementation of ML models.