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Feasibility of machine learning analysis for the identification of patients with possible primary ciliary dyskinesia.

Orphanet journal of rare diseases
BACKGROUND: Significant diagnostic delays are common in primary ciliary dyskinesia (PCD), a rare disease that is significantly underdiagnosed. Scalable screening methods could improve early identification and health outcomes.

Statistical and machine-learning assessment of attitudinal, knowledge, and perceptual factors on diabetes awareness in Kuwait.

BMC medical informatics and decision making
OBJECTIVES: The primary objective was to identify and analyze the factors that impact diabetes awareness and perception among diabetic and non-diabetic participants. The study also sought to assess the effectiveness of current health awareness progra...

Machine learning model development and validation using SHAP: predicting 28-day mortality risk in pulmonary fibrosis patients.

BMC medical informatics and decision making
BACKGROUND: Early prediction of mortality risk within 28 days of admission is crucial for personalized treatment in patients with pulmonary fibrosis (PF). This study aims to develop a predictive model for 28-day mortality risk in PF patients using in...

Prevalence and factors associated with HIV drug resistance among adult persons living with HIV/AIDS in nine countries of Sub-Saharan Africa using population-based HIV impact assessments: 2015-2019.

BMC public health
INTRODUCTION: HIV drug resistance (HIVDR) remains a significant challenge in sub-Saharan Africa (SSA) due to limited effective Treatment and healthcare resources vary. Using the first widely available HIVDR surveillance data in SSA, we calculated the...

Deep learning automatic segmentation and radiomics model for diagnosing pancreatic solid neoplasms in MRI.

BMC cancer
BACKGROUND: To develop and validate a deep learning tool for the automatic segmentation of pancreatic solid neoplasms and to establish a radiomics model for diagnosing these solid neoplasms in MRI.

Prediction model for depression risk in middle-aged and elderly patients with metabolic syndrome: a nomogram and interpretable machine learning approach based on CHARLS.

BMC psychiatry
BACKGROUND: Individuals with metabolic syndrome (MetS) are more prone to depression, which is a significant complication impacting quality of life. This research seeks to create and validate predictive models for assessing depression risk in patients...

Predicting outcomes in head and neck cancer using CT images via transfer learning.

BMC medical imaging
BACKGROUND: Accurate preoperative risk stratification for patients with head and neck (H&N) cancer remained a critical challenge, as long-term survival rates are poor despite aggressive multimodality treatment. While deep learning models showed promi...

Machine learning model based on preoperative MRI and clinical data for predicting pancreatic fistula after pancreaticoduodenectomy.

BMC medical imaging
OBJECTIVE: To establish and validate a machine learning model using preoperative multi-sequence MRI radiomic features and clinical data to predict pancreatic fistula after pancreaticoduodenectomy (PD).

Explainable machine learning models for predicting of protein-energy wasting in patients on maintenance haemodialysis.

BMC nephrology
BACKGROUND: Protein-energy wasting (PEW) is a common complication of patients on maintenance haemodialysis (MHD) and is strongly associated with poor clinical outcomes; early identification and timely nutritional interventions are essential. The aim ...