Explainable machine learning models for early Alzheimer's disease detection using multimodal clinical data.

Background: Alzheimer's disease (AD) represents a significant global health challenge requiring early and accurate prediction for effective intervention. While machine learning models demonstrate promising capabilities in AD prediction, their black-box nature limits clinical adoption due to a lack of interpretability and transparency.

Objective: This study aims to develop and evaluate explainable artificial intelligence (XAI) frameworks for AD prediction using comprehensive multimodal patient data, with a focus on enhancing model interpretability through SHAP and LIME techniques.

Enhancing endoscopic precision: the role of artificial intelligence in modern gastroenterology.

Background: Endoscopy remains the gold standard for gastrointestinal (GI) diagnostics, enabling direct visualization and intervention within the GI tract. However, diagnostic accuracy and procedural outcomes vary significantly depending on the endoscopist's skill and experience, leading to potential missed lesions and inconsistent patient care. The integration of artificial intelligence (AI) into endoscopic practice offers a promising solution to address these limitations and enhance diagnostic precision.

AI-driven diagnosis of Lassa fever: Evidence from Nigerian clinical records.

Background: Neglected Tropical Diseases (NTDs), particularly Lassa fever, remain a significant public health challenge in Nigeria, often presenting with symptoms similar to malaria. These similarities contribute to misdiagnoses, delayed treatments, and increased mortality. The need for rapid and accurate disease differentiation has created an opportunity for machine learning applications in medical diagnostics.

Charts, Calendars, Questionnaires & Intake Forms: Navigating the Options when Caring for Patients with Migraine.

Purpose Of Review: To describe the different options available to collect clinical data for headache care via intake forms, questionnaires, diary, and electronic health record (EHR) and practical ways to optimize clinic flow and data integration in this era of information overload.

Recent Findings: Digital technologies can be leveraged to help with data integration and processing of information. New technologies and progress with data integration have the potential to help streamline flow and improve healthcare outcomes.

When Is the Broken Heart Most Dangerous? Assessing Risk Factors to Predict Inpatient Death in Takotsubo Cardiomyopathy: Analysis of the National Inpatient Sample for 2021.

Background: Takotsubo cardiomyopathy (TC) has a similar clinical presentation to acute coronary syndromes (ACS). As the prevalence and influence on clinical decisions of this condition are being increasingly recognized, prognostic factors have yet to be established. We applied known near-term acute coronary syndrome mortality risk factors to determine their prognostic value in TC.

Intraosseous vs. intravenous access in out-of-hospital cardiac arrest: a systematic review and meta-analysis of clinical outcomes.

Background: Out-of-hospital cardiac arrest (OHCA) is a critical emergency with low survival rates despite advancements in prehospital care. Timely vascular access for medication administration is essential, with intravenous (IV) and intraosseous (IO) access as primary strategies. While IO offers rapid and reliable access under challenging conditions, its effectiveness compared to IV access remains uncertain.

Machine learning techniques for stroke prediction: A systematic review of algorithms, datasets, and regional gaps.

Background: Stroke is a leading cause of mortality and disability worldwide, with approximately 15 million people suffering strokes annually. Machine learning (ML) techniques have emerged as powerful tools for stroke prediction, enabling early identification of risk factors through data-driven approaches. However, the clinical utility and performance characteristics of these approaches require systematic evaluation.

Enhancing Parkinson's disease prediction using meta-heuristic optimized machine learning models.

Parkinson's disease is a progressive neurological disorder affecting movement and cognition. Early detection is crucial but challenging with traditional methods. This study applies meta-heuristic optimization to enhance machine learning prediction models.

A scoping review of stroke rehabilitation in Africa: interventions, barriers, and research gaps.

Stroke is a leading cause of disability worldwide, with low- and middle-income countries (LMICs), particularly in Africa, experiencing an increasing burden due to rising incidences driven by urbanization, lifestyle changes, and non-communicable diseases. This scoping review maps stroke rehabilitation interventions in Africa, identifying barriers to implementation and adherence, and highlighting research gaps to inform future policy and clinical practices. A literature search was conducted across PubMed, Scopus, Web of Science, Embase, and African Journals Online (AJOL), supplemented by grey literature from WHO reports and government publications.

A review of proton beam therapy's role in glioma management.

Gliomas pose significant therapeutic challenges due to limited survival and risks of treatment-related toxicity. Proton beam therapy (PBT) offers a precise radiation delivery method, minimizing damage to healthy brain tissue compared to conventional radiotherapy. This review synthesizes findings from 15 studies (2000-January 2024) from PubMed, Google Scholar, Science Direct, Cochrane Library, and Directory of Open Access Journals.

Enhancing Alzheimer's disease prediction using random forest: A novel framework combining backward feature elimination and ant colony optimization.

Background: Alzheimer's disease (AD) represents a significant global health challenge due to its increasing prevalence and the limitations of current diagnostic approaches. Early detection is crucial as pathological changes occur 10-15 years before clinical symptoms manifest, yet current diagnostic methods typically identify the disease at moderate to advanced stages. Machine learning techniques offer promising solutions for early prediction, but face challenges related to feature selection and hyperparameter optimization.

Examining the perception of residency training among Nigerian medical students: a nationwide pilot survey.

Background: Residency training is critical for preparing medical graduates for independent practice, yet Nigeria's healthcare system faces challenges like resource constraints and brain drain, potentially shaping medical students' perceptions of residency. This study examines Nigerian medical students' attitudes, expectations, and concerns regarding residency training.

Methods: A nationwide, multi-center, cross-sectional study was conducted among 687 undergraduate medical students across Nigeria's six geopolitical zones from May to July 2024.

Stacked Ensemble Learning for Classification of Parkinson's Disease Using Telemonitoring Vocal Features.

Parkinson's disease (PD) is a progressive neurodegenerative condition that impairs motor and non-motor functions. Early and accurate diagnosis is critical for effective management and care. Leveraging machine learning (ML) techniques, this study aimed to develop a robust prediction system for PD using a stacked ensemble learning approach, addressing challenges such as imbalanced datasets and feature optimization.

Effectiveness of mobile stroke units in reducing time to thrombolysis in acute ischemic stroke: a scoping review.

Background: Timely thrombolysis within the golden hour (≤ 60 min from onset) is critical for minimizing disability in acute ischemic stroke (AIS). Mobile stroke units (MSUs) enable prehospital thrombolysis, with effectiveness varying by urban versus rural settings, the presence of an onboard neurologist, and telemedicine models. This study maps evidence on MSU effectiveness in reducing time to thrombolysis in AIS compared to standard emergency medical services (EMS), examines factors modulating effectiveness (e.

Isolated pulmonary valve endocarditis in a 7-year-old Nigerian girl: a case report.

Background: Right-sided infective endocarditis is a rare clinical entity, with isolated pulmonary valve infective endocarditis being extremely uncommon. Infective endocarditis carries a high mortality rate and significant complications, making early identification and prompt management crucial in improving outcomes. This case highlights an unusual presentation of right-sided infective endocarditis isolated to the pulmonic valve in a pediatric patient with no apparent preexisting heart disease.

Stroke incidence, presentation, and outcomes in malaria: a review of current evidence.

Malaria, a global health challenge, remains a leading cause of morbidity and mortality, particularly in sub-Saharan Africa, Southeast Asia, and South America. While traditionally associated with fever and systemic complications, the neurological impact of malaria, including stroke, has become a significant concern. This review aims to examine the incidence, clinical presentation, and outcomes of stroke in individuals with malaria, highlighting the role of malaria in both ischaemic and haemorrhagic strokes.

Comparative analysis of machine learning models for coronary artery disease prediction with optimized feature selection.

Background: Coronary artery disease (CAD) is a major global cause of death, necessitating early, accurate prediction for better management. Traditional diagnostics are often invasive, costly, and less accessible. Machine learning (ML) offers a non-invasive alternative, but high-dimensional data and redundancy can hinder performance.

Online Gambling and Its Prevalence Among Nigerian Medical and Dental Students.

With a global rise in online gambling platforms, medical undergraduates are increasingly engaging themselves in gambling as a means of surviving and escaping challenges peculiar to their course of study. The objective of this research was to determine online gambling prevalence among Nigerian medical (MBBS) and dental (BDS) students to gain knowledge and understanding of the factors influencing this behavior. A cross-sectional study design was employed using the South Oaks Gambling Screen (SOGS), which recruited medical and dental students from selected medical and dental schools involving all geo-political zones in Nigeria.

Therapeutic Potential of GLP-1 Receptor Agonists in Heart Failure with Preserved Ejection Fraction (HFpEF) in Obese Patients.

Purpose Of Review: Heart failure with preserved ejection fraction (HFpEF) is increasingly prevalent among individuals with obesity, primarily due to metabolic dysfunction and structural cardiac remodeling. This review explores the emerging therapeutic role of glucagon-like peptide-1 receptor agonists (GLP-1 RAs) in managing HFpEF in obese populations.

Recent Findings: Recent clinical trials, including the STEP-HFpEF and SUMMIT studies, have shown that GLP-1 RAs such as semaglutide and tirzepatide significantly reduce body weight (13.

Telepsychiatry in Africa: overcoming barriers to access and care.

Telepsychiatry presents a transformative opportunity to address Africa's significant mental health challenges. With a high prevalence of psychiatric disorders and limited access to care, particularly in rural and marginalized communities, innovative solutions are urgently needed. This paper has explored the potential of telepsychiatry to overcome geographical barriers and address the shortage of mental health professionals in Africa.

COVID-19 and cognitive impairment: a review of the emerging evidence.

The recent surge of COVID-19 cases has raised concerns about its potential long-term effects on cognitive function. This review explores the growing body of research investigating the link between COVID-19 infection and cognitive impairment. Studies employing observational, longitudinal, and case-control designs reveal a concerning prevalence of cognitive impairment in survivors, affecting domains like attention, memory, executive function, and processing speed.