Obesity

Predicting dementia in people with Parkinson's disease.

Parkinson's disease (PD) exhibits a variety of symptoms, with approximately 25% of patients experiencing mild cognitive impairment and 45% developing dementia within ten years of diagnosis. Predicting this progression and identifying its causes remains challenging. Our study utilizes machine learning and multimodal data from the UK Biobank to explore the predictability of Parkinson's dementia (PDD...

Prediction of Glomerular Filtration Rate Following Partial Nephrectomy for Localized Renal Cell Carcinoma with Different Machine Learning Techniques.

: Partial nephrectomy (PN) is the preferred option for treating localized cT1 renal cell carcinoma (RCC), as it preserves renal function in most patients and offers non-inferior oncological outcomes compared to radical nephrectomy. In this study, we aimed to construct a predictive model for estimating the glomerular filtration rate (GFR) at one year after PN in patients with RCC, using various mac...

Past, Present and Future Perspectives of Forensic Genetics.

Forensic genetics has experienced remarkable advancements over the past decades, evolving from the analysis of a limited number of DNA segments to com...

Leveraging explainable artificial intelligence with ensemble of deep learning model for dementia prediction to enhance clinical decision support systems.

The prevalence of dementia is growing worldwide due to the fast ageing of the population. Dementia is an intricate illness that is frequently produced...

Revolutionizing sleep disorder diagnosis: A Multi-Task learning approach optimized with genetic and Q-Learning techniques.

Adequate sleep is crucial for maintaining a healthy lifestyle, and its deficiency can lead to various sleep-related disorders. Identifying these disor...

Development, Validation, and Deployment of a Time-Dependent Machine Learning Model for Predicting One-Year Mortality Risk in Critically Ill Patients with Heart Failure.

Heart failure (HF) ranks among the foremost causes of mortality globally, exhibiting particularly high prevalence and significant impact within inten...

Pharmacokinetic and pharmacodynamic alterations in older people: what we know so far.

INTRODUCTION: Healthcare professionals face increasing challenges when managing older patients, a group characterized by significant interindividual v...

Prescriptive analytics decision-making system for cardiovascular disease prediction in long COVID patients using advanced reinforcement learning algorithms.

In recent years Covid-19 impact is causing unprecedented difficulties worldwide, affecting lifestyle choices. The post-pandemic era has made this even...

Machine learning-based prediction of restless legs syndrome using digital phenotypes from wearables and smartphone data.

Restless legs syndrome (RLS) is a relatively common neurosensory disorder that causes an irresistible urge for leg movement. RLS causes sleep disturba...

RiM: Record, Improve and Maintain Physical Well-being using Federated Learning

In academic settings, the demanding environment often forces students to prioritize academic performance over their physical well-being. Moreover, p...

Interactive Diabetes Risk Prediction Using Explainable Machine Learning: A Dash-Based Approach with SHAP, LIME, and Comorbidity Insights

This study presents a web-based interactive health risk prediction tool designed to assess diabetes risk using machine learning models. Built on the...

A Conjoint Graph Representation Learning Framework for Hypertension Comorbidity Risk Prediction

The comorbidities of hypertension impose a heavy burden on patients and society. Early identification is necessary to prompt intervention, but it re...

Navigating challenging patient factors in systemic therapy for head and neck cancer.

Patients with head and neck cancer often present with complex challenges due to a substantial comorbidity burden, including substance use disorders, a...

Development and Validation of Comorbidity Severity Adjustment Methods in Mortality Models for Acute Cerebrovascular Disease Using Survival and Machine Learning Analyses.

: This study aimed to develop and validate comorbidity-based severity adjustment methods for acute cerebrovascular disease by recalibrating the Charls...

Machine learning-based predictive modelling of mental health in Rwandan Youth.

Globally, mental disorders are a significant burden, particularly in low- and middle-income countries, with high prevalence in Rwanda, especially amon...

Prediction of Delirium Risk in Mild Cognitive Impairment Using Time-Series data, Machine Learning and Comorbidity Patterns -- A Retrospective Study

Delirium represents a significant clinical concern characterized by high morbidity and mortality rates, particularly in patients with mild cognitive...

Machine Learning Meets Transparency in Osteoporosis Risk Assessment: A Comparative Study of ML and Explainability Analysis

The present research tackles the difficulty of predicting osteoporosis risk via machine learning (ML) approaches, emphasizing the use of explainable...

NDDRF 2.0: An update and expansion of risk factor knowledge base for personalized prevention of neurodegenerative diseases.

INTRODUCTION: Neurodegenerative diseases (NDDs) are chronic diseases caused by brain neuron degeneration, requiring systematic integration of risk fac...

Identifying GAP43, NMU, and TEX29 as Potential Prognostic Biomarkers for COPD Combined With Lung Cancer Patients Using Machine Learning.

Chronic obstructive pulmonary disease (COPD) and lung cancer, frequently comorbid conditions intricately linked through smoking, represent significant...

Integrating Artificial Intelligence in the Diagnosis and Management of Metabolic Syndrome: A Comprehensive Review.

BACKGROUND: Metabolic syndrome (MetS) is a progressive chronic pathophysiological state characterised by abdominal obesity, hypertension, hyperglycaem...