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Comorbidity

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Development and Validation of a Prediction Model for Co-Occurring Moderate-to-Severe Anxiety Symptoms in First-Episode and Drug Naïve Patients With Major Depressive Disorder.

Depression and anxiety
Moderate-to-severe anxiety symptoms are severe and common in patients with major depressive disorder (MDD) and have a significant impact on MDD patients and their families. The main objective of this study was to develop a risk prediction model for ...

Predicting Depression, Anxiety, and Their Comorbidity among Patients with Breast Cancer in China Using Machine Learning: A Multisite Cross-Sectional Study.

Depression and anxiety
Depression and anxiety are highly prevalent among patients with breast cancer. We tested the capacity of personal resources (psychological resilience, social support, and process of recovery) for predicting depression, anxiety, and comorbid depressio...

Novel Alzheimer's Disease Stating Based on Comorbidities-Informed Graph Neural Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Alzheimer's Disease (AD), the most prevalent form of dementia, requires early prediction for timely intervention. Leveraging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), our study employs Graph Neural Networks (GNNs) for multi-cl...

The Role of Computed Tomography and Artificial Intelligence in Evaluating the Comorbidities of Chronic Obstructive Pulmonary Disease: A One-Stop CT Scanning for Lung Cancer Screening.

International journal of chronic obstructive pulmonary disease
Chronic obstructive pulmonary disease (COPD) is a major cause of morbidity and mortality worldwide. Comorbidities in patients with COPD significantly increase morbidity, mortality, and healthcare costs, posing a significant burden on the management o...

Machine learning-based prediction of mortality risk in AIDS patients with comorbid common AIDS-related diseases or symptoms.

Frontiers in public health
OBJECTIVE: Early assessment and intervention of Acquired Immune Deficiency Syndrome (AIDS) patients at high risk of mortality is critical. This study aims to develop an optimally performing mortality risk prediction model for AIDS patients with comor...

Explainable machine learning model for assessing health status in patients with comorbid coronary heart disease and depression: Development and validation study.

International journal of medical informatics
BACKGROUND: Coronary heart disease (CHD) and depression frequently co-occur, significantly impacting patient outcomes. However, comprehensive health status assessment tools for this complex population are lacking. This study aimed to develop and vali...

AI-driven health analysis for emerging respiratory diseases: A case study of Yemen patients using COVID-19 data.

Mathematical biosciences and engineering : MBE
In low-income and resource-limited countries, distinguishing COVID-19 from other respiratory diseases is challenging due to similar symptoms and the prevalence of comorbidities. In Yemen, acute comorbidities further complicate the differentiation bet...

Utilizing machine learning algorithms for predicting Anxiety-Depression Comorbidity Syndrome in Gastroenterology Inpatients (ADCS-GI).

BMC psychiatry
BACKGROUND: Accurately diagnosing Anxiety-Depression Comorbidity Syndrome in Gastroenterology Inpatients (ADCS-GI) shows significant challenges as traditional diagnostic methods fail to meet expectations due to patient hesitance and non-psychiatric h...

Extracting Multifaceted Characteristics of Patients With Chronic Disease Comorbidity: Framework Development Using Large Language Models.

JMIR medical informatics
BACKGROUND: Research on chronic multimorbidity has increasingly become a focal point with the aging of the population. Many studies in this area require detailed patient characteristic information. However, the current methods for extracting such inf...