AIMC Topic: Middle Aged

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Development and validation of a machine learning-based model to predict the risk of hospitalization death in hospitalized patients with AECOPD.

Scientific reports
Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is a leading cause of hospitalization and death in COPD patients. Machine learning (ML) approach is powerful but has a "black box" issue with an undirect interpretation of the ML te...

Comparing Generative Artificial Intelligence and Mental Health Professionals for Clinical Decision-Making With Trauma-Exposed Populations: Vignette-Based Experimental Study.

JMIR mental health
BACKGROUND: Trauma exposure is highly prevalent and associated with various health issues. However, health care professionals can exhibit trauma-related diagnostic overshadowing bias, leading to misdiagnosis and inadequate treatment of trauma-exposed...

Predicting All-Cause Mortality in Diabetic Patients 2 Years in Advance Using Aggregated EHR Data and Machine Learning.

Journal of medical systems
This study presents a machine learning-driven model predicting all-cause mortality two years in advance using administrative health data focused on diabetic patients. Integrating hospitalization records, emergency department data, demographics, and c...

Explainable machine learning algorithm predicting working memory performance in Parkinson's disease using task-fMRI.

Journal of neurology
BACKGROUND: Parkinson's disease (PD) is a neurodegenerative disorder that affects both motor and cognitive functions, particularly working memory (WM). Machine learning offers an advantage for decoding complex brain activity patterns, but its applica...

Clinical implementation of an AI-enabled ECG for hypertrophic cardiomyopathy detection.

Heart (British Cardiac Society)
BACKGROUND: Hypertrophic cardiomyopathy (HCM) is often underdiagnosed. Artificial intelligence (AI)-based notification of HCM suspicion on a 12-lead ECG has been proposed to assist patient identification and evaluation. However, there has been no stu...

Neural predictors of hidden, persistent psychological states at work.

Proceedings of the National Academy of Sciences of the United States of America
Common workplace challenges such as feeling overwhelmed, burned out, or disengaged often remain hidden due to fear of judgment or social norms, contributing to rising mental health crises and organizational dysfunction. This study presents a brain-ba...

The exhaled breath pattern as a potential method for biometrics identification.

Scientific reports
Conventional biometric identification methods relying on Personally Identifiable Information (PII) pose significant challenges concerning privacy and security. Volatile organic compounds (VOCs) in exhaled breath are unique to individuals and can serv...

Predicting one-year overall survival in patients with AITL using machine learning algorithms: a multicenter study.

Scientific reports
Angioimmunoblastic T-cell lymphoma (AITL) is a life-threatening hematological malignancy. For patients with poor prognosis, especially those with expected survival less than 1 year, the benefits from traditional regimens are extremely limited. Theref...

Development of a serum protein biomarker panel for the diagnosis of pancreatic ductal adenocarcinoma using a machine learning approach.

Scientific reports
Early detection of pancreatic ductal adenocarcinoma (PDA) remains a major clinical challenge due to the lack of reliable biomarkers. We developed and validated a machine learning (ML)-based serum protein biomarker panel to enhance PDA diagnosis. Seru...

A radiomics model predicts progression from mild cognitive impairment to alzheimer's disease using structural MRI.

Scientific reports
The aim of this study is to build and validate a model based on structural magnetic resonance imaging (sMRI) to predict the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD). A total of 343 patients with MCI were selected fro...