AIMC Topic: Middle Aged

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Unsupervised machine learning clustering approach for hospitalized COVID-19 pneumonia patients.

BMC pulmonary medicine
BACKGROUND: Identification of distinct clinical phenotypes of diseases can guide personalized treatment. This study aimed to classify hospitalized COVID-19 pneumonia subgroups using an unsupervised machine learning approach.

Comparison of time-to-event machine learning models in predicting biliary complication and mortality rate in liver transplant patients.

Scientific reports
Post-Liver transplantation (LT) survival rates stagnate, with biliary complications (BC) as a major cause of death. We analyzed longitudinal data with a median 19-month follow-up. BC was diagnosed with ultrasounds and MRCP. Missing data was imputed u...

Advancing Alzheimer's disease risk prediction: development and validation of a machine learning-based preclinical screening model in a cross-sectional study.

BMJ open
OBJECTIVES: Alzheimer's disease (AD) poses a significant challenge for individuals aged 65 and older, being the most prevalent form of dementia. Although existing AD risk prediction tools demonstrate high accuracy, their complexity and limited access...

Analyzing patterns of frequent mental distress in Alzheimer's patients: A generative AI approach.

Journal of the National Medical Association
This study tackles creating Python code for beginners with generative AI and analyzing trends in mental distress among Alzheimer's patients in the US (2015-2022 CDC data). It guides beginners through using AI to generate code for visualizing these tr...

Machine learning-based unsupervised phenotypic clustering analysis of patients with IgA nephropathy: Distinct therapeutic responses of different groups.

Chinese medical journal
BACKGROUND: Immunoglobulin A nephropathy (IgAN) has a heterogeneous clinical presentation. Comparison of different IgAN subgroups may facilitate the application of more targeted therapies. This study was aimed to distinct disease phenotypes in IgAN a...

Deep Learning Reconstruction Combined With Conventional Acceleration Improves Image Quality of 3 T Brain MRI and Does Not Impact Quantitative Diffusion Metrics.

Investigative radiology
OBJECTIVES: Deep learning reconstruction of magnetic resonance imaging (MRI) allows to either improve image quality of accelerated sequences or to generate high-resolution data. We evaluated the interaction of conventional acceleration and Deep Resol...

Developing approaches to incorporate donor-lung computed tomography images into machine learning models to predict severe primary graft dysfunction after lung transplantation.

American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons
Primary graft dysfunction (PGD) is a common complication after lung transplantation associated with poor outcomes. Although risk factors have been identified, the complex interactions between clinical variables affecting PGD risk are not well underst...

Characterizing drivers of change in intraoperative cerebral saturation using supervised machine learning.

Journal of clinical monitoring and computing
Regional cerebral oxygen saturation (rSO) is used to monitor cerebral perfusion with emerging evidence that optimization of rSO may improve neurological and non-neurological outcomes. To manipulate rSO an understanding of the variables that drive its...

Artificial Intelligence Job Substitution Risks, Digital Self-efficacy, and Mental Health Among Employees.

Journal of occupational and environmental medicine
OBJECTIVE: Artificial intelligence (AI) becomes increasingly integrated into the workplace, its associated job substitution risks for employees are more evident, resulting in significant repercussions for their well-being. This study tries to elucida...

A Hybrid Machine Learning CT-Based Radiomics Nomogram for Predicting Cancer-Specific Survival in Curatively Resected Colorectal Cancer.

Academic radiology
RATIONALE AND OBJECTIVES: To develop and validate a computed tomography-based radiomics nomogram for cancer-specific survival (CSS) prediction in curatively resected colorectal cancer (CRC), and its performance was compared with the American Joint Co...