AIMC Topic: Machine Learning

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Machine learning-based stratification of mild cognitive impairment in Parkinson's disease: a multicenter cross-sectional analysis.

BMC medical informatics and decision making
BACKGROUND: Cognitive impairment is a prominent non-motor manifestation of Parkinson's disease (PD) and is associated with reduced quality of life, increased mortality, and higher healthcare utilization. We aimed to develop and externally validate a ...

Looking back to move forward: can historical clinical trial data and machine learning drive change in participant recruitment in anticipation of future value assessments?

Trials
Drug development is an expensive endeavor, with costs averaging $879.3 million and only 14.3% of them ultimately securing regulatory approval. One fundamental challenge is ensuring that the enrolled patient population in a clinical trial accurately r...

BiMA-DTI: a bidirectional Mamba-Attention hybrid framework for enhanced drug-target interaction prediction.

BMC biology
BACKGROUND: Predicting drug-target interactions (DTIs) is essential for accelerating drug discovery, yet traditional experimental methods are time-consuming and costly. Computational approaches, especially those using machine learning and deep learni...

Identifying subjective life expectancy risk factors in physically active and inactive middle-aged and older adults using machine learning models.

BMC public health
BACKGROUND: Physical activity is a key focus in the field of public health, and subjective life expectancy is closely associated with individuals' physical and psychological well-being. This study aimed to identify the risk factors for subjective lif...

Predicting carotid plaques in metabolic dysfunction-associated steatotic liver disease using machine learning and SHAP interpretation.

Scientific reports
Cardiovascular disease (CVD) remains the most common cause of death worldwide. Carotid plaque is an indicator of subclinical CVDs. Metabolic dysfunction-associated steatotic liver disease (MASLD) is a risk factor for atherosclerotic CVDs. We aimed to...

Prediction of stillbirth using machine learning methods.

Scientific reports
This study developed a machine learning model to predict stillbirth using retrospective data from 32,953 singleton pregnancies at multi-centers in South Korea. Variables were collected at baseline, E1 (before 13 weeks of pregnancy), and T0 (before 28...

Incidence and severity of aortic stenosis according to machine learning predicted risk of atrial fibrillation.

Scientific reports
Atrial fibrillation (AF) and aortic stenosis (AS) are two common progressive conditions affecting older persons that share pathobiological pathways. Early detection of AS is critical for improving outcomes, but no prediction tool exists to inform dec...

Construction and validation of a cross-sectional risk classification model for hypoproteinemia in single-center maintenance hemodialysis patient.

Scientific reports
Hypoproteinemia is a common complication across patients receiving maintenance hemodialysis (MHD). Moreover, it is associated with increased risks of cardiovascular events, infection risk, and mortality. This study aimed to construct a classification...

Pathology image-based predictive model for individual survival time of early-stage lung adenocarcinoma patients.

Scientific reports
The tumor microenvironment (TME) is associated with tumor prognosis, immunotherapy response, and prognosis in patients. Here, we hypothesized that the entire TME in pathology image is associated with the survival time prediction. To address this hypo...

Intelligent microstructure materials for diagnosis and treatment of osteoarthritis: progress and AI-enpowered future.

Bone research
Osteoarthritis (OA) is a widespread joint disorder that has emerged as a significant global healthcare challenge. Over the past decade, advancements in material science and medicine have transformed the development of functional materials aimed at ad...