AIMC Topic: Risk Assessment

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AI-powered spatial cell phenomics enhances risk stratification in non-small cell lung cancer.

Nature communications
Risk stratification remains a critical challenge in non-small cell lung cancer patients for optimal therapy selection. In this study, we develop an artificial intelligence-powered spatial cellomics approach that combines histology, multiplex immunofl...

Machine learning models for predicting renal injury in patients with gout.

Renal failure
BACKGROUND: Renal injury is a severe complication among individuals diagnosed with gout. This research constructed a machine learning predictive model to assess renal injury risk in gout patients.

An interpretable machine learning model predicts the interactive and cumulative risks of different environmental chemical exposures on depression.

Translational psychiatry
Humans are exposed to a multitude of environmental chemical mixtures (ECMs) in daily life that may influence depression risk. While prior studies have shown individual ECM exposures to depression, the cumulative and interactive effects of multiple co...

Comparative study of coronary artery disease prediction: conventional QRISK3 versus enhanced machine learning models combined with particle swarm optimisation algorithm.

Open heart
BACKGROUND: Coronary artery disease (CAD) is one of the biggest causes of mortality worldwide. Risk stratification for early detection is essential for the primary prevention of CAD. QRISK3 is known to overestimate future CAD risk in some populations...

Multiple polygenic score approach in colorectal cancer risk prediction.

Scientific reports
Recent studies have demonstrated that for various diseases, incorporating polygenic risk scores (PRSs) for other traits and diseases into the PRS-based risk prediction model may improve predictive performance - known as Multiple Polygenic Score (MPS)...

Transparent AI-driven personalized risk prediction system for acute kidney injury after total hip arthroplasty.

Scientific reports
Acute kidney injury is a common and severe complication following total hip arthroplasty, particularly in elderly or high-risk patients with chronic conditions, significantly increasing morbidity and mortality rates. Traditional prediction methods of...

Multimodal Multitask Learning for Predicting Depression Severity and Suicide Risk Using Pretrained Audio and Text Embeddings: Methodology Development and Application.

JMIR medical informatics
BACKGROUND: Depression is a critical psychological disorder necessitating urgent assessment and treatment, given its strong association with increased suicide risk (SR). Effective management hinges on promptly identifying individuals with high depres...

Machine learning-driven risk stratification for distant metastasis in gastric cancer: A comparative study of clinical features and composite indices integrated models.

PloS one
OBJECTIVE: Distant metastasis (DM) of gastric cancer (GC) represents a significant health challenge due to its high mortality rates, necessitating advancements in early detection and management strategies. The objective of this study was to create a ...

BRCAGenie: A machine learning-driven 43-gene polygenic risk score model for precision prediction of breast cancer survival.

Journal of translational medicine
BACKGROUND: Breast cancer is one of the most prevalent malignancies globally, imposing a substantial disease burden. Its inherent heterogeneity complicates prognosis and treatment, underscoring the need for accurate survival prediction models to guid...

Cardiovascular disease detection: A hybrid machine learning-AI framework for personalized diagnosis and risk assessment.

PloS one
Cardiovascular disease (CVD) is considered the number one killer disease in the world, underlining the importance of the application of more accurate diagnostic and therapeutic tools. Traditional screening procedures usually do not provide identifica...