Researchers created an AI tool leveraging clinical and radiology data to accurately predict patients at risk of intimate partner violence (IPV).
Key Details
- 1NIH-funded study trained AI models on structured clinical data and unstructured text, including radiology reports.
- 2Models evaluated data from nearly 850 IPV-affected female patients and 5,200 matched controls.
- 3Three models were developed: structured data only, unstructured text only, and multimodal (fusion) combining both.
- 4The fusion model achieved 88% accuracy and greater stability, outperforming single-modality models.
- 5Both tabular and fusion models predicted IPV risk an average of three years before intervention center enrollment.
- 6Emergency radiologist Bhati Khurana was senior author; further clinical integration is planned via EMR tools.
Why It Matters
Proactive AI-based IPV risk identification can significantly improve early intervention and patient outcomes, with radiology playing a key role in detection. This approach illustrates how imaging and informatics can address public health challenges beyond traditional diagnostics.
Source
EurekAlert
Related News
•EurekAlert
NIH-Backed AI Model Predicts Cancer Survival Using Single-Cell Data
Researchers have developed scSurvival, a machine learning tool that uses single-cell tumor data to accurately predict cancer patient survival and identify high-risk cell populations.
•EurekAlert
Deep Learning Pathomics Platform Improves Immunotherapy Prediction in Lung Cancer
A deep learning pathomics platform accurately predicts immunotherapy response in metastatic NSCLC using routine pathology slides.
•EurekAlert
AI Pathology Model Outperforms PD-L1 in Predicting NSCLC Immunotherapy Response
MD Anderson's Path-IO machine learning platform accurately predicts immunotherapy responses in metastatic non-small cell lung cancer, surpassing current biomarker standards.