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
FDA Approves Johns Hopkins AI Tool for Early Sepsis Detection
FDA clears an AI-driven system developed by Johns Hopkins to detect sepsis up to 48 hours earlier and reduce mortality rates.
•EurekAlert
AI-Driven Handheld Endomicroscope Enhances Early Cancer Detection
Researchers develop PrecisionView, a handheld AI-powered endomicroscope for real-time, high-resolution cancer diagnostics.
•EurekAlert
AI Model Uses EKG and EHR Data to Predict Sudden Cardiac Arrest
Researchers have developed AI models that analyze EKG and EHR data to predict risk of sudden cardiac arrest in the general population.