Multimodal Patient Outcome Prediction through Explainable AI in Clinical Finance Decision Systems
Keywords:
explainable AI, multimodal prediction, patient outcomes, electronic health recordsAbstract
Explore clinical finance decision-making in multimodal patient outcome prediction systems utilising explainable AI (XAI) frameworks. Deep learning architectures predict patient care and costs using EHRs, genetic sequencing, wearable sensors, and billing databases. Physicians and financial analysts trust straightforward medical and insurance decisions. Attention and feature attribution explain global and individual model reasoning, whereas stronger fusion connects temporal, categorical, and continuous modalities. Machine learning vs. black-box neural prediction accuracy, calibration, and durability. To balance clinical effectiveness and economic prudence, XAI-powered multimodal models may optimise resource allocation, financial risk, and precision healthcare therapies.
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