Personalized health monitoring has become increasingly salient as more patients are inclined toward preventive care. The inclusion of visual and textual patient data may improve the accuracy of monitoring and reduce the time for treatment. Current methods tend to employ static attention processes or unimodal analysis, which limit the methods’ ability to consider complex relationships within medical images, wearable sensor data, and textual health records. Many methods also fail to provide the tailored personalized insight needed for patients who differ on such patient characteristics. In order to address the above issues, the Reinforcement-Learned Multimodal Transformer Framework (RL-MMT) integrates visual and linguistic data to personalize health monitoring. This framework encodes medical images and textual data in a seamless way, using a multimodal transformer, while a reinforcement-learning (RL) agent quickly updates dynamically varying attention weights across modalities.. Prioritizing the most important patient information enhances forecast accuracy and flexibility. The recommended system can monitor chronic illnesses and detect early signs of diabetes and cardiovascular disease by analyzing data from wearable devices and electronic health records. Experimental findings reveal that RL-MMT outperforms multimodal and unimodal techniques in health risk assessments and treatment timing.
Kumar et al. (Thu,) studied this question.