Deep Learning-Driven Real-Time Multimodal Healthcare Data Synthesis
Keywords:
Deep learning, healthcare, clinical trials, X-ray imagesAbstract
In recent years, the healthcare sector has witnessed an exponential surge in data generation from various sources. This data influx has opened new avenues for researchers to construct models and analytics, enhancing patient healthcare. While research and applications in prediction and classification have prospered, numerous challenges persist in optimizing healthcare comprehensively. Challenges encompass improving physician performance, curbing healthcare costs, and uncovering novel disease treatments. Physicians often grapple with time-consuming tasks, resulting in fatigue and occasional misdiagnoses. Automating such tasks can save time, enabling healthcare professionals to concentrate on elevating care quality. Health datasets comprise multiple modalities, such as structured sequences, unstructured text, images, ECG, and EEG signals. Leveraging these diverse data types necessitates effective methods. Moreover, the healthcare landscape is hindered by limited treatment options reaching the market, with many potential solutions failing in clinical trials. Machine learning models can enhance clinical trial outcomes and consequently elevate patient treatment quality. This paper addresses these issues through the development of a multimodal deep learning framework. It generates text reports and aids physicians in clinical practice, offering a multifaceted approach to address the diverse challenges in healthcare. The intended objective is to create a generative model capable of generating chest X-ray images and their corresponding textual reports.
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