Predictive Modeling in Healthcare using Deep Learning: A Comprehensive Review
Keywords:
Convolutional neural networks, Deep learning, Federated Learning, Healthcare, LSTM, Predictive modeling, Retrieval-augmented generation, TransformersAbstract
Predictive modelling is essential to the healthcare sector for enhancing clinical outcomes and to maximize scarce resources in the healthcare system. The increasing digitization of healthcare systems creates a vast amount of data, including electronic health records (EHRs), medical imaging, genome data, and data from wearable devices. Traditional analytical techniques frequently cannot be applied to the analysis of this high-dimensional and heterogeneous data. Many of these challenges are addressed by a subfield of artificial intelligence (AI) known as deep learning, which constructs hierarchical representations directly from raw data, without relying on much feature engineering. This study offers a systematic review of predictive modeling in healthcare with deep learning, which includes popular architectures like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Transformer-based systems, and hybrid multi-modal systems. In addition to summarizing previous research, this review critically compares these architectures in terms of their strengths and weaknesses, data needs, and application to specific prediction tasks in healthcare. Applications are explained for disease prediction, medical imaging, patient monitoring in ICUs, personalized medicine, and prediction of health in populations. The study also provides a detailed discussion on key open problems such as interpretability, data quality, algorithmic fairness, regulatory compliance, and clinical integration, as well as new trends, such as federated learning, explainable AI, foundation models, Vision Transformers, and Retrieval-Augmented Generation (RAG) in healthcare. In conclusion, the evidence suggests that deep learning has significant promise of changing the landscape of healthcare delivery, but achieving this will involve continued collaboration among many disciplines to build trust, transparency, and safe clinical deployment.
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