Year: 2025
Type: book-chapter
Abstract: The modern healthcare landscape is overwhelmed by data, including extensive Electronic Health Records (EHRs) and many clinical notes and reports. This rapid growth seems promising, but it complicates real medical intelligence. The massive amount of information is still disorganized and varied, which limits its predictive capability. To address this problem, we present a new predictive healthcare framework aimed at improving early disease detection and proactive diagnosis. Our solution combines advanced Natural Language Processing (NLP) with strong Big Data analytics. We use powerful deep learning models like BERT and LSTM networks for effective text mining. These models uncover hidden, clinically relevant information and recognize subtle patterns in large medical text collections. Importantly, we connect these sophisticated models with scalable and distributed platforms like Apache Spark and Hadoop, enabling our system to process and analyze extensive real-world datasets quickly and efficiently. The experimental results support our method: NLP-driven feature extraction significantly increases the accuracy of machine learning classifiers and reduces the time needed for a dependable diagnosis. More than just boosting performance, we have included Explainable Artificial Intelligence (XAI) features to make the model's decisions clear and understandable, which is essential for earning the trust of clinicians. Overall, this work changes raw, unstructured medical text into useful information, paving the way for truly proactive, scalable, and patient-centered healthcare analytics.
Institution: ITM University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W7128538740