2026· E3S Web of Conferences· 0 citations· 10 references
TL;DR
This approach combines semantic understanding of clinical narratives with structural modeling of patient-disease-treatment relationships and successfully validates synthetic EHR data utility for privacy-preserving healthcare AI development while addressing critical requirements necessary for clinical decision support system.
Abstract
This paper presents a comprehensive multi-modal artificial intelligence framework for the prediction of disease from electronic health records that integrates ClinicalBERT natural language processing with graph neural networks, temporal modeling and explainability analysis. Using Synthea synthetic EHR dat with SNOMED CT codes from 1,171 patients, our approach combines semantic understanding of clinical narratives with structural modeling of patient-disease-treatment relationships. The system achieves predictive performance with macro-averaged F1 score of 0.4512 and AUC of 0.9071 across six chronic conditions, demonstrating outstanding results for diabetes (F1=0.900) and hypertension (F1=0.949). Novel contributions include temporal progression forecasting over 12-month periods using LSTM-Transformer hybrid architecture and comprehensive explainability framework providing gradient-based feature importance analysis and automated clinical reasoning generation. The frameworks successfully validates synthetic EHR data utility for privacy-preserving healthcare AI development while addressing critical requirements necessary for clinical decision support system.
A hybrid framework for a comprehensive evaluation of machine learning and transformer-based models for cardiovascular risk prediction by integrating structured clinical data with unstructured clinical narratives is included in this study. The structured data includes demographic and diagnostic variables like patient information and test results, while unstructured data is derived from electronic health records such as clinical notes. Through preprocessing, feature engineering, and semantic embedding using transformer-based models, the system leverages the complementary strengths of both data types. By analysing and processing this unstructured information, this project aims to improve the predictions for heart disease. The results show that it is feasible to make better predictions and gain valuable insights by merging these two types of data. Such analysis is essential because structured data alone often overlooks fine-grained clinical indicators found in narrative texts. Integrating unstructured data allows for a more holistic view of patient health, leading to earlier detection, personalized interventions, and improved decision-making in clinical settings.
The rapid growth of medical data and the increasing complexity of clinical diagnostics requires effective computational frameworks to help people in healthcare industry. This paper presents an integrated intelligent healthcare system which is aimed at improving disease prediction and personalized drug recommendations. Based on the recent advancements in Artificial Intelligence, the proposed system uses hybrid deep learning architecture. First, it makes use of Transformer-based Natural Language Processing (NLP) models to analyze clinical symptoms and Electronic Health Records (EHR), which greatly increases diagnostic accuracy compared to traditional decision-tree methods. Second, the system combines the Graph Neural Networks (GNNs) with an ontology driven databases like DrugBank and UMLS to provide safe and effective drug recommendations. This module specifically models drug to drug interactions and checks for safety using OpenFDA label data, which ensures reliable treatment planning. By merging symptom-based disease inference with the reinforcement learning based personalization, this approach fills some of the significant gaps in automated healthcare. It offers scalable solution for precision medicine and better resource allocation.
Gayathri Tippani, Datta Sai, K. Sai et al.· 2026 6th International Confe...· 0 citations
This paper summarizes the main challenges currently facing, including medical data privacy and labeling problems, interpretability and clinical credibility barriers of the model, and systemic barriers to multimodal fusion.
Abstract Background Health care systems generate vast amounts of unstructured text, such as clinical notes, which capture nuanced patient experiences, clinical reasoning, and subtle indicators of health status. While health system research has traditionally relied upon structured data, natural language processing (NLP) enables the extraction of this rich textual information. Leveraging NLP could improve the identification and characterization of underreported adverse drug events (ADEs). Objective The primary objective of this study was to train and evaluate multiple NLP models, including both previously published architectures and a novel model, for the identification of ADEs from clinical notes. Methods Electronic medical records from the Manitoba Primary Care Research Network (MaPCReN) were used in this study. Clinical notes were annotated to indicate the presence of a possible ADE, the associated words or phrases, and the corresponding drug. A subselection algorithm was applied to ensure sufficient representation of notes containing ADEs for training a robust classifier. The cohort was restricted to patients aged 55 years and older and was annotated in 2 waves: Wave 1 comprised primary care encounter notes selected for a temporally linked emergency department (ED) visit, enriching it for acute presentations, while Wave 2 relaxed this requirement, and its acuity composition was uncharacterized. The annotated data were split into training and test sets. NLP models—including BioBERT, BlueBERT, a large language model (LLM) classifier, and an LLM embeddings–based classifier—were trained on both original clinical notes and notes reformatted into Subjective-Objective-Assessment-Plan (SOAP) structure. To approximate a clinician-inspired reasoning workflow, Mistral-7b-Instruct was used to extract presenting symptoms and generate a ranked list of potential etiologies. Model performance was evaluated using precision, recall, and F1-score. Results Of the 1085 annotated encounter notes, 355 included ADEs. Across 9 modeling approaches evaluated over 5 random seeds, models trained on SOAP-rewritten notes generally outperformed those trained on original notes. The SOAP Rewrite+ LLM Embeddings Classifier (GritLM-SOAP) achieved the highest mean F1-score (72.53%; 95% CI 62.40%‐81.82%), while BioBERT-SOAP weighted achieved the highest mean recall (76.34%; 95% CI 62.69%‐89.66%). End-to-end span-level extraction (named entity recognition+relation extraction) on ADE-positive test notes achieved a mean relaxed F1-score of 0.455, with relation extraction identified as the bottleneck. Conclusions Effective detection of ADEs in clinical notes may benefit from NLP models that approximate the clinical reasoning of health care providers. While the SOAP Rewrite+ LLM Embeddings Classifier demonstrated a reasonable balance of precision and recall, there is room for improvement as models evolve.
Alan Katz, Abhishek Dhankar, Gillian Fransoo et al.· Journal of Medical Internet...· 0 citations
A Large Language Model-guided retrieval-aware framework for generating realistic synthetic EHR data on a large scale to train ML models to predict cardiovascular risks accurately and achieves high performance in predicting CVD.
Kavitha Bai A. S, J. Somasekar· Journal of Vascular Diseases· 0 citations
Cardiovascular diseases (CVDs) are among the leading causes of mortality worldwide. Early detection and risk stratification are critical for preventive care. Traditional machine learning (ML) models can predict heart disease risk but often lack interpretability and fail to integrate with real-time clinical data. Recent advances in fine-tuned large language models (Custom GPT) offer natural language explanations but are limited by insufficient interoperability with heterogeneous healthcare data sources. This study aims to design and evaluate a Model Context Protocol (MCP)–enabled Custom GPT framework that integrates ML-based predictive models with external healthcare systems—including EHRs, laboratory APIs, and wearable devices—to deliver context-aware, explainable, and clinically actionable heart disease risk predictions. The experimental evaluation was conducted on a validated cardiovascular dataset containing 303 patient records and 14 clinically relevant attributes derived from publicly available clinical repositories. Experimental evaluation demonstrated improved predictive accuracy (approximately 88% with the XGBoost ensemble) and robustness compared to standalone models. MCP integration enabled dynamic contextual awareness, reduced latency in tool orchestration, and enriched interpretability through RAG-based explanations. Clinician and patient evaluations confirmed enhanced usability and transparency. This approach paves the way for broader adoption of agentic AI in clinical workflows.
Neha Gupta, Bhawna Singla· Journal of Electronic &...· 0 citations
