HYBRID AI MODEL FOR HL7 DATA PROCESSING AND SEMANTIC INTEROPERABILITY

AI health assistant combining transformers and XGBoost for multilingual care

CIMAS HealthMate is proposed, a hybrid multilingual VHA that integrates transformer-based natural language processing (NLP) with an explainable extreme gradient boosting (XGBoost) decision model to provide accurate and transparent symptom triage.

Natural Language Processing for Prediction of Chronic Diseases from Electronic Health Records

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 Reproducible Protocol for Developing and Evaluating Explainable Artificial Intelligence Frameworks in Healthcare Analytics.

Explainable Artificial Intelligence (XAI) is increasingly recognized as an indispensable tool for building trustworthy AI systems in healthcare, where transparency and the ability to explain decisions are essential components of clinical decision-making. This publication presents a reproducible experimental approach to developing and evaluating explainable AI systems for healthcare analytics. The developed pipeline integrates the steps of data preprocessing, predictive modeling, interpretation generation, and evaluation into one seamless workflow that can be applied to both structured clinical data and medical imaging datasets. Ensemble machine learning models have demonstrated strong predictive performance on structured tabular datasets, whereas deep learning models are effective for learning complex patterns in medical imaging data. Techniques such as SHAP, LIME, and Grad-CAM are global and local explanations that facilitate model interpretation. Very helpful. The quantitative assessment of the framework spans many different metrics such as accuracy, precision, recall, F1 score, ROC-AUC, explanation metrics, fidelity, and stability. The results indicate that when the experimental conditions are controlled, the framework demonstrated improved predictive performance and explanation quality under the evaluated experimental conditions. As a document guided by protocol, this piece of work backs the reproducibility and scalability, the persistent implementation by other living beings. This transparent, understandable AI model is the foundation upon which clinical decision-making support and healthcare analytics systems gain trust and usage on a large scale.

Machine Learning-Augmented AI System for Medical Report Analysis, Clinical Summarization, and Diagnostic Decision Support

The extensive adoption of electronic health records has necessitated the development of automated systems that are capable of understanding unstructured clinical documents. Medical records, such as lab results, radiology findings, and discharge summaries, thus make manual analysis a slow and error-prone process. The paper introduces an AI-driven medical report analysis framework that employs natural language processing and deep learning to automatically locate and interpret the clinically significant information. The system proposed in this paper first preprocesses the medical text to identify the major entities such as diseases, symptoms, and drugs, and then translates them into structured clinical data. An attention-based neural model is used to produce brief analytical summaries, which help clinical decision-making. Experimentally, it was found that the proposed system not only outperformed the manual process in accuracy but also reduced the time. The framework, therefore, increases the efficiency of healthcare and opens up the potential for better utilization of electronic medical records.

Tutorial: guidance on the use of large language models for medical research

This entry-level tutorial aims to equip healthcare professionals with the tools necessary to effectively integrate LLMs into clinical practice, ensuring that these powerful technologies are applied in a safe, reliable, and impactful manner.

Diagnosis classification in EMR data using latent representations and SNOMED-CT mapping for improved medical data integration.