An Interpretable Clinical Transformer Framework (ICTF) for Disease Prediction using EHR

• Author(s): Tabitha Susan Philip; Dr. Balamurugan S
• Paper ID: 1717948
• Page: 2568-2572
• Published Date: 19-05-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 11 May-2026

Abstract

Electronic health records (EHRs) contain large amounts of unstructured clinical texts that require analysis beyond traditional approaches. While transformer models like BioBERT (bidirectional encoder representations from transformers for biomedical text mining) have greatly improved prediction accuracy, their black-box nature reduces trust in clinical applications. This paper provides an overview of recent research papers on clinical NLP tasks and reveals the lack of interpretability in conjunction with data standardization. This study presents ICTF (interpretable clinical transformer framework), which uses a dual pipeline approach to compare machine learning methods with BioBERT. This method also incorporates SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model agnostic Explanations) to provide explanation post-prediction, assisting clinicians in interpreting predictions. The ICTF framework will predict disease labels while providing visualization maps using the MTSamples dataset available on Kaggle.

Keywords

Natural Language Processing (NLP), BioBERT, Interpretability, Electronic Health Records (EHR), SHAP, LIME, Disease Prediction.

Citations

IRE Journals:
Tabitha Susan Philip, Dr. Balamurugan S "An Interpretable Clinical Transformer Framework (ICTF) for Disease Prediction using EHR" Iconic Research And Engineering Journals Volume 9 Issue 11 2026 Page 2568-2572 https://doi.org/10.64388/IREV9I11-1717948

IEEE:
Tabitha Susan Philip, Dr. Balamurugan S "An Interpretable Clinical Transformer Framework (ICTF) for Disease Prediction using EHR" Iconic Research And Engineering Journals, 9(11) https://doi.org/10.64388/IREV9I11-1717948