International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 02 | Feb 2026
p-ISSN: 2395-0072
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EARLY DETECTION OF BONE MARROW GRAFT REJECTION USING LARGE LANGUAGE MODELS Mrs. Y. Naga Lavanya1,Vinitha.G2, Ajay.B3, Anjan vivek.J4, Shiva.G5 1Assistant Professor, Department of IT, TKR College of Engineering and Technology, Telangana, India 2,3,4,5B.Tech Students, Department of IT, TKR College of Engineering and Technology, Telangana, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Bone marrow transplantation is a critical
language models (LLMs), offer an opportunity to enhance predictive healthcare analytics by integrating structured laboratory data with unstructured clinical notes [2,3,5]. In this project, we leverage Google Gemini 2.5 Flash LLM via Lang Chain to analyze hematological parameters, cytokine levels, doctor’s notes, and pathology reports to predict the risk of bone marrow graft rejection [6,7]. The system provides a quantitative risk score, a categorical rejection status, and a brief explanation for the prediction, enabling clinicians to make informed decisions quickly [4,7]. The proposed framework combines real time AI predictions with a Django-based web interface, ensuring accessibility, scalability, and interpretability [1,7]. By integrating AI driven insights with clinical workflows, this system aims to improve patient outcomes and reduce the incidence of undetected early graft rejection [2,4].
therapeutic intervention for patients with hematological disorders, yet graft rejection remains a significant cause of morbidity and mortality. Early detection of graft rejection is essential for timely medical intervention and improved patient outcomes. This work proposes an intelligent system leveraging Google Gemini 2.5 Flash Large Language Models (LLMs) via Lang Chain to predict the risk of bone marrow graft rejection. The system accepts patient-specific hematological parameters, cytokine levels, clinical notes, and pathology reports as input, and generates a rejection risk score, categorical rejection status, and a concise explanation. A Django-based web interface allows healthcare professionals to input patient data seamlessly and receive AI-generated predictions in real time. The model is trained and validated using a structured dataset containing hematological features and historical rejection outcomes. Experimental results demonstrate that the system effectively identifies early and severe rejection scenarios, enabling proactive clinical decision-making. This approach integrates natural language understanding with clinical data analytics, offering a scalable solution for personalized patient monitoring. By combining AI interpretability with ease of use, the proposed framework provides a robust platform for augmenting traditional clinical practices, reducing diagnostic latency, and enhancing overall transplant care quality.
1.1Challenges inConventionalGraft Rejection Monitoring Despite clinical advancements, early detection of bone marrow graft rejection remains challenging due to the fragmented nature of patient data and delayed symptom manifestation. Conventional approaches primarily depend on periodic laboratory investigations such as complete blood counts, cytokine profiling, and biopsy results, combined with clinician experience. These methods often fail to capture subtle early warning signals embedded in unstructured data like physician notes, discharge summaries, and pathology narratives. Moreover, manual interpretation introduces subjectivity and may delay critical interventions, increasing the risk of graft failure and adverse patient outcomes. Hence, there is a growing need for intelligent systems capable of continuous, holistic, and objective monitoring.
Keywords: Bone Marrow Transplantation (BMT), Graft Rejection, Early Detection, Large Language Models (LLMs), Clinical Decision Support System, Natural Language Processing (NLP), Electronic Health Records (EHR), Immunological Biomarkers.
1. INTRODUCTION Bone marrow transplantation (BMT) is a life-saving procedure for patients suffering from hematological malignancies, immunodeficiency disorders, and other severe blood-related conditions [2,3]. Despite advancements in transplant techniques and immunosuppressive therapies, graft rejection remains a major complication, often leading to patient morbidity or mortality [4]. Early identification of graft rejection is critical for initiating timely interventions and improving patient survival rates [2,4]. Traditional monitoring methods rely heavily on periodic laboratory tests and clinical judgment, which can be time-consuming and prone to subjective interpretation [2]. Recent advances in artificial intelligence, particularly machine learning and large
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1.2 AI-Driven Framework for Early Graft Rejection Prediction The proposed system addresses these limitations by integrating large language models with structured and unstructured clinical data to enable proactive graft rejection prediction. By leveraging hematological parameters, immunological markers, and narrative clinical documentation, the AI model identifies complex patterns indicative of early immune response abnormalities. The framework delivers an interpretable risk score, classification
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