Aug 2026· Journal of Biomedical Informatics· Vol 181, pp.
105089
· 0 citations· 39 references
Medicine
TL;DR
Initial experiments on heart-failure-focused clinical question answering show that CGX improves evidence retrieval quality and perceived answer reliability over conventional retrieval methods, while reducing total graph construction time by 69.7% under the same input corpus and hardware setting.
Abstract
Knowledge graphs are increasingly used to organize and retrieve complex medical information, yet existing graph-based retrieval systems often suffer from high construction costs, limited scalability as knowledge grows, and limited interpretability in clinical practice. These challenges are amplified in cardiovascular medicine, where data are heterogeneous, noisy, and linked by complex relationships. In this work, we present CGX, a domain-oriented GraphRAG framework that mirrors clinical reasoning for explainable heart failure analysis. CGX structures cardiovascular knowledge into a three-layer hierarchy that spans patient-level observations, guideline-based evidence, and standardized ontologies. An OCR-enhanced preprocessing pipeline combined with a zero-shot biomedical transformer converts PDF-based biomedical literature/guidelines and machine-readable clinical narratives into semantic triples, reducing error propagation compared with vanilla RAG. A Hybrid U-Retrieval mechanism then exploits the graph topology through top-down summary retrieval and bottom-up path refinement, producing explicit evidence chains that support each answer. Initial experiments on heart-failure-focused clinical question answering show that CGX improves evidence retrieval quality and perceived answer reliability over conventional retrieval methods, while reducing total graph construction time by 69.7% under the same input corpus and hardware setting. Under blinded expert clinical evaluation, CGX reduces the rate of clinically risky answers from 12.4%-14.0% to 8.3%, alongside substantially higher scores across all five expert-rated Likert criteria compared with both baselines. These results suggest that CGX offers a scalable and reusable GraphRAG architecture for integrating structured medical knowledge with large language models to support trustworthy clinical decision-making.
Clinical records contain rich evidence about patient state, but converting that evidence into reliable, structured knowledge graphs remains difficult because extraction errors, ontology mismatch, missing relations, and temporal ambiguity can propagate into downstream systems. We propose a clinical knowledge graph construction and refinement framework that combines multi-agent relation proposal, ontology-aware normalization, deterministic evidence scoring, and JEPA-based latent refinement. Rather than treating a clinical knowledge graph as a static extraction artifact, we treat it as a predictive patient-state representation. For each admission, the system constructs an evidence-scored graph from structured MIMIC-IV records and inferred clinical cross-links, then learns to recover held-out clinical relations from the observed graph context. We evaluate the refiner with leakage-free leave-one-out edge recovery (MRR and Hits@k) and held-out batch-mask evaluation (AUC and MRR). To isolate the contribution of discharge-note context, we compare a note-embedding-free configuration with a note-augmented configuration that injects real discharge-note representations only into note-grounded entities. Under the same cohort and evaluation protocol, entity-grounded note injection improves overall leave-one-out MRR by 31% relative improvement.
Kushagra Yadav, N. Prabhath, Amit Lamba et al.· 0 citations
Biomedical knowledge discovery and clinical natural language processing increasingly rely on foundation models, but these approaches face persistent challenges, including incomplete curated knowledge, instability in language model outputs, and ambiguity in clinical interpretation. This dissertation argues that reliable biomedical AI requires integrating structured knowledge and foundation models through complementary control mechanisms rather than relying on any single method. To support this argument, the dissertation presents a unified framework across molecular and clinical domains. It develops an iterative prompt refinement approach with retrieval-augmented generation to improve the reliability of biomedical relation extraction, introduces graph transformer models to infer gene–gene relations and augment incomplete pathway knowledge, and proposes ontology-grounded retrieval and normalization pipelines to improve semantic alignment in multidisciplinary clinical notes. Together, these contributions define a layered reliability framework in which graph-based inference addresses knowledge incompleteness, retrieval-augmented prompt control mitigates instability, and ontology grounding reduces semantic ambiguity, providing a foundation for more reliable biomedical AI systems.
Despite rapid progress in retrieval-augmented generation (RAG), existing systems still struggle to retrieve both fine-grained entity-level evidence and higher-level abstract knowledge from biomedical literature. Graph-based RAG offers a promising alternative to flat retrieval, and hierarchical Graph RAG extends this idea by enabling coarse-to-fine retrieval across multiple levels of abstraction. Its effectiveness, however, depends on hierarchy construction choices, uneven community connectivity, and query-dependent layer choice. These challenges are particularly important in literature-derived biomedical graphs, where the hierarchy is constructed automatically from documents rather than specified in advance. We introduce HAGRAG, a hierarchical attributed Graph RAG framework for PubMed-derived diabetes literature. HAGRAG constructs multi-level attributed communities, summarizes them across layers, indexes them using hierarchical navigable similarity search, and supports layer-aware filtering during retrieval. It further evaluates alternative community detection algorithms and layer-weighting strategies to determine how hierarchy design affects retrieval behaviour. Across comparisons with baseline systems, HAGRAG achieves the strongest overall performance, including accuracy of 0.80, relevancy of 1.00, recall of 0.54, and mean semantic similarity of 0.65. Answer quality is further assessed through NLI-based entailment checks and independent diabetes-clinician validation. The results show that Leiden produces the most coherent hierarchy, while retrieval quality remains sensitive to clustering and weighting choices. Overall, the findings show that effective hierarchical Graph RAG depends not only on graph augmentation, but on constructing, navigating, and weighting the hierarchy.
Maneeha Rani, B. K. Mishra, Dhavalkumar Thakker et al.· Machine Learning: Science an...· 0 citations
The increasing volume and heterogeneity of healthcare data pose significant challenges for developing reliable and interpretable clinical decision support systems. Conventional machine learning approaches often struggle to integrate structured electronic health records, real-time patient inputs, and unstructured clinical narratives at scale, limiting their effectiveness in complex medical settings. This study proposes a scalable, knowledge graph driven big data framework for explainable clinical decision support that unifies heterogeneous healthcare data into a semantically structured representation. The framework integrates RDF-based semantic modeling, domain-specific natural language processing for entity extraction, and graph-based reasoning to map patient-reported symptoms to evidence-based treatment guidelines. Large-scale clinical data from the MIMIC-III database comprising over 40,000 hospital admissions real-time patient records, and international clinical protocols from the International Diabetes Federation (IDF) are incorporated to enable dynamic, data-driven decision making. Experimental evaluation demonstrates strong predictive performance in detecting critical diabetic conditions under controlled settings, achieving perfect precision and recall for hypoglycemia and a recall of 0.90 for diabetic ketoacidosis. A macro-averaged F1-score of 0.79 is achieved across all four diabetic condition classes, comparing favorably with rule-based clinical decision support systems and classical machine learning baselines while offering superior explainability. In addition to predictive accuracy, the framework provides transparent, traceable decision paths through knowledge graph reasoning addressing key challenges of interpretability and trust in clinical AI systems. The results highlight the effectiveness of knowledge graph based big data integration for scalable, explainable, and guideline-compliant clinical decision support. The proposed framework is generalizable to other data-intensive healthcare applications, offering a robust foundation for next-generation big data analytics and intelligent decision systems.
Ubaid Ul Rehman, Hufsa Mohsin, Ghulam Mustafa et al.· Journal of Big Data· 0 citations
Large language models (LLMs) are increasingly proposed for healthcare decision support, but their evaluations still reward single-answer accuracy rather than reasoning about interventions, mechanisms, harms, evidence, and uncertainty. We propose a reproducible, graph-centered evaluation framework for intervention-oriented LLM behavior in healthcare and stress-test it in a cardiovascular pilot. The framework has four components: (i) a domain causal knowledge graph in which assertions are first-class, provenance-preserving nodes with stable identifiers; (ii) a scenario-conditioned subgraph extraction step that, given any clinical scenario, retrieves the relevant reified-assertion subgraph; (iii) four controlled grounding conditions that vary how the retrieved subgraph is composed into the model's context (ungrounded C1, knowledge-graph C2, causal-graph C3, integrated C4); and (iv) an automated scoring pipeline, anchored on assertion identifiers, that computes intervention accuracy, and other evaluation measures on a single pass. To test the framework, we built a category-balanced scenario generator across eight reasoning failure modes and instantiated it on a cardiovascular graph. The metric panel discriminates conditions along interpretable, non-redundant axes: C4 obtains the strongest causal edge F1 (0.838), adverse-effect F1 (0.833), evidence accuracy (0.738), and unsupported claim rate (0.114), while C1 obtains the highest raw intervention accuracy (0.948) with no measurable causal or evidential grounding.
Existing general-purpose biomedical knowledge graphs tend to focus on disease mechanisms and drug repurposing, leaving multiomic and wellness-relevant content underrepresented. KRAKEN (Knowledge Research & Analysis Kit for Evidence Networks) addresses this gap by integrating existing graphs (including Translator KG Open, RTX-KG2, and ROBOKOP) with specialized sources such as RefMet, LIPID MAPS, NIH Common Data Elements, Polygenic Score Catalog, and derived wellness measures including biological age and biological BMI. The resulting graph spans ∼15M nodes and ∼113M edges across 62 entity types. KRAKEN adopts the Biolink Model as its semantic layer, ensuring compatibility with standardized resources emerging from the NIH NCATS Biomedical Data Translator program. A lightweight, modular build system rebuilds the full graph (including entity resolution), with peak memory consumption <48 GB, and supports flexible inclusion or exclusion of sources, allowing the user to scope the graph to a domain of interest. Built-in analytical tools include multi-hop reasoning, subgraph extraction, text, vector and hybrid entity search, and enrichment analyses, all accessible through an interactive web interface, a REST API, and a Model Context Protocol server, the last enabling direct consumption by agentic and LLM-based systems. KRAKEN is freely available at https://app.krakenkg.com. GRAPHICAL ABSTRACT
Amy K. Glen, D. Witherington, Trent Leslie et al.· bioRxiv· 0 citations