Search Broadly, Seek Evidence on Both Sides, Decide Narrowly: Evidence-Admissible GraphRAG for Longitudinal Clinical Event Verification

Xingtao Lin; Yubo Feng; Weixin Liu; Hangqi Ren; Junchao Zhou; Caiwan Sun; You Chen

cs.AIAug 22, 2026

Search Broadly, Seek Evidence on Both Sides, Decide Narrowly: Evidence-Admissible GraphRAG for Longitudinal Clinical Event Verification

Authors: Xingtao Lin, Yubo Feng, Weixin Liu, Hangqi Ren, Junchao Zhou, Caiwan Sun, You Chen

Organizations: Vanderbilt University · Vanderbilt University Medical Center (VUMC)

Abstract

Longitudinal clinical event-relation verification determines whether a patient record supports a specified relation among two or more clinical events. This task is challenging because evidence is distributed across structured records, notes, laboratory trajectories, encounters, and time, while negation, temporal mismatch, repeated documentation, and conflicting findings can make retrieved information appear relevant without establishing the relation. We present MedEventGraph-RAG, an evidence-admissible framework that represents event occurrences in a patient-specific graph and links each occurrence to source evidence, including structured rows, note spans, timestamps, and numerical trajectories. Given a verification query specifying events, relation, and clinical scope, the graph guides discovery of candidate event chains and retrieves evidence from both supporting and contradicting sides. A query-specific evidence contract filters information by patient identity, scope, occurrence binding, and source traceability before a separate assessor determines supported, conflicting, refuted, or insufficient outcomes. Across ten protocols on i2b2, n2c2, MIMIC-IV, and LUNGUAGE, MedEventGraph-RAG achieves balanced accuracies of 78.6, 67.3, and 96.8 on temporal, medication-adverse-event, and recorded-order verification, improving over the strongest matched baselines by 26.9, 4.9, and 30.4 points. Under evidence masking, it reaches 92.2 balanced accuracy with no false-support predictions. When intermediate events are hidden, it recovers complete source-traceable event chains in 57.9% of i2b2 and 70.0% of LUNGUAGE cases. These results show that separating broad evidence discovery from narrow evidence-admissible assessment improves longitudinal clinical verification and reduces unsupported conclusions.

Figures & tables

Figure 1: MedEventGraph-RAG in one view. Existing RAG paradigms may retrieve relevant but non-evidential content, whereas our framework binds event paths to source contexts and isolates assessment from search signals.

Figure 2: Patient event graphs index source-resolvable contexts; adaptive two-sided discovery builds a query-specific bundle, and only admissible evidence reaches the fixed-bundle assessor.

Method	T1/T2/T3 BA
MedGraphRAG	72.3/54.4/78.8
EHR-RAG	56.5/56.6/70.0
HippoRAG 2	57.9/61.9/64.8
KG 2 RAG	61.0/56.6/70.2
LightRAG	71.3/52.2/76.8
GRAG	57.7/56.2/68.4

Table 2: Matched-index retrieval. Artifacts, identifiers, context budget, and assessor are held fixed and only the online retrieval operator changes; bold marks the best value per task. Inference cost is reported in the supplement.

Stage	BA	F1	FSR $\downarrow$	Ctx-R	Chain-R
E1 bound graph–context	70.0	69.9	23.3	46.0	52.5
E2 + two-sided	72.5	72.2	16.7	63.1	47.5
E3 + isolated axes	75.0	74.7	15.0	63.1	47.5
E4 + replanning	75.8	75.7	15.8	60.0	55.0
E5 + memory	76.2	76.1	15.8	60.0	55.0

Table 3: Cumulative ablation, percent, on a frozen balanced 240-query set; Chain-R is complete-chain recall. Stages add search over the patient graph with its occurrence-bound context index (E1), source-bound two-sided retrieval (E2), evidence-isolated dual-axis verification (E3), verifier-guided replanning (E4, our B6), and retrieval-only procedural memory (E5, our B7), under the same candidate contract, assessor, and retrieval budget. E1–E3 decide from a coupled boundary and E4–E5 from an isolated one; E1–E3 hold the candidate superset fixed, while E4–E5 may extend it because that adaptive change is the treatment measured.

Explore similar work

Oct 5, 2026

Auditable Clinical Timeline Reconstruction with Provenance-Aware Evidence Graphs

A patient-timeline reconstruction system is auditable only if it keeps the mentions behind each answer, records how facts were revised, and declines to answer when the evidence is not in the text. This study tests these three properties on a fully synthetic corpus (1,000 patients, 3,353 notes, 220 revision edges). Two provenance-aware Evidence Graph operators reduced the node-plus-edge count to 67% and 63% (77-78% of serialized size) while preserving every answer and mention link across 6,813 query points answerable by recency; a fixed-window baseline returned no value for 53.4% of points, unflagged. On evidence-unavailable controls that announce the omission, a BioClinicalBERT gate and a zero-shot LLM gate responded mainly to the announcement. On marker-free controls, BERT abstained on 0 of 81 notes while its accuracy fell from 93.8% to 59.3% across all three relation classes; the LLM's coverage fell from 75.6% to 27.7% on notes its own model family judged undeterminable. Against 482 regenerated gold spans, the LLM's cited evidence reached recall 0.850 and precision 0.864; BERT's span head, trained without span labels, did not localize evidence. A temporally versioned provenance graph stored abstentions as typed, queryable edges. The clean task admits a 0.651-accuracy shortcut, and results describe implementation behaviour on synthetic data, not clinical performance.

Judith Jeyafreeda Andrew

Public Health Unit, Medical Information Department, Bordeaux University Hospital (CHU Bordeaux), Bordeaux, France.

Data Provenance Clinical QA Longitudinal EHR Modeling Evidence Retrieval Temporal KGs

Ask PDF

May 21, 2026

ChronoMedKG: A Temporally-Grounded Biomedical Knowledge Graph and Benchmark for Clinical Reasoning

Biomedical knowledge graphs (KGs) treat disease associations as static facts, but temporal information is crucial for clinical reasoning, e.g., a symptom diagnostic of one disease at age 3 may imply a different disease at age 13. Existing KGs such as PrimeKG, Hetionet, and iKraph do not encode when a finding becomes clinically relevant over the course of a disease. This limits their usefulness for longitudinal clinical reasoning and retrieval augmentation. We introduce ChronoMedKG, a temporal biomedical knowledge graph that contains 460,497 evidence-linked triples (filtered from 13M raw extractions) covering 13,431 diseases. Each association is tied to temporal components like onset window or progression stage, which are backed by PMID-traceable evidence and a multi-signal credibility score. The graph is constructed through a disease-autonomous multi-agent pipeline in which multiple frontier LLMs independently extract knowledge from PubMed and PMC literature. Only those relations are kept that are supported by multi-model consensus, survive credibility filtering, as well as ontology alignment. ChronoMedKG scored 92.7% agreement against Orphadata and adds temporal grounding for 6,250 diseases absent from HPOA, Orphadata, and Phenopackets, including 1,657 Orphanet-coded rare diseases. We further introduce ChronoTQA, a benchmark of 3,341 questions across eight task types (six temporal plus two static controls), with a 12-question supplementary probe. Frontier LLMs lose roughly 30 points moving from static to temporal questions; ChronoMedKG retrieval rescues 47-65% of their long-tail failures, against 17-29% for HPOA-RAG. As such, ChronoMedKG provides a crucial temporal axis for retrieval-augmented clinical systems that was previously absent.

Md Shamim Ahmed, Farzaneh Firoozbakht, Lukas Galke Poech +2

University of Southern Denmark · University of Hamburg

KG Construction Healthcare Graph Retrieval-Augmented Generation Biomedical QA Knowledge Representation Clinical Reasoning+2

Ask PDF

Sep 16, 2026

EviGen: Predictive Evidence Scaffolding for Verifiable Clinical Rationale Generation

Longitudinal electronic health records (EHRs) capture years of patient history across notes, codes, labs, and procedures, and contain evidence needed to reason about likely clinical outcomes. However, comprehensive clinician review of these records is impractical, and LLM-based processing is costly and often unreliable, missing some relevant observations while hallucinating others. We therefore propose EviGen, a three-layer framework for verifiable clinical rationale generation that addresses these challenges. The first layer is a patient-conditioned retriever that uses learnable queries to find evidence predictive of, not just textually relevant to, a clinical outcome and ranks it by prediction attribution scores. The second layer is an LLM generator that consumes this ranked evidence as a scaffold to produce a clinical rationale grounded in the retrieved spans. The third layer is a process-supervised verifier that checks the generated rationale at the reasoning-step level, flagging unreliable claims. Across three medical prediction datasets, EviGen improves prediction performance and rationale faithfulness over full-context LLM and RAG baselines, and is preferred by clinical reviewers in a usability evaluation.

Fengnan Li, Heman Burre, Liwen Sun +2

Duke University

LLM Answer Verification Clinical Outcome Prediction Evidence-Grounded Generation Longitudinal EHR Modeling Evidence Retrieval

Ask PDF