ChronoMedKG: A Temporally-Grounded Biomedical Knowledge Graph and Benchmark for Clinical Reasoning
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Computer Science > Computation and Language
arXiv:2605.22734 (cs)
[Submitted on 21 May 2026]
Title:ChronoMedKG: A Temporally-Grounded Biomedical Knowledge Graph and Benchmark for Clinical Reasoning
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Abstract: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.
| Comments: | 9 pages main text plus appendices, 8 figures. Dataset and benchmark paper. ChronoMedKG released under CC BY 4.0 and ChronoTQA/code under MIT (Zenodo: https://doi.org/10.5281/zenodo.19697542). Under review |
| Subjects: | Computation and Language (cs.CL) |
| ACM classes: | I.2.7; I.2.4; H.3.3; J.3 |
| Cite as: | arXiv:2605.22734 [cs.CL] |
| (or arXiv:2605.22734v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.22734
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled ChronoMedKG: A Temporally-Grounded Biomedical Knowledge Graph and Benchmark for Clinical Reasoning, by Md Shamim Ahmed and 4 other authors
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