ArticleJAMIA open2026
Real-time clinical analytics at scale: a platform built on large language models-powered knowledge graphs.
Article in JAMIA open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: The increasing volume of clinical trial documents presents a significant challenge for biomedical researchers who must analyze vast amounts of unstructured and structured data. Traditional methods are no longer feasible given the scale and complexity of modern clinical trials. Materials and Methods: We introduce a large-scale clinical analytics platform, ClinicalMind, that integrates Large Language Models (LLMs) with Knowledge Graph technology to perform real-time clinical analytics over 110 000 clinical documents and 60 000 electronic medical records. The system employs a 2-phase graph update strategy and hardware acceleration to increase the accuracy and speed. Results: Our platform achieves an average query delay of 1.7 seconds with high accuracy (BLEU score: 0.85, ROUGE score: 0.92). The system can process and analyze thousands of clinical documents in real-time, significantly outperforming existing methods. Discussion: These results demonstrate that combining LLMs with a continuously updated knowledge graph enables scalable, low-latency clinical analytics across large and heterogeneous data sources. The observed performance gains highlight the potential of this approach to support real-time clinical decision-making and large-scale evidence synthesis, addressing key limitations of existing document-centric and retrieval-based methods. Conclusion: We demonstrate that our platform offers an efficient, scalable solution for real-time clinical analytics, enabling rapid analysis of large-scale clinical document collections.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.