Evidence map›Paper›PMID 39875929›Full record

ArticleBMC medical informatics and decision making2025

Discovering patient groups in sequential electronic healthcare data using unsupervised representation learning.

Jingteng Li, Kimberley R Zakka, John Booth, Louise Rigny, Samiran Ray, Mario Cortina-Borja, Payam Barnaghi, Neil Sebire

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Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

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.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Jingteng Li *Great Ormond Street Institute of Child Health, University College London, London, UK.
Kimberley R Zakka *Great Ormond Street Institute of Child Health, University College London, London, UK.
John BoothGreat Ormond Street Institute of Child Health, University College London, London, UK.
Louise RignyData Research Innovation and Virtual Environment, Great Ormond Street Hospital for Children, London, UK.
Samiran RayGreat Ormond Street Institute of Child Health, University College London, London, UK.
Mario Cortina-BorjaGreat Ormond Street Institute of Child Health, University College London, London, UK.
Payam BarnaghiGreat Ormond Street Institute of Child Health, University College London, London, UK.
Neil SebireGreat Ormond Street Institute of Child Health, University College London, London, UK. neil.sebire@gosh.nhs.uk.

Funding

Great Ormond Street Hospital for Children 21PP30
6 · The paper itself

Abstract

introductionUnsupervised feature learning methods inspired by natural language processing (NLP) models are capable of constructing patient-specific features from longitudinal Electronic Health Records (EHR).

designWe applied document embedding algorithms to real-world paediatric intensive care (PICU) EHR data to extract patient-specific features from 1853 patients' PICU journeys using 647 unique lab tests and medication events. We evaluated the clinical utility of the patient features via a K-means clustering analysis.

resultsWe trained a document embedding model under a unique evaluation pipeline and obtained latent patient feature vectors for all 1853 patients. We performed unsupervised clustering to the patient vectors as a downstream analysis and obtained 5 distinct clusters via hyperparameter optimisation. Significant variations (p<0.0001) within both patient characteristics and surgery intervention and diagnostic profiles were detected.

conclusionThe K-means clustering results demonstrated the clinical utilities of the patient-specific features learned from the embedding algorithms. The latent patient features obtained via the embedding process enabled direct applications of other machine learning algorithms. Future work will focus on utilising the temporal information within EHR and extending EHR embedding algorithms to develop personalised patient journey predictions.

Indexed as

Electronic Health RecordsNatural Language ProcessingUnsupervised Machine LearningAlgorithmsChildCluster AnalysisHumans

Identifiers

PMID39875929
PMCPMC11776155

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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.