Evidence map›Paper›PMID 37271308›Full record

ArticleJournal of biomedical informatics2023

Graph-based clinical recommender: Predicting specialists procedure orders using graph representation learning.

Sajjad Fouladvand, Federico Reyes Gomez, Hamed Nilforoshan, Matthew Schwede, Morteza Noshad, Olivia Jee, Jiaxuan You, Rok Sosic, Jure Leskovec, Jonathan Chen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of biomedical informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Sajjad FouladvandBiomedical Informatics Research, Stanford University, Stanford, CA, United States of America. Electronic address: sajjadf@stanford.edu.
Federico Reyes GomezComputer Science Department, Stanford University, Stanford, CA, United States of America. Electronic address: frg100@alumni.stanford.edu.
Hamed NilforoshanComputer Science Department, Stanford University, Stanford, CA, United States of America. Electronic address: hamedn@cs.stanford.edu.
Matthew SchwedeBiomedical Informatics Research, Stanford University, Stanford, CA, United States of America. Electronic address: mschwede@stanford.edu.
Morteza NoshadBiomedical Informatics Research, Stanford University, Stanford, CA, United States of America. Electronic address: noshad@stanford.edu.
Olivia JeePrimary Care and Population Health, Stanford University, Stanford, CA, United States of America. Electronic address: ojee@stanford.edu.
Jiaxuan YouComputer Science Department, Stanford University, Stanford, CA, United States of America. Electronic address: jiaxuan@cs.stanford.edu.
Rok SosicComputer Science Department, Stanford University, Stanford, CA, United States of America. Electronic address: rok@cs.stanford.edu.
Jure LeskovecComputer Science Department, Stanford University, Stanford, CA, United States of America. Electronic address: jure@cs.stanford.edu.
Jonathan ChenBiomedical Informatics Research, Stanford University, Stanford, CA, United States of America; Division of Hospital Medicine, Stanford University, Stanford, CA, United States of America; Clinical Excellence Research Center, Stanford University, Stanford, CA, United States of America. Electronic address: jonc101@stanford.edu.

Funding

Measuring and Predicting Appropriate Antibiotic Use to Combat Resistant BacteriaR01AI178121 · NIAID · STANFORD UNIVERSITY · PI JONATHAN H. CHEN · 2023 to 2026
$3.1M
Machine Learning Clinical Order Recommendations for Specialty Consultation CareR56LM013365 · NLM · STANFORD UNIVERSITY · PI CHEN, JONATHAN H. · 2020 to 2020
$394k
NIAID NIH HHS R01 AI178121NLM NIH HHS R56 LM013365
6 · The paper itself

Abstract

objectiveTo determine whether graph neural network based models of electronic health records can predict specialty consultation care needs for endocrinology and hematology more accurately than the standard of care checklists and other conventional medical recommendation algorithms in the literature.

methodsDemand for medical expertise far outstrips supply, with tens of millions in the US alone with deficient access to specialty care. Rather than potentially months long delays to initiate diagnostic workup and medical treatment with a specialist, referring primary care supported by an automated recommender algorithm could anticipate and directly initiate patient evaluation that would otherwise be needed at subsequent a specialist appointment. We propose a novel graph representation learning approach with a heterogeneous graph neural network to model structured electronic health records and formulate recommendation/prediction of subsequent specialist orders as a link prediction problem.

resultsModels are trained and assessed in two specialty care sites: endocrinology and hematology. Our experimental results show that our model achieves an 8% improvement in ROC-AUC for endocrinology (ROC-AUC = 0.88) and 5% improvement for hematology (ROC-AUC = 0.84) personalized procedure recommendations over prior medical recommender systems. These recommender algorithm approaches provide medical procedure recommendations for endocrinology referrals more effectively than manual clinical checklists (recommender: precision = 0.60, recall = 0.27, F1-score = 0.37) vs. (checklist: precision = 0.16, recall = 0.28, F1-score = 0.20), and similarly for hematology referrals (recommender: precision = 0.44, recall = 0.38, F1-score = 0.41) vs. (checklist: precision = 0.27, recall = 0.71, F1-score = 0.39).

conclusionEmbedding graph neural network models into clinical care can improve digital specialty consultation systems and expand the access to medical experience of prior similar cases.

Indexed as

AlgorithmsNeural Networks, ComputerElectronic Health RecordsEndocrinologyHematologyHumansReferral and ConsultationElectronic medical consultationEndocrinologyGraph neural networksHematology

Identifiers

PMID37271308
PMCPMC12140122

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

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.