Evidence map›Paper›PMID 28442772›Full record

ArticleScientific reports2017

Discriminative and Distinct Phenotyping by Constrained Tensor Factorization.

Yejin Kim, Robert El-Kareh, Jimeng Sun, Hwanjo Yu, Xiaoqian Jiang

Abstract read
In one paragraph

Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Review
  2. Article
  3. Unraveling Complex Temporal Patterns in EHRs via Robust Irregular Tensor Factorization.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025
    Article
  4. Article
  5. LogPar: Logistic PARAFAC2 Factorization for Temporal Binary Data with Missing Values.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2020
    Article
  6. Article
  7. Article
  8. Article
  9. Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis.Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management · 2019
    Article
  10. Distributed Tensor Decomposition for Large Scale Health Analytics.Proceedings of the ... International World-Wide Web Conference. International WWW Conference · 2019
    Article
  11. CP Tensor Decomposition with Cannot-Link Intermode Constraints.Proceedings of the ... SIAM International Conference on Data Mining. SIAM International Conference on Data Mining · 2019
    Article
  12. Discriminative Sleep Patterns of Alzheimer's Disease via Tensor Factorization.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019
    Article
  13. Article
  14. Article
  15. Article
  16. Phenotyping through Semi-Supervised Tensor Factorization (PSST).AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018
    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

5 authors.

Yejin KimDepartment of Creative IT Engineering, Pohang University of Science and Technology, Pohang, Korea.
Robert El-KarehDepartment of Biomedical Informatics, UC San Diego, La Jolla, CA, United States.
Jimeng SunSchool of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, United States.
Hwanjo YuDepartment of Computer Science and Engineering, Pohang University of Science and Technology, Pohang, Korea. hwanjoyu@postech.ac.kr.
Xiaoqian JiangDepartment of Biomedical Informatics, UC San Diego, La Jolla, CA, United States. x1jiang@ucsd.edu.

Funding

iCONCUR: informed CONsent for Clinical data and biosample Use for ResearchR01HG008802 · NHGRI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI JIANG, XIAOQIAN, KIM, HYEON-EUI · 2015 to 2017
$1.3M
SHARE: Statistical Health Information Release with Differential PrivacyR01GM114612 · NIGMS · EMORY UNIVERSITY · PI XIONG, LI · 2015 to 2017
$1.0M
Protection of Records: Privacy (PReP) Technology for Medical ResearchR00LM011392 · NLM · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI JIANG, XIAOQIAN · 2013 to 2015
$650k
Protecting the pRivacy Of Genomes in REsearch StudieS (PROGRESS)R00HG008175 · NHGRI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI WANG, SHUANG · 2016 to 2018
$527k
Protect Privacy of Healthcare Data in the CloudR21LM012060 · NLM · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI JIANG, XIAOQIAN · 2014 to 2015
$376k
NHGRI NIH HHS R00 HG008175NHGRI NIH HHS R01 HG008802NIGMS NIH HHS R01 GM114612NLM NIH HHS R00 LM011392NLM NIH HHS R21 LM012060
6 · The paper itself

Abstract

Adoption of Electronic Health Record (EHR) systems has led to collection of massive healthcare data, which creates oppor- tunities and challenges to study them. Computational phenotyping offers a promising way to convert the sparse and complex data into meaningful concepts that are interpretable to healthcare givers to make use of them. We propose a novel su- pervised nonnegative tensor factorization methodology that derives discriminative and distinct phenotypes. We represented co-occurrence of diagnoses and prescriptions in EHRs as a third-order tensor, and decomposed it using the CP algorithm. We evaluated discriminative power of our models with an Intensive Care Unit database (MIMIC-III) and demonstrated superior performance than state-of-the-art ICU mortality calculators (e.g., APACHE II, SAPS II). Example of the resulted phenotypes are sepsis with acute kidney injury, cardiac surgery, anemia, respiratory failure, heart failure, cardiac arrest, metastatic cancer (requiring ICU), end-stage dementia (requiring ICU and transitioned to comfort-care), intraabdominal conditions, and alcohol abuse/withdrawal.

Indexed as

Computer SimulationDecision Support TechniquesElectronic Health RecordsPhenotypeHumansModels, Statistical

Identifiers

PMID28442772
PMCPMC5430728

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.