Evidence map›Paper›PMID 33581828›Full record

ArticleArtificial intelligence in medicine2021

Modeling multivariate clinical event time-series with recurrent temporal mechanisms.

Jeong Min Lee, Milos Hauskrecht

Abstract read
In one paragraph

Article in Artificial intelligence in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Review
  6. Article
  7. Review
  8. Article
  9. Personalized event prediction for Electronic Health Records.Artificial intelligence in medicine · 2023
    Article
  10. Article
  11. Article
  12. Article
  13. Neural Clinical Event Sequence Prediction through Personalized Online Adaptive Learning.Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ) · 2021
    Article
  14. Improving Prediction of Low-Prior Clinical Events with Simultaneous General Patient-State Representation Learning.Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ) · 2021
    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

2 authors.

Jeong Min LeeDepartment of Computer Science, University of Pittsburgh, Pittsburgh, PA 15260, USA. Electronic address: jlee@cs.pitt.edu.
Milos HauskrechtDepartment of Computer Science, University of Pittsburgh, Pittsburgh, PA 15260, USA. Electronic address: milos@pitt.edu.

Funding

Real-time detection of deviations in clinical care in ICU data streamsR01GM088224 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI CLERMONT, GILLES, COOPER, GREGORY F. · 2009 to 2017
$3.7M
NIGMS NIH HHS R01 GM088224
6 · The paper itself

Abstract

In this work, we propose a novel autoregressive event time-series model that can predict future occurrences of multivariate clinical events. Our model represents multivariate event time-series using different temporal mechanisms aimed to fit different temporal characteristics of the time-series. In particular, information about distant past is modeled through the hidden state space defined by an LSTM-based model, information on recently observed clinical events is modeled through discriminative projections, and information about periodic (repeated) events is modeled using a special recurrent mechanism based on probability distributions of inter-event gaps compiled from past data. We evaluate our proposed model on electronic health record (EHRs) data derived from MIMIC-III dataset. We show that our new model equipped with the above temporal mechanisms leads to improved prediction performance compared to multiple baselines.

Indexed as

Electronic Health RecordsNeural Networks, ComputerForecastingHumansTimeClinical time seriesEvent time series predictionModeling electronic health record dataRecurrent neural networkSequential models

Identifiers

PMID33581828
PMCPMC7943294

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Registered trials

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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.