Evidence map›Paper›PMID 35756359›Full record

ArticleMachine learning with applications2022

Time-to-event modeling for hospital length of stay prediction for COVID-19 patients.

Yuxin Wen, Md Fashiar Rahman, Yan Zhuang, Michael Pokojovy, Honglun Xu, Peter McCaffrey, Alexander Vo, Eric Walser, Scott Moen, Tzu-Liang Bill Tseng

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Machine learning with applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Observational
  4. Article
  5. Determinants of Length of Stay for Medical Inpatients Using Survival Analysis.International journal of environmental research and public health · 2024
    Article
  6. Article
  7. 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.

Yuxin WenDale E. and Sarah Ann Fowler School of Engineering, Chapman University, Orange, CA 92866, USA.
Md Fashiar RahmanDepartment of Industrial, Manufacturing and Systems Engineering, The University of Texas at El Paso, El Paso, TX 79968, USA.
Yan ZhuangDepartment of Biomedical Engineering, Sichuan University, Chengdu, Sichuan 610065, China.
Michael PokojovyDepartment of Mathematical Sciences, The University of Texas at El Paso, El Paso, TX 79968, USA.
Honglun XuDepartment of Industrial, Manufacturing and Systems Engineering, The University of Texas at El Paso, El Paso, TX 79968, USA.
Peter McCaffreyThe University of Texas Medical Branch, Galveston, TX 77550, USA.
Alexander VoThe University of Texas Medical Branch, Galveston, TX 77550, USA.
Eric WalserThe University of Texas Medical Branch, Galveston, TX 77550, USA.
Scott MoenThe University of Texas Medical Branch, Galveston, TX 77550, USA.
Tzu-Liang Bill TsengDepartment of Industrial, Manufacturing and Systems Engineering, The University of Texas at El Paso, El Paso, TX 79968, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Providing timely patient care while maintaining optimal resource utilization is one of the central operational challenges hospitals have been facing throughout the pandemic. Hospital length of stay (LOS) is an important indicator of hospital efficiency, quality of patient care, and operational resilience. Numerous researchers have developed regression or classification models to predict LOS. However, conventional models suffer from the lack of capability to make use of typically censored clinical data. We propose to use time-to-event modeling techniques, also known as survival analysis, to predict the LOS for patients based on individualized information collected from multiple sources. The performance of six proposed survival models is evaluated and compared based on clinical data from COVID-19 patients.

Indexed as

COVID-19Deep learningLength of staySurvival analysisTime-to-event modeling

Identifiers

PMID35756359
PMCPMC9213016

What OpenQuestion holds

Textmetadata
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.