Evidence map›Paper›PMID 38444563›Full record

ArticleCleaner environmental systems2024

Predicting the Climate Impact of Healthcare Facilities Using Gradient Boosting Machines.

Hao Yin, Bhavna Sharma, Howard Hu, Fei Liu, Mehak Kaur, Gary Cohen, Rob McConnell, Sandrah P Eckel

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cleaner environmental systems, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
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

8 authors.

Hao YinDepartment of Economics, University of Southern California, Los Angeles, California, USA, 90089.
Bhavna SharmaSchool of Architecture, University of Southern California, Los Angeles, California, USA, 90089.
Howard HuDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA, 90033.
Fei LiuDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA, 90033.
Mehak KaurDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA, 90033.
Gary CohenHealth Care Without Harm, Boston, Massachusetts, USA, 20190.
Rob McConnellDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA, 90033.
Sandrah P EckelDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA, 90033.

Funding

Translational Research Support CoreP30ES007048 · NIEHS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI ROB S MCCONNELL · 1996 to 2026
$46.4M
NIEHS NIH HHS P30 ES007048
6 · The paper itself

Abstract

Health care accounts for 9-10% of greenhouse gas (GHG) emissions in the United States. Strategies for monitoring these emissions at the hospital level are needed to decarbonize the sector. However, data collection to estimate emissions is challenging, especially for smaller hospitals. We explored the potential of gradient boosting machines (GBM) to impute missing data on resource consumption in the 2020 survey of a consortium of 283 hospitals participating in Practice Greenhealth. GBM imputed missing values for selected variables in order to predict electricity use and beef consumption (R

Indexed as

Climate changeHealthcare facilityMachine learningMissing data imputationSustainability

Identifiers

PMID38444563
PMCPMC10909736

What OpenQuestion holds

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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.