Deep learning models for the prediction of acute postoperative pain in PACU for video‐assisted thoracoscopic surgery

Deep learning model can predict postoperative acute pain events based on patients' basic information and intraoperative vital signs.

Authors

Qinye Shi
Jianhong Xu (ORCID: https://orcid.org/0000-0002-0524-4368)
Lijia Peng
Jiannan He
Jiangqin He
Shuai Wang (ORCID: https://orcid.org/0000-0001-6838-1151)
Cao Zhang (ORCID: https://orcid.org/0000-0001-7868-8464)
Xingyuan Liang (ORCID: https://orcid.org/0009-0001-6183-0628)

Institutions

Communication University of Zhejiang (CN)
Zhejiang University (CN)
Southeast University (CN)

Publication Details

Journal: BMC Medical Research Methodology
Published: 2024-10-07
DOI: https://doi.org/10.1186/s12874-024-02357-5
Citations: 1
Primary Topic: Lung Cancer Diagnosis and Treatment
Type: article
Field-Weighted Citation Impact: 0.42

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article

Deep learning models for the prediction of acute postoperative pain in PACU for video‐assisted thoracoscopic surgery

Qinye Shi, Jianhong Xu, Lijia Peng, Jiannan He et al.

Oct 7, 2024

1 citations

BMC Medical Research Methodology

Lung Cancer Diagnosis and Treatment

0.42

article

Deep learning models for the prediction of acute postoperative pain in PACU for video‐assisted thoracoscopic surgery

Qinye Shi, Jianhong Xu, Lijia Peng, Jiannan He, Jiangqin He, Shuai Wang, Cao Zhang, Xingyuan Liang

October 7, 2024article en

1 citations

Abstract

Deep learning model can predict postoperative acute pain events based on patients' basic information and intraoperative vital signs.

BMC Medical Research MethodologyVol. 24(1)

Communication University of Zhejiang (CN), Zhejiang University (CN), Southeast University (CN)

Openalex Percentile: Top 32%

Lung Cancer Diagnosis and Treatment

0.42

DOI Open Access PDF OpenAlex

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Deep learning models for the prediction of acute postoperative pain in PACU for video‐assisted thoracoscopic surgery — Qinye Shi, Jianhong Xu, et al. · BMC Medical Research Methodology (2024) | TGRS Research Map | TGRS