| アイテムタイプ |
itemtype_ver1(1) |
| 公開日 |
2024-10-18 |
| タイトル |
|
|
タイトル |
Development of a machine learning-based risk model for postoperative complications of lung cancer surgery |
|
言語 |
en |
| 著者 |
Kadomatsu, Yuka
Emoto, Ryo
Kubo, Yoko
Nakanishi, Keita
Ueno, Harushi
Kato, Taketo
Nakamura, Shota
Mizuno, Tetsuya
Matsui, Shigeyuki
Chen-Yoshikawa, Toyofumi Fengshi
|
| アクセス権 |
|
|
アクセス権 |
open access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 権利 |
|
|
権利情報 |
This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s00595-024-02878-y |
|
言語 |
en |
| 内容記述 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
Purpose: To develop a comorbidity risk score specifically for lung resection surgeries. Methods: We reviewed the medical records of patients who underwent lung resections for lung cancer, and developed a risk model using data from 2014 to 2017 (training dataset), validated using data from 2018 to 2019 (validation dataset). Forty variables were analyzed, including 35 factors related to the patient’s overall condition and five factors related to surgical techniques and tumor-related factors. The risk model for postoperative complications was developed using an elastic net regularized generalized linear model. The performance of the risk model was evaluated using receiver operating characteristic curves and compared with the Charlson Comorbidity Index (CCI). Results: The rate of postoperative complications was 34.7% in the training dataset and 21.9% in the validation dataset. The final model consisted of 20 variables, including age, surgical-related factors, respiratory function tests, and comorbidities, such as chronic obstructive pulmonary disease, a history of ischemic heart disease, and 12 blood test results. The area under the curve (AUC) for the developed risk model was 0.734, whereas the AUC for the CCI was 0.521 in the validation dataset. Conclusions: The new machine learning model could predict postoperative complications with acceptable accuracy. |
|
言語 |
en |
| 内容記述 |
|
|
内容記述タイプ |
Other |
|
内容記述 |
Online Published: 19 June 2024 |
|
言語 |
en |
| 出版者 |
|
|
出版者 |
Springer |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| 資源タイプ |
|
|
資源タイプresource |
http://purl.org/coar/resource_type/c_6501 |
|
タイプ |
journal article |
| 出版タイプ |
|
|
出版タイプ |
AM |
|
出版タイプResource |
http://purl.org/coar/version/c_ab4af688f83e57aa |
| 関連情報 |
|
|
関連タイプ |
isVersionOf |
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.1007/s00595-024-02878-y |
| 収録物識別子 |
|
|
収録物識別子タイプ |
PISSN |
|
収録物識別子 |
0941-1291 |
| 書誌情報 |
en : Surgery Today
巻 54,
p. 1482-1489,
発行日 2024-12
|
| ファイル公開日 |
|
|
日付 |
2025-06-19 |
|
日付タイプ |
Available |
CM_Surgerytoday_main_article.pdf (373 KB)
