Cargando…
Prediction of Prolonged Length of Hospital Stay After Cancer Surgery Using Machine Learning on Electronic Health Records: Retrospective Cross-sectional Study
BACKGROUND: Postoperative length of stay is a key indicator in the management of medical resources and an indirect predictor of the incidence of surgical complications and the degree of recovery of the patient after cancer surgery. Recently, machine learning has been used to predict complex medical...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7939945/ https://www.ncbi.nlm.nih.gov/pubmed/33616544 http://dx.doi.org/10.2196/23147 |
_version_ | 1783661845901475840 |
---|---|
author | Jo, Yong-Yeon Han, JaiHong Park, Hyun Woo Jung, Hyojung Lee, Jae Dong Jung, Jipmin Cha, Hyo Soung Sohn, Dae Kyung Hwangbo, Yul |
author_facet | Jo, Yong-Yeon Han, JaiHong Park, Hyun Woo Jung, Hyojung Lee, Jae Dong Jung, Jipmin Cha, Hyo Soung Sohn, Dae Kyung Hwangbo, Yul |
author_sort | Jo, Yong-Yeon |
collection | PubMed |
description | BACKGROUND: Postoperative length of stay is a key indicator in the management of medical resources and an indirect predictor of the incidence of surgical complications and the degree of recovery of the patient after cancer surgery. Recently, machine learning has been used to predict complex medical outcomes, such as prolonged length of hospital stay, using extensive medical information. OBJECTIVE: The objective of this study was to develop a prediction model for prolonged length of stay after cancer surgery using a machine learning approach. METHODS: In our retrospective study, electronic health records (EHRs) from 42,751 patients who underwent primary surgery for 17 types of cancer between January 1, 2000, and December 31, 2017, were sourced from a single cancer center. The EHRs included numerous variables such as surgical factors, cancer factors, underlying diseases, functional laboratory assessments, general assessments, medications, and social factors. To predict prolonged length of stay after cancer surgery, we employed extreme gradient boosting classifier, multilayer perceptron, and logistic regression models. Prolonged postoperative length of stay for cancer was defined as bed-days of the group of patients who accounted for the top 50% of the distribution of bed-days by cancer type. RESULTS: In the prediction of prolonged length of stay after cancer surgery, extreme gradient boosting classifier models demonstrated excellent performance for kidney and bladder cancer surgeries (area under the receiver operating characteristic curve [AUC] >0.85). A moderate performance (AUC 0.70-0.85) was observed for stomach, breast, colon, thyroid, prostate, cervix uteri, corpus uteri, and oral cancers. For stomach, breast, colon, thyroid, and lung cancers, with more than 4000 cases each, the extreme gradient boosting classifier model showed slightly better performance than the logistic regression model, although the logistic regression model also performed adequately. We identified risk variables for the prediction of prolonged postoperative length of stay for each type of cancer, and the importance of the variables differed depending on the cancer type. After we added operative time to the models trained on preoperative factors, the models generally outperformed the corresponding models using only preoperative variables. CONCLUSIONS: A machine learning approach using EHRs may improve the prediction of prolonged length of hospital stay after primary cancer surgery. This algorithm may help to provide a more effective allocation of medical resources in cancer surgery. |
format | Online Article Text |
id | pubmed-7939945 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-79399452021-03-12 Prediction of Prolonged Length of Hospital Stay After Cancer Surgery Using Machine Learning on Electronic Health Records: Retrospective Cross-sectional Study Jo, Yong-Yeon Han, JaiHong Park, Hyun Woo Jung, Hyojung Lee, Jae Dong Jung, Jipmin Cha, Hyo Soung Sohn, Dae Kyung Hwangbo, Yul JMIR Med Inform Original Paper BACKGROUND: Postoperative length of stay is a key indicator in the management of medical resources and an indirect predictor of the incidence of surgical complications and the degree of recovery of the patient after cancer surgery. Recently, machine learning has been used to predict complex medical outcomes, such as prolonged length of hospital stay, using extensive medical information. OBJECTIVE: The objective of this study was to develop a prediction model for prolonged length of stay after cancer surgery using a machine learning approach. METHODS: In our retrospective study, electronic health records (EHRs) from 42,751 patients who underwent primary surgery for 17 types of cancer between January 1, 2000, and December 31, 2017, were sourced from a single cancer center. The EHRs included numerous variables such as surgical factors, cancer factors, underlying diseases, functional laboratory assessments, general assessments, medications, and social factors. To predict prolonged length of stay after cancer surgery, we employed extreme gradient boosting classifier, multilayer perceptron, and logistic regression models. Prolonged postoperative length of stay for cancer was defined as bed-days of the group of patients who accounted for the top 50% of the distribution of bed-days by cancer type. RESULTS: In the prediction of prolonged length of stay after cancer surgery, extreme gradient boosting classifier models demonstrated excellent performance for kidney and bladder cancer surgeries (area under the receiver operating characteristic curve [AUC] >0.85). A moderate performance (AUC 0.70-0.85) was observed for stomach, breast, colon, thyroid, prostate, cervix uteri, corpus uteri, and oral cancers. For stomach, breast, colon, thyroid, and lung cancers, with more than 4000 cases each, the extreme gradient boosting classifier model showed slightly better performance than the logistic regression model, although the logistic regression model also performed adequately. We identified risk variables for the prediction of prolonged postoperative length of stay for each type of cancer, and the importance of the variables differed depending on the cancer type. After we added operative time to the models trained on preoperative factors, the models generally outperformed the corresponding models using only preoperative variables. CONCLUSIONS: A machine learning approach using EHRs may improve the prediction of prolonged length of hospital stay after primary cancer surgery. This algorithm may help to provide a more effective allocation of medical resources in cancer surgery. JMIR Publications 2021-02-22 /pmc/articles/PMC7939945/ /pubmed/33616544 http://dx.doi.org/10.2196/23147 Text en ©Yong-Yeon Jo, JaiHong Han, Hyun Woo Park, Hyojung Jung, Jae Dong Lee, Jipmin Jung, Hyo Soung Cha, Dae Kyung Sohn, Yul Hwangbo. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 22.02.2021. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Jo, Yong-Yeon Han, JaiHong Park, Hyun Woo Jung, Hyojung Lee, Jae Dong Jung, Jipmin Cha, Hyo Soung Sohn, Dae Kyung Hwangbo, Yul Prediction of Prolonged Length of Hospital Stay After Cancer Surgery Using Machine Learning on Electronic Health Records: Retrospective Cross-sectional Study |
title | Prediction of Prolonged Length of Hospital Stay After Cancer Surgery Using Machine Learning on Electronic Health Records: Retrospective Cross-sectional Study |
title_full | Prediction of Prolonged Length of Hospital Stay After Cancer Surgery Using Machine Learning on Electronic Health Records: Retrospective Cross-sectional Study |
title_fullStr | Prediction of Prolonged Length of Hospital Stay After Cancer Surgery Using Machine Learning on Electronic Health Records: Retrospective Cross-sectional Study |
title_full_unstemmed | Prediction of Prolonged Length of Hospital Stay After Cancer Surgery Using Machine Learning on Electronic Health Records: Retrospective Cross-sectional Study |
title_short | Prediction of Prolonged Length of Hospital Stay After Cancer Surgery Using Machine Learning on Electronic Health Records: Retrospective Cross-sectional Study |
title_sort | prediction of prolonged length of hospital stay after cancer surgery using machine learning on electronic health records: retrospective cross-sectional study |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7939945/ https://www.ncbi.nlm.nih.gov/pubmed/33616544 http://dx.doi.org/10.2196/23147 |
work_keys_str_mv | AT joyongyeon predictionofprolongedlengthofhospitalstayaftercancersurgeryusingmachinelearningonelectronichealthrecordsretrospectivecrosssectionalstudy AT hanjaihong predictionofprolongedlengthofhospitalstayaftercancersurgeryusingmachinelearningonelectronichealthrecordsretrospectivecrosssectionalstudy AT parkhyunwoo predictionofprolongedlengthofhospitalstayaftercancersurgeryusingmachinelearningonelectronichealthrecordsretrospectivecrosssectionalstudy AT junghyojung predictionofprolongedlengthofhospitalstayaftercancersurgeryusingmachinelearningonelectronichealthrecordsretrospectivecrosssectionalstudy AT leejaedong predictionofprolongedlengthofhospitalstayaftercancersurgeryusingmachinelearningonelectronichealthrecordsretrospectivecrosssectionalstudy AT jungjipmin predictionofprolongedlengthofhospitalstayaftercancersurgeryusingmachinelearningonelectronichealthrecordsretrospectivecrosssectionalstudy AT chahyosoung predictionofprolongedlengthofhospitalstayaftercancersurgeryusingmachinelearningonelectronichealthrecordsretrospectivecrosssectionalstudy AT sohndaekyung predictionofprolongedlengthofhospitalstayaftercancersurgeryusingmachinelearningonelectronichealthrecordsretrospectivecrosssectionalstudy AT hwangboyul predictionofprolongedlengthofhospitalstayaftercancersurgeryusingmachinelearningonelectronichealthrecordsretrospectivecrosssectionalstudy |