Cargando…
An Artificial Intelligence Model to Predict the Mortality of COVID-19 Patients at Hospital Admission Time Using Routine Blood Samples: Development and Validation of an Ensemble Model
BACKGROUND: COVID-19, which is accompanied by acute respiratory distress, multiple organ failure, and death, has spread worldwide much faster than previously thought. However, at present, it has limited treatments. OBJECTIVE: To overcome this issue, we developed an artificial intelligence (AI) model...
Autores principales: | , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7759509/ https://www.ncbi.nlm.nih.gov/pubmed/33301414 http://dx.doi.org/10.2196/25442 |
_version_ | 1783627124215644160 |
---|---|
author | Ko, Hoon Chung, Heewon Kang, Wu Seong Park, Chul Kim, Do Wan Kim, Seong Eun Chung, Chi Ryang Ko, Ryoung Eun Lee, Hooseok Seo, Jae Ho Choi, Tae-Young Jaimes, Rafael Kim, Kyung Won Lee, Jinseok |
author_facet | Ko, Hoon Chung, Heewon Kang, Wu Seong Park, Chul Kim, Do Wan Kim, Seong Eun Chung, Chi Ryang Ko, Ryoung Eun Lee, Hooseok Seo, Jae Ho Choi, Tae-Young Jaimes, Rafael Kim, Kyung Won Lee, Jinseok |
author_sort | Ko, Hoon |
collection | PubMed |
description | BACKGROUND: COVID-19, which is accompanied by acute respiratory distress, multiple organ failure, and death, has spread worldwide much faster than previously thought. However, at present, it has limited treatments. OBJECTIVE: To overcome this issue, we developed an artificial intelligence (AI) model of COVID-19, named EDRnet (ensemble learning model based on deep neural network and random forest models), to predict in-hospital mortality using a routine blood sample at the time of hospital admission. METHODS: We selected 28 blood biomarkers and used the age and gender information of patients as model inputs. To improve the mortality prediction, we adopted an ensemble approach combining deep neural network and random forest models. We trained our model with a database of blood samples from 361 COVID-19 patients in Wuhan, China, and applied it to 106 COVID-19 patients in three Korean medical institutions. RESULTS: In the testing data sets, EDRnet provided high sensitivity (100%), specificity (91%), and accuracy (92%). To extend the number of patient data points, we developed a web application (BeatCOVID19) where anyone can access the model to predict mortality and can register his or her own blood laboratory results. CONCLUSIONS: Our new AI model, EDRnet, accurately predicts the mortality rate for COVID-19. It is publicly available and aims to help health care providers fight COVID-19 and improve patients’ outcomes. |
format | Online Article Text |
id | pubmed-7759509 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-77595092020-12-31 An Artificial Intelligence Model to Predict the Mortality of COVID-19 Patients at Hospital Admission Time Using Routine Blood Samples: Development and Validation of an Ensemble Model Ko, Hoon Chung, Heewon Kang, Wu Seong Park, Chul Kim, Do Wan Kim, Seong Eun Chung, Chi Ryang Ko, Ryoung Eun Lee, Hooseok Seo, Jae Ho Choi, Tae-Young Jaimes, Rafael Kim, Kyung Won Lee, Jinseok J Med Internet Res Original Paper BACKGROUND: COVID-19, which is accompanied by acute respiratory distress, multiple organ failure, and death, has spread worldwide much faster than previously thought. However, at present, it has limited treatments. OBJECTIVE: To overcome this issue, we developed an artificial intelligence (AI) model of COVID-19, named EDRnet (ensemble learning model based on deep neural network and random forest models), to predict in-hospital mortality using a routine blood sample at the time of hospital admission. METHODS: We selected 28 blood biomarkers and used the age and gender information of patients as model inputs. To improve the mortality prediction, we adopted an ensemble approach combining deep neural network and random forest models. We trained our model with a database of blood samples from 361 COVID-19 patients in Wuhan, China, and applied it to 106 COVID-19 patients in three Korean medical institutions. RESULTS: In the testing data sets, EDRnet provided high sensitivity (100%), specificity (91%), and accuracy (92%). To extend the number of patient data points, we developed a web application (BeatCOVID19) where anyone can access the model to predict mortality and can register his or her own blood laboratory results. CONCLUSIONS: Our new AI model, EDRnet, accurately predicts the mortality rate for COVID-19. It is publicly available and aims to help health care providers fight COVID-19 and improve patients’ outcomes. JMIR Publications 2020-12-23 /pmc/articles/PMC7759509/ /pubmed/33301414 http://dx.doi.org/10.2196/25442 Text en ©Hoon Ko, Heewon Chung, Wu Seong Kang, Chul Park, Do Wan Kim, Seong Eun Kim, Chi Ryang Chung, Ryoung Eun Ko, Hooseok Lee, Jae Ho Seo, Tae-Young Choi, Rafael Jaimes, Kyung Won Kim, Jinseok Lee. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 23.12.2020. 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 the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Ko, Hoon Chung, Heewon Kang, Wu Seong Park, Chul Kim, Do Wan Kim, Seong Eun Chung, Chi Ryang Ko, Ryoung Eun Lee, Hooseok Seo, Jae Ho Choi, Tae-Young Jaimes, Rafael Kim, Kyung Won Lee, Jinseok An Artificial Intelligence Model to Predict the Mortality of COVID-19 Patients at Hospital Admission Time Using Routine Blood Samples: Development and Validation of an Ensemble Model |
title | An Artificial Intelligence Model to Predict the Mortality of COVID-19 Patients at Hospital Admission Time Using Routine Blood Samples: Development and Validation of an Ensemble Model |
title_full | An Artificial Intelligence Model to Predict the Mortality of COVID-19 Patients at Hospital Admission Time Using Routine Blood Samples: Development and Validation of an Ensemble Model |
title_fullStr | An Artificial Intelligence Model to Predict the Mortality of COVID-19 Patients at Hospital Admission Time Using Routine Blood Samples: Development and Validation of an Ensemble Model |
title_full_unstemmed | An Artificial Intelligence Model to Predict the Mortality of COVID-19 Patients at Hospital Admission Time Using Routine Blood Samples: Development and Validation of an Ensemble Model |
title_short | An Artificial Intelligence Model to Predict the Mortality of COVID-19 Patients at Hospital Admission Time Using Routine Blood Samples: Development and Validation of an Ensemble Model |
title_sort | artificial intelligence model to predict the mortality of covid-19 patients at hospital admission time using routine blood samples: development and validation of an ensemble model |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7759509/ https://www.ncbi.nlm.nih.gov/pubmed/33301414 http://dx.doi.org/10.2196/25442 |
work_keys_str_mv | AT kohoon anartificialintelligencemodeltopredictthemortalityofcovid19patientsathospitaladmissiontimeusingroutinebloodsamplesdevelopmentandvalidationofanensemblemodel AT chungheewon anartificialintelligencemodeltopredictthemortalityofcovid19patientsathospitaladmissiontimeusingroutinebloodsamplesdevelopmentandvalidationofanensemblemodel AT kangwuseong anartificialintelligencemodeltopredictthemortalityofcovid19patientsathospitaladmissiontimeusingroutinebloodsamplesdevelopmentandvalidationofanensemblemodel AT parkchul anartificialintelligencemodeltopredictthemortalityofcovid19patientsathospitaladmissiontimeusingroutinebloodsamplesdevelopmentandvalidationofanensemblemodel AT kimdowan anartificialintelligencemodeltopredictthemortalityofcovid19patientsathospitaladmissiontimeusingroutinebloodsamplesdevelopmentandvalidationofanensemblemodel AT kimseongeun anartificialintelligencemodeltopredictthemortalityofcovid19patientsathospitaladmissiontimeusingroutinebloodsamplesdevelopmentandvalidationofanensemblemodel AT chungchiryang anartificialintelligencemodeltopredictthemortalityofcovid19patientsathospitaladmissiontimeusingroutinebloodsamplesdevelopmentandvalidationofanensemblemodel AT koryoungeun anartificialintelligencemodeltopredictthemortalityofcovid19patientsathospitaladmissiontimeusingroutinebloodsamplesdevelopmentandvalidationofanensemblemodel AT leehooseok anartificialintelligencemodeltopredictthemortalityofcovid19patientsathospitaladmissiontimeusingroutinebloodsamplesdevelopmentandvalidationofanensemblemodel AT seojaeho anartificialintelligencemodeltopredictthemortalityofcovid19patientsathospitaladmissiontimeusingroutinebloodsamplesdevelopmentandvalidationofanensemblemodel AT choitaeyoung anartificialintelligencemodeltopredictthemortalityofcovid19patientsathospitaladmissiontimeusingroutinebloodsamplesdevelopmentandvalidationofanensemblemodel AT jaimesrafael anartificialintelligencemodeltopredictthemortalityofcovid19patientsathospitaladmissiontimeusingroutinebloodsamplesdevelopmentandvalidationofanensemblemodel AT kimkyungwon anartificialintelligencemodeltopredictthemortalityofcovid19patientsathospitaladmissiontimeusingroutinebloodsamplesdevelopmentandvalidationofanensemblemodel AT leejinseok anartificialintelligencemodeltopredictthemortalityofcovid19patientsathospitaladmissiontimeusingroutinebloodsamplesdevelopmentandvalidationofanensemblemodel AT kohoon artificialintelligencemodeltopredictthemortalityofcovid19patientsathospitaladmissiontimeusingroutinebloodsamplesdevelopmentandvalidationofanensemblemodel AT chungheewon artificialintelligencemodeltopredictthemortalityofcovid19patientsathospitaladmissiontimeusingroutinebloodsamplesdevelopmentandvalidationofanensemblemodel AT kangwuseong artificialintelligencemodeltopredictthemortalityofcovid19patientsathospitaladmissiontimeusingroutinebloodsamplesdevelopmentandvalidationofanensemblemodel AT parkchul artificialintelligencemodeltopredictthemortalityofcovid19patientsathospitaladmissiontimeusingroutinebloodsamplesdevelopmentandvalidationofanensemblemodel AT kimdowan artificialintelligencemodeltopredictthemortalityofcovid19patientsathospitaladmissiontimeusingroutinebloodsamplesdevelopmentandvalidationofanensemblemodel AT kimseongeun artificialintelligencemodeltopredictthemortalityofcovid19patientsathospitaladmissiontimeusingroutinebloodsamplesdevelopmentandvalidationofanensemblemodel AT chungchiryang artificialintelligencemodeltopredictthemortalityofcovid19patientsathospitaladmissiontimeusingroutinebloodsamplesdevelopmentandvalidationofanensemblemodel AT koryoungeun artificialintelligencemodeltopredictthemortalityofcovid19patientsathospitaladmissiontimeusingroutinebloodsamplesdevelopmentandvalidationofanensemblemodel AT leehooseok artificialintelligencemodeltopredictthemortalityofcovid19patientsathospitaladmissiontimeusingroutinebloodsamplesdevelopmentandvalidationofanensemblemodel AT seojaeho artificialintelligencemodeltopredictthemortalityofcovid19patientsathospitaladmissiontimeusingroutinebloodsamplesdevelopmentandvalidationofanensemblemodel AT choitaeyoung artificialintelligencemodeltopredictthemortalityofcovid19patientsathospitaladmissiontimeusingroutinebloodsamplesdevelopmentandvalidationofanensemblemodel AT jaimesrafael artificialintelligencemodeltopredictthemortalityofcovid19patientsathospitaladmissiontimeusingroutinebloodsamplesdevelopmentandvalidationofanensemblemodel AT kimkyungwon artificialintelligencemodeltopredictthemortalityofcovid19patientsathospitaladmissiontimeusingroutinebloodsamplesdevelopmentandvalidationofanensemblemodel AT leejinseok artificialintelligencemodeltopredictthemortalityofcovid19patientsathospitaladmissiontimeusingroutinebloodsamplesdevelopmentandvalidationofanensemblemodel |