EASY-APP
Acute pancreatitis (AP) is a potentially severe or even fatal inflammation of the pancreas. Early identification of patients at high risk for developing a severe course of the disease is crucial for preventing organ failure and death. Most of the former predictive scores require many parameters or a...
| Autores: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:ddd.uab.cat:292263 |
| Acceso en línea: | https://ddd.uab.cat/record/292263 https://dx.doi.org/urn:doi:10.1002/ctm2.842 |
| Access Level: | acceso abierto |
| Palabra clave: | Acute pancreatitis Artificial intelligence Severity prediction |
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EASY-APPAn artificial intelligence model and application for early and easy prediction of severity in acute pancreatitisKui, BalázsPintér, JózsefMolontay, RolandNagy, MarcellFarkas, NelliGede, NoémiVincze, ÁronBajor, JuditGódi, SzilárdCzimmer, JózsefSzabó, ImreIllés, AnitaSarlós, PatríciaHágendorn, RolandPár, GabriellaPapp, MáriaVitalis, Zsuzsanna|||0000-0001-8198-5312Kovács, GyörgyFehér, EszterFöldi, IldikóIzbéki, FerencGajdán, LászlóFejes, RolandNémeth, Balázs CsabaTörök, ImolaFarkas, HunorMickevicius, ArtautasSallinen, VilleGaleev, ShamilRamírez Maldonado, Elena|||0000-0001-9213-7212Párniczky, AndreaErőss, BálintHegyi, Péter JenőMárta, KatalinVáncsa, SzilárdSutton, RobertSzatmary, PeterLatawiec, DianeHalloran, Chrisde-Madaria, Enrique|||0000-0002-2412-9541Pando, Elizabeth|||0000-0001-6898-5502Alberti Delgado, Piero Arturo|||0000-0003-3512-0814Gómez-Jurado, Maria José|||0000-0002-5757-0691Tantau, AlinaSzentesi, AndreaHegyi, PéterAcute pancreatitisArtificial intelligenceSeverity predictionAcute pancreatitis (AP) is a potentially severe or even fatal inflammation of the pancreas. Early identification of patients at high risk for developing a severe course of the disease is crucial for preventing organ failure and death. Most of the former predictive scores require many parameters or at least 24 h to predict the severity; therefore, the early therapeutic window is often missed. The early achievable severity index (EASY) is a multicentre, multinational, prospective and observational study (ISRCTN10525246). The predictions were made using machine learning models. We used the scikit-learn, xgboost and catboost Python packages for modelling. We evaluated our models using fourfold cross-validation, and the receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), and accuracy metrics were calculated on the union of the test sets of the cross-validation. The most critical factors and their contribution to the prediction were identified using a modern tool of explainable artificial intelligence called SHapley Additive exPlanations (SHAP). The prediction model was based on an international cohort of 1184 patients and a validation cohort of 3543 patients. The best performing model was an XGBoost classifier with an average AUC score of 0.81 ± 0.033 and an accuracy of 89.1%, and the model improved with experience. The six most influential features were the respiratory rate, body temperature, abdominal muscular reflex, gender, age and glucose level. Using the XGBoost machine learning algorithm for prediction, the SHAP values for the explanation and the bootstrapping method to estimate confidence, we developed a free and easy-to-use web application in the Streamlit Python-based framework (http://easy-app.org/). The EASY prediction score is a practical tool for identifying patients at high risk for severe AP within hours of hospital admission. The web application is available for clinicians and contributes to the improvement of the model.Universitat Autònoma de Barcelona 22022-01-0120222022-01-01Articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/292263https://dx.doi.org/urn:doi:10.1002/ctm2.842reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:2922632026-06-06T12:50:31Z |
| dc.title.none.fl_str_mv |
EASY-APP An artificial intelligence model and application for early and easy prediction of severity in acute pancreatitis |
| title |
EASY-APP |
| spellingShingle |
EASY-APP Kui, Balázs Acute pancreatitis Artificial intelligence Severity prediction |
| title_short |
EASY-APP |
| title_full |
EASY-APP |
| title_fullStr |
EASY-APP |
| title_full_unstemmed |
EASY-APP |
| title_sort |
EASY-APP |
| dc.creator.none.fl_str_mv |
Kui, Balázs Pintér, József Molontay, Roland Nagy, Marcell Farkas, Nelli Gede, Noémi Vincze, Áron Bajor, Judit Gódi, Szilárd Czimmer, József Szabó, Imre Illés, Anita Sarlós, Patrícia Hágendorn, Roland Pár, Gabriella Papp, Mária Vitalis, Zsuzsanna|||0000-0001-8198-5312 Kovács, György Fehér, Eszter Földi, Ildikó Izbéki, Ferenc Gajdán, László Fejes, Roland Németh, Balázs Csaba Török, Imola Farkas, Hunor Mickevicius, Artautas Sallinen, Ville Galeev, Shamil Ramírez Maldonado, Elena|||0000-0001-9213-7212 Párniczky, Andrea Erőss, Bálint Hegyi, Péter Jenő Márta, Katalin Váncsa, Szilárd Sutton, Robert Szatmary, Peter Latawiec, Diane Halloran, Chris de-Madaria, Enrique|||0000-0002-2412-9541 Pando, Elizabeth|||0000-0001-6898-5502 Alberti Delgado, Piero Arturo|||0000-0003-3512-0814 Gómez-Jurado, Maria José|||0000-0002-5757-0691 Tantau, Alina Szentesi, Andrea Hegyi, Péter |
| author |
Kui, Balázs |
| author_facet |
Kui, Balázs Pintér, József Molontay, Roland Nagy, Marcell Farkas, Nelli Gede, Noémi Vincze, Áron Bajor, Judit Gódi, Szilárd Czimmer, József Szabó, Imre Illés, Anita Sarlós, Patrícia Hágendorn, Roland Pár, Gabriella Papp, Mária Vitalis, Zsuzsanna|||0000-0001-8198-5312 Kovács, György Fehér, Eszter Földi, Ildikó Izbéki, Ferenc Gajdán, László Fejes, Roland Németh, Balázs Csaba Török, Imola Farkas, Hunor Mickevicius, Artautas Sallinen, Ville Galeev, Shamil Ramírez Maldonado, Elena|||0000-0001-9213-7212 Párniczky, Andrea Erőss, Bálint Hegyi, Péter Jenő Márta, Katalin Váncsa, Szilárd Sutton, Robert Szatmary, Peter Latawiec, Diane Halloran, Chris de-Madaria, Enrique|||0000-0002-2412-9541 Pando, Elizabeth|||0000-0001-6898-5502 Alberti Delgado, Piero Arturo|||0000-0003-3512-0814 Gómez-Jurado, Maria José|||0000-0002-5757-0691 Tantau, Alina Szentesi, Andrea Hegyi, Péter |
| author_role |
author |
| author2 |
Pintér, József Molontay, Roland Nagy, Marcell Farkas, Nelli Gede, Noémi Vincze, Áron Bajor, Judit Gódi, Szilárd Czimmer, József Szabó, Imre Illés, Anita Sarlós, Patrícia Hágendorn, Roland Pár, Gabriella Papp, Mária Vitalis, Zsuzsanna|||0000-0001-8198-5312 Kovács, György Fehér, Eszter Földi, Ildikó Izbéki, Ferenc Gajdán, László Fejes, Roland Németh, Balázs Csaba Török, Imola Farkas, Hunor Mickevicius, Artautas Sallinen, Ville Galeev, Shamil Ramírez Maldonado, Elena|||0000-0001-9213-7212 Párniczky, Andrea Erőss, Bálint Hegyi, Péter Jenő Márta, Katalin Váncsa, Szilárd Sutton, Robert Szatmary, Peter Latawiec, Diane Halloran, Chris de-Madaria, Enrique|||0000-0002-2412-9541 Pando, Elizabeth|||0000-0001-6898-5502 Alberti Delgado, Piero Arturo|||0000-0003-3512-0814 Gómez-Jurado, Maria José|||0000-0002-5757-0691 Tantau, Alina Szentesi, Andrea Hegyi, Péter |
| author2_role |
author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author |
| dc.contributor.none.fl_str_mv |
Universitat Autònoma de Barcelona |
| dc.subject.none.fl_str_mv |
Acute pancreatitis Artificial intelligence Severity prediction |
| topic |
Acute pancreatitis Artificial intelligence Severity prediction |
| description |
Acute pancreatitis (AP) is a potentially severe or even fatal inflammation of the pancreas. Early identification of patients at high risk for developing a severe course of the disease is crucial for preventing organ failure and death. Most of the former predictive scores require many parameters or at least 24 h to predict the severity; therefore, the early therapeutic window is often missed. The early achievable severity index (EASY) is a multicentre, multinational, prospective and observational study (ISRCTN10525246). The predictions were made using machine learning models. We used the scikit-learn, xgboost and catboost Python packages for modelling. We evaluated our models using fourfold cross-validation, and the receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), and accuracy metrics were calculated on the union of the test sets of the cross-validation. The most critical factors and their contribution to the prediction were identified using a modern tool of explainable artificial intelligence called SHapley Additive exPlanations (SHAP). The prediction model was based on an international cohort of 1184 patients and a validation cohort of 3543 patients. The best performing model was an XGBoost classifier with an average AUC score of 0.81 ± 0.033 and an accuracy of 89.1%, and the model improved with experience. The six most influential features were the respiratory rate, body temperature, abdominal muscular reflex, gender, age and glucose level. Using the XGBoost machine learning algorithm for prediction, the SHAP values for the explanation and the bootstrapping method to estimate confidence, we developed a free and easy-to-use web application in the Streamlit Python-based framework (http://easy-app.org/). The EASY prediction score is a practical tool for identifying patients at high risk for severe AP within hours of hospital admission. The web application is available for clinicians and contributes to the improvement of the model. |
| publishDate |
2022 |
| dc.date.none.fl_str_mv |
2 2022-01-01 2022 2022-01-01 |
| dc.type.none.fl_str_mv |
Article http://purl.org/coar/resource_type/c_6501 VoR http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| dc.type.openaire.fl_str_mv |
info:eu-repo/semantics/article |
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article |
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https://ddd.uab.cat/record/292263 https://dx.doi.org/urn:doi:10.1002/ctm2.842 |
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https://ddd.uab.cat/record/292263 https://dx.doi.org/urn:doi:10.1002/ctm2.842 |
| dc.language.none.fl_str_mv |
Inglés eng |
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Inglés |
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eng |
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open access http://purl.org/coar/access_right/c_abf2 https://creativecommons.org/licenses/by/4.0/ |
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info:eu-repo/semantics/openAccess |
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open access http://purl.org/coar/access_right/c_abf2 https://creativecommons.org/licenses/by/4.0/ |
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openAccess |
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application/pdf |
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reponame:Dipòsit Digital de Documents de la UAB instname:Universitat Autònoma de Barcelona |
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