Open Source Repository and Online Calculator of Prediction Models for Diagnosis and Prognosis in Oncology
(1) Background: The main aim was to develop a prototype application that would serve as an open-source repository for a curated subset of predictive and prognostic models regarding oncology, and provide a user-friendly interface for the included models to allow online calculation. The focus of the a...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Iva Halilaj [verfasserIn] Cary Oberije [verfasserIn] Avishek Chatterjee [verfasserIn] Yvonka van Wijk [verfasserIn] Nastaran Mohammadian Rad [verfasserIn] Prabash Galganebanduge [verfasserIn] Elizaveta Lavrova [verfasserIn] Sergey Primakov [verfasserIn] Yousif Widaatalla [verfasserIn] Anke Wind [verfasserIn] Philippe Lambin [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
In: Biomedicines - MDPI AG, 2014, 10(2022), 11, p 2679 |
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| Übergeordnetes Werk: |
volume:10 ; year:2022 ; number:11, p 2679 |
| Links: |
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| DOI / URN: |
10.3390/biomedicines10112679 |
|---|
| Katalog-ID: |
DOAJ02590471X |
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| 520 | |a (1) Background: The main aim was to develop a prototype application that would serve as an open-source repository for a curated subset of predictive and prognostic models regarding oncology, and provide a user-friendly interface for the included models to allow online calculation. The focus of the application is on providing physicians and health professionals with patient-specific information regarding treatment plans, survival rates, and side effects for different expected treatments. (2) Methods: The primarily used models were the ones developed by our research group in the past. This selection was completed by a number of models, addressing the same cancer types but focusing on other outcomes that were selected based on a literature search in PubMed and Medline databases. All selected models were publicly available and had been validated TRIPOD (Transparent Reporting of studies on prediction models for Individual Prognosis Or Diagnosis) type 3 or 2b. (3) Results: The open source repository currently incorporates 18 models from different research groups, evaluated on datasets from different countries. Model types included logistic regression, Cox regression, and recursive partition analysis (decision trees). (4) Conclusions: An application was developed to enable physicians to complement their clinical judgment with user-friendly patient-specific predictions using models that have received internal/external validation. Additionally, this platform enables researchers to display their work, enhancing the use and exposure of their models. | ||
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10.3390/biomedicines10112679 doi (DE-627)DOAJ02590471X (DE-599)DOAJafc1a62aa9804c6e8d5ca4c53f8a5749 DE-627 ger DE-627 rakwb eng QH301-705.5 Iva Halilaj verfasserin aut Open Source Repository and Online Calculator of Prediction Models for Diagnosis and Prognosis in Oncology 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier (1) Background: The main aim was to develop a prototype application that would serve as an open-source repository for a curated subset of predictive and prognostic models regarding oncology, and provide a user-friendly interface for the included models to allow online calculation. The focus of the application is on providing physicians and health professionals with patient-specific information regarding treatment plans, survival rates, and side effects for different expected treatments. (2) Methods: The primarily used models were the ones developed by our research group in the past. This selection was completed by a number of models, addressing the same cancer types but focusing on other outcomes that were selected based on a literature search in PubMed and Medline databases. All selected models were publicly available and had been validated TRIPOD (Transparent Reporting of studies on prediction models for Individual Prognosis Or Diagnosis) type 3 or 2b. (3) Results: The open source repository currently incorporates 18 models from different research groups, evaluated on datasets from different countries. Model types included logistic regression, Cox regression, and recursive partition analysis (decision trees). (4) Conclusions: An application was developed to enable physicians to complement their clinical judgment with user-friendly patient-specific predictions using models that have received internal/external validation. Additionally, this platform enables researchers to display their work, enhancing the use and exposure of their models. web application open-source software prediction models prediction tool decision support Biology (General) Cary Oberije verfasserin aut Avishek Chatterjee verfasserin aut Yvonka van Wijk verfasserin aut Nastaran Mohammadian Rad verfasserin aut Prabash Galganebanduge verfasserin aut Elizaveta Lavrova verfasserin aut Sergey Primakov verfasserin aut Yousif Widaatalla verfasserin aut Anke Wind verfasserin aut Philippe Lambin verfasserin aut In Biomedicines MDPI AG, 2014 10(2022), 11, p 2679 (DE-627)750370483 (DE-600)2720867-9 22279059 nnns volume:10 year:2022 number:11, p 2679 https://doi.org/10.3390/biomedicines10112679 kostenfrei https://doaj.org/article/afc1a62aa9804c6e8d5ca4c53f8a5749 kostenfrei https://www.mdpi.com/2227-9059/10/11/2679 kostenfrei https://doaj.org/toc/2227-9059 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 11, p 2679 |
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10.3390/biomedicines10112679 doi (DE-627)DOAJ02590471X (DE-599)DOAJafc1a62aa9804c6e8d5ca4c53f8a5749 DE-627 ger DE-627 rakwb eng QH301-705.5 Iva Halilaj verfasserin aut Open Source Repository and Online Calculator of Prediction Models for Diagnosis and Prognosis in Oncology 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier (1) Background: The main aim was to develop a prototype application that would serve as an open-source repository for a curated subset of predictive and prognostic models regarding oncology, and provide a user-friendly interface for the included models to allow online calculation. The focus of the application is on providing physicians and health professionals with patient-specific information regarding treatment plans, survival rates, and side effects for different expected treatments. (2) Methods: The primarily used models were the ones developed by our research group in the past. This selection was completed by a number of models, addressing the same cancer types but focusing on other outcomes that were selected based on a literature search in PubMed and Medline databases. All selected models were publicly available and had been validated TRIPOD (Transparent Reporting of studies on prediction models for Individual Prognosis Or Diagnosis) type 3 or 2b. (3) Results: The open source repository currently incorporates 18 models from different research groups, evaluated on datasets from different countries. Model types included logistic regression, Cox regression, and recursive partition analysis (decision trees). (4) Conclusions: An application was developed to enable physicians to complement their clinical judgment with user-friendly patient-specific predictions using models that have received internal/external validation. Additionally, this platform enables researchers to display their work, enhancing the use and exposure of their models. web application open-source software prediction models prediction tool decision support Biology (General) Cary Oberije verfasserin aut Avishek Chatterjee verfasserin aut Yvonka van Wijk verfasserin aut Nastaran Mohammadian Rad verfasserin aut Prabash Galganebanduge verfasserin aut Elizaveta Lavrova verfasserin aut Sergey Primakov verfasserin aut Yousif Widaatalla verfasserin aut Anke Wind verfasserin aut Philippe Lambin verfasserin aut In Biomedicines MDPI AG, 2014 10(2022), 11, p 2679 (DE-627)750370483 (DE-600)2720867-9 22279059 nnns volume:10 year:2022 number:11, p 2679 https://doi.org/10.3390/biomedicines10112679 kostenfrei https://doaj.org/article/afc1a62aa9804c6e8d5ca4c53f8a5749 kostenfrei https://www.mdpi.com/2227-9059/10/11/2679 kostenfrei https://doaj.org/toc/2227-9059 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 11, p 2679 |
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10.3390/biomedicines10112679 doi (DE-627)DOAJ02590471X (DE-599)DOAJafc1a62aa9804c6e8d5ca4c53f8a5749 DE-627 ger DE-627 rakwb eng QH301-705.5 Iva Halilaj verfasserin aut Open Source Repository and Online Calculator of Prediction Models for Diagnosis and Prognosis in Oncology 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier (1) Background: The main aim was to develop a prototype application that would serve as an open-source repository for a curated subset of predictive and prognostic models regarding oncology, and provide a user-friendly interface for the included models to allow online calculation. The focus of the application is on providing physicians and health professionals with patient-specific information regarding treatment plans, survival rates, and side effects for different expected treatments. (2) Methods: The primarily used models were the ones developed by our research group in the past. This selection was completed by a number of models, addressing the same cancer types but focusing on other outcomes that were selected based on a literature search in PubMed and Medline databases. All selected models were publicly available and had been validated TRIPOD (Transparent Reporting of studies on prediction models for Individual Prognosis Or Diagnosis) type 3 or 2b. (3) Results: The open source repository currently incorporates 18 models from different research groups, evaluated on datasets from different countries. Model types included logistic regression, Cox regression, and recursive partition analysis (decision trees). (4) Conclusions: An application was developed to enable physicians to complement their clinical judgment with user-friendly patient-specific predictions using models that have received internal/external validation. Additionally, this platform enables researchers to display their work, enhancing the use and exposure of their models. web application open-source software prediction models prediction tool decision support Biology (General) Cary Oberije verfasserin aut Avishek Chatterjee verfasserin aut Yvonka van Wijk verfasserin aut Nastaran Mohammadian Rad verfasserin aut Prabash Galganebanduge verfasserin aut Elizaveta Lavrova verfasserin aut Sergey Primakov verfasserin aut Yousif Widaatalla verfasserin aut Anke Wind verfasserin aut Philippe Lambin verfasserin aut In Biomedicines MDPI AG, 2014 10(2022), 11, p 2679 (DE-627)750370483 (DE-600)2720867-9 22279059 nnns volume:10 year:2022 number:11, p 2679 https://doi.org/10.3390/biomedicines10112679 kostenfrei https://doaj.org/article/afc1a62aa9804c6e8d5ca4c53f8a5749 kostenfrei https://www.mdpi.com/2227-9059/10/11/2679 kostenfrei https://doaj.org/toc/2227-9059 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 11, p 2679 |
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10.3390/biomedicines10112679 doi (DE-627)DOAJ02590471X (DE-599)DOAJafc1a62aa9804c6e8d5ca4c53f8a5749 DE-627 ger DE-627 rakwb eng QH301-705.5 Iva Halilaj verfasserin aut Open Source Repository and Online Calculator of Prediction Models for Diagnosis and Prognosis in Oncology 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier (1) Background: The main aim was to develop a prototype application that would serve as an open-source repository for a curated subset of predictive and prognostic models regarding oncology, and provide a user-friendly interface for the included models to allow online calculation. The focus of the application is on providing physicians and health professionals with patient-specific information regarding treatment plans, survival rates, and side effects for different expected treatments. (2) Methods: The primarily used models were the ones developed by our research group in the past. This selection was completed by a number of models, addressing the same cancer types but focusing on other outcomes that were selected based on a literature search in PubMed and Medline databases. All selected models were publicly available and had been validated TRIPOD (Transparent Reporting of studies on prediction models for Individual Prognosis Or Diagnosis) type 3 or 2b. (3) Results: The open source repository currently incorporates 18 models from different research groups, evaluated on datasets from different countries. Model types included logistic regression, Cox regression, and recursive partition analysis (decision trees). (4) Conclusions: An application was developed to enable physicians to complement their clinical judgment with user-friendly patient-specific predictions using models that have received internal/external validation. Additionally, this platform enables researchers to display their work, enhancing the use and exposure of their models. web application open-source software prediction models prediction tool decision support Biology (General) Cary Oberije verfasserin aut Avishek Chatterjee verfasserin aut Yvonka van Wijk verfasserin aut Nastaran Mohammadian Rad verfasserin aut Prabash Galganebanduge verfasserin aut Elizaveta Lavrova verfasserin aut Sergey Primakov verfasserin aut Yousif Widaatalla verfasserin aut Anke Wind verfasserin aut Philippe Lambin verfasserin aut In Biomedicines MDPI AG, 2014 10(2022), 11, p 2679 (DE-627)750370483 (DE-600)2720867-9 22279059 nnns volume:10 year:2022 number:11, p 2679 https://doi.org/10.3390/biomedicines10112679 kostenfrei https://doaj.org/article/afc1a62aa9804c6e8d5ca4c53f8a5749 kostenfrei https://www.mdpi.com/2227-9059/10/11/2679 kostenfrei https://doaj.org/toc/2227-9059 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 11, p 2679 |
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10.3390/biomedicines10112679 doi (DE-627)DOAJ02590471X (DE-599)DOAJafc1a62aa9804c6e8d5ca4c53f8a5749 DE-627 ger DE-627 rakwb eng QH301-705.5 Iva Halilaj verfasserin aut Open Source Repository and Online Calculator of Prediction Models for Diagnosis and Prognosis in Oncology 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier (1) Background: The main aim was to develop a prototype application that would serve as an open-source repository for a curated subset of predictive and prognostic models regarding oncology, and provide a user-friendly interface for the included models to allow online calculation. The focus of the application is on providing physicians and health professionals with patient-specific information regarding treatment plans, survival rates, and side effects for different expected treatments. (2) Methods: The primarily used models were the ones developed by our research group in the past. This selection was completed by a number of models, addressing the same cancer types but focusing on other outcomes that were selected based on a literature search in PubMed and Medline databases. All selected models were publicly available and had been validated TRIPOD (Transparent Reporting of studies on prediction models for Individual Prognosis Or Diagnosis) type 3 or 2b. (3) Results: The open source repository currently incorporates 18 models from different research groups, evaluated on datasets from different countries. Model types included logistic regression, Cox regression, and recursive partition analysis (decision trees). (4) Conclusions: An application was developed to enable physicians to complement their clinical judgment with user-friendly patient-specific predictions using models that have received internal/external validation. Additionally, this platform enables researchers to display their work, enhancing the use and exposure of their models. web application open-source software prediction models prediction tool decision support Biology (General) Cary Oberije verfasserin aut Avishek Chatterjee verfasserin aut Yvonka van Wijk verfasserin aut Nastaran Mohammadian Rad verfasserin aut Prabash Galganebanduge verfasserin aut Elizaveta Lavrova verfasserin aut Sergey Primakov verfasserin aut Yousif Widaatalla verfasserin aut Anke Wind verfasserin aut Philippe Lambin verfasserin aut In Biomedicines MDPI AG, 2014 10(2022), 11, p 2679 (DE-627)750370483 (DE-600)2720867-9 22279059 nnns volume:10 year:2022 number:11, p 2679 https://doi.org/10.3390/biomedicines10112679 kostenfrei https://doaj.org/article/afc1a62aa9804c6e8d5ca4c53f8a5749 kostenfrei https://www.mdpi.com/2227-9059/10/11/2679 kostenfrei https://doaj.org/toc/2227-9059 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 11, p 2679 |
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Open Source Repository and Online Calculator of Prediction Models for Diagnosis and Prognosis in Oncology |
| abstract |
(1) Background: The main aim was to develop a prototype application that would serve as an open-source repository for a curated subset of predictive and prognostic models regarding oncology, and provide a user-friendly interface for the included models to allow online calculation. The focus of the application is on providing physicians and health professionals with patient-specific information regarding treatment plans, survival rates, and side effects for different expected treatments. (2) Methods: The primarily used models were the ones developed by our research group in the past. This selection was completed by a number of models, addressing the same cancer types but focusing on other outcomes that were selected based on a literature search in PubMed and Medline databases. All selected models were publicly available and had been validated TRIPOD (Transparent Reporting of studies on prediction models for Individual Prognosis Or Diagnosis) type 3 or 2b. (3) Results: The open source repository currently incorporates 18 models from different research groups, evaluated on datasets from different countries. Model types included logistic regression, Cox regression, and recursive partition analysis (decision trees). (4) Conclusions: An application was developed to enable physicians to complement their clinical judgment with user-friendly patient-specific predictions using models that have received internal/external validation. Additionally, this platform enables researchers to display their work, enhancing the use and exposure of their models. |
| abstractGer |
(1) Background: The main aim was to develop a prototype application that would serve as an open-source repository for a curated subset of predictive and prognostic models regarding oncology, and provide a user-friendly interface for the included models to allow online calculation. The focus of the application is on providing physicians and health professionals with patient-specific information regarding treatment plans, survival rates, and side effects for different expected treatments. (2) Methods: The primarily used models were the ones developed by our research group in the past. This selection was completed by a number of models, addressing the same cancer types but focusing on other outcomes that were selected based on a literature search in PubMed and Medline databases. All selected models were publicly available and had been validated TRIPOD (Transparent Reporting of studies on prediction models for Individual Prognosis Or Diagnosis) type 3 or 2b. (3) Results: The open source repository currently incorporates 18 models from different research groups, evaluated on datasets from different countries. Model types included logistic regression, Cox regression, and recursive partition analysis (decision trees). (4) Conclusions: An application was developed to enable physicians to complement their clinical judgment with user-friendly patient-specific predictions using models that have received internal/external validation. Additionally, this platform enables researchers to display their work, enhancing the use and exposure of their models. |
| abstract_unstemmed |
(1) Background: The main aim was to develop a prototype application that would serve as an open-source repository for a curated subset of predictive and prognostic models regarding oncology, and provide a user-friendly interface for the included models to allow online calculation. The focus of the application is on providing physicians and health professionals with patient-specific information regarding treatment plans, survival rates, and side effects for different expected treatments. (2) Methods: The primarily used models were the ones developed by our research group in the past. This selection was completed by a number of models, addressing the same cancer types but focusing on other outcomes that were selected based on a literature search in PubMed and Medline databases. All selected models were publicly available and had been validated TRIPOD (Transparent Reporting of studies on prediction models for Individual Prognosis Or Diagnosis) type 3 or 2b. (3) Results: The open source repository currently incorporates 18 models from different research groups, evaluated on datasets from different countries. Model types included logistic regression, Cox regression, and recursive partition analysis (decision trees). (4) Conclusions: An application was developed to enable physicians to complement their clinical judgment with user-friendly patient-specific predictions using models that have received internal/external validation. Additionally, this platform enables researchers to display their work, enhancing the use and exposure of their models. |
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The focus of the application is on providing physicians and health professionals with patient-specific information regarding treatment plans, survival rates, and side effects for different expected treatments. (2) Methods: The primarily used models were the ones developed by our research group in the past. This selection was completed by a number of models, addressing the same cancer types but focusing on other outcomes that were selected based on a literature search in PubMed and Medline databases. All selected models were publicly available and had been validated TRIPOD (Transparent Reporting of studies on prediction models for Individual Prognosis Or Diagnosis) type 3 or 2b. (3) Results: The open source repository currently incorporates 18 models from different research groups, evaluated on datasets from different countries. Model types included logistic regression, Cox regression, and recursive partition analysis (decision trees). (4) Conclusions: An application was developed to enable physicians to complement their clinical judgment with user-friendly patient-specific predictions using models that have received internal/external validation. 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