Effectiveness, Explainability and Reliability of Machine Meta-Learning Methods for Predicting Mortality in Patients with COVID-19: Results of the Brazilian COVID-19 Registry Article Swipe
Bruno Barbosa Miranda de Paiva
,
Polianna Delfino-Pereira
,
Cláudio Moisés Valiense de Andrade
,
Virgínia Mara Reis Gomes
,
Maria Clara Pontello Barbosa Lima
,
Maíra Viana Rego Souza-Silva
,
Marcelo Carneiro
,
Karina Paula Medeiros Prado Martins
,
Thaís Lorenna Souza Sales
,
Rafael Lima Rodrigues de Carvalho
,
Magda Carvalho Pires
,
Lucas Emanuel Ferreira Ramos
,
Rafael T. Silva
,
Adriana Falangola Benjamin Bezerra
,
Alexandre Vargas Schwarzbold
,
Aline Gabrielle Sousa Nunes
,
Amanda de Oliveira Maurílio
,
Ana Luiza Bahia Alves Scotton
,
André Soares de Moura Costa
,
Andriele Abreu Castro
,
Bárbara Lopes Farace
,
Christiane Corrêa Rodrigues Cimini
,
Cíntia Alcântara De Carvalho
,
Daniel Vitório Silveira
,
Daniela Ponce
,
Elayne Crestani Pereira
,
Euler Roberto Fernandes Manenti
,
Evelin Paola de Almeida Cenci
,
Fernanda Barbosa Lucas
,
Fernanda d’Athayde Rodrigues
,
Fernando Anschau
,
Fernando Antônio Botoni
,
Fernando Graça Aranha
,
Frederico Bartolazzi
,
Gisele Alsina Nader Bastos
,
Giovanna Grünewald Vietta
,
Guilherme Fagundes Nascimento
,
Helena Carolina Noal
,
Helena Duani
,
Heloísa Reniers Vianna
,
Henrique Cerqueira Guimarães
,
Isabela Moraes Gomes
,
Jamille Hemétrio Salles Martins Costa
,
Jéssica Rayane Corrêa Silva da Fonseca
,
Júlia Di Sabatino Santos Guimarães
,
Júlia Drumond Parreiras de Morais
,
Juliana Machado‐Rugolo
,
Joanna d’Arc Lyra Batista
,
Joice Coutinho de Alvarenga
,
José Miguel Chatkin
,
Karen Brasil Ruschel
,
Leila Beltrami Moreira
,
Leonardo Seixas de Oliveira
,
Liege Barella Zandoná
,
Lílian Santos Pinheiro
,
Luanna da Silva Monteiro
,
Lucas de Deus Sousa
,
Luciane Kopittke
,
Luciano de Souza Viana
,
Luís César de Castro
,
Luisa Argolo Assis
,
Luisa Elem Almeid Santos
,
Máderson Alvares de Souza Cabral
,
Magda César Raposo
,
Maiara Anschau Floriani
,
Maria Angélica Pires Ferreira
,
Maria Aparecida Camargos Bicalho
,
Mariana Frizzo de Godoy
,
Matheus Carvalho Alves Nogueira
,
Meire Pereira de Figueiredo
,
Milton Henriques Guimarães Júnior
,
Mônica Aparecida de Paula de Sordi
,
Natália da Cunha Severino Sampaio
,
Neimy Ramos de Oliveira
,
Pedro Ledic Assaf
,
Raquel Lutkmeier
,
Reginaldo Aparecido Valácio
,
Renan Goulart Finger
,
Roberta Senger
,
Rochele Mosmann Menezes
,
Rufino de Freitas Silva
,
Saionara Cristina Francisco
,
Silvana Mangeon Mereilles Guimarães
,
Silvia Ferreira Araújo
,
Talita Fischer Oliveira
,
Tatiana Kurtz
,
Tatiani Oliveira Fereguetti
,
Thainara Conceição de Oliveira
,
Thulio Henrique Oliveira Diniz
,
Yara Cristina Neves Marques Barbosa Ribeiro
,
Yuri Carlotto Ramires
,
Marcos André Gonçalves
,
Milena Soriano Marcolino
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1101/2021.11.01.21265527
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1101/2021.11.01.21265527
Objective To provide a thorough comparative study among state-of-the-art machine learning methods and statistical methods for determining in-hospital mortality in COVID-19 patients using data upon hospital admission; to study the reliability of the predictions of the most effective methods by correlating the probability of the outcome and the accuracy of the methods; to investigate how explainable are the predictions produced by the most effective methods. Materials and Methods De-identified data were obtained from COVID-19 positive patients in 36 participating hospitals, from March 1 to September 30, 2020. Demographic, comorbidity, clinical presentation and laboratory data were used as training data to develop COVID-19 mortality prediction models. Multiple machine learning and traditional statistics models were trained on this prediction task using a folded cross-validation procedure, from which we assessed performance and interpretability metrics. Results The Stacking of machine learning models improved over the previous state-of-the-art results by more than 26% in predicting the class of interest (death), achieving 87.1% of AUROC and macro F1 of 73.9%. We also show that some machine learning models can be very interpretable and reliable, yielding more accurate predictions while providing a good explanation for the ‘why’. Conclusion The best results were obtained using the meta-learning ensemble model – Stacking. State-of the art explainability techniques such as SHAP-values can be used to draw useful insights into the patterns learned by machine-learning algorithms. Machine-learning models can be more explainable than traditional statistics models while also yielding highly reliable predictions.
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- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2021.11.01.21265527
- https://www.medrxiv.org/content/medrxiv/early/2021/11/02/2021.11.01.21265527.full.pdf
- OA Status
- green
- Cited By
- 1
- References
- 44
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3211057558
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3211057558Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2021.11.01.21265527Digital Object Identifier
- Title
-
Effectiveness, Explainability and Reliability of Machine Meta-Learning Methods for Predicting Mortality in Patients with COVID-19: Results of the Brazilian COVID-19 RegistryWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-11-02Full publication date if available
- Authors
-
Bruno Barbosa Miranda de Paiva, Polianna Delfino-Pereira, Cláudio Moisés Valiense de Andrade, Virgínia Mara Reis Gomes, Maria Clara Pontello Barbosa Lima, Maíra Viana Rego Souza-Silva, Marcelo Carneiro, Karina Paula Medeiros Prado Martins, Thaís Lorenna Souza Sales, Rafael Lima Rodrigues de Carvalho, Magda Carvalho Pires, Lucas Emanuel Ferreira Ramos, Rafael T. Silva, Adriana Falangola Benjamin Bezerra, Alexandre Vargas Schwarzbold, Aline Gabrielle Sousa Nunes, Amanda de Oliveira Maurílio, Ana Luiza Bahia Alves Scotton, André Soares de Moura Costa, Andriele Abreu Castro, Bárbara Lopes Farace, Christiane Corrêa Rodrigues Cimini, Cíntia Alcântara De Carvalho, Daniel Vitório Silveira, Daniela Ponce, Elayne Crestani Pereira, Euler Roberto Fernandes Manenti, Evelin Paola de Almeida Cenci, Fernanda Barbosa Lucas, Fernanda d’Athayde Rodrigues, Fernando Anschau, Fernando Antônio Botoni, Fernando Graça Aranha, Frederico Bartolazzi, Gisele Alsina Nader Bastos, Giovanna Grünewald Vietta, Guilherme Fagundes Nascimento, Helena Carolina Noal, Helena Duani, Heloísa Reniers Vianna, Henrique Cerqueira Guimarães, Isabela Moraes Gomes, Jamille Hemétrio Salles Martins Costa, Jéssica Rayane Corrêa Silva da Fonseca, Júlia Di Sabatino Santos Guimarães, Júlia Drumond Parreiras de Morais, Juliana Machado‐Rugolo, Joanna d’Arc Lyra Batista, Joice Coutinho de Alvarenga, José Miguel Chatkin, Karen Brasil Ruschel, Leila Beltrami Moreira, Leonardo Seixas de Oliveira, Liege Barella Zandoná, Lílian Santos Pinheiro, Luanna da Silva Monteiro, Lucas de Deus Sousa, Luciane Kopittke, Luciano de Souza Viana, Luís César de Castro, Luisa Argolo Assis, Luisa Elem Almeid Santos, Máderson Alvares de Souza Cabral, Magda César Raposo, Maiara Anschau Floriani, Maria Angélica Pires Ferreira, Maria Aparecida Camargos Bicalho, Mariana Frizzo de Godoy, Matheus Carvalho Alves Nogueira, Meire Pereira de Figueiredo, Milton Henriques Guimarães Júnior, Mônica Aparecida de Paula de Sordi, Natália da Cunha Severino Sampaio, Neimy Ramos de Oliveira, Pedro Ledic Assaf, Raquel Lutkmeier, Reginaldo Aparecido Valácio, Renan Goulart Finger, Roberta Senger, Rochele Mosmann Menezes, Rufino de Freitas Silva, Saionara Cristina Francisco, Silvana Mangeon Mereilles Guimarães, Silvia Ferreira Araújo, Talita Fischer Oliveira, Tatiana Kurtz, Tatiani Oliveira Fereguetti, Thainara Conceição de Oliveira, Thulio Henrique Oliveira Diniz, Yara Cristina Neves Marques Barbosa Ribeiro, Yuri Carlotto Ramires, Marcos André Gonçalves, Milena Soriano MarcolinoList of authors in order
- Landing page
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https://doi.org/10.1101/2021.11.01.21265527Publisher landing page
- PDF URL
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https://www.medrxiv.org/content/medrxiv/early/2021/11/02/2021.11.01.21265527.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.medrxiv.org/content/medrxiv/early/2021/11/02/2021.11.01.21265527.full.pdfDirect OA link when available
- Concepts
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Interpretability, Machine learning, Artificial intelligence, Computer science, Reliability (semiconductor), Coronavirus disease 2019 (COVID-19), Predictive modelling, Medicine, Quantum mechanics, Disease, Infectious disease (medical specialty), Pathology, Power (physics), PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2023: 1Per-year citation counts (last 5 years)
- References (count)
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44Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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