This paper is in the following e-collection/theme issue:

Machine Learning (1501) Artificial Intelligence (1421) Ontologies, Classifications, and Coding (243)

Published on in Vol 8, No 4 (2020): April

A Deep Artificial Neural Network−Based Model for Prediction of Underlying Cause of Death From Death Certificates: Algorithm Development and Validation

A Deep Artificial Neural Network−Based Model for Prediction of Underlying Cause of Death From Death Certificates: Algorithm Development and Validation

A Deep Artificial Neural Network−Based Model for Prediction of Underlying Cause of Death From Death Certificates: Algorithm Development and Validation

Authors of this article:

Louis Falissard1, 2 Author Orcid Image ;   Claire Morgand1 Author Orcid Image ;   Sylvie Roussel1 Author Orcid Image ;   Claire Imbaud1 Author Orcid Image ;   Walid Ghosn1 Author Orcid Image ;   Karim Bounebache1 Author Orcid Image ;   Grégoire Rey1 Author Orcid Image
Article Authors Cited by (19) Tweetations (2) Metrics

Journals

  1. Della Mea V, Popescu M, Roitero K. Underlying cause of death identification from death certificates using reverse coding to text and a NLP based deep learning approach. Informatics in Medicine Unlocked 2020;21:100456 View
  2. Chou P, Chien T, Yang T, Yeh Y, Chou W, Yeh C. Predicting Active NBA Players Most Likely to Be Inducted into the Basketball Hall of Famers Using Artificial Neural Networks in Microsoft Excel: Development and Usability Study. International Journal of Environmental Research and Public Health 2021;18(8):4256 View
  3. Tey S, Liu C, Chien T, Hsu C, Chan K, Chen C, Cheng T, Wu W. Predicting the 14-Day Hospital Readmission of Patients with Pneumonia Using Artificial Neural Networks (ANN). International Journal of Environmental Research and Public Health 2021;18(10):5110 View
  4. Lin J, Chien T, Wang L, Chou W. An artificial neural network model to predict the mortality of COVID-19 patients using routine blood samples at the time of hospital admission. Medicine 2021;100(28):e26532 View
  5. Bernstam E, Shireman P, Meric‐Bernstam F, N. Zozus M, Jiang X, Brimhall B, Windham A, Schmidt S, Visweswaran S, Ye Y, Goodrum H, Ling Y, Barapatre S, Becich M. Artificial intelligence in clinical and translational science: Successes, challenges and opportunities. Clinical and Translational Science 2022;15(2):309 View
  6. Chen H, Chien T, Chen L, Yeh Y, Ma S, Lee H. An app for predicting nurse intention to quit the job using artificial neural networks (ANNs) in Microsoft Excel. Medicine 2022;101(11) View
  7. Tang L, Korona-Bailey J, Zaras D, Roberts A, Mukhopadhyay S, Espy S, Walsh C. Using Natural Language Processing to Predict Fatal Drug Overdose From Autopsy Narrative Text: Algorithm Development and Validation Study. JMIR Public Health and Surveillance 2023;9:e45246 View
  8. Vargas-Herrera J, Miki J, Wong L, Monzón J, Villanueva R. Automatização da codificação e seleção das causas de óbitos no Peru: estudo descritivo, 2016-2019. Epidemiologia e Serviços de Saúde 2023;32(3) View
  9. Vargas-Herrera J, Miki J, Wong L, Monzón J, Villanueva R. Automated coding and selection of causes of death in Peru: a descriptive study, 2016-2019. Epidemiologia e Serviços de Saúde 2023;32(3) View
  10. Boyer L, Falissard B, Nuss P, Collin C, Duret S, Rabbani M, De Chefdebien I, Tonelli I, Llorca P, Fond G. Real-world effectiveness of long-acting injectable antipsychotic treatments in a nationwide cohort of 12,373 patients with schizophrenia-spectrum disorders. Molecular Psychiatry 2023;28(9):3709 View
  11. Pita Ferreira P, Godinho Simões D, Pinto de Carvalho C, Duarte F, Fernandes E, Casaca Carvalho P, Loff J, Soares A, Albuquerque M, Pinto-Leite P, Peralta-Santos A. Real-Time Classification of Causes of Death Using AI: Sensitivity Analysis. JMIR AI 2023;2:e40965 View
  12. Lefèvre T, Tournois L. Artificial Intelligence and Diagnostics in Medicine and Forensic Science. Diagnostics 2023;13(23):3554 View
  13. Fang X, Huang S, Yin Y, Chen T, Liao Z, Zhong W. Advancing Underlying Cause of Death Inference Through Wide and Deep Model. China CDC Weekly 2024;6(21):487 View
  14. Mertes P, Morgand C, Barach P, Jurkolow G, Assmann K, Dufetelle E, Susplugas V, Alauddin B, Yavordios P, Tourres J, Dumeix J, Capdevila X. Validation of a natural language processing algorithm using national reporting data to improve identification of anesthesia-related ADVerse evENTs: The “ADVENTURE” study. Anaesthesia Critical Care & Pain Medicine 2024;43(4):101390 View
  15. Ge H, Gao K, Li S, Wang W, Chen Q, Lin X, Huan Z, Su X, Yang X. An Automatic Approach Designed for Inference of the Underlying Cause-of-Death of Citizens. International Journal of Environmental Research and Public Health 2021;18(5):2414 View
  16. Yang X, Ma H, Gao K, Ge H. An Automated Method of Causal Inference of the Underlying Cause of Death of Citizens. Life 2022;12(8):1134 View

Books/Policy Documents

  1. Lefèvre T, Colineaux H, Morgand C, Tournois L, Delpierre C. Artificial Intelligence in Covid-19. View