Cohort profile for development of machine learning models to predict healthcare-related adverse events (Demeter): clinical objectives, data requirements for modelling and overview of data set for 2016–2018
Purpose In-hospital health-related adverse events (HAEs) are a major concern for hospitals worldwide. In high-income countries, approximately 1 in 10 patients experience HAEs associated with their hospital stay. Estimating the risk of an HAE at the individual patient level as accurately as possible...
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2023-08-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/13/8/e070929.full |
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| author | Caroline Landelle Ursula von Schenck Jean-Luc Bosson Christophe Cancé Svetlana Artemova Rui Fa Daniel Stoessel Hadiseh Nowparast Rostami Pierre-Ephrem Madiot Jean-Marie Januel Daniel Pagonis Meghann Gallouche Frederic Olive Alexandre Moreau-Gaudry Sigurd Prieur |
| author_facet | Caroline Landelle Ursula von Schenck Jean-Luc Bosson Christophe Cancé Svetlana Artemova Rui Fa Daniel Stoessel Hadiseh Nowparast Rostami Pierre-Ephrem Madiot Jean-Marie Januel Daniel Pagonis Meghann Gallouche Frederic Olive Alexandre Moreau-Gaudry Sigurd Prieur |
| author_sort | Caroline Landelle |
| collection | DOAJ |
| description | Purpose In-hospital health-related adverse events (HAEs) are a major concern for hospitals worldwide. In high-income countries, approximately 1 in 10 patients experience HAEs associated with their hospital stay. Estimating the risk of an HAE at the individual patient level as accurately as possible is one of the first steps towards improving patient outcomes. Risk assessment can enable healthcare providers to target resources to patients in greatest need through adaptations in processes and procedures. Electronic health data facilitates the application of machine-learning methods for risk analysis. We aim, first to reveal correlations between HAE occurrence and patients’ characteristics and/or the procedures they undergo during their hospitalisation, and second, to build models that allow the early identification of patients at an elevated risk of HAE.Participants 143 865 adult patients hospitalised at Grenoble Alpes University Hospital (France) between 1 January 2016 and 31 December 2018.Findings to date In this set-up phase of the project, we describe the preconditions for big data analysis using machine-learning methods. We present an overview of the retrospective de-identified multisource data for a 2-year period extracted from the hospital’s Clinical Data Warehouse, along with social determinants of health data from the National Institute of Statistics and Economic Studies, to be used in machine learning (artificial intelligence) training and validation. No supplementary information or evaluation on the part of medical staff will be required by the information system for risk assessment.Future plans We are using this data set to develop predictive models for several general HAEs including secondary intensive care admission, prolonged hospital stay, 7-day and 30-day re-hospitalisation, nosocomial bacterial infection, hospital-acquired venous thromboembolism, and in-hospital mortality. |
| format | Article |
| id | doaj-art-07123d31bb3f4e519b801559ae203acb |
| institution | Kabale University |
| issn | 2044-6055 |
| language | English |
| publishDate | 2023-08-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-07123d31bb3f4e519b801559ae203acb2024-11-14T17:00:09ZengBMJ Publishing GroupBMJ Open2044-60552023-08-0113810.1136/bmjopen-2022-070929Cohort profile for development of machine learning models to predict healthcare-related adverse events (Demeter): clinical objectives, data requirements for modelling and overview of data set for 2016–2018Caroline Landelle0Ursula von Schenck1Jean-Luc Bosson2Christophe Cancé3Svetlana Artemova4Rui Fa5Daniel Stoessel6Hadiseh Nowparast Rostami7Pierre-Ephrem Madiot8Jean-Marie Januel9Daniel Pagonis10Meghann Gallouche11Frederic Olive12Alexandre Moreau-Gaudry13Sigurd Prieur14Public Health Department, CHU Grenoble Alpes, Grenoble, FranceLife Science Analytics, Elsevier BV, Berlin, GermanyPublic Health Department, INSERM CIC1406, CHU Grenoble Alpes, Grenoble, FrancePublic Health Department, INSERM CIC1406, CHU Grenoble Alpes, Grenoble, FrancePublic Health Department, INSERM CIC1406, CHU Grenoble Alpes, Grenoble, FranceElsevier Health Analytics, London, UKLife Science Analytics, Elsevier BV, Berlin, GermanyLife Science Analytics, Elsevier BV, Berlin, GermanyDigital Services Management, CHU Grenoble Alpes, Grenoble, FranceTIMC, CNRS UMR5525, Université Grenoble Alpes, Grenoble, FrancePublic Health Department, CHU Grenoble Alpes, Grenoble, FranceTIMC, CNRS UMR5525, Université Grenoble Alpes, Grenoble, FrancePublic Health Department, CHU Grenoble Alpes, Grenoble, FrancePublic Health Department, INSERM CIC1406, CHU Grenoble Alpes, Grenoble, FranceLife Science Analytics, Elsevier BV, Berlin, GermanyPurpose In-hospital health-related adverse events (HAEs) are a major concern for hospitals worldwide. In high-income countries, approximately 1 in 10 patients experience HAEs associated with their hospital stay. Estimating the risk of an HAE at the individual patient level as accurately as possible is one of the first steps towards improving patient outcomes. Risk assessment can enable healthcare providers to target resources to patients in greatest need through adaptations in processes and procedures. Electronic health data facilitates the application of machine-learning methods for risk analysis. We aim, first to reveal correlations between HAE occurrence and patients’ characteristics and/or the procedures they undergo during their hospitalisation, and second, to build models that allow the early identification of patients at an elevated risk of HAE.Participants 143 865 adult patients hospitalised at Grenoble Alpes University Hospital (France) between 1 January 2016 and 31 December 2018.Findings to date In this set-up phase of the project, we describe the preconditions for big data analysis using machine-learning methods. We present an overview of the retrospective de-identified multisource data for a 2-year period extracted from the hospital’s Clinical Data Warehouse, along with social determinants of health data from the National Institute of Statistics and Economic Studies, to be used in machine learning (artificial intelligence) training and validation. No supplementary information or evaluation on the part of medical staff will be required by the information system for risk assessment.Future plans We are using this data set to develop predictive models for several general HAEs including secondary intensive care admission, prolonged hospital stay, 7-day and 30-day re-hospitalisation, nosocomial bacterial infection, hospital-acquired venous thromboembolism, and in-hospital mortality.https://bmjopen.bmj.com/content/13/8/e070929.full |
| spellingShingle | Caroline Landelle Ursula von Schenck Jean-Luc Bosson Christophe Cancé Svetlana Artemova Rui Fa Daniel Stoessel Hadiseh Nowparast Rostami Pierre-Ephrem Madiot Jean-Marie Januel Daniel Pagonis Meghann Gallouche Frederic Olive Alexandre Moreau-Gaudry Sigurd Prieur Cohort profile for development of machine learning models to predict healthcare-related adverse events (Demeter): clinical objectives, data requirements for modelling and overview of data set for 2016–2018 BMJ Open |
| title | Cohort profile for development of machine learning models to predict healthcare-related adverse events (Demeter): clinical objectives, data requirements for modelling and overview of data set for 2016–2018 |
| title_full | Cohort profile for development of machine learning models to predict healthcare-related adverse events (Demeter): clinical objectives, data requirements for modelling and overview of data set for 2016–2018 |
| title_fullStr | Cohort profile for development of machine learning models to predict healthcare-related adverse events (Demeter): clinical objectives, data requirements for modelling and overview of data set for 2016–2018 |
| title_full_unstemmed | Cohort profile for development of machine learning models to predict healthcare-related adverse events (Demeter): clinical objectives, data requirements for modelling and overview of data set for 2016–2018 |
| title_short | Cohort profile for development of machine learning models to predict healthcare-related adverse events (Demeter): clinical objectives, data requirements for modelling and overview of data set for 2016–2018 |
| title_sort | cohort profile for development of machine learning models to predict healthcare related adverse events demeter clinical objectives data requirements for modelling and overview of data set for 2016 2018 |
| url | https://bmjopen.bmj.com/content/13/8/e070929.full |
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