• Funding : ANR, Communauté Urbaine d'Arras
  • Start year :
  • 2023

Description

The proposed thesis will aim to set up a monitoring system that triggers alerts for caregivers by analyzing patient data collected recurrently during the hospital care of these patients. These are biological, physiological and medical device parameters. The analysis of patient data or parameters is intended on a continuous basis as soon as they are admitted to the hospital. The alerts will be related to anomalies in patient data (biological/ physiological/medical devices) threatening the vital prognosis. The main issues will be:

  • the interoperability of information systems in order to centralize patient data (biological/physiological/medical devices) and analyze them jointly;
  • the selection of the relevant features to be analyzed and the values that can trigger an alert;
  • the selection of hospital care pathways (care services) during which the system will apply monitoring;
  • the definition of a severity index and a reliability index for an alert;
  • the selection of the recipient(s) of the alerts and the transmission of the alerts themselves via an appropriate channel.

The intended monitoring system will rely on artificial intelligence methods (machine learning and explainable AI). A preliminary work of data aggregation from different sources and possible data cleaning will be carried out. Machine learning models of various types (random forests, boosted trees) will be trained and evaluated on that data. The scikit-learn library can be used for this purpose. Explanations of predictions can be computed using the PyXAI library developed at CRIL. The predictions and their explanations will be evaluated by the physicians participating in the project. This innovative project aiming at improving hospital care safety, will be carried out with the collaboration of various hospital practitioners and managers from the Arras hospital center.

References

  • Bailey TC, Chen Y, Mao Y, Lu C, Hackmann G, Micek ST, Heard KM, Faulkner KM, «««< HEAD Kollef MH. A trial of a real-time alert for clinical deterioration in patients hospitalized on general medical wards. J Hosp Med. 2013 May;8(5):236-42. doi: 10.1002/jhm.2009. Epub 2013 Feb 25. PMID: 23440923. ======= Kollef MH. A trial of a real-time alert for clinical deterioration in patients hospitalized on general medical wards. J Hosp Med. 2013 May;8(5):236-42. doi: 10.1002/jhm.2009. Epub 2013 Feb 25. PMID: 23440923.

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  • Yi Mao, Wenlin Chen, Yixin Chen, Chenyang Lu, Marin Kollef, and Thomas Bailey. 2012. An integrated data mining approach to real-time clinical monitoring and deterioration warning. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD ‘12). Association for Computing Machinery, New York, NY, USA, 1140–1148. https://doi.org/10.1145/2339530.2339709

  • Gilles Audemard, Steve Bellart, Louenas Bounia, Frederic Koriche, Jean-Marie «««< HEAD Lagniez, Pierre Marquis, On Preferred Abductive Explanations for Decision Trees and Random Forests. in Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}, International Joint Conferences on Artificial Intelligence Organization, pp. 643-650, 2022.

  • Ryma Boumazouza, Fahima Cheikh-Alili, Bertrand Mazure, and Karim Tabia. 2021. ASTERYX: A model-Agnostic SaT-basEd appRoach for sYmbolic and score-based eXplanations. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (CIKM ‘21). https://doi.org/10.1145/3459637.3482321 ======= Lagniez, Pierre Marquis, On Preferred Abductive Explanations for Decision Trees and Random Forests. in Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}, International Joint Conferences on Artificial Intelligence Organization, pp. 643-650, 2022.

  • Ryma Boumazouza, Fahima Cheikh-Alili, Bertrand Mazure, and Karim Tabia. 2021. ASTERYX: A model-Agnostic SaT-basEd appRoach for sYmbolic and score-based eXplanations. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (CIKM ‘21). https://doi.org/10.1145/3459637.3482321

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