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Obsolete personal information update system: towards the prevention of falls in the elderly

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Abstract

Falls stand for a prevalent problem among the elderly and a significant public health concern. In recent years, a growing number of apps have been developed to assist in terms of the delivery of more effective and efficient falls prevention programs. All of these apps rely on a massive elderly personal database gathered from hospitals, mutual health groups, and other organizations that help the elderly. Information on an older adult is constantly changing, and it may become obsolete at any time, contradicting what we currently know about the same person. As a result, it needs to be checked and updated on a regular basis in order to maintain database consistency and hence provide a better service. This research work describes an Obsolete Personal Information Update System (OIUS) developed as part of the elderly-fall prevention project. Our OIUS intends to control and update the information gathered about each older adult in real-time, to provide consistent information on demand, and to provide tailored interventions to carers and fall-risk patients. The method discussed here is based upon a polynomial-time algorithm built on top of a causal Bayesian network that models the older adults data. The outcome is presented as an AND-OR recommendation Tree with a certain level of accuracy. On an aged personal information base, we perform an empirical study for such a model. Experiments corroborate our OIUS’s viability and effectiveness.

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Notes

  1. https://www.cdc.gov

  2. https://www.who.int/news-room/fact-sheets/detail/ageing-and-health

  3. http://www.elsat2020.org/en

  4. https://agrum.gitlab.io/

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Acknowledgements

The present work is part of the ELSAT2020Footnote 3 project, which is co-financed by the European Union with the European Regional Development Fund, the French state and the Hauts de France Region Council. It is also supported by the PEJC project (20PEJC 08-03) fund from the Tunisian ministry of higher education and scientific research. A tiny part of the data used for the simulation has been invested in previous work and may be found in https://doi.org/10.1016/j.procs.2021.08.120 and https://doi.org/10.1016/j.procs.2021.08.020. The experts who provided the estimates for the used causal Bayesian model and the University Hospital physicians who validated our scenarios are sincerely thanked for their active participation.

Author information

Authors and Affiliations

  1. ISITCom, University of Sousse, Sousse, 4011, Tunisia

    Salma Chaieb

  2. CES Lab, ENIS, University of Sfax, Sfax, 3038, Tunisia

    Salma Chaieb, Ali Ben Mrad & Brahim Hnich

  3. ISAAS, University of Sfax, Sfax, 1013, Tunisia

    Ali Ben Mrad

  4. FSM, University of Monastir, Monastir, 5000, Tunisia

    Brahim Hnich

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  1. Salma Chaieb
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Correspondence to Salma Chaieb.

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An early version of this paper appeared on the quant-ph arXiv as arxiv:2101.10132

Appendices

Appendix A: Variables description

Table 6 CBN variables for elderly fall
Full size table

Appendix B: User interface

We designed a first test application in the form of a desktop interface, as demonstrated in Fig. 12, to allow users (e.g., physicians) to manipulate our system and show the effects of the user’s manipulation. Our application was implemented under the Windows environment, with the pyAgrum libraryFootnote 4. As displayed in Fig. 12, the designed interface includes the different variables of the RB. Thus, when entering observations, a drop-down list containing all the possible values of each variable is displayed to the user. The latter selects the appropriate one.

Fig. 12
figure 12

User interface 1

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We simulate scenario 3 given in Table 4, which is related to a particular elderly patient. Once the user enters the new observation (Numberoffalls = f3 :≥ 5) in the treated scenario), the other old observations on the same older adult will be filled in automatically. Once the observations are validated, the new observation will be recorded in the database row reserved for the concerned patient. Then, our system checks if there is a contradiction between onew = f3 and the other observations. In case of contradiction on one or more observations, the system displays an alert message (see Fig. 13) highlighting the variables likely to be a direct or indirect cause of this contradiction.

Fig. 13
figure 13

User interface 2

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The physicians reacted positively to the first trial version of our system, and some of them stated that working with it is advantageous to a great extend. Furthermore, our system had no detrimental impact on users; none of the doctors’ good decisions were influenced, even when the algorithm supplied an inaccurate answer. It has led us to assert that our system could be valuable in terms of supporting physicians and other users at the level of detecting abnormal behaviors, exploring and identifying all possible reasons, and deciding how to handle these situations as early as possible to avoid any potential risk.

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Chaieb, S., Mrad, A.B. & Hnich, B. Obsolete personal information update system: towards the prevention of falls in the elderly. Appl Intell 53, 18061–18084 (2023). https://doi.org/10.1007/s10489-022-04289-3

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