Exploring polypharmacy with artificial intelligence: data analysis protocol
Abstract Background Polypharmacy is common among older adults and it represents a public health concern, due to the negative health impacts potentially associated with the use of several medications. However, the large number of medication combinations and sequences of use makes it complicated for t...
| Main Authors: | , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2021-07-01
|
| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12911-021-01583-x |
| _version_ | 1818643309738852352 |
|---|---|
| author | Caroline Sirois Richard Khoury Audrey Durand Pierre-Luc Deziel Olga Bukhtiyarova Yohann Chiu Denis Talbot Alexandre Bureau Philippe Després Christian Gagné François Laviolette Anne-Marie Savard Jacques Corbeil Thierry Badard Sonia Jean Marc Simard |
| author_facet | Caroline Sirois Richard Khoury Audrey Durand Pierre-Luc Deziel Olga Bukhtiyarova Yohann Chiu Denis Talbot Alexandre Bureau Philippe Després Christian Gagné François Laviolette Anne-Marie Savard Jacques Corbeil Thierry Badard Sonia Jean Marc Simard |
| author_sort | Caroline Sirois |
| collection | DOAJ |
| description | Abstract Background Polypharmacy is common among older adults and it represents a public health concern, due to the negative health impacts potentially associated with the use of several medications. However, the large number of medication combinations and sequences of use makes it complicated for traditional statistical methods to predict which therapy is genuinely associated with health outcomes. The project aims to use artificial intelligence (AI) to determine the quality of polypharmacy among older adults with chronic diseases in the province of Québec, Canada. Methods We will use data from the Quebec Integrated Chronic Disease Surveillance System (QICDSS). QICDSS contains information about prescribed medications in older adults in Quebec collected over 20 years. It also includes diagnostic codes and procedures, and sociodemographic data linked through a unique identification number for each individual. Our research will be structured around three interconnected research axes: AI, Health, and Law&Ethics. The AI research axis will develop algorithms for finding frequent patterns of medication use that correlate with health events, considering data locality and temporality (explainable AI or XAI). The Health research axis will translate these patterns into polypharmacy indicators relevant to public health surveillance and clinicians. The Law&Ethics axis will assess the social acceptability of the algorithms developed using AI tools and the indicators developed by the Heath axis and will ensure that the developed indicators neither discriminate against any population group nor increase the disparities already present in the use of medications. Discussion The multi-disciplinary research team consists of specialists in AI, health data, statistics, pharmacy, public health, law, and ethics, which will allow investigation of polypharmacy from different points of view and will contribute to a deeper understanding of the clinical, social, and ethical issues surrounding polypharmacy and its surveillance, as well as the use of AI for health record data. The project results will be disseminated to the scientific community, healthcare professionals, and public health decision-makers in peer-reviewed publications, scientific meetings, and reports. The diffusion of the results will ensure the confidentiality of individual data. |
| first_indexed | 2024-12-16T23:56:55Z |
| format | Article |
| id | doaj.art-87eca5839d5449e785da59045dfec2c4 |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-12-16T23:56:55Z |
| publishDate | 2021-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-87eca5839d5449e785da59045dfec2c42022-12-21T22:11:11ZengBMCBMC Medical Informatics and Decision Making1472-69472021-07-012111810.1186/s12911-021-01583-xExploring polypharmacy with artificial intelligence: data analysis protocolCaroline Sirois0Richard Khoury1Audrey Durand2Pierre-Luc Deziel3Olga Bukhtiyarova4Yohann Chiu5Denis Talbot6Alexandre Bureau7Philippe Després8Christian Gagné9François Laviolette10Anne-Marie Savard11Jacques Corbeil12Thierry Badard13Sonia Jean14Marc Simard15Faculty of Pharmacy, Université LavalFaculty of Science and Engineering, Department of Computer Science and Software Engineering, Université LavalFaculty of Science and Engineering, Department of Computer Science and Software Engineering, Université LavalFaculty of Law, Université LavalFaculty of Pharmacy, Université LavalFaculty of Pharmacy, Université LavalFaculty of Medicine, Department of Social and Preventive Medicine, Université LavalFaculty of Medicine, Department of Social and Preventive Medicine, Université LavalFaculty of Science and Engineering, Department of Physics, Physical Engineering and Optics, Université LavalFaculty of Science and Engineering, Department of Electrical and Computer Engineering, Université LavalFaculty of Science and Engineering, Department of Electrical and Computer Engineering, Université LavalFaculty of Law, Université LavalFaculty of Medicine, Department of Molecular Medicine, Université LavalFaculty of Forestry, Geography and Geomatics, Department of Geomatic Science, Université LavalQuebec National Institute of Public HealthQuebec National Institute of Public HealthAbstract Background Polypharmacy is common among older adults and it represents a public health concern, due to the negative health impacts potentially associated with the use of several medications. However, the large number of medication combinations and sequences of use makes it complicated for traditional statistical methods to predict which therapy is genuinely associated with health outcomes. The project aims to use artificial intelligence (AI) to determine the quality of polypharmacy among older adults with chronic diseases in the province of Québec, Canada. Methods We will use data from the Quebec Integrated Chronic Disease Surveillance System (QICDSS). QICDSS contains information about prescribed medications in older adults in Quebec collected over 20 years. It also includes diagnostic codes and procedures, and sociodemographic data linked through a unique identification number for each individual. Our research will be structured around three interconnected research axes: AI, Health, and Law&Ethics. The AI research axis will develop algorithms for finding frequent patterns of medication use that correlate with health events, considering data locality and temporality (explainable AI or XAI). The Health research axis will translate these patterns into polypharmacy indicators relevant to public health surveillance and clinicians. The Law&Ethics axis will assess the social acceptability of the algorithms developed using AI tools and the indicators developed by the Heath axis and will ensure that the developed indicators neither discriminate against any population group nor increase the disparities already present in the use of medications. Discussion The multi-disciplinary research team consists of specialists in AI, health data, statistics, pharmacy, public health, law, and ethics, which will allow investigation of polypharmacy from different points of view and will contribute to a deeper understanding of the clinical, social, and ethical issues surrounding polypharmacy and its surveillance, as well as the use of AI for health record data. The project results will be disseminated to the scientific community, healthcare professionals, and public health decision-makers in peer-reviewed publications, scientific meetings, and reports. The diffusion of the results will ensure the confidentiality of individual data.https://doi.org/10.1186/s12911-021-01583-xPolypharmacyIndicatorsMedicationsArtificial intelligenceEthicsSocial acceptability |
| spellingShingle | Caroline Sirois Richard Khoury Audrey Durand Pierre-Luc Deziel Olga Bukhtiyarova Yohann Chiu Denis Talbot Alexandre Bureau Philippe Després Christian Gagné François Laviolette Anne-Marie Savard Jacques Corbeil Thierry Badard Sonia Jean Marc Simard Exploring polypharmacy with artificial intelligence: data analysis protocol BMC Medical Informatics and Decision Making Polypharmacy Indicators Medications Artificial intelligence Ethics Social acceptability |
| title | Exploring polypharmacy with artificial intelligence: data analysis protocol |
| title_full | Exploring polypharmacy with artificial intelligence: data analysis protocol |
| title_fullStr | Exploring polypharmacy with artificial intelligence: data analysis protocol |
| title_full_unstemmed | Exploring polypharmacy with artificial intelligence: data analysis protocol |
| title_short | Exploring polypharmacy with artificial intelligence: data analysis protocol |
| title_sort | exploring polypharmacy with artificial intelligence data analysis protocol |
| topic | Polypharmacy Indicators Medications Artificial intelligence Ethics Social acceptability |
| url | https://doi.org/10.1186/s12911-021-01583-x |
| work_keys_str_mv | AT carolinesirois exploringpolypharmacywithartificialintelligencedataanalysisprotocol AT richardkhoury exploringpolypharmacywithartificialintelligencedataanalysisprotocol AT audreydurand exploringpolypharmacywithartificialintelligencedataanalysisprotocol AT pierrelucdeziel exploringpolypharmacywithartificialintelligencedataanalysisprotocol AT olgabukhtiyarova exploringpolypharmacywithartificialintelligencedataanalysisprotocol AT yohannchiu exploringpolypharmacywithartificialintelligencedataanalysisprotocol AT denistalbot exploringpolypharmacywithartificialintelligencedataanalysisprotocol AT alexandrebureau exploringpolypharmacywithartificialintelligencedataanalysisprotocol AT philippedespres exploringpolypharmacywithartificialintelligencedataanalysisprotocol AT christiangagne exploringpolypharmacywithartificialintelligencedataanalysisprotocol AT francoislaviolette exploringpolypharmacywithartificialintelligencedataanalysisprotocol AT annemariesavard exploringpolypharmacywithartificialintelligencedataanalysisprotocol AT jacquescorbeil exploringpolypharmacywithartificialintelligencedataanalysisprotocol AT thierrybadard exploringpolypharmacywithartificialintelligencedataanalysisprotocol AT soniajean exploringpolypharmacywithartificialintelligencedataanalysisprotocol AT marcsimard exploringpolypharmacywithartificialintelligencedataanalysisprotocol |