extended-abstract

L’intelligence hybride pour prédire l’évolution des maladies chroniques: Hybrid intelligence to predict chronic disease progression

Authors:

Chadia Ed-driouch,

Pierre-Antoine Gourraud,

Cédric DumasAuthors Info & Claims

IHM '22 Adjunct: Adjunct Proceedings of the 33rd Conference on l'Interaction Humain-Machine

Article No.: 1, Pages 1 - 5

https://doi.org/10.1145/3502178.3529109

Published: 01 November 2022 Publication History

Abstract

In personalized medicine, care individualization is a key challenge to provide a more adapted and personalized clinical decision for each patient. The access to large amounts of medical data with the available computational power as well as the evolution of Artificial Intelligence (AI) algorithms allow to overcome this challenge. However, the application of AI algorithms must enable efficient and accurate communication with physicians. The complementarity between human and artificial intelligence holds the most promise for a safe and innovative medical future. We propose a new personalized medicine approach to improve the results of machine learning algorithms and overcome some barriers to the adoption of AI in medical practice. The proposed approach is based on human-machine interaction to create predictive models of chronic disease progression, especially for multiple sclerosis (MS).

References

[1]

Bassem I. Yamout and Raed Alroughani. 2018. Multiple Sclerosis. Seminars in Neurology 38, 2 (2018), 212–225. https://doi.org/10.1055/s-0038-1649502

[2]

Clare Walton, Rachel King, Lindsay Rechtman, Wendy Kaye, Emmanuelle Leray, Ruth Ann Marrie, Neil Robertson, Nicholas La Rocca, Bernard Uitdehaag, Ingrid van der Mei, Mitchell Wallin, Anne Helme, Ceri Angood Napier, Nick Rijke, and Peer Baneke. 2020. Rising prevalence of multiple sclerosis worldwide: Insights from the Atlas of MS, third edition. Multiple Sclerosis Journal 26, 14 (Dec. 2020), 1816–1821. https://doi.org/10.1177/1352458520970841 Publisher: SAGE Publications Ltd STM.

[3]

Pierre-Antoine Gourraud, Roland Henry, Bruce AC Cree, Jason C Crane, Antoine Lizee, Marram P Olson, Adam V. Santaniello, Esha Datta, Alyssa H. Zhu, Carolyn J. Bevan, Jeffrey M. Gelfand, Jennifer A. Graves, Douglas E. Goodin, Ari Green, H.-Christian von Büdingen, Emmanuelle Waubant, Scott S. Zamvil, Elizabeth Crabtree-Hartman, Sarah Nelson, Sergio E. Baranzini, and Stephen L. Hauser. 2014. Precision medicine in chronic disease management: the MS BioScreen. Annals of neurology 76, 5 (Nov. 2014), 633–642. https://doi.org/10.1002/ana.24282

[4]

Vittorio Martinelli, M Rodegher, Lucia Moiola, and Giacomo Comi. 2004. Late onset multiple sclerosis: Clinical characteristics, prognostic factors and differential diagnosis. Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology 25 Suppl 4 (Dec. 2004), S350–5. https://doi.org/10.1007/s10072-004-0339-8

[5]

Tatjana Reynders, Miguel D’haeseleer, Jacques De Keyser, Guy Nagels, and Marie B. D’hooghe. 2017. Definition, prevalence and predictive factors of benign multiple sclerosis. eNeurologicalSci 7 (June 2017), 37–43. https://doi.org/10.1016/j.ensci.2017.05.002

[6]

Caspar E. P. van Munster and Bernard M. J. Uitdehaag. 2017. Outcome Measures in Clinical Trials for Multiple Sclerosis. CNS Drugs 31, 3 (2017), 217–236. https://doi.org/10.1007/s40263-017-0412-5

[7]

Pavel Hamet and Johanne Tremblay. 2017. Artificial intelligence in medicine. Metabolism: Clinical and Experimental 69S (April 2017), S36–S40. https://doi.org/10.1016/j.metabol.2017.01.011

[8]

Chris Giordano, Meghan Brennan, Basma Mohamed, Parisa Rashidi, François Modave, and Patrick Tighe. 2021. Accessing Artificial Intelligence for Clinical Decision-Making. Frontiers in Digital Health 3 (June 2021), 645232. https://doi.org/10.3389/fdgth.2021.645232

[9]

I. Glenn Cohen, Ruben Amarasingham, Anand Shah, Bin Xie, and Bernard Lo. 2014. The legal and ethical concerns that arise from using complex predictive analytics in health care. Health Affairs (Project Hope) 33, 7 (July 2014), 1139–1147. https://doi.org/10.1377/hlthaff.2014.0048

[10]

James Shaw, Frank Rudzicz, Trevor Jamieson, and Avi Goldfarb. 2019. Artificial Intelligence and the Implementation Challenge. Journal of Medical Internet Research 21, 7 (July 2019), e13659. https://doi.org/10.2196/13659

[11]

Jonathan Waring, Charlotta Lindvall, and Renato Umeton. 2020. Automated machine learning: Review of the state-of-the-art and opportunities for healthcare. Artificial Intelligence in Medicine 104 (April 2020), 101822. https://doi.org/10.1016/j.artmed.2020.101822

[12]

Ibrahim Habli, Tom Lawton, and Zoe Porter. 2020. Artificial intelligence in health care: accountability and safety. Bulletin of the World Health Organization 98, 4 (April 2020), 251–256. https://doi.org/10.2471/BLT.19.237487

[13]

Ariadne A. Nichol, Jason N. Batten, Meghan C. Halley, Julia K. Axelrod, Pamela L. Sankar, and Mildred K. Cho. 2021. A Typology of Existing Machine Learning-Based Predictive Analytic Tools Focused on Reducing Costs and Improving Quality in Health Care: Systematic Search and Content Analysis. Journal of Medical Internet Research 23, 6 (June 2021), e26391. https://doi.org/10.2196/26391

[14]

Yifan Xiang, Lanqin Zhao, Zhenzhen Liu, Xiaohang Wu, Jingjing Chen, Erping Long, Duoru Lin, Yi Zhu, Chuan Chen, Zhuoling Lin, and Haotian Lin. 2020. Implementation of artificial intelligence in medicine: Status analysis and development suggestions. Artificial Intelligence in Medicine 102 (Jan. 2020), 101780. https://doi.org/10.1016/j.artmed.2019.101780

Digital Library

[15]

Sahil Sandhu, Anthony L. Lin, Nathan Brajer, Jessica Sperling, William Ratliff, Armando D. Bedoya, Suresh Balu, Cara O’Brien, and Mark P. Sendak. 2020. Integrating a Machine Learning System Into Clinical Workflows: Qualitative Study. Journal of Medical Internet Research 22, 11 (Nov. 2020), e22421. https://doi.org/10.2196/22421

[16]

Cécile Petitgand, Aude Motulsky, Jean-Louis Denis, and Catherine Régis. 2020. Investigating the Barriers to Physician Adoption of an Artificial Intelligence- Based Decision Support System in Emergency Care: An Interpretative Qualitative Study. Studies in Health Technology and Informatics 270 (June 2020), 1001–1005. https://doi.org/10.3233/SHTI200312

[17]

Lawrence A. Lynn. 2019. Artificial intelligence systems for complex decision-making in acute care medicine: a review. Patient Safety in Surgery 13 (2019), 6. https://doi.org/10.1186/s13037-019-0188-2

[18]

E. Leray, T. Moreau, A. Fromont, and G. Edan. [n.d.]. Epidemiology of multiple sclerosis. 172, 1([n. d.]), 3–13. https://doi.org/10.1016/j.neurol.2015.10.006

[19]

Tomas Kalincik. [n.d.]. Multiple Sclerosis Relapses: Epidemiology, Outcomes and Management. A Systematic Review. 44, 4([n. d.]), 199–214. https://doi.org/10.1159/000382130 Publisher: Karger Publishers.

[20]

Nicola De Stefano, Maria Stromillo, Antonio Giorgio, Marco Battaglini, Maria Bartolozzi, Maria Amato, and Maria Pia Sormani. [n.d.]. Long-term assessment of No Evidence of Disease Activity in relapsing-remitting MS. 85 ([n. d.]). https://doi.org/10.1212/WNL.0000000000002105

[21]

Abhimanyu S. Ahuja. 2019. The impact of artificial intelligence in medicine on the future role of the physician. PeerJ 7 (Oct. 2019). https://doi.org/10.7717/peerj.7702

[22]

RH Gross, SH Sillau, AE Miller, C Farrell, and SC Krieger. 2019. The Multiple Sclerosis Severity Score: fluctuations and prognostic ability in a longitudinal cohort of patients with MS. Multiple Sclerosis Journal - Experimental, Translational and Clinical 5, 1 (March 2019). https://doi.org/10.1177/2055217319837254

[23]

Loren A. Rolak. 2003. Multiple Sclerosis: It’s Not The Disease You Thought It Was. Clinical Medicine and Research 1, 1 (Jan. 2003), 57–60. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1069023/

Cited By

Ed-Driouch CMars FGourraud PDumas C(2022)Addressing the Challenges and Barriers to the Integration of Machine Learning into Clinical Practice: An Innovative Method to Hybrid Human–Machine IntelligenceSensors10.3390/s2221831322:21(8313)Online publication date: 29-Oct-2022
https://doi.org/10.3390/s22218313

Index Terms

L’intelligence hybride pour prédire l’évolution des maladies chroniques: Hybrid intelligence to predict chronic disease progression
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI theory, concepts and models
    2. Interaction techniques
      1. Pointing
  2. Visualization
    1. Visualization techniques

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cover image ACM Conferences

IHM '22 Adjunct: Adjunct Proceedings of the 33rd Conference on l'Interaction Humain-Machine

April 2022

32 pages

ISBN:9781450391986

DOI:10.1145/3502178

Copyright © 2022 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 November 2022

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Extended-abstract
Research
Refereed limited

Funding Sources

Région Pays de la Loire
European Union?s Horizon 2020
National Research Agency

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IHM '22

Sponsor:

SIGCHI

IHM '22: 33e conférence internationale francophone sur l'Interaction Humain-Machine

April 5 - 8, 2022

Namur, Belgium

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Overall Acceptance Rate 103 of 199 submissions, 52%

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Cited By

Ed-Driouch CMars FGourraud PDumas C(2022)Addressing the Challenges and Barriers to the Integration of Machine Learning into Clinical Practice: An Innovative Method to Hybrid Human–Machine IntelligenceSensors10.3390/s2221831322:21(8313)Online publication date: 29-Oct-2022
https://doi.org/10.3390/s22218313

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