Predictive health intelligence: Potential, limitations and sense making

Marco Roccetti; Marco Roccetti

doi:10.3934/mbe.2023460

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 6: 10459-10463. doi: 10.3934/mbe.2023460

Previous Article Next Article

Editorial Special Issues

Predictive health intelligence: Potential, limitations and sense making

Marco Roccetti ^,

Department of Computer Science and Engineering, University of Bologna, Bologna, Italy

Received: 03 April 2023 Revised: 03 April 2023 Accepted: 07 April 2023 Published: 07 April 2023

We discuss the new paradigm of predictive health intelligence, based on the use of modern deep learning algorithms and big biomedical data, along the various dimensions of: a) its potential, b) the limitations it encounters, and c) the sense it makes. We conclude by reasoning on the idea that viewing data as the unique source of sanitary knowledge, fully abstracting from human medical reasoning, may affect the scientific credibility of health predictions.
- predictive health intelligence,
- artificial intelligence,
- health predictions,
- machine and deep learning,
- data-fitting,
- data-interpreting
Citation: Marco Roccetti. Predictive health intelligence: Potential, limitations and sense making[J]. Mathematical Biosciences and Engineering, 2023, 20(6): 10459-10463. doi: 10.3934/mbe.2023460

Related Papers:

Abstract

We discuss the new paradigm of predictive health intelligence, based on the use of modern deep learning algorithms and big biomedical data, along the various dimensions of: a) its potential, b) the limitations it encounters, and c) the sense it makes. We conclude by reasoning on the idea that viewing data as the unique source of sanitary knowledge, fully abstracting from human medical reasoning, may affect the scientific credibility of health predictions.

References

[1]	P. L. Bokonda, M. Sidibe, N. Souissi, K. Ouazzani-Touhami, Machine learning model for predicting epidemics, Computers, 12 (2023), 54. https://doi.org/10.3390/computers12030054 doi: 10.3390/computers12030054
[2]	L. Casini, M. Roccetti, A cross-regional analysis of the COVID-19 spread during the 2020 Italian vacation period: Results from three computational models are compared, Sensors, 20 (2020), 7319. https://doi.org/10.3390/s20247319 doi: 10.3390/s20247319
[3]	R. Cappi, L. Casini, D. Tosi, M. Roccetti, Questioning the seasonality of SARS-COV-2: A Fourier spectral analysis, BMJ Open, 12 (2022), e061602. https://doi.org/10.1136/bmjopen-2022-061602 doi: 10.1136/bmjopen-2022-061602
[4]	M. Roccetti, Excess mortality and COVID-19 deaths in Italy: A peak comparison study Math. Biosci. Eng., 20 (2023), 7042–7055. https://doi.org/10.3934/mbe.2023304
[5]	S. Yang, F. Zhu, X. Ling, Q. Liu, P. Zhao, Intelligent health care: Applications of deep learning in computational medicine, Front. Genet., 12 (2021). https://doi.org/10.3389/fgene.2021.607471
[6]	L. Ma, F. Zhang, End-to-end predictive intelligence diagnosis in brain tumor using lightweight neural network. Appl. Soft Comput., 111 (2021), 107666. https://doi.org/10.1016/j.asoc.2021.107666
[7]	P. Ni, G. Li, P. C. H. Kung, V. Chang, StaResGRU-CNN with CMedLMs: A stacked residual GRU-CNN with pre-trained biomedical language models for predictive intelligence, Appl. Soft Comput., 113 (2021), 107975. https://doi.org/10.1016/j.asoc.2021.107975 doi: 10.1016/j.asoc.2021.107975
[8]	N. Dong, M. Zhai, L. Chang, C. Wu, A self-adaptive approach for white blood cell classification towards point-of-care testing, Appl. Soft Comput., 111 (2021), 107709. https://doi.org/10.1016/j.asoc.2021.107709 doi: 10.1016/j.asoc.2021.107709
[9]	K. Chong, K. Li, Z. Guo, K. Ja, E. Leung, S. Zhao, et al., Dining-out behavior as a proxy for the superspreading potential of SARS-CoV-2 infections: Modeling analysis, JMIR Public Health Surveill., 9 (2023), e44251. https://doi.org/10.2196/44251 doi: 10.2196/44251
[10]	S. P. Philips, Artificial intelligence and predictive algorithms in medicine, Can. Fam. Phys., 68 (2022), 570-572. https://doi.org/10.46747/cfp.6808570 doi: 10.46747/cfp.6808570
[11]	S. Mirri, G. Delnevo, M. Roccetti, Is a COVID-19 second wave possible in Emilia-Romagna (Italy)? Forecasting a future outbreak with particulate pollution and machine learning, Computation, 8 (2020), 74. https://doi.org/10.3390/computation8030074 doi: 10.3390/computation8030074
[12]	L. Shen, L. Margolies, J. H. Rothstein, E. Fluder, R. McBride, et al., Deep Learning to Improve Breast Cancer Detection on Screening Mammography, Sci. Rep., 9 (2019), 12495. https://doi.org/10.1038/s41598-019-48995-4 doi: 10.1038/s41598-019-48995-4
[13]	W. Lotter, A. R. Diab, B. Haslam, J. G. Kim, G. Grisot, E. Wu, et al., Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach, Nature Med., 27 (2021), 244–249. https://doi.org/10.1038/s41591-020-01174-9 doi: 10.1038/s41591-020-01174-9
[14]	B. Seligman, S. Tuljapurkar, D. Rehkopf, Machine learning approaches to the social determinants of health in the health and retirement study, SSM Popul. Health, 4 (2018), 95–99. https://doi.org/10.1016/j.ssmph.2017.11.008 doi: 10.1016/j.ssmph.2017.11.008
[15]	C. R. Clark, M. J. Ommerborn, K. Moran, K. Brooks, J. Haas, et al., Predicting self-rated health across the life course: Health equity insights from machine learning models, J. Gen. Intern. Med., 36 (2021), 1181–1188. https://doi.org/10.1007/s11606-020-06438-1 doi: 10.1007/s11606-020-06438-1
[16]	M. Roccetti, G. Delnevo, L. Casini, G. Cappiello, Is bigger always better? A controversial journey to the center of machine learning design, with uses and misuses of big data for predicting water meter failures, J. Big Data, 6 (2019). https://doi.org/10.1186/s40537-019-0235-y
[17]	N. Chomsky, I. Roberts, J. Watumull, The false promise of ChatGPT, The New York Times, (2023). Available online, March, 8, 2023.
[18]	J. Pearl, Radical empiricism and machine learning research, J. Causal Infer., 9 (2021), 78–82. https://doi.org/10.1515/jci-2021-0006 doi: 10.1515/jci-2021-0006
[19]	M. Roccetti, G. Delnevo, L. Casini, G. Cappiello, Modeling COVID-19 diffusion with intelligent computational techniques is not working, what are we doing wrong?, In: 4th International Conference on Human Interaction and Emerging Technologies: Future Applications, IHIET – AI (2021). https://doi.org/10.1007/978-3-030-74009-2_61
[20]	G. Marcus, Hoping for the best as AI evolves, Commun. ACM, 64 (2023), 6–7. https://doi.org/10.1145/3583078 doi: 10.1145/3583078
[21]	F. Corradini, R. Gorrieri, M. Roccetti, Performance preorder and competitive equivalence, Acta Inform., 34 (1997), 805–835. https://doi.org/10.1007/s002360050107 doi: 10.1007/s002360050107
[22]	E. S. Davis, G. Marcus, Computational limits don't fully explain human cognitive limitations, Behav. Brain Sci., 43 (2020), E7. https://doi.org/10.1017/S0140525X19001651 doi: 10.1017/S0140525X19001651

Reader Comments

Your name:*

Email:*
© 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)