Adaptive data-driven selection of sequences of biological and cognitive markers in pre-clinical diagnosis of dementia.

Wyss, Patric; Ginsbourger, David; Shou, Haochang; Davatzikos, Christos; Klöppel, Stefan; Abdulkadir, Ahmed (2023). Adaptive data-driven selection of sequences of biological and cognitive markers in pre-clinical diagnosis of dementia. Scientific Reports, 13(1), p. 6406. Nature Publishing Group 10.1038/s41598-023-32867-z

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Effective clinical decision procedures must balance multiple competing objectives such as time-to-decision, acquisition costs, and accuracy. We describe and evaluate POSEIDON, a data-driven method for PrOspective SEquentIal DiagnOsis with Neutral zones to individualize clinical classifications. We evaluated the framework with an application in which the algorithm sequentially proposes to include cognitive, imaging, or molecular markers if a sufficiently more accurate prognosis of clinical decline to manifest Alzheimer's disease is expected. Over a wide range of cost parameter data-driven tuning lead to quantitatively lower total cost compared to ad hoc fixed sets of measurements. The classification accuracy based on all longitudinal data from participants that was acquired over 4.8 years on average was 0.89. The sequential algorithm selected 14 percent of available measurements and concluded after an average follow-up time of 0.74 years at the expense of 0.05 lower accuracy. Sequential classifiers were competitive from a multi-objective perspective since they could dominate fixed sets of measurements by making fewer errors using less resources. Nevertheless, the trade-off of competing objectives depends on inherently subjective prescribed cost parameters. Thus, despite the effectiveness of the method, the implementation into consequential clinical applications will remain controversial and evolve around the choice of cost parameters.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > Institute of Social and Preventive Medicine (ISPM)
08 Faculty of Science > Department of Mathematics and Statistics > Institute of Mathematical Statistics and Actuarial Science
04 Faculty of Medicine > University Psychiatric Services > University Hospital of Geriatric Psychiatry and Psychotherapy

UniBE Contributor:

Wyss, Patric, Ginsbourger, David, Klöppel, Stefan

Subjects:

600 Technology > 610 Medicine & health
300 Social sciences, sociology & anthropology > 360 Social problems & social services
500 Science > 510 Mathematics

ISSN:

2045-2322

Publisher:

Nature Publishing Group

Funders:

[4] Swiss National Science Foundation

Language:

English

Submitter:

Pubmed Import

Date Deposited:

20 Apr 2023 11:17

Last Modified:

27 Apr 2023 15:05

Publisher DOI:

10.1038/s41598-023-32867-z

PubMed ID:

37076487

BORIS DOI:

10.48350/181870

URI:

https://boris.unibe.ch/id/eprint/181870

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