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Longitudinal Time-to-Event Graph Mining Pipeline for Musculoskeletal Injury Forecasting.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Longitudinal Time-to-Event Graph Mining Pipeline for Musculoskeletal Injury Forecasting./
作者:	Peterson, Kyle Donald.
面頁冊數:	1 online resource (158 pages)
附註:	Source: Dissertations Abstracts International, Volume: 83-04, Section: B.
Contained By:	Dissertations Abstracts International83-04B.
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28651484click for full text (PQDT)
ISBN:	9798544296461

Longitudinal Time-to-Event Graph Mining Pipeline for Musculoskeletal Injury Forecasting.
Peterson, Kyle Donald.

Longitudinal Time-to-Event Graph Mining Pipeline for Musculoskeletal Injury Forecasting. - 1 online resource (158 pages)

Source: Dissertations Abstracts International, Volume: 83-04, Section: B.

Thesis (Ph.D.)--The University of Iowa, 2021.

Includes bibliographical references

Injury is pervasive to sport and is an important yet challenging forecasting problem with high practical value. Athlete monitoring technologies are deployed for health and well-being surveillance, but incidence rates have not decreased in proportion to their widespread use. Existing injury prediction algorithms have deployed off-the-shelf machine learning solutions, leading to mis-formulated approaches that often fail to appreciate the complexities of an athlete's ever-evolving physiology. This has resulted in a gap between machine learning capabilities and their actual effectiveness in mitigating athletic injuries. The focus of this thesis is to contribute a practical and accurate forecasting framework to mitigate sports-related, non-contact musculoskeletal injuries. Chapter 1 positions my research perspective under the theory of complexity. I formulate injury forecasting as a longitudinal time-to-event problem and specify three specific aims addressed in Chapters 2-4: 1) select an intra-individual dynamic graph construction approach, 2) develop a longitudinal time-to-event forecasting model for dynamic graphs, and 3) design an architecture to identify temporal edges contributing to an athlete's injury forecast. This thesis ends by threading Chapters 2-4 into an end-to-end graph mining pipeline for sport epidemiology. An athlete monitoring dataset from University of Iowa Department of Intercollegiate Athletics serves as an applied example. Athlete-specific dynamic graphs are constructed from longitudinal force plate data. The longitudinal time-to-event model was trained and tested to forecast musculoskeletal injury from the dynamic graph dataset. Athlete-specific injury forecasts are then explained where novel inferences into the temporal interactions leading to injury are unveiled.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798544296461Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Longitudinal time-to-event graph mining pipelineIndex Terms--Genre/Form:

542853
Electronic books.

Longitudinal Time-to-Event Graph Mining Pipeline for Musculoskeletal Injury Forecasting.
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