Data Preprocessing Techniques for AI and Machine Learning Readiness: Scoping Review of Wearable Sensor Data in Cancer Care.

Autor:	Ortiz BL; Department of Pediatrics, Hematology and Oncology Division, Michigan Medicine, University of Michigan Health System, Ann Arbor, MI, United States., Gupta V; School of Applied Computational Sciences, Meharry Medical College, Nashville, TN, United States., Kumar R; Department of Pediatrics, Hematology and Oncology Division, Michigan Medicine, University of Michigan Health System, Ann Arbor, MI, United States., Jalin A; Department of Pediatrics, Hematology and Oncology Division, Michigan Medicine, University of Michigan Health System, Ann Arbor, MI, United States., Cao X; Department of Pediatrics, Hematology and Oncology Division, Michigan Medicine, University of Michigan Health System, Ann Arbor, MI, United States., Ziegenbein C; Department of Pediatrics, Hematology and Oncology Division, Michigan Medicine, University of Michigan Health System, Ann Arbor, MI, United States.; Autonomous Systems Research Department, Peraton Labs, Basking Ridge, NJ, United States., Singhal A; School of Applied Computational Sciences, Meharry Medical College, Nashville, TN, United States., Tewari M; Department of Biomedical Engineering, College of Engineering, University of Michigan, Ann Arbor, MI, United States.; Rogel Comprehensive Cancer Center, University of Michigan, Ann Arbor, MI, United States.; VA Ann Arbor Healthcare System, Ann Arbor, MI, United States.; Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States.; Department of Internal Medicine, University of Michigan, Ann Arbor, MI, United States., Choi SW; Department of Pediatrics, Hematology and Oncology Division, Michigan Medicine, University of Michigan Health System, Ann Arbor, MI, United States.; Rogel Comprehensive Cancer Center, University of Michigan, Ann Arbor, MI, United States.
Jazyk:	angličtina
Zdroj:	JMIR mHealth and uHealth [JMIR Mhealth Uhealth] 2024 Sep 27; Vol. 12, pp. e59587. Date of Electronic Publication: 2024 Sep 27.
DOI:	10.2196/59587
Abstrakt:	Background: Wearable sensors are increasingly being explored in health care, including in cancer care, for their potential in continuously monitoring patients. Despite their growing adoption, significant challenges remain in the quality and consistency of data collected from wearable sensors. Moreover, preprocessing pipelines to clean, transform, normalize, and standardize raw data have not yet been fully optimized. Objective: This study aims to conduct a scoping review of preprocessing techniques used on raw wearable sensor data in cancer care, specifically focusing on methods implemented to ensure their readiness for artificial intelligence and machine learning (AI/ML) applications. We sought to understand the current landscape of approaches for handling issues, such as noise, missing values, normalization or standardization, and transformation, as well as techniques for extracting meaningful features from raw sensor outputs and converting them into usable formats for subsequent AI/ML analysis. Methods: We systematically searched IEEE Xplore, PubMed, Embase, and Scopus to identify potentially relevant studies for this review. The eligibility criteria included (1) mobile health and wearable sensor studies in cancer, (2) written and published in English, (3) published between January 2018 and December 2023, (4) full text available rather than abstracts, and (5) original studies published in peer-reviewed journals or conferences. Results: The initial search yielded 2147 articles, of which 20 (0.93%) met the inclusion criteria. Three major categories of preprocessing techniques were identified: data transformation (used in 12/20, 60% of selected studies), data normalization and standardization (used in 8/20, 40% of the selected studies), and data cleaning (used in 8/20, 40% of the selected studies). Transformation methods aimed to convert raw data into more informative formats for analysis, such as by segmenting sensor streams or extracting statistical features. Normalization and standardization techniques usually normalize the range of features to improve comparability and model convergence. Cleaning methods focused on enhancing data reliability by handling artifacts like missing values, outliers, and inconsistencies. Conclusions: While wearable sensors are gaining traction in cancer care, realizing their full potential hinges on the ability to reliably translate raw outputs into high-quality data suitable for AI/ML applications. This review found that researchers are using various preprocessing techniques to address this challenge, but there remains a lack of standardized best practices. Our findings suggest a pressing need to develop and adopt uniform data quality and preprocessing workflows of wearable sensor data that can support the breadth of cancer research and varied patient populations. Given the diverse preprocessing techniques identified in the literature, there is an urgency for a framework that can guide researchers and clinicians in preparing wearable sensor data for AI/ML applications. For the scoping review as well as our research, we propose a general framework for preprocessing wearable sensor data, designed to be adaptable across different disease settings, moving beyond cancer care. (©Bengie L Ortiz, Vibhuti Gupta, Rajnish Kumar, Aditya Jalin, Xiao Cao, Charles Ziegenbein, Ashutosh Singhal, Muneesh Tewari, Sung Won Choi. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 27.09.2024.)
Databáze:	MEDLINE
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