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A brief review of methods for determining time-activity patterns in California.
J Air Waste Manag Assoc
January 2025
Department of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, California, USA.
In this review paper, we provide a comprehensive overview of approaches for collecting time-activity pattern (TAP) data from individuals, a crucial component in understanding human behavior and its implications across various fields such as urban planning, environmental science, and, particularly, public health in relation to air pollution exposures.Furthermore, our paper introduces and critically evaluates several emerging methods for TAP data collection. These novel approaches, including but not limited to Google Location History, iPhone Significant Locations, and crowdsourced smartphone location data, offer unprecedented granularity in tracking human activities.
View Article and Find Full Text PDFA Systematic Approach to Prioritise Diagnostically Useful Findings for Inclusion in Electronic Health Records as Discrete Data to Improve Clinical Artificial Intelligence Tools and Genomic Research.
Clin Oncol (R Coll Radiol)
December 2024
Department of Pathology and Laboratory Medicine, Medical College of Wisconsin, 9200 W Wisconsin Ave, Milwaukee, WI 53226, USA; Department of Pathology, Yale School of Medicine, 20 York Street, Ste East Pavilion 2-631, New Haven, CT 06510, USA. Electronic address:
Aims: The recent widespread use of electronic health records (EHRs) has opened the possibility for innumerable artificial intelligence (AI) tools to aid in genomics, phenomics, and other research, as well as disease prevention, diagnosis, and therapy. Unfortunately, much of the data contained in EHRs are not optimally structured for even the most sophisticated AI approaches. There are very few published efforts investigating methods for recording discrete data in EHRs that would not slow current clinical workflows or ways to prioritise patient characteristics worth recording.
View Article and Find Full Text PDFParticipant Contributions to Person-Generated Health Data Research Using Mobile Devices: Scoping Review.
J Med Internet Res
January 2025
Biomedical Informatics & Data Science Section, The Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Background: Mobile devices offer an emerging opportunity for research participants to contribute person-generated health data (PGHD). There is little guidance, however, on how to best report findings from studies leveraging those data. Thus, there is a need to characterize current reporting practices so as to better understand the potential implications for producing reproducible results.
View Article and Find Full Text PDFMeasuring Talker Age Estimates Through Crowdsourced Listeners' Ratings: A Pilot Study for Voice Research.
J Speech Lang Hear Res
January 2025
Department of Otolaryngology-Head and Neck Surgery, New York University Grossman School of Medicine, NY.
Purpose: Most auditory-perceptual voice research utilizes the judgments of trained listeners rather than everyday listeners with no previous training in speech pathology. Online crowdsourcing of behavioral data from untrained participants is rapidly increasing in popularity but has yet to be a common procedure for auditory-perceptual studies of the voice. The objective of this pilot study was to assess the functionality of this model for judgments of voice by using an online experiment platform to replicate a lab-based, voice-specific age estimation study.
View Article and Find Full Text PDFPHARAOH: A collaborative crowdsourcing platform for phenotyping and regional analysis of histology.
Nat Commun
January 2025
Princess Margaret Cancer Centre, 101 College Street, Toronto, ON, Canada.
Deep learning has proven capable of automating key aspects of histopathologic analysis. However, its context-specific nature and continued reliance on large expert-annotated training datasets hinders the development of a critical mass of applications to garner widespread adoption in clinical/research workflows. Here, we present an online collaborative platform that streamlines tissue image annotation to promote the development and sharing of custom computer vision models for PHenotyping And Regional Analysis Of Histology (PHARAOH; https://www.
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