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Full text release has been delayed at the author's request until August 16, 2026

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Integration of Digital Health Resources for Deep Phenotypic Remote Monitoring of Patient Health

Powell, Joseph R
http://orcid.org/0000-0002-0798-2196
http://rave.ohiolink.edu/etdc/view?acc_num=case1721399628773685

Abstract Details

2024, Doctor of Philosophy, Case Western Reserve University, Systems Biology and Bioinformatics.
The rapid advancement of personal wearable devices has allowed for the inception of novel applications of deep phenotyping for characterization of disease. The need to advance deep phenotyping and analysis methods for personalized wearable devices is crucial to the advancement of personalized remote patient monitoring. We developed an end-to-end digital health infrastructure designed for fast, secure, and effective patient recruitment, data collection, and analysis reporting. We analyzed the efficacy of patient recruitment through our end-to-end patient interface and found that recruitment methods from traditional means such as through clinical sources and university sources resulted in more consents ([0.015, 0.030]; p << 0.001) and more active patients initially (2 = 23.65; p < 0.005). Additionally, we noted that online recruitment through Facebook advertising and Google advertising produced a more ethnically diverse population compared to regional clinical recruitment (2 = 231.47; p < 0.001). We investigated the use of the previously reported NightSignal algorithm, originally developed for SARS-CoV-2 detection, on the detection of abnormal resting heart rate observations for cardiothoracic surgical patients collected through our infrastructure. We found The NightSignal algorithm had a sensitivity of 81%, a specificity of 75%, a negative predictive value of 97%, and a positive predictive value of 28% for the detection of postoperative events. When compared to patients who did not experience a postoperative event, patients who did experience a postoperative event had a significantly higher proportion of red alerts issued by the NightSignal algorithm during the first 30 days after surgery (0.325 vs. 0.063; p<0.05)]. Finally, we then investigated the potential for latent subgroup identification using physiological parameters generated from personal wearable devices. We found latent subgroups at 30-days, 60-days, and 90-days post-operatively. Each latent group was well separated by silhouette score, and the mean time series of each of these latent subgroups was statistically distinct from other groups. This work highlights the importance of utilizing the deep phenotypic resources available to researchers and contributes to the growing body of knowledge dedicated to personalized medicine and improved patient outcomes for minimal to severe disease.
Mark Cameron (Committee Chair)
Jing Li (Committee Member)
Wai Hong Wilson Tang (Committee Member)
Xiao Li (Advisor)
123 p.
Bioinformatics; Biomedical Research
Informatics, Machine Learning, Clinical, Translational Science, Time Series, Bioinformatics, Biomedical

Recommended Citations

Citations

  • Powell, J. R. (2024). Integration of Digital Health Resources for Deep Phenotypic Remote Monitoring of Patient Health [Doctoral dissertation, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1721399628773685

    APA Style (7th edition)

  • Powell, Joseph. Integration of Digital Health Resources for Deep Phenotypic Remote Monitoring of Patient Health. 2024. Case Western Reserve University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1721399628773685.

    MLA Style (8th edition)

  • Powell, Joseph. "Integration of Digital Health Resources for Deep Phenotypic Remote Monitoring of Patient Health." Doctoral dissertation, Case Western Reserve University, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=case1721399628773685

    Chicago Manual of Style (17th edition)

case1721399628773685
© 2024, all rights reserved.
This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.