General feature selection technique supporting sex-debiasing in chronic illness algorithms validated using wearable device data
Abstract In tasks involving human health condition data, feature selection is heavily affected by data types, the complexity of the condition manifestation, and the variability in physiological presentation. One type of variability often overlooked or oversimplified is the effect of biological sex....
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | npj Women's Health |
| Online Access: | https://doi.org/10.1038/s44294-024-00041-z |
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| author | Jamison H. Burks Lauryn Keeler Bruce Patrick Kasl Severine Soltani Varun Viswanath Wendy Hartogensis Stephan Dilchert Frederick M. Hecht Subhasis Dasgupta Ilkay Altintas Amarnath Gupta Ashley E. Mason Benjamin L. Smarr |
| author_facet | Jamison H. Burks Lauryn Keeler Bruce Patrick Kasl Severine Soltani Varun Viswanath Wendy Hartogensis Stephan Dilchert Frederick M. Hecht Subhasis Dasgupta Ilkay Altintas Amarnath Gupta Ashley E. Mason Benjamin L. Smarr |
| author_sort | Jamison H. Burks |
| collection | DOAJ |
| description | Abstract In tasks involving human health condition data, feature selection is heavily affected by data types, the complexity of the condition manifestation, and the variability in physiological presentation. One type of variability often overlooked or oversimplified is the effect of biological sex. As females have been chronically underrepresented in clinical research, we know less about how conditions manifest in females. Innovations in wearable technology have enabled individuals to generate high temporal resolution data for extended periods of time. With millions of days of data now available, additional feature selection pipelines should be developed to systematically identify sex-dependent variability in data, along with the effects of how many per-person data are included in analysis. Here we present a set of statistical approaches as a technique for identifying sex-dependent physiological and behavioral manifestations of complex diseases starting from longitudinal data, which are evaluated on diabetes, hypertension, and their comorbidity. |
| format | Article |
| id | doaj-art-6a7b0d1ab11c40dca1dceca18e560650 |
| institution | Kabale University |
| issn | 2948-1716 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Women's Health |
| spelling | doaj-art-6a7b0d1ab11c40dca1dceca18e5606502024-12-22T12:56:33ZengNature Portfolionpj Women's Health2948-17162024-11-012111110.1038/s44294-024-00041-zGeneral feature selection technique supporting sex-debiasing in chronic illness algorithms validated using wearable device dataJamison H. Burks0Lauryn Keeler Bruce1Patrick Kasl2Severine Soltani3Varun Viswanath4Wendy Hartogensis5Stephan Dilchert6Frederick M. Hecht7Subhasis Dasgupta8Ilkay Altintas9Amarnath Gupta10Ashley E. Mason11Benjamin L. Smarr12Shu Chien-Gene Lay Department of Bioengineering, University of California San DiegoUC San Diego Health Department of Biomedical Informatics, University of California San DiegoShu Chien-Gene Lay Department of Bioengineering, University of California San DiegoBioinformatics and Systems Biology, University of California San DiegoDepartment of Electrical and Computer Engineering, University of California San DiegoOsher Center for Integrative Health, University of California San FranciscoDepartment of Management, Zicklin School of Business, Baruch College, The City University of New YorkOsher Center for Integrative Health, University of California San FranciscoSan Diego Supercomputer Center, University of California San DiegoSan Diego Supercomputer Center, University of California San DiegoSan Diego Supercomputer Center, University of California San DiegoOsher Center for Integrative Health, University of California San FranciscoShu Chien-Gene Lay Department of Bioengineering, University of California San DiegoAbstract In tasks involving human health condition data, feature selection is heavily affected by data types, the complexity of the condition manifestation, and the variability in physiological presentation. One type of variability often overlooked or oversimplified is the effect of biological sex. As females have been chronically underrepresented in clinical research, we know less about how conditions manifest in females. Innovations in wearable technology have enabled individuals to generate high temporal resolution data for extended periods of time. With millions of days of data now available, additional feature selection pipelines should be developed to systematically identify sex-dependent variability in data, along with the effects of how many per-person data are included in analysis. Here we present a set of statistical approaches as a technique for identifying sex-dependent physiological and behavioral manifestations of complex diseases starting from longitudinal data, which are evaluated on diabetes, hypertension, and their comorbidity.https://doi.org/10.1038/s44294-024-00041-z |
| spellingShingle | Jamison H. Burks Lauryn Keeler Bruce Patrick Kasl Severine Soltani Varun Viswanath Wendy Hartogensis Stephan Dilchert Frederick M. Hecht Subhasis Dasgupta Ilkay Altintas Amarnath Gupta Ashley E. Mason Benjamin L. Smarr General feature selection technique supporting sex-debiasing in chronic illness algorithms validated using wearable device data npj Women's Health |
| title | General feature selection technique supporting sex-debiasing in chronic illness algorithms validated using wearable device data |
| title_full | General feature selection technique supporting sex-debiasing in chronic illness algorithms validated using wearable device data |
| title_fullStr | General feature selection technique supporting sex-debiasing in chronic illness algorithms validated using wearable device data |
| title_full_unstemmed | General feature selection technique supporting sex-debiasing in chronic illness algorithms validated using wearable device data |
| title_short | General feature selection technique supporting sex-debiasing in chronic illness algorithms validated using wearable device data |
| title_sort | general feature selection technique supporting sex debiasing in chronic illness algorithms validated using wearable device data |
| url | https://doi.org/10.1038/s44294-024-00041-z |
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