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Similarity matrix-based anomaly detection for clinical intervention

The use of digital phenotyping methods in clinical care has allowed for improved investigation of spatiotemporal behaviors of patients. Moreover, detecting abnormalities in mobile sensor data patterns can be instrumental in identifying potential changes in symptomology. We propose a method that temp...

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Detalles Bibliográficos
Autores principales: D’Mello, Ryan, Melcher, Jennifer, Torous, John
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9163116/
https://www.ncbi.nlm.nih.gov/pubmed/35654843
http://dx.doi.org/10.1038/s41598-022-12792-3
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author D’Mello, Ryan
Melcher, Jennifer
Torous, John
author_facet D’Mello, Ryan
Melcher, Jennifer
Torous, John
author_sort D’Mello, Ryan
collection PubMed
description The use of digital phenotyping methods in clinical care has allowed for improved investigation of spatiotemporal behaviors of patients. Moreover, detecting abnormalities in mobile sensor data patterns can be instrumental in identifying potential changes in symptomology. We propose a method that temporally aligns sensor data in order to achieve interpretable measures of similarity between time points. These computed measures can then be used for anomaly detection, baseline routine computation, and trajectory clustering. In addition, we apply this method on a study of 695 college participants, as well as on a patient with worsening anxiety and depression. With varying temporal constraints, we find mild correlations between changes in routine and clinical scores. Furthermore, in our experiment on an individual with elevated depression and anxiety, we are able to cluster GPS trajectories, allowing for improved understanding and visualization of routines with respect to symptomology. In the future, we aim to apply this method on individuals that undergo data collection for longer periods of time, thus allowing for a better understanding of long-term routines and signals for clinical intervention.
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spelling pubmed-91631162022-06-05 Similarity matrix-based anomaly detection for clinical intervention D’Mello, Ryan Melcher, Jennifer Torous, John Sci Rep Article The use of digital phenotyping methods in clinical care has allowed for improved investigation of spatiotemporal behaviors of patients. Moreover, detecting abnormalities in mobile sensor data patterns can be instrumental in identifying potential changes in symptomology. We propose a method that temporally aligns sensor data in order to achieve interpretable measures of similarity between time points. These computed measures can then be used for anomaly detection, baseline routine computation, and trajectory clustering. In addition, we apply this method on a study of 695 college participants, as well as on a patient with worsening anxiety and depression. With varying temporal constraints, we find mild correlations between changes in routine and clinical scores. Furthermore, in our experiment on an individual with elevated depression and anxiety, we are able to cluster GPS trajectories, allowing for improved understanding and visualization of routines with respect to symptomology. In the future, we aim to apply this method on individuals that undergo data collection for longer periods of time, thus allowing for a better understanding of long-term routines and signals for clinical intervention. Nature Publishing Group UK 2022-06-02 /pmc/articles/PMC9163116/ /pubmed/35654843 http://dx.doi.org/10.1038/s41598-022-12792-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
D’Mello, Ryan
Melcher, Jennifer
Torous, John
Similarity matrix-based anomaly detection for clinical intervention
title Similarity matrix-based anomaly detection for clinical intervention
title_full Similarity matrix-based anomaly detection for clinical intervention
title_fullStr Similarity matrix-based anomaly detection for clinical intervention
title_full_unstemmed Similarity matrix-based anomaly detection for clinical intervention
title_short Similarity matrix-based anomaly detection for clinical intervention
title_sort similarity matrix-based anomaly detection for clinical intervention
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9163116/
https://www.ncbi.nlm.nih.gov/pubmed/35654843
http://dx.doi.org/10.1038/s41598-022-12792-3
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