Prediction of viral symptoms using wearable technology and artificial intelligence: A pilot study in healthcare workers.
Conventional testing and diagnostic methods for infections like SARS-CoV-2 have limitations for population health management and public policy. We hypothesize that daily changes in autonomic activity, measured through off-the-shelf technologies together with app-based cognitive assessments, may be u...
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Public Library of Science (PLoS)
2021
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oai:doaj.org-article:ee2654e949b2420f94e8be0678b18d832021-12-02T20:07:53ZPrediction of viral symptoms using wearable technology and artificial intelligence: A pilot study in healthcare workers.1932-620310.1371/journal.pone.0257997https://doaj.org/article/ee2654e949b2420f94e8be0678b18d832021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0257997https://doaj.org/toc/1932-6203Conventional testing and diagnostic methods for infections like SARS-CoV-2 have limitations for population health management and public policy. We hypothesize that daily changes in autonomic activity, measured through off-the-shelf technologies together with app-based cognitive assessments, may be used to forecast the onset of symptoms consistent with a viral illness. We describe our strategy using an AI model that can predict, with 82% accuracy (negative predictive value 97%, specificity 83%, sensitivity 79%, precision 34%), the likelihood of developing symptoms consistent with a viral infection three days before symptom onset. The model correctly predicts, almost all of the time (97%), individuals who will not develop viral-like illness symptoms in the next three days. Conversely, the model correctly predicts as positive 34% of the time, individuals who will develop viral-like illness symptoms in the next three days. This model uses a conservative framework, warning potentially pre-symptomatic individuals to socially isolate while minimizing warnings to individuals with a low likelihood of developing viral-like symptoms in the next three days. To our knowledge, this is the first study using wearables and apps with machine learning to predict the occurrence of viral illness-like symptoms. The demonstrated approach to forecasting the onset of viral illness-like symptoms offers a novel, digital decision-making tool for public health safety by potentially limiting viral transmission.Pierre-François D'HaeseVictor FinomoreDmitry LesnikLaura KornhauserTobias SchaeferPeter E KonradSally HodderClay MarshAli R RezaiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0257997 (2021) |
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DOAJ |
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Medicine R Science Q |
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Medicine R Science Q Pierre-François D'Haese Victor Finomore Dmitry Lesnik Laura Kornhauser Tobias Schaefer Peter E Konrad Sally Hodder Clay Marsh Ali R Rezai Prediction of viral symptoms using wearable technology and artificial intelligence: A pilot study in healthcare workers. |
| description |
Conventional testing and diagnostic methods for infections like SARS-CoV-2 have limitations for population health management and public policy. We hypothesize that daily changes in autonomic activity, measured through off-the-shelf technologies together with app-based cognitive assessments, may be used to forecast the onset of symptoms consistent with a viral illness. We describe our strategy using an AI model that can predict, with 82% accuracy (negative predictive value 97%, specificity 83%, sensitivity 79%, precision 34%), the likelihood of developing symptoms consistent with a viral infection three days before symptom onset. The model correctly predicts, almost all of the time (97%), individuals who will not develop viral-like illness symptoms in the next three days. Conversely, the model correctly predicts as positive 34% of the time, individuals who will develop viral-like illness symptoms in the next three days. This model uses a conservative framework, warning potentially pre-symptomatic individuals to socially isolate while minimizing warnings to individuals with a low likelihood of developing viral-like symptoms in the next three days. To our knowledge, this is the first study using wearables and apps with machine learning to predict the occurrence of viral illness-like symptoms. The demonstrated approach to forecasting the onset of viral illness-like symptoms offers a novel, digital decision-making tool for public health safety by potentially limiting viral transmission. |
| format |
article |
| author |
Pierre-François D'Haese Victor Finomore Dmitry Lesnik Laura Kornhauser Tobias Schaefer Peter E Konrad Sally Hodder Clay Marsh Ali R Rezai |
| author_facet |
Pierre-François D'Haese Victor Finomore Dmitry Lesnik Laura Kornhauser Tobias Schaefer Peter E Konrad Sally Hodder Clay Marsh Ali R Rezai |
| author_sort |
Pierre-François D'Haese |
| title |
Prediction of viral symptoms using wearable technology and artificial intelligence: A pilot study in healthcare workers. |
| title_short |
Prediction of viral symptoms using wearable technology and artificial intelligence: A pilot study in healthcare workers. |
| title_full |
Prediction of viral symptoms using wearable technology and artificial intelligence: A pilot study in healthcare workers. |
| title_fullStr |
Prediction of viral symptoms using wearable technology and artificial intelligence: A pilot study in healthcare workers. |
| title_full_unstemmed |
Prediction of viral symptoms using wearable technology and artificial intelligence: A pilot study in healthcare workers. |
| title_sort |
prediction of viral symptoms using wearable technology and artificial intelligence: a pilot study in healthcare workers. |
| publisher |
Public Library of Science (PLoS) |
| publishDate |
2021 |
| url |
https://doaj.org/article/ee2654e949b2420f94e8be0678b18d83 |
| work_keys_str_mv |
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