Emergence of digital biomarkers to predict and modify treatment efficacy: machine learning study
Objectives Development of digital biomarkers to predict treatment response to a digital behavioural intervention.Design Machine learning using random forest classifiers on data generated through the use of a digital therapeutic which delivers behavioural therapy to treat cardiometabolic disease. Dat...
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2019-07-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/9/7/e030710.full |
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| author | Nicole L Guthrie Jason Carpenter Katherine L Edwards Kevin J Appelbaum Sourav Dey David M Eisenberg David L Katz Mark A Berman |
| author_facet | Nicole L Guthrie Jason Carpenter Katherine L Edwards Kevin J Appelbaum Sourav Dey David M Eisenberg David L Katz Mark A Berman |
| author_sort | Nicole L Guthrie |
| collection | DOAJ |
| description | Objectives Development of digital biomarkers to predict treatment response to a digital behavioural intervention.Design Machine learning using random forest classifiers on data generated through the use of a digital therapeutic which delivers behavioural therapy to treat cardiometabolic disease. Data from 13 explanatory variables (biometric and engagement in nature) generated in the first 28 days of a 12-week intervention were used to train models. Two levels of response to treatment were predicted: (1) systolic change ≥10 mm Hg (SC model), and (2) shift down to a blood pressure category of elevated or better (ER model). Models were validated using leave-one-out cross validation and evaluated using area under the curve receiver operating characteristics (AUROC) and specificity- sensitivity. Ability to predict treatment response with a subset of nine variables, including app use and baseline blood pressure, was also tested (models SC-APP and ER-APP).Setting Data generated through ad libitum use of a digital therapeutic in the USA.Participants Deidentified data from 135 adults with a starting blood pressure ≥130/80, who tracked blood pressure for at least 7 weeks using the digital therapeutic.Results The SC model had an AUROC of 0.82 and a sensitivity of 58% at a specificity of 90%. The ER model had an AUROC of 0.69 and a sensitivity of 32% at a specificity at 91%. Dropping explanatory variables related to blood pressure resulted in an AUROC of 0.72 with a sensitivity of 42% at a specificity of 90% for the SC-APP model and an AUROC of 0.53 for the ER-APP model.Conclusions Machine learning was used to transform data from a digital therapeutic into digital biomarkers that predicted treatment response in individual participants. Digital biomarkers have potential to improve treatment outcomes in a digital behavioural intervention. |
| format | Article |
| id | doaj-art-d1175a7c79834bbabe36bd17c5372782 |
| institution | OA Journals |
| issn | 2044-6055 |
| language | English |
| publishDate | 2019-07-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-d1175a7c79834bbabe36bd17c53727822025-08-20T02:18:39ZengBMJ Publishing GroupBMJ Open2044-60552019-07-019710.1136/bmjopen-2019-030710Emergence of digital biomarkers to predict and modify treatment efficacy: machine learning studyNicole L Guthrie0Jason Carpenter1Katherine L Edwards2Kevin J Appelbaum3Sourav Dey4David M Eisenberg5David L Katz6Mark A Berman71 Better Therapeutics LLC, San Francisco, California, USA2 Manifold, Inc, Oakland, California, USA1 Better Therapeutics LLC, San Francisco, California, USA1 Better Therapeutics LLC, San Francisco, California, USA2 Manifold, Inc, Oakland, California, USA3 Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA1 Better Therapeutics LLC, San Francisco, California, USA1 Better Therapeutics LLC, San Francisco, California, USAObjectives Development of digital biomarkers to predict treatment response to a digital behavioural intervention.Design Machine learning using random forest classifiers on data generated through the use of a digital therapeutic which delivers behavioural therapy to treat cardiometabolic disease. Data from 13 explanatory variables (biometric and engagement in nature) generated in the first 28 days of a 12-week intervention were used to train models. Two levels of response to treatment were predicted: (1) systolic change ≥10 mm Hg (SC model), and (2) shift down to a blood pressure category of elevated or better (ER model). Models were validated using leave-one-out cross validation and evaluated using area under the curve receiver operating characteristics (AUROC) and specificity- sensitivity. Ability to predict treatment response with a subset of nine variables, including app use and baseline blood pressure, was also tested (models SC-APP and ER-APP).Setting Data generated through ad libitum use of a digital therapeutic in the USA.Participants Deidentified data from 135 adults with a starting blood pressure ≥130/80, who tracked blood pressure for at least 7 weeks using the digital therapeutic.Results The SC model had an AUROC of 0.82 and a sensitivity of 58% at a specificity of 90%. The ER model had an AUROC of 0.69 and a sensitivity of 32% at a specificity at 91%. Dropping explanatory variables related to blood pressure resulted in an AUROC of 0.72 with a sensitivity of 42% at a specificity of 90% for the SC-APP model and an AUROC of 0.53 for the ER-APP model.Conclusions Machine learning was used to transform data from a digital therapeutic into digital biomarkers that predicted treatment response in individual participants. Digital biomarkers have potential to improve treatment outcomes in a digital behavioural intervention.https://bmjopen.bmj.com/content/9/7/e030710.full |
| spellingShingle | Nicole L Guthrie Jason Carpenter Katherine L Edwards Kevin J Appelbaum Sourav Dey David M Eisenberg David L Katz Mark A Berman Emergence of digital biomarkers to predict and modify treatment efficacy: machine learning study BMJ Open |
| title | Emergence of digital biomarkers to predict and modify treatment efficacy: machine learning study |
| title_full | Emergence of digital biomarkers to predict and modify treatment efficacy: machine learning study |
| title_fullStr | Emergence of digital biomarkers to predict and modify treatment efficacy: machine learning study |
| title_full_unstemmed | Emergence of digital biomarkers to predict and modify treatment efficacy: machine learning study |
| title_short | Emergence of digital biomarkers to predict and modify treatment efficacy: machine learning study |
| title_sort | emergence of digital biomarkers to predict and modify treatment efficacy machine learning study |
| url | https://bmjopen.bmj.com/content/9/7/e030710.full |
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