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Applied Sciences
Volume 11
Issue 12
10.3390/app11125576
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Article

A Digital Twins Machine Learning Model for Forecasting Disease Progression in Stroke Patients

by
Angier Allen
1,
Anna Siefkas
1,
Emily Pellegrini
1,
Hoyt Burdick
2,3,
Gina Barnes
1,
Jacob Calvert
1,
Qingqing Mao
1,* and
Ritankar Das
1
1
Dascena, Inc., 12333 Sowden Rd Ste B PMB 65148, Houston, TX 77080, USA
2
Cabell Huntington Hospital, 1340 Hal Greer Boulevard, Huntington, WV 25701, USA
3
School of Medicine, Marshall University, 600 Medical Center Drive, Huntington, WV 25701, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Anthony C. Boucouvalas
Appl. Sci. 2021, 11(12), 5576; https://doi.org/10.3390/app11125576
Received: 23 April 2021 / Revised: 22 May 2021 / Accepted: 7 June 2021 / Published: 16 June 2021
(This article belongs to the Section Computing and Artificial Intelligence)
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Abstract

Background: Machine learning methods have been developed to predict the likelihood of a given event or classify patients into two or more diagnostic categories. Digital twin models, which forecast entire trajectories of patient health data, have potential applications in clinical trials and patient management. Methods: In this study, we apply a digital twin model based on a variational autoencoder to a population of patients who went on to experience an ischemic stroke. The digital twin’s ability to model patient clinical features was assessed with regard to its ability to forecast clinical measurement trajectories leading up to the onset of the acute medical event and beyond using International Classification of Diseases (ICD) codes for ischemic stroke and lab values as inputs. Results: The simulated patient trajectories were virtually indistinguishable from real patient data, with similar feature means, standard deviations, inter-feature correlations, and covariance structures on a withheld test set. A logistic regression adversary model was unable to distinguish between the real and simulated data area under the receiver operating characteristic (ROC) curve (AUCadversary = 0.51). Conclusion: Through accurate projection of patient trajectories, this model may help inform clinical decision making or provide virtual control arms for efficient clinical trials. View Full-Text
Keywords: digital twins; variational autoencoder; machine learning; algorithm; stroke; disease forecasting digital twins; variational autoencoder; machine learning; algorithm; stroke; disease forecasting
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MDPI and ACS Style

Allen, A.; Siefkas, A.; Pellegrini, E.; Burdick, H.; Barnes, G.; Calvert, J.; Mao, Q.; Das, R. A Digital Twins Machine Learning Model for Forecasting Disease Progression in Stroke Patients. Appl. Sci. 2021, 11, 5576. https://doi.org/10.3390/app11125576

AMA Style

Allen A, Siefkas A, Pellegrini E, Burdick H, Barnes G, Calvert J, Mao Q, Das R. A Digital Twins Machine Learning Model for Forecasting Disease Progression in Stroke Patients. Applied Sciences. 2021; 11(12):5576. https://doi.org/10.3390/app11125576

Chicago/Turabian Style

Allen, Angier, Anna Siefkas, Emily Pellegrini, Hoyt Burdick, Gina Barnes, Jacob Calvert, Qingqing Mao, and Ritankar Das. 2021. "A Digital Twins Machine Learning Model for Forecasting Disease Progression in Stroke Patients" Applied Sciences 11, no. 12: 5576. https://doi.org/10.3390/app11125576

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