Digital Twin Generators for Disease Modeling
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866913339578253312 |
|---|---|
| author | Alam, Nameyeh Basilico, Jake Bertolini, Daniele Chetty, Satish Casie D'Angelo, Heather Douglas, Ryan Fisher, Charles K. Fuller, Franklin Gomes, Melissa Gupta, Rishabh Lang, Alex Loukianov, Anton Mak-McCully, Rachel Murray, Cary Pham, Hanalei Qiao, Susanna Ryapolova-Webb, Elena Smith, Aaron Theoharatos, Dimitri Tolwani, Anil Tramel, Eric W. Vidovszky, Anna Viduya, Judy Walsh, Jonathan R. |
| author_facet | Alam, Nameyeh Basilico, Jake Bertolini, Daniele Chetty, Satish Casie D'Angelo, Heather Douglas, Ryan Fisher, Charles K. Fuller, Franklin Gomes, Melissa Gupta, Rishabh Lang, Alex Loukianov, Anton Mak-McCully, Rachel Murray, Cary Pham, Hanalei Qiao, Susanna Ryapolova-Webb, Elena Smith, Aaron Theoharatos, Dimitri Tolwani, Anil Tramel, Eric W. Vidovszky, Anna Viduya, Judy Walsh, Jonathan R. |
| contents | A patient's digital twin is a computational model that describes the evolution of their health over time. Digital twins have the potential to revolutionize medicine by enabling individual-level computer simulations of human health, which can be used to conduct more efficient clinical trials or to recommend personalized treatment options. Due to the overwhelming complexity of human biology, machine learning approaches that leverage large datasets of historical patients' longitudinal health records to generate patients' digital twins are more tractable than potential mechanistic models. In this manuscript, we describe a neural network architecture that can learn conditional generative models of clinical trajectories, which we call Digital Twin Generators (DTGs), that can create digital twins of individual patients. We show that the same neural network architecture can be trained to generate accurate digital twins for patients across 13 different indications simply by changing the training set and tuning hyperparameters. By introducing a general purpose architecture, we aim to unlock the ability to scale machine learning approaches to larger datasets and across more indications so that a digital twin could be created for any patient in the world. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_01488 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Digital Twin Generators for Disease Modeling Alam, Nameyeh Basilico, Jake Bertolini, Daniele Chetty, Satish Casie D'Angelo, Heather Douglas, Ryan Fisher, Charles K. Fuller, Franklin Gomes, Melissa Gupta, Rishabh Lang, Alex Loukianov, Anton Mak-McCully, Rachel Murray, Cary Pham, Hanalei Qiao, Susanna Ryapolova-Webb, Elena Smith, Aaron Theoharatos, Dimitri Tolwani, Anil Tramel, Eric W. Vidovszky, Anna Viduya, Judy Walsh, Jonathan R. Machine Learning A patient's digital twin is a computational model that describes the evolution of their health over time. Digital twins have the potential to revolutionize medicine by enabling individual-level computer simulations of human health, which can be used to conduct more efficient clinical trials or to recommend personalized treatment options. Due to the overwhelming complexity of human biology, machine learning approaches that leverage large datasets of historical patients' longitudinal health records to generate patients' digital twins are more tractable than potential mechanistic models. In this manuscript, we describe a neural network architecture that can learn conditional generative models of clinical trajectories, which we call Digital Twin Generators (DTGs), that can create digital twins of individual patients. We show that the same neural network architecture can be trained to generate accurate digital twins for patients across 13 different indications simply by changing the training set and tuning hyperparameters. By introducing a general purpose architecture, we aim to unlock the ability to scale machine learning approaches to larger datasets and across more indications so that a digital twin could be created for any patient in the world. |
| title | Digital Twin Generators for Disease Modeling |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.01488 |