Patient-centered data science: an integrative framework for evaluating and predicting clinical outcomes in the digital health era
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866910557411475456 |
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| author | Amoei, Mohsen Poenaru, Dan |
| author_facet | Amoei, Mohsen Poenaru, Dan |
| contents | This study proposes a novel, integrative framework for patient-centered data science in the digital health era. We developed a multidimensional model that combines traditional clinical data with patient-reported outcomes, social determinants of health, and multi-omic data to create comprehensive digital patient representations. Our framework employs a multi-agent artificial intelligence approach, utilizing various machine learning techniques including large language models, to analyze complex, longitudinal datasets. The model aims to optimize multiple patient outcomes simultaneously while addressing biases and ensuring generalizability. We demonstrate how this framework can be implemented to create a learning healthcare system that continuously refines strategies for optimal patient care. This approach has the potential to significantly improve the translation of digital health innovations into real-world clinical benefits, addressing current limitations in AI-driven healthcare models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_02677 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Patient-centered data science: an integrative framework for evaluating and predicting clinical outcomes in the digital health era Amoei, Mohsen Poenaru, Dan Machine Learning Artificial Intelligence Computers and Society This study proposes a novel, integrative framework for patient-centered data science in the digital health era. We developed a multidimensional model that combines traditional clinical data with patient-reported outcomes, social determinants of health, and multi-omic data to create comprehensive digital patient representations. Our framework employs a multi-agent artificial intelligence approach, utilizing various machine learning techniques including large language models, to analyze complex, longitudinal datasets. The model aims to optimize multiple patient outcomes simultaneously while addressing biases and ensuring generalizability. We demonstrate how this framework can be implemented to create a learning healthcare system that continuously refines strategies for optimal patient care. This approach has the potential to significantly improve the translation of digital health innovations into real-world clinical benefits, addressing current limitations in AI-driven healthcare models. |
| title | Patient-centered data science: an integrative framework for evaluating and predicting clinical outcomes in the digital health era |
| topic | Machine Learning Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2408.02677 |