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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.02661 |
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| _version_ | 1866909062720913408 |
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| author | Faruqui, Syed Hasib Akhter Alaeddini, Adel Du, Yan Li, Shiyu Sharma, Kumar Wang, Jing |
| author_facet | Faruqui, Syed Hasib Akhter Alaeddini, Adel Du, Yan Li, Shiyu Sharma, Kumar Wang, Jing |
| contents | Background: Type 2 diabetes (T2D) is a prevalent chronic disease with a significant risk of serious health complications and negative impacts on the quality of life. Given the impact of individual characteristics and lifestyle on the treatment plan and patient outcomes, it is crucial to develop precise and personalized management strategies. Artificial intelligence (AI) provides great promise in combining patterns from various data sources with nurses' expertise to achieve optimal care. Methods: This is a 6-month ancillary study among T2D patients (n = 20, age = 57 +- 10). Participants were randomly assigned to an intervention (AI, n=10) group to receive daily AI-generated individualized feedback or a control group without receiving the daily feedback (non-AI, n=10) in the last three months. The study developed an online nurse-in-the-loop predictive control (ONLC) model that utilizes a predictive digital twin (PDT). The PDT was developed using a transfer-learning-based Artificial Neural Network. The PDT was trained on participants self-monitoring data (weight, food logs, physical activity, glucose) from the first three months, and the online control algorithm applied particle swarm optimization to identify impactful behavioral changes for maintaining the patient's glucose and weight levels for the next three months. The ONLC provided the intervention group with individualized feedback and recommendations via text messages. The PDT was re-trained weekly to improve its performance. Findings: The trained ONLC model achieved >=80% prediction accuracy across all patients while the model was tuned online. Participants in the intervention group exhibited a trend of improved daily steps and stable or improved total caloric and total carb intake as recommended. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_02661 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Nurse-in-the-Loop Artificial Intelligence for Precision Management of Type 2 Diabetes in a Clinical Trial Utilizing Transfer-Learned Predictive Digital Twin Faruqui, Syed Hasib Akhter Alaeddini, Adel Du, Yan Li, Shiyu Sharma, Kumar Wang, Jing Machine Learning Artificial Intelligence Background: Type 2 diabetes (T2D) is a prevalent chronic disease with a significant risk of serious health complications and negative impacts on the quality of life. Given the impact of individual characteristics and lifestyle on the treatment plan and patient outcomes, it is crucial to develop precise and personalized management strategies. Artificial intelligence (AI) provides great promise in combining patterns from various data sources with nurses' expertise to achieve optimal care. Methods: This is a 6-month ancillary study among T2D patients (n = 20, age = 57 +- 10). Participants were randomly assigned to an intervention (AI, n=10) group to receive daily AI-generated individualized feedback or a control group without receiving the daily feedback (non-AI, n=10) in the last three months. The study developed an online nurse-in-the-loop predictive control (ONLC) model that utilizes a predictive digital twin (PDT). The PDT was developed using a transfer-learning-based Artificial Neural Network. The PDT was trained on participants self-monitoring data (weight, food logs, physical activity, glucose) from the first three months, and the online control algorithm applied particle swarm optimization to identify impactful behavioral changes for maintaining the patient's glucose and weight levels for the next three months. The ONLC provided the intervention group with individualized feedback and recommendations via text messages. The PDT was re-trained weekly to improve its performance. Findings: The trained ONLC model achieved >=80% prediction accuracy across all patients while the model was tuned online. Participants in the intervention group exhibited a trend of improved daily steps and stable or improved total caloric and total carb intake as recommended. |
| title | Nurse-in-the-Loop Artificial Intelligence for Precision Management of Type 2 Diabetes in a Clinical Trial Utilizing Transfer-Learned Predictive Digital Twin |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2401.02661 |