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Better Treatment of Hypothyroidism: The Merge of Modeling, Biosensor Data and Machine Learning in the Pharmacy

Prosjektbeskrivelse

Hypothyroidism affects approximately 5% of the population, with many patients experiencing suboptimal treatment despite standard hormone replacement therapy (THRT). This project aims to revolutionize THRT by integrating biosensor data, machine learning (ML), and pharmacokinetic modeling to improve dosage precision and treatment outcomes. By leveraging noninvasive physiological data, such as heart rate variability (HRV), physical activity, and sleep patterns, we will enhance our understanding of patient responses to hormone therapy and provide personalized treatment recommendations. The project will be conducted in collaboration with pharmacies, bringing innovative clinical research closer to patients while strengthening regional healthcare networks.

Mål

The primary goal of this project is to develop and implement a data-driven system that optimizes thyroid hormone dosage based on multimodal physiological data. By refining the LEVdose tool, a decision-support system for personalized THRT, we aim to:

  • Improve treatment accuracy by integrating ML predictions with biochemical and patient-reported data.
  • Reduce the time needed for dosage adjustments, enhancing patient quality of life (QoL).
  • Strengthen regional healthcare collaboration by conducting clinical studies in pharmacies, making research more accessible to patients.

Metode

The project combines clinical research, pharmacokinetic modeling, and ML in an innovative study framework:

  1. Data Collection: Patients will be recruited through pharmacies and provided with biosensors to measure HRV, sleep, and physical activity. Blood samples and health surveys will be collected.
  2. Model Development: ML algorithms will analyze longitudinal patient data, identifying patterns that correlate with treatment efficacy and predicting optimal hormone dosage adjustments.
  3. Clinical Implementation: The improved LEVdose tool will be tested in a randomized clinical trial, evaluating its ability to personalize THRT based on sensor-driven insights.

 

Konklusjon

By integrating cutting-edge technology and clinical expertise, this project aims to set a new standard in personalized hypothyroidism treatment, improving patient outcomes and advancing digital healthcare solutions.