Improving MEDication Adherence through Person Centered Care and Adaptive Interventions
|Title||Improving MEDication Adherence through Person Centered Care and Adaptive Interventions|
|Summary||Improving MEDication Adherence through Person Centered Care and Adaptive Interventions|
|References|| Definitions, variants, and causes of nonadherence with medication: a challenge for tailored interventions:
Predictors of Medication Adherence Using a Multidimensional Adherence Model in Patients with Heart Failure: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2603618/
A machine learning approach for medication adherence monitoring using body-worn sensors: https://ieeexplore.ieee.org/document/7459425
Machine Learning Classification of Medication Adherence in Patients with Movement Disorders Using Non-Wearable Sensors: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5729888/
|Prerequisites||Courses Artificial Intelligence, learning systems. Good knowledge in applied data science/machine learning. Ability to implement state-of-the-art machine learning algorithms in a programming environment of your choice.|
|Supervisor||Alexander Galozy, Sławomir Nowaczyk|
Medications nonadherence is major public health concern leading to increased mortality, morbidity and billions in increased costs for out-of-plan treatment. Doctors and health care providers a like, have a keen interest to alleviate this problem by finding out the reasons for medication non-adherence and designing appropriate interventions with a high likelihood of success. Patient behavior is extraordinary complex and reasons for patients to not taking their medications are varied and individual.
The main goal of this thesis is to identify the reason for secondary medication non-adherence utilizing a combination of smart home sensor data, data from electronic health records and questionnaires.
The student is expected to perform literature review on the subject of secondary medication adherence, smart home environments and machine learning on medical data (especially electronic health records). In the medical domain, it is quite important to explain the reasons for a particular classification. The student should analyze the tradeoffs between better classification performance and interpretability.