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<p>Medication non-adherence and non-compliance left unaddressed can compound
into severe medical problems for patients. Identifying patients that are likely to
become non-adherent can help reduce these problems. Despite these benefits, monitoring adherence at scale is cost-prohibitive. Social forums offer an easily accessible,
affordable, and timely alternative to the traditional methods based on claims data.
This study investigates the potential of medication adherence prediction based on
social forum data for diabetes and fibromyalgia therapies by using transfer learning
from the Medical Expenditure Panel Survey (MEPS).
</p><p><br></p>
<p>Predictive adherence models are developed by using both survey and social forums
data and different random forest (RF) techniques. The first of these implementations
uses binned inputs from k-means clustering. The second technique is based on ternary
trees instead of the widely used binary decision trees. These techniques are able to
handle missing data, a prevalent characteristic of social forums data.
</p><p><br></p>
<p>The results of this study show that transfer learning between survey models and
social forum models is possible. Using MEPS survey data and the techniques listed
above to derive RF models, less than 5% difference in accuracy was observed between
the MEPS test dataset and the social forum test dataset. Along with these RF
techniques, another RF implementation with imputed means for the missing values
was developed and shown to predict adherence for social forum patients with an
accuracy >70%.
</p>
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<p>This thesis shows that a model trained with verified survey data can be used
to complement traditional medical adherence models by predicting adherence from
unverified, self-reported data in a dynamic and timely manner. Furthermore, this
model provides a method for discovering objective insights from subjective social
reports. Additional investigation is needed to improve the prediction accuracy of the
proposed model and to assess biases that may be inherent to self-reported adherence
measures in social health networks.
</p>
</div>
</div>
</div>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/7411589 |
| Date | 17 January 2019 |
| Creators | Kyle Haas (5931056) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/Transfer_Learning_for_Medication_Adherence_Prediction_from_Social_Forums_Self-Reported_Data/7411589 |
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