Using Similarity Metrics on Real World Data and Patient Treatment Pathways to Recommend the Next Treatment.

Non-small-cell lung cancer (NSCLC) is one of the most prevalent types of lung cancer and continues to have an ominous five year survival rate. Considerable work has been accomplished in analyzing the viability of the treatments offered to NSCLC patients; however, while many of these treatments have performed better over populations of diagnosed NSCLC patients, a specific treatment may not be the most effective therapy for a given patient. Coupling both patient similarity metrics using the Gower similarity metric and prior treatment knowledge, we were able to demonstrate how patient analytics can complement clinical efforts in recommending the next best treatment. Our retrospective and exploratory results indicate that a majority of patients are not recommended the best surviving therapy once they require a new therapy. This investigation lays the groundwork for treatment recommendation using analytics, but more investigation is required to analyze patient outcomes beyond survival.

Medication Adherence Prediction Through Online Social Forums: A Case Study of Fibromyalgia.

BACKGROUND: Medication nonadherence can compound into severe medical problems for patients. Identifying patients who are likely to become nonadherent may help reduce these problems. Data-driven machine learning models can predict medication adherence by using selected indicators from patients' past health records. Sources of data for these models traditionally fall under two main categories: (1) proprietary data from insurance claims, pharmacy prescriptions, or electronic medical records and (2) survey data collected from representative groups of patients. Models developed using these data sources often are limited because they are proprietary, subject to high cost, have limited scalability, or lack timely accessibility. These limitations suggest that social health forums might be an alternate source of data for adherence prediction. Indeed, these data are accessible, affordable, timely, and available at scale. However, they can be inaccurate. OBJECTIVE: This paper proposes a medication adherence machine learning model for fibromyalgia therapies that can mitigate the inaccuracy of social health forum data. METHODS: Transfer learning is a machine learning technique that allows knowledge acquired from one dataset to be transferred to another dataset. In this study, predictive adherence models for the target disease were first developed by using accurate but limited survey data. These models were then used to predict medication adherence from health social forum data. Random forest, an ensemble machine learning technique, was used to develop the predictive models. This transfer learning methodology is demonstrated in this study by examining data from the Medical Expenditure Panel Survey and the PatientsLikeMe social health forum. RESULTS: When the models are carefully designed, less than a 5% difference in accuracy is observed between the Medical Expenditure Panel Survey and the PatientsLikeMe medication adherence predictions for fibromyalgia treatments. This design must take into consideration the mapping between the predictors and the outcomes in the two datasets. CONCLUSIONS: This study exemplifies the potential and limitations of transfer learning in medication adherence-predictive models based on survey data and social health forum data. The proposed approach can make timely medication adherence monitoring cost-effective and widely accessible. Additional investigation is needed to improve the robustness of the approach and extend its applicability to other therapies and other sources of data.

Sequential therapy with ramucirumab and/or checkpoint inhibitors for non-small-cell lung cancer in routine practice.

Aim: To describe treatment patterns and outcomes for advanced/metastatic non-small-cell lung cancer (aNSCLC) treated with single-agent or combination ramucirumab (ramucirumab-based) and/or immune checkpoint inhibitor (ICI-based) therapy. Materials & methods: Retrospective study of aNSCLC patients (n = 4054) identified in the Flatiron Health database, who received at least two treatment lines including ramucirumab- and/or ICI-based regimens between December 2014 and May 2017. Results: Median overall survival (95% CI) from aNSCLC diagnosis was 29.3 (25.5-33.0) months for patients receiving sequential ramucirumab- and ICI-based therapy (n = 245), 15.1 (12.6-18.2) months for patients receiving sequences including ramucirumab- without ICI-based therapy (n = 112), and 23.1 (21.9-24.2) months for patients receiving ICI-based therapy without ramucirumab-based therapy in sequence (n = 3697). Conclusion: Results provide real-world survival estimates for aNSCLC treated with sequences including ramucirumab- and/or ICI-based therapies.

Leveraging user search behavior to design personalized browsing interfaces for healthcare Web sites.

Understanding and leveraging user search behavior is increasingly becoming a key component towards improving web sites functionality for the health care consumer and provider. In this study we propose to leverage user search behavior to design user-tailored browsing interfaces to better support locating information in healthcare websites at the point-of-need.

Visualization of usability and functionality of a professional website through web-mining.

Functional interface design requires understanding of the information system structure and the user. Web logs record user interactions with the interface, and thus provide some insight into user search behavior and efficiency of the search process. The present study uses a data-mining approach with techniques such as association rules, clustering and classification, to visualize the usability and functionality of a digital library through in depth analyses of web logs.