Browsing by Author "Hosseini, Mahmood"

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    Novel Approach to Cluster Patient-Generated Data Into Actionable Topics: Case Study of a Web-Based Breast Cancer Forum
    (JMIR, 2018) Jones, Josette; Pradhan, Meeta; Hosseini, Masoud; Kulanthaivel, Anand; Hosseini, Mahmood; Biohealth Informatics, School of Informatics and Computing
    Background: The increasing use of social media and mHealth apps has generated new opportunities for health care consumers to share information about their health and well-being. Information shared through social media contains not only medical information but also valuable information about how the survivors manage disease and recovery in the context of daily life. Objective: The objective of this study was to determine the feasibility of acquiring and modeling the topics of a major online breast cancer support forum. Breast cancer patient support forums were selected to discover the hidden, less obvious aspects of disease management and recovery. Methods: First, manual topic categorization was performed using qualitative content analysis (QCA) of each individual forum board. Second, we requested permission from the Breastcancer.org Community for a more in-depth analysis of the postings. Topic modeling was then performed using open source software Machine Learning Language Toolkit, followed by multiple linear regression (MLR) analysis to detect highly correlated topics among the different website forums. Results: QCA of the forums resulted in 20 categories of user discussion. The final topic model organized >4 million postings into 30 manageable topics. Using qualitative analysis of the topic models and statistical analysis, we grouped these 30 topics into 4 distinct clusters with similarity scores of ≥0.80; these clusters were labeled Symptoms & Diagnosis, Treatment, Financial, and Family & Friends. A clinician review confirmed the clinical significance of the topic clusters, allowing for future detection of actionable items within social media postings. To identify the most significant topics across individual forums, MLR demonstrated that 6 topics—based on the Akaike information criterion values ranging from −642.75 to −412.32—were statistically significant. Conclusions: The developed method provides an insight into the areas of interest and concern, including those not ascertainable in the clinic. Such topics included support from lay and professional caregivers and late side effects of therapy that consumers discuss in social media and may be of interest to clinicians. The developed methods and results indicate the potential of social media to inform the clinical workflow with regards to the impact of recovery on daily life. [JMIR Med Inform 2018;6(4):e45]
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    A Water Demand Prediction Model for Central Indiana
    (AAAI, 2018) Shah, Setu; Hosseini, Mahmood; Miled, Zina Ben; Shafer, Rebecca; Berube, Steve; Electrical and Computer Engineering, School of Engineering and Technology
    Due to the limited natural water resources and the increase in population, managing water consumption is becoming an increasingly important subject worldwide. In this paper, we present and compare different machine learning models that are able to predict water demand for Central Indiana. The models are developed for two different time scales: daily and monthly. The input features for the proposed model include weather conditions (temperature, rainfall, snow), social features (holiday, median income), date (day of the year, month), and operational features (number of customers, previous water demand levels). The importance of these input features as accurate predictors is investigated. The results show that daily and monthly models based on recurrent neural networks produced the best results with an average error in prediction of 1.69% and 2.29%, respectively for 2016. These models achieve a high accuracy with a limited set of input features.