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Browsing by Author "Peng, Bo"
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Item Applications of Data Mining in Healthcare(2019-05) Peng, Bo; Mohler, George; Dundar, Murat; Zheng, Jiang YuWith increases in the quantity and quality of healthcare related data, data mining tools have the potential to improve people’s standard of living through personalized and predictive medicine. In this thesis we improve the state-of-the-art in data mining for several problems in the healthcare domain. In problems such as drug-drug interaction prediction and Alzheimer’s Disease (AD) biomarkers discovery and prioritization, current methods either require tedious feature engineering or have unsatisfactory performance. New effective computational tools are needed that can tackle these complex problems. In this dissertation, we develop new algorithms for two healthcare problems: high-order drug-drug interaction prediction and amyloid imaging biomarker prioritization in Alzheimer’s Disease. Drug-drug interactions (DDIs) and their associated adverse drug reactions (ADRs) represent a significant detriment to the public h ealth. Existing research on DDIs primarily focuses on pairwise DDI detection and prediction. Effective computational methods for high-order DDI prediction are desired. In this dissertation, I present a deep learning based model D 3 I for cardinality-invariant and order-invariant high-order DDI pre- diction. The proposed models achieve 0.740 F1 value and 0.847 AUC value on high-order DDI prediction, and outperform classical methods on order-2 DDI prediction. These results demonstrate the strong potential of D 3 I and deep learning based models in tackling the prediction problems of high-order DDIs and their induced ADRs. The second problem I consider in this thesis is amyloid imaging biomarkers discovery, for which I propose an innovative machine learning paradigm enabling precision medicine in this domain. The paradigm tailors the imaging biomarker discovery process to individual characteristics of a given patient. I implement this paradigm using a newly developed learning-to-rank method PLTR. The PLTR model seamlessly integrates two objectives for joint optimization: pushing up relevant biomarkers and ranking among relevant biomarkers. The empirical study of PLTR conducted on the ADNI data yields promising results to identify and prioritize individual-specific amyloid imaging biomarkers based on the individual’s structural MRI data. The resulting top ranked imaging biomarkers have the potential to aid personalized diagnosis and disease subtyping.Item Hybrid Collaborative Filtering Methods for Recommending Search Terms to Clinicians(Elsevier, 2021) Ren, Zhiyun; Peng, Bo; Schleyer, Titus K.; Ning, Xia; Medicine, School of MedicineWith increasing and extensive use of electronic health records (EHR), clinicians are often challenged in retrieving relevant patient information efficiently and effectively to arrive at a diagnosis. While using the search function built into an EHR can be more useful than browsing in a voluminous patient record, it is cumbersome and repetitive to search for the same or similar information on similar patients. To address this challenge, there is a critical need to build effective recommender systems that can recommend search terms to clinicians accurately. In this study, we developed a hybrid collaborative filtering model to recommend search terms for a specific patient to a clinician. The model draws on information from patients' clinical encounters and the searches that were performed during them. To generate recommendations, the model uses search terms which are (1) frequently co-occurring with the ICD codes recorded for the patient and (2) highly relevant to the most recent search terms. In one variation of the model (Hybrid Collaborative Filtering Method for Healthcare, or HCFMH), we use only the most recent ICD codes assigned to the patient, and in the other (Co-occurrence Pattern based HCFMH, or cpHCFMH), all ICD codes. We have conducted comprehensive experiments to evaluate the proposed model. These experiments demonstrate that our model outperforms state-of-the-art baseline methods for top-N search term recommendation on different data sets.Item Prioritization of Cognitive Assessments in Alzheimer's Disease via Learning to Rank using Brain Morphometric Data(IEEE Xplore, 2019-05-01) Peng, Bo; Yao, Xiaohui; Risacher, Shannon L.; Saykin, Andrew J.; Shen, Li; Ning, Xia; Radiology and Imaging Sciences, School of MedicineWe propose an innovative machine learning paradigm enabling precision medicine for prioritizing cognitive assessments according to their relevance to Alzheimer’s disease at the individual patient level. The paradigm tailors the cognitive biomarker discovery and cognitive assessment selection process to the brain morphometric characteristics of each individual patient. We implement this paradigm using a newly developed learning-to-rank method PLTR. Our empirical study on the ADNI data yields promising results to identify and prioritize individual-specific cognitive biomarkers as well as cognitive assessment tasks based on the individual’s structural MRI data. The resulting top ranked cognitive biomarkers and assessment tasks have the potential to aid personalized diagnosis and disease subtyping.