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You searched for +publisher:"University of Notre Dame" +contributor:("Nitesh V. Chawla, Committee Member"). Showing records 1 – 3 of 3 total matches.

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University of Notre Dame

1. Darcy A. Davis. Network-Centric Data Mining for Medical Applications</h1>.

Degree: Computer Science and Engineering, 2012, University of Notre Dame

Faced with unsustainable costs and enormous amounts of under-utilized data, health care needs more efficient practices, research, and tools to harness the benefits of data. These methods create a feedback loop where computational tools guide and facilitate research, leading to improved biological knowledge and clinical standards, which will in turn generate better data. In order to facilitate the necessary changes, better tools are needed for assessing risk and optimizing treatments, which further require better understanding of disease interdependencies, genetic influence, and translation into a patient’s future. This dissertation explores network-centric data mining approaches for benefit in multiple stages of this feedback loop: from better understanding of disease mechanisms to development of novel clinical tools for personalized and prospective medicine. Applications include predicting personalized patient disease risk based on medical history, optimizing NICU nursing schedules to reduce negative effects, and predicting novel disease-gene interactions. Advisors/Committee Members: Zoltan Toroczkai, Committee Member, Nitesh V. Chawla, Committee Member, Predrag Radivojac, Committee Member, Scott Emrich, Committee Member.

Subjects/Keywords: bioinformatics; disease gene candidate detection; personalized medicine; clinical informatics; network science; translational biology; data mining; heterogeneous networks; link prediction

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Davis, D. A. (2012). Network-Centric Data Mining for Medical Applications</h1>. (Thesis). University of Notre Dame. Retrieved from https://curate.nd.edu/show/df65v694x9x

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Davis, Darcy A.. “Network-Centric Data Mining for Medical Applications</h1>.” 2012. Thesis, University of Notre Dame. Accessed January 20, 2021. https://curate.nd.edu/show/df65v694x9x.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Davis, Darcy A.. “Network-Centric Data Mining for Medical Applications</h1>.” 2012. Web. 20 Jan 2021.

Vancouver:

Davis DA. Network-Centric Data Mining for Medical Applications</h1>. [Internet] [Thesis]. University of Notre Dame; 2012. [cited 2021 Jan 20]. Available from: https://curate.nd.edu/show/df65v694x9x.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Davis DA. Network-Centric Data Mining for Medical Applications</h1>. [Thesis]. University of Notre Dame; 2012. Available from: https://curate.nd.edu/show/df65v694x9x

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Notre Dame

2. Ryan Nicholas Lichtenwalter. Beyond Static Data: Tackling Class Imbalance and Concept Drift in Data Streams, Link Persistence and Prediction in Dynamic Networks, and Autonomous Composition in Computer Music</h1>.

Degree: Computer Science and Engineering, 2009, University of Notre Dame

This thesis tackles the fundamental issues of streaming data in different challenging scenarios. First, the confounding problem of class imbalance and concept drift is considered, and a novel and competitive classification framework is proposed to address this challenge. The proposed methodology outperforms the contemporary methods on a number of different datasets. Second, the thesis looks at the problem of dynamic networks, specifically the challenges of link persistence and link prediction. This is the first work to formally cast the problem of link prediction as a class imbalance problem, and it greatly outperforms a number of contemporary and popular methods. The third form of streaming data is in the domain of music. Bach chorales are first uniquely transformed into a feature vector space, and then a sliding window approach is used to generate classifiers for subsequent autonomous music composition. Advisors/Committee Members: W. Philip Kegelmeyer, Committee Member, Patrick J. Flynn, Committee Member, Nitesh V. Chawla, Committee Member.

Subjects/Keywords: stream mining; class imbalance; link prediction; link persistence; data streams; ensembles; schenkerian analysis; concept drift; classification; autonomous composition; social networks; computer music

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Lichtenwalter, R. N. (2009). Beyond Static Data: Tackling Class Imbalance and Concept Drift in Data Streams, Link Persistence and Prediction in Dynamic Networks, and Autonomous Composition in Computer Music</h1>. (Thesis). University of Notre Dame. Retrieved from https://curate.nd.edu/show/ws859c70f89

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Lichtenwalter, Ryan Nicholas. “Beyond Static Data: Tackling Class Imbalance and Concept Drift in Data Streams, Link Persistence and Prediction in Dynamic Networks, and Autonomous Composition in Computer Music</h1>.” 2009. Thesis, University of Notre Dame. Accessed January 20, 2021. https://curate.nd.edu/show/ws859c70f89.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Lichtenwalter, Ryan Nicholas. “Beyond Static Data: Tackling Class Imbalance and Concept Drift in Data Streams, Link Persistence and Prediction in Dynamic Networks, and Autonomous Composition in Computer Music</h1>.” 2009. Web. 20 Jan 2021.

Vancouver:

Lichtenwalter RN. Beyond Static Data: Tackling Class Imbalance and Concept Drift in Data Streams, Link Persistence and Prediction in Dynamic Networks, and Autonomous Composition in Computer Music</h1>. [Internet] [Thesis]. University of Notre Dame; 2009. [cited 2021 Jan 20]. Available from: https://curate.nd.edu/show/ws859c70f89.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lichtenwalter RN. Beyond Static Data: Tackling Class Imbalance and Concept Drift in Data Streams, Link Persistence and Prediction in Dynamic Networks, and Autonomous Composition in Computer Music</h1>. [Thesis]. University of Notre Dame; 2009. Available from: https://curate.nd.edu/show/ws859c70f89

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Notre Dame

3. Darcy A Davis. Predicting Individual Disease Risk Based on Medical History</h1>.

Degree: Computer Science and Engineering, 2008, University of Notre Dame

The monumental cost of health care, especially for chronic disease treatment, is quickly becoming unmanageable. This crisis has motivated the drive towards preventative medicine, where the primary concern is recognizing disease risk and taking action at the earliest signs. However, universal testing is neither time nor cost efficient. We propose CARE, a Collaborative Assessment and Recommendation Engine, which relies only on a patient’s medical history using ICD-9-CM codes in order to predict future diseases risks. CARE combines collaborative filtering methods with clustering to predict each patient’s greatest disease risks based on their own medical history and that of similar patients. We also describe an Iterative version, ICARE, which incorporates ensemble concepts for improved performance. These novel systems require no specialized information and provide predictions for medical conditions of all kinds in a single run. We present experimental results on a large Medicare dataset, demonstrating that CARE and ICARE perform well at capturing future disease risks. Advisors/Committee Members: Patrick J. Flynn, Committee Member, Kevin W. Bowyer, Committee Member, Nitesh V. Chawla, Committee Member.

Subjects/Keywords: collaborative filtering; disease prediction

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Davis, D. A. (2008). Predicting Individual Disease Risk Based on Medical History</h1>. (Thesis). University of Notre Dame. Retrieved from https://curate.nd.edu/show/pg15bc40k5d

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Davis, Darcy A. “Predicting Individual Disease Risk Based on Medical History</h1>.” 2008. Thesis, University of Notre Dame. Accessed January 20, 2021. https://curate.nd.edu/show/pg15bc40k5d.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Davis, Darcy A. “Predicting Individual Disease Risk Based on Medical History</h1>.” 2008. Web. 20 Jan 2021.

Vancouver:

Davis DA. Predicting Individual Disease Risk Based on Medical History</h1>. [Internet] [Thesis]. University of Notre Dame; 2008. [cited 2021 Jan 20]. Available from: https://curate.nd.edu/show/pg15bc40k5d.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Davis DA. Predicting Individual Disease Risk Based on Medical History</h1>. [Thesis]. University of Notre Dame; 2008. Available from: https://curate.nd.edu/show/pg15bc40k5d

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

.