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Text Mining and Topic Modeling for Social and Medical Decision Support
Title
Text Mining and Topic Modeling for Social and Medical Decision Support.
Creator
Hurtado, Jose Luis, Zhu, Xingquan, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Effective decision support plays vital roles in people's daily life, as well as for professional practitioners such as health care providers. Without correct information and timely derived knowledge, a decision is often suboptimal and may result in signi cant nancial loss or compromises of the performance. In this dissertation, we study text mining and topic modeling and propose to use text mining methods, in combination with topic models, to discover knowledge from texts popularly available...
Show moreEffective decision support plays vital roles in people's daily life, as well as for professional practitioners such as health care providers. Without correct information and timely derived knowledge, a decision is often suboptimal and may result in signi cant nancial loss or compromises of the performance. In this dissertation, we study text mining and topic modeling and propose to use text mining methods, in combination with topic models, to discover knowledge from texts popularly available from a wide variety of sources, such as research publications, news, medical diagnose notes, and further employ discovered knowledge to assist social and medical decision support. Examples of such decisions include hospital patient readmission prediction, which is a national initiative for health care cost reduction, academic research topics discovery and trend modeling, and social preference modeling for friend recommendation in social networks etc. To carry out text mining, our research, in Chapter 3, first emphasizes on single document analyzing to investigate textual stylometric features for user pro ling and recognition. Our research confirms that by using properly designed features, it is possible to identify the authors who wrote the article, using a number of sample articles written by the author as the training data. This study serves as the base to assert that text mining is a powerful tool for capturing knowledge in texts for better decision making. In the Chapter 4, we advance our research from single documents to documents with interdependency relationships, and propose to model and predict citation relationship between documents. Given a collection of documents with known linkage relationships, our research will discover e ective features to train prediction models, and predict the likelihood of two documents involving a citation relationships. This study will help accurately model social network linkage relationships, and can be used to assist e ective decision making for friend recommendation in social networking, and reference recommendation in scienti c writing etc. In the Chapter 5, we advance a topic discovery and trend prediction principle to discover meaningful topics from a set of data collection, and further model the evolution trend of the topic. By proposing techniques to discover topics from text, and using temporal correlation between trend for prediction, our techniques can be used to summarize a large collection of documents as meaningful topics, and further forecast the popularity of the topic in a near future. This study can help design systems to discover popular topics in social media, and further assist resource planning and scheduling based on the discovered topics and the their evolution trend. In the Chapter 6, we employ both text mining and topic modeling to the medical domain for effective decision making. The goal is to discover knowledge from medical notes to predict the risk of a patient being re-admitted in a near future. Our research emphasizes on the challenge that re-admitted patients are only a small portion of the patient population, although they bring signficant financial loss. As a result, the datasets are highly imbalanced which often result in poor accuracy for decision making. Our research will propose to use latent topic modeling to carryout localized sampling, and combine models trained from multiple copies of sampled data for accurate prediction. This study can be directly used to assist hospital re-admission assessment for early warning and decision support. The text mining and topic modeling techniques investigated in the dissertation can be applied to many other domains, involving texts and social relationships, towards pattern and knowledge based e ective decision making.
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Date Issued
2016
PURL
http://purl.flvc.org/fau/fd/FA00004782, http://purl.flvc.org/fau/fd/FA00004782
Subject Headings
Social sciences--Research--Methodology., Data mining., Machine learning., Database searching., Discourse analysis--Data processing., Communication--Network analysis., Medical care--Quality control.
Format
Document (PDF)
Reducing impedance mismatch in SQL embeddings for object-oriented programming languages
Title
Reducing impedance mismatch in SQL embeddings for object-oriented programming languages.
Creator
Hurtado, Jose Luis, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
We survey and compare the different major mechanisms for embedding the relational database language SQL in object-oriented programming languages such as Java and C#, with regard to how much impedance mismatch these embeddings suffer. Here impedance mismatch refers to clarity and performance difficulties that arise because of the nature of the embedding. Because of the central position in the information technology industry of object-oriented programs that access SQL-based relational database...
Show moreWe survey and compare the different major mechanisms for embedding the relational database language SQL in object-oriented programming languages such as Java and C#, with regard to how much impedance mismatch these embeddings suffer. Here impedance mismatch refers to clarity and performance difficulties that arise because of the nature of the embedding. Because of the central position in the information technology industry of object-oriented programs that access SQL-based relational database systems, reducing impedance mismatch is generally recognized in that industry as an important practical problem. We argue for the suitability of SQL as a database language, and hence for the desirability of keeping SQL as the view provided by a SQL embedding. We make the case that SQLJ, a SQL embedding for Java in which it appears that Java directly supports SQL commands, is the kind of SQL embedding that suffers the least impedance mismatch, when compared with call-level interfaces and object-relational mappings. We propose extensions to SQLJ that would reduce its impedance mismatch even further.
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Date Issued
2012
PURL
http://purl.flvc.org/FAU/3355565
Subject Headings
Object-oriented programming (Computer science), SQL (Computer program language), Java (Computer program language), Relational databases
Format
Document (PDF)