Record linkage, in a genealogical context, is the process of identifying individuals from multiple sources which refer to the same real-world entity. Current solutions focus on the individuals in question and on complex rules developed by human experts. Genealogical databases are highly-structured with relationships existing between the individuals and other instances. These relationships can be utilized and human involvement greatly minimized by using a filtered structured neural network. These neural networks, using traditional back-propagation methods, are biased in a way to make the network human readable. The results show an increase in precision and recall when pedigree data is available and used.

Swedish households are borrowing like never before: the debt to income ratio, consumption loans and the amount of debt registered with the Swedish Enforcement Authority have been steadily increasing over the last decade. Meanwhile, life-changing events such as unemployment, divorce and sickness have been identified as drivers of over-indebtedness. The objective of this thesis is to establish whether there is a link between life-changing events and the level of debt with the SEA on the aggregate level. The data comes from various Swedish government agencies between 2010 – 2019. The method applied is a population weighted Ordinary Least Square model with time and municipality fixed effects. The results indicate that an increase in unemployment and divorce rates correlate with an average increase in the share of the population indebted to the Swedish Enforcement Authority. This applies to both men and women, but the association is stronger for men.

The admission of students into Counselor Education programs aims to select those applicants with the best potential to successfully complete the graduate program as well as acquire effective clinical skills. The primary method of measuring achievement potential for graduate students in Counselor Education programs represents Graduate Record Examination Scores (GRE); minimal scores on the examination are required for program admission. This study examined the relationship between student achievement of clinical skills and incoming GRE scores. The study participants were 47 master's level Counselor Education students who were enrolled in Practicum in Counselor Education (MHS 6800) in the spring, summer, and fall semesters of 2002 as well as the spring, summer, and fall semesters of 2003. All students were required to submit videotapes of live counseling sessions conducted in the Community Counseling Clinic at the University of Central Florida. The videotapes were rated using the Global Scale for Rating Helper Responses, developed by George Gazda. An independent samples t-test was utilized to assess between group differences for the sample participants. The study results showed no significant differences between the demonstration of clinical skills in Counselor Education students with a GRE score over 1000 and those students with a GRE score below 1000. The study results suggested that other factors outside of those skills measured by the GRE might attribute to successful acquirement and demonstration of clinical counseling skills.

Background: Both clinical pathways (CPs) and electronic health records (EHRs) increase the quality and efficiency of health care; however, no known studies have examined the integration of CPs into the EHR during an organizational EHR launch. Aim: To understand how nursing leadership perceives the nursing practice changes that accompanied the transition from paper to EHR-based CPs. Methods: A case study design was utilized, focusing on CPs utilized by one acute care unit within a tertiary care organization. Findings: Transfer of paper CPs into an EHR not built for the Canadian health care context proved to be difficult. In the integration process, a single paper document became spread throughout the EHR. EHR-based CPs are not as clear, and represent a larger documentation burden, than their paper counterparts. Conclusion: Nursing agency has been greatly affected by the change in format of CPs. Further exploration of nursing agency regarding CPs is warranted.

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Since the advent of electronic data processing, organizations have accrued vast amounts of data contained in multiple databases with no reliable global unique identifier. These databases were developed by different departments for different purposes at different times. Organizing and analyzing these data for human services requires linking records from all sources. RL (Record Linkage) is a process that connects records that are related to the identical or a sufficiently similar entity from multiple heterogeneous databases. RL is a data and compute intensive, mission critical process. The process must be efficient enough to process big data and effective enough to provide accurate matches. We have evaluated an RL system that is currently in use by a local health and human services department. We found that they were using the typical approach that was offered by Fellegi and Sunter with tuple-by-tuple processing, using the Soundex as the primary approximate string matching method. The Soundex has been found to be unreliable both as a phonetic and as an approximate string matching method. We found that their data, in many cases, has more than one value per field, suggesting that the data were queried from a 5NF data base. Consider that if a woman has been married 3 times, she may have up to 4 last names on record. This query process produced more than one tuple per database/entity apparently generating a Cartesian product of this data. In many cases, more than a dozen tuples were observed for a single database/entity. This approach is both ineffective and inefficient. An effective RL method should handle this multi-data without redundancy and use edit-distance for approximate string matching. However, due to high computational complexity, edit-distance will not scale well with big data problems. We developed two methodologies for resolving the aforementioned issues: PSH and ALIM. PSH – The Probabilistic Signature Hash is a composite method that increases the speed of Damerau-Levenshtein edit-distance. It combines signature filtering, probabilistic hashing, length filtering and prefix pruning to increase the speed of edit-distance. It is also lossless because it does not lose any true positive matches. ALIM – Aggregate Link and Iterative Match is a graph-based record linkage methodology that uses a multi-graph to store demographic data about people. ALIM performs string matching as records are inserted into the graph. ALIM eliminates data redundancy and stores the relationships between data. We tested PSH for string comparison and found it to be approximately 6,000 times faster than DL. We tested it against the trie-join methods and found that they are up to 6.26 times faster but lose between 10 and 20 percent of true positives. We tested ALIM against a method currently in use by a local health and human services department and found ALIM to produce significantly more matches (even with more restrictive match criteria) and that ALIM ran more than twice as fast. ALIM handles the multi-data problem and PSH allows the use of edit-distance comparison in this RL model. ALIM is more efficient and effective than a currently implemented RL system. This model can also be expanded to perform social network analysis and temporal data modeling. For human services, temporal modeling can reveal how policy changes and treatments affect clients over time and social network analysis can determine the effects of these on whole families by facilitating family linkage. / Computer and Information Science

The immutability, validity and confidentiality of an audit log is crucial when operating over sensitive data to comply to standard data regulations (e.g., HIPAA). Despite its critical needs, state-of-the-art privacy-preserving audit log schemes (e.g., Ghostor (NSDI '20), Calypso (VLDB '19)) do not fully obtain a high level of privacy, integrity, and immutability simultaneously, in which certain information (e.g., user identities) is still leaked in the log. In this work, we propose Harpocrates, a new privacy-preserving and immutable audit log scheme. Harpocrates permits data store, share, and access operations to be recorded in the audit log without leaking sensitive information (e.g., data identifier, user identity), while permitting the validity of data operations to be publicly verifiable. Harpocrates makes use of blockchain techniques to achieve immutability and avoid a single point of failure, while cryptographic zero-knowledge proofs are harnessed for confidentiality and public verifiability. We analyze the security of our proposed technique and prove that it achieves non-malleability and indistinguishability. We fully implemented Harpocrates and evaluated its performance on a real blockchain system (i.e., Hyperledger Fabric) deployed on a commodity platform (i.e., Amazon EC2). Experimental results demonstrated that Harpocrates is highly scalable and achieves practical performance. / Master of Science / Audit logs are an essential part of data storage systems as they allow to check if the system is working as intended. They are usually maintained on a server, a server with ill intentions can easily modify records of the log and make it appear that the system is working correctly. To store these records in an un-modifiable manner, prior works have leveraged special audit log storing mechanisms for e.g., blockchain due to its immutable nature. However, these works do not focus on the privacy of the records which is a crucial aspect for conforming to certain data regulations like HIPAA. In our work, we propose Harpocrates, an immutable and privacy-preserving audit log platform that supports recording operations (share/access) on sensitive data. Harpocrates leverages blockchain to achieve immutability of the audit log. Harpocrates use specific cryptographic primitives to achieve public verifiability and confidentiality of the audit log. Real world deployment of Harpocrates shows that it is practical and achieves strong security guarantees.