Hypertension is a prevalent risk factor for cardiovascular disease and premature mortality. Telemonitoring can be used to provide a communication pipeline between patients and clinicians for diagnosing hypertension and staging early intervention. However, it takes healthcare resources to monitor patients and identify patients at risk of experiencing a hypertensive event. To reduce the burden on the health care system, we present an automated early warning system to predict patients at risk of a hypertensive event. We first construct a fusion model that utilizes a dual stage attention mechanism to determine whether a hypertensive event occurs in the next seven days and compare its performance to XGBoost and logistic regression. Then, we measure its performance in an early warning system to determine whether it can detect the onset of the first hypertensive event for each patient. With the best threshold, the early warning system using this model has an F1 score of 0.61.

OBJECTIVE: While patients often contribute data for research, they want researchers to protect their data. As part of a participatory design of privacy-enhancing software, this study explored patients' perceptions of privacy protection in research using their healthcare data. MATERIALS AND METHODS: We conducted 4 focus groups with 27 patients on privacy-enhancing software using the nominal group technique. We provided participants with an open source software prototype to demonstrate privacy-enhancing features and elicit privacy concerns. Participants generated ideas on benefits, risks, and needed additional information. Following a thematic analysis of the results, we deployed an online questionnaire to identify consensus across all 4 groups. Participants were asked to rank-order benefits and risks. Themes around "needed additional information" were rated by perceived importance on a 5-point Likert scale. RESULTS: Participants considered "allowance for minimum disclosure" and "comprehensive privacy protection that is not currently available" as the most important benefits when using the privacy-enhancing prototype software. The most concerning perceived risks were "additional checks needed beyond the software to ensure privacy protection" and the "potential of misuse by authorized users." Participants indicated a desire for additional information with 6 of the 11 themes receiving a median participant rating of "very necessary" and rated "information on the data custodian" as "essential." CONCLUSIONS: Patients recognize not only the benefits of privacy-enhancing software, but also inherent risks. Patients desire information about how their data are used and protected. Effective patient engagement, communication, and transparency in research may improve patients' comfort levels, alleviate patients' concerns, and thus promote ethical research.

Effective use of data involving personal or sensitive information often requires different people to have access to personal information, which significantly reduces the personal privacy of those whose data is stored and increases risk of identity theft, data leaks, or social engineering attacks. Our research studies the tradeoffs between privacy and utility of personal information for human decision making. Using a record-linkage scenario, this paper presents a controlled study of how varying degrees of information availability influences the ability to effectively use personal information. We compared the quality of human decision-making using a visual interface that controls the amount of personal information available using visual markup to highlight data discrepancies. With this interface, study participants who viewed only 30% of data content had decision quality similar to those who had full 100% access. The results demonstrate that it is possible to greatly limit the amount of personal information available to human decision makers without negatively affecting utility or human effectiveness. However, the findings also show there is a limit to how much data can be hidden before negatively influencing the quality of judgment in decisions involving person-level data. Despite the reduced accuracy with extreme data hiding, the study demonstrates that with proper interface designs, many correct decisions can be made with even legally de-identified data that is fully masked (74.5% accuracy with fully-masked data compared to 84.1% with full access). Thus, when legal requirements only allow for de-identified data access, use of well-designed interface can significantly improve data utility.

OBJECTIVE: Record linkage to integrate uncoordinated databases is critical in biomedical research using Big Data. Balancing privacy protection against the need for high quality record linkage requires a human-machine hybrid system to safely manage uncertainty in the ever changing streams of chaotic Big Data. METHODS: In the computer science literature, private record linkage is the most published area. It investigates how to apply a known linkage function safely when linking two tables. However, in practice, the linkage function is rarely known. Thus, there are many data linkage centers whose main role is to be the trusted third party to determine the linkage function manually and link data for research via a master population list for a designated region. Recently, a more flexible computerized third-party linkage platform, Secure Decoupled Linkage (SDLink), has been proposed based on: (1) decoupling data via encryption, (2) obfuscation via chaffing (adding fake data) and universe manipulation; and (3) minimum information disclosure via recoding. RESULTS: We synthesize this literature to formalize a new framework for privacy preserving interactive record linkage (PPIRL) with tractable privacy and utility properties and then analyze the literature using this framework. CONCLUSIONS: Human-based third-party linkage centers for privacy preserving record linkage are the accepted norm internationally. We find that a computer-based third-party platform that can precisely control the information disclosed at the micro level and allow frequent human interaction during the linkage process, is an effective human-machine hybrid system that significantly improves on the linkage center model both in terms of privacy and utility.

Population informatics is the systematic study of populations via secondary analysis of massive data collections about people, called the social genome. A major challenge in building the social genome is the difficulty in data integration of heterogeneous and uncoordinated data while protecting the confidentiality of the data subjects. Here, we present our work in designing a flexible computerized third party linkage platform, Secure Decoupled Linkage (SDLink), which can provide both privacy protection and accurate high quality integrated data using a hybrid human-machine data integration system. Our evaluation results show that chaffing used in combination with universe manipulation is very effective in blocking inferences during the clerical review process.

Conventional sequential pattern mining methods may meet inherent difficulties in mining databases with long sequences and noise. They may generate a huge number of short and trivial patterns but fail to find interesting patterns approximately shared by many sequences. In this paper, we propose the theme of approximate sequential pattern mining roughly defined as identifying patterns approximately shared by many sequences. We present an efficient and effective algorithm, ApproxMAP, to mine consensus patterns from large sequence databases in two steps. First, sequences are clustered by similarity. Then, consensus patterns are mined directly from each cluster through multiple alignment. We use a real case study to illustrate the effectiveness of ApproxMAP.

No abstract available.