We report the sexual transmission of Coxiella burnetii from a man with occupationally acquired Q fever to his wife. Fifteen days after coitus, his wife also developed serologically proven acute Q fever. C. burnetii DNA sequences were detected by polymerase chain reaction (PCR) performed on semen samples obtained from the husband at 4 and 15 months after the onset of acute Q fever, but PCR results were variable at 23 months, indicating the presence of few organisms.

It is becoming increasingly common to construct databases from information automatically culled from many heterogeneous sources. For example, a research publication database can be constructed by automatically extracting titles, authors, and conference information from papers and their references. A common difficulty in consolidating data from multiple sources is that records are referenced in a variety of ways (e.g. abbreviations, aliases, and misspellings). Therefore, it can be difficult to construct a single, standard representation to present to the user. We refer to the task of constructing this representation as canonicalization. Despite its importance, there is very little existing work on canonicalization.In this paper, we explore the use of edit distance measures to construct a canonical representation that is "central" in the sense that it is most similar to each of the disparate records. This approach reduces the impact of noisy records on the canonical representation. Furthermore, because the user may prefer different styles of canonicalization, we show how different edit distance costs can result in different forms of canonicalization. For example, reducing the cost of character deletions can result in representations that favor abbreviated forms over expanded forms (e.g. KDD versus Conference on Knowledge Discovery and Data Mining). We describe how to learn these costs from a small amount of manually annotated data using stochastic hill-climbing. Additionally, we investigate feature-based methods to learn ranking preferences over canonicalizations. We empirically evaluate our approach on a real-world publications database and show that our learning method results in a canonicalization solution that is robust to errors and easily customizable to user preferences.

Background Data science offers an unparalleled opportunity to identify new insights into many aspects of human life with recent advances in health care. Using data science in digital health raises significant challenges regarding data privacy, transparency, and trustworthiness. Recent regulations enforce the need for a clear legal basis for collecting, processing, and sharing data, for example, the European Union’s General Data Protection Regulation (2016) and the United Kingdom’s Data Protection Act (2018). For health care providers, legal use of the electronic health record (EHR) is permitted only in clinical care cases. Any other use of the data requires thoughtful considerations of the legal context and direct patient consent. Identifiable personal and sensitive information must be sufficiently anonymized. Raw data are commonly anonymized to be used for research purposes, with risk assessment for reidentification and utility. Although health care organizations have internal policies defined for information governance, there is a significant lack of practical tools and intuitive guidance about the use of data for research and modeling. Off-the-shelf data anonymization tools are developed frequently, but privacy-related functionalities are often incomparable with regard to use in different problem domains. In addition, tools to support measuring the risk of the anonymized data with regard to reidentification against the usefulness of the data exist, but there are question marks over their efficacy. Objective In this systematic literature mapping study, we aim to alleviate the aforementioned issues by reviewing the landscape of data anonymization for digital health care. Methods We used Google Scholar, Web of Science, Elsevier Scopus, and PubMed to retrieve academic studies published in English up to June 2020. Noteworthy gray literature was also used to initialize the search. We focused on review questions covering 5 bottom-up aspects: basic anonymization operations, privacy models, reidentification risk and usability metrics, off-the-shelf anonymization tools, and the lawful basis for EHR data anonymization. Results We identified 239 eligible studies, of which 60 were chosen for general background information; 16 were selected for 7 basic anonymization operations; 104 covered 72 conventional and machine learning–based privacy models; four and 19 papers included seven and 15 metrics, respectively, for measuring the reidentification risk and degree of usability; and 36 explored 20 data anonymization software tools. In addition, we also evaluated the practical feasibility of performing anonymization on EHR data with reference to their usability in medical decision-making. Furthermore, we summarized the lawful basis for delivering guidance on practical EHR data anonymization. Conclusions This systematic literature mapping study indicates that anonymization of EHR data is theoretically achievable; yet, it requires more research efforts in practical implementations to balance privacy preservation and usability to ensure more reliable health care applications.

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