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Deep learning continues to rapidly evolve and is now demonstrating remarkable potential for numerous medical prediction tasks. However, realizing deep learning models that generalize across healthcare organizations is challenging. This is due, in part, to the inherent siloed nature of these organizations and patient privacy requirements. To address this problem, we illustrate how split learning can enable collaborative training of deep learning models across disparate and privately maintained health datasets, while keeping the original records and model parameters private. We introduce a new privacy-preserving distributed learning framework that offers a higher level of privacy compared to conventional federated learning. We use several biomedical imaging and electronic health record (EHR) datasets to show that deep learning models trained via split learning can achieve highly similar performance to their centralized and federated counterparts while greatly improving computational efficiency and reducing privacy risks., (©2023 AMIA - All rights reserved.)

Objective:  To summarize notable research contributions published in 2017 on data sharing and privacy issues in medical informatics., Methods:  An extensive search of PubMed/Medline, Web of Science, ACM Digital Library, IEEE Xplore, and AAAI Digital Library was conducted to uncover the scientific contributions published in 2017 that addressed issues of biomedical data sharing, with a focus on data access and privacy. The selection process was based on three steps: (i) a selection of candidate best papers, (ii) the review of the candidate best papers by a team of international experts with respect to six predefined criteria, and (iii) the selection of the best papers by the editorial board of the Yearbook Results: Five best papers were selected. They cover the lifecycle of biomedical data collection, use, and sharing. The papers introduce 1) consenting strategies for emerging environments, 2) software for searching and retrieving datasets in organizationally distributed environments, 3) approaches to measure the privacy risks of sharing new data increasingly utilized in research and the clinical setting (e.g., genomic), 4) new cryptographic techniques for querying clinical data for cohort discovery, and 5) novel game theoretic strategies for publishing summary information about genome-phenome studies that balance the utility of the data with potential privacy risks to the participants of such studies., Conclusion:  The papers illustrated that there is no one-size-fitsall solution to privacy while working with biomedical data. At the same time, the papers show that there are opportunities for leveraging newly emerging technologies to enable data use while minimizing privacy risks., Competing Interests: Disclosure The authors report no conflicts of interest in this work., (Georg Thieme Verlag KG Stuttgart.)

Objective: Healthcare analytics research increasingly involves the construction of predictive models for disease targets across varying patient cohorts using electronic health records (EHRs). To facilitate this process, it is critical to support a pipeline of tasks: (1) cohort construction, (2) feature construction, (3) cross-validation, (4) feature selection, and (5) classification. To develop an appropriate model, it is necessary to compare and refine models derived from a diversity of cohorts, patient-specific features, and statistical frameworks. The goal of this work is to develop and evaluate a predictive modeling platform that can be used to simplify and expedite this process for health data., Methods: To support this goal, we developed a PARAllel predictive MOdeling (PARAMO) platform which (1) constructs a dependency graph of tasks from specifications of predictive modeling pipelines, (2) schedules the tasks in a topological ordering of the graph, and (3) executes those tasks in parallel. We implemented this platform using Map-Reduce to enable independent tasks to run in parallel in a cluster computing environment. Different task scheduling preferences are also supported., Results: We assess the performance of PARAMO on various workloads using three datasets derived from the EHR systems in place at Geisinger Health System and Vanderbilt University Medical Center and an anonymous longitudinal claims database. We demonstrate significant gains in computational efficiency against a standard approach. In particular, PARAMO can build 800 different models on a 300,000 patient data set in 3h in parallel compared to 9days if running sequentially., Conclusion: This work demonstrates that an efficient parallel predictive modeling platform can be developed for EHR data. This platform can facilitate large-scale modeling endeavors and speed-up the research workflow and reuse of health information. This platform is only a first step and provides the foundation for our ultimate goal of building analytic pipelines that are specialized for health data researchers., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Objective: The goal of this research is to learn how the editorial staffs of bioinformatics and medical informatics journals provide support for cross-community exposure. Models such as co-citation and co-author analysis measure the relationships between researchers; but they do not capture how environments that support knowledge transfer across communities are organized., Methods: In this paper, we propose a social network analysis model to study how editorial boards integrate researchers from disparate communities. We evaluate our model by building relational networks based on the editorial boards of approximately 40 journals that serve as research outlets in medical informatics and bioinformatics. We track the evolution of editorial relationships through a longitudinal investigation over the years 2000 through 2005., Results: Our findings suggest that there are research journals that support the collocation of editorial board members from the bioinformatics and medical informatics communities. Network centrality metrics indicate that editorial board members are located in the intersection of the communities and that the number of individuals in the intersection is growing with time., Conclusions: Social network analysis methods provide insight into the relationships between the medical informatics and bioinformatics communities. The number of editorial board members facilitating the publication intersection of the communities has grown, but the intersection remains dependent on a small group of individuals and fragile.

OBJECTIVE:Hepatorenal Syndrome (HRS) is a devastating form of acute kidney injury (AKI) in advanced liver disease patients with high morbidity and mortality, but phenotyping algorithms have not yet been developed using large electronic health record (EHR) databases. We evaluated and compared multiple phenotyping methods to achieve an accurate algorithm for HRS identification. MATERIALS AND METHODS:A national retrospective cohort of patients with cirrhosis and AKI admitted to 124 Veterans Affairs hospitals was assembled from electronic health record data collected from 2005 to 2013. AKI was defined by the Kidney Disease: Improving Global Outcomes criteria. Five hundred and four hospitalizations were selected for manual chart review and served as the gold standard. Electronic Health Record based predictors were identified using structured and free text clinical data, subjected through NLP from the clinical Text Analysis Knowledge Extraction System. We explored several dimension reduction techniques for the NLP data, including newer high-throughput phenotyping and word embedding methods, and ascertained their effectiveness in identifying the phenotype without structured predictor variables. With the combined structured and NLP variables, we analyzed five phenotyping algorithms: penalized logistic regression, naïve Bayes, support vector machines, random forest, and gradient boosting. Calibration and discrimination metrics were calculated using 100 bootstrap iterations. In the final model, we report odds ratios and 95% confidence intervals. RESULTS:The area under the receiver operating characteristic curve (AUC) for the different models ranged from 0.73 to 0.93; with penalized logistic regression having the best discriminatory performance. Calibration for logistic regression was modest, but gradient boosting and support vector machines were superior. NLP identified 6985 variables; a priori variable selection performed similarly to dimensionality reduction using high-throughput phenotyping and semantic similarity informed clustering (AUC of 0.81 - 0.82). CONCLUSION:This study demonstrated improved phenotyping of a challenging AKI etiology, HRS, over ICD-9 coding. We also compared performance among multiple approaches to EHR-derived phenotyping, and found similar results between methods. Lastly, we showed that automated NLP dimension reduction is viable for acute illness.

Abstract: A medical specialty indicates the skills needed by health care providers to conduct key procedures or make critical judgments. However, documentation about specialties may be lacking or inaccurately specified in a health care institution. Thus, we propose to leverage diagnosis histories to recognize medical specialties that exist in practice. Such specialties that are highly recognizable through diagnosis histories are de facto diagnosis specialties. We aim to recognize de facto diagnosis specialties that are listed in the Health Care Provider Taxonomy Code Set (HPTCS) and discover those that are unlisted. First, to recognize the former, we use similarity and supervised learning models. Next, to discover de facto diagnosis specialties unlisted in the HPTCS, we introduce a general discovery‐evaluation framework. In this framework, we use a semi‐supervised learning model and an unsupervised learning model, from which the discovered specialties are subsequently evaluated by the similarity and supervised learning models used in recognition. To illustrate the potential for these approaches, we collect 2 data sets of 1 year of diagnosis histories from a large academic medical center: One is a subset of the other except for additional information useful for network analysis. The results indicate that 12 core de facto diagnosis specialties listed in the HPTCS are highly recognizable. Additionally, the semi‐supervised learning model discovers a specialty for breast cancer on the smaller data set based on network analysis, while the unsupervised learning model confirms this discovery and suggests an additional specialty for Obesity on the larger data set. The potential correctness of these 2 specialties is reinforced by the evaluation results that they are highly recognizable by similarity and supervised learning models in comparison with 12 core de facto diagnosis specialties listed in the HPTCS. [ABSTRACT FROM AUTHOR]

Background The increased use of human biological material for cell-based research and clinical interventions poses risks to the privacy of patients and donors, including the possibility of re-identification of individuals from anonymized cell lines and associated genetic data. These risks will increase as technologies and databases used for re-identification become affordable and more sophisticated. Policies that require ongoing linkage of cell lines to donors' clinical information for research and regulatory purposes, and existing practices that limit research participants' ability to control what is done with their genetic data, amplify the privacy concerns. Discussion To date, the privacy issues associated with cell-based research and interventions have not received much attention in the academic and policymaking contexts. This paper, arising out of a multi-disciplinary workshop, aims to rectify this by outlining the issues, proposing novel governance strategies and policy recommendations, and identifying areas where further evidence is required to make sound policy decisions. The authors of this paper take the position that existing rules and norms can be reasonably extended to address privacy risks in this context without compromising emerging developments in the research environment, and that exceptions from such rules should be justified using a case-by-case approach. In developing new policies, the broader framework of regulations governing cellbased research and related areas must be taken into account, as well as the views of impacted groups, including scientists, research participants and the general public. Summary This paper outlines deliberations at a policy development workshop focusing on privacy challenges associated with cell-based research and interventions. The paper provides an overview of these challenges, followed by a discussion of key themes and recommendations that emerged from discussions at the workshop. The paper concludes that privacy risks associated with cell-based research and interventions should be addressed through evidencebased policy reforms that account for both well-established legal and ethical norms and current knowledge about actual or anticipated harms. The authors also call for research studies that identify and address gaps in understanding of privacy risks. [ABSTRACT FROM AUTHOR]

Health information technologies facilitate the collection of massive quantities of patient-level data. A growing body of research demonstrates that such information can support novel, large-scale biomedical investigations at a fraction of the cost of traditional prospective studies. While healthcare organizations are being encouraged to share these data in a de-identified form, there is hesitation over concerns that it will allow corresponding patients to be re-identified. Currently proposed technologies to anonymize clinical data may make unrealistic assumptions with respect to the capabilities of a recipient to ascertain a patients identity. We show that more pragmatic assumptions enable the design of anonymization algorithms that permit the dissemination of detailed clinical profiles with provable guarantees of protection. We demonstrate this strategy with a dataset of over one million medical records and show that 192 genotype-phenotype associations can be discovered with fidelity equivalent to non-anonymized clinical data. [ABSTRACT FROM AUTHOR]

Abstract: Objectives: Healthcare organizations must adopt measures to uphold their patients’ right to anonymity when sharing sensitive records, such as DNA sequences, to publicly accessible databanks. This is often achieved by suppressing patient identifiable information; however, such a practice is insufficient because the same organizations may disclose identified patient information, devoid of the sensitive information, for other purposes and patients’ organization-visit patterns, or trails, can re-identify records to the identities from which they were derived. There exist various algorithms that healthcare organizations can apply to ascertain when a patient''s record is susceptible to trail re-identification, but they require organizations to exchange information regarding the identities of their patients prior to data protection certification. In this paper, we introduce an algorithmic approach to formally thwart trail re-identification in a secure setting. Methods and materials: We present a framework that allows data holders to securely collaborate through a third party. In doing so, healthcare organizations keep all sensitive information in an encrypted state until the third party certifies that the data to be disclosed satisfies a formal data protection model. The model adopted for this work is an extended form of k-unlinkability, a protection model that, until this work, was applied in a non-secure setting only. Given the framework and protection model, we develop an algorithm to generate data that satisfies the protection model. In doing so, we enable healthcare organizations to prevent trail re-identification without revealing identified information. Results: Theoretically, we prove that the proposed data protection model does not leak information, even in the context of an organization''s prior knowledge. Empirically, we use real world hospital discharge records to demonstrate that, while the secure protocol induces additional suppression of patient information in comparison to an existing non-secure approach, the quantity of data disclosed by the secure protocol remains substantial. For instance, in a population of over 7700 sickle cell anemia patients, the non-secure protocol discloses 99.48% of DNA records whereas the secure protocol permits the disclosure of 99.41%. Conclusions: Our results demonstrate healthcare organizations can collaborate to disclose significant quantities of personal biomedical data without violating their anonymity in the process. [Copyright &y& Elsevier]

Abstract: Purpose: Medical records must often be stripped of patient identifiers, or de-identified, before being shared. De-identification by humans is time-consuming, and existing software is limited in its generality. The open source MITRE Identification Scrubber Toolkit (MIST) provides an environment to support rapid tailoring of automated de-identification to different document types, using automatically learned classifiers to de-identify and protect sensitive information. Methods: MIST was evaluated with four classes of patient records from the Vanderbilt University Medical Center: discharge summaries, laboratory reports, letters, and order summaries. We trained and tested MIST on each class of record separately, as well as on pooled sets of records. We measured precision, recall, F-measure and accuracy at the word level for the detection of patient identifiers as designated by the HIPAA Safe Harbor Rule. Results: MIST was applied to medical records that differed in the amounts and types of protected health information (PHI): lab reports contained only two types of PHI (dates, names) compared to discharge summaries, which were much richer. Performance of the de-identification tool depended on record class; F-measure results were 0.996 for order summaries, 0.996 for discharge summaries, 0.943 for letters and 0.934 for laboratory reports. Experiments suggest the tool requires several hundred training exemplars to reach an F-measure of at least 0.9. Conclusions: The MIST toolkit makes possible the rapid tailoring of automated de-identification to particular document types and supports the transition of the de-identification software to medical end users, avoiding the need for developers to have access to original medical records. We are making the MIST toolkit available under an open source license to encourage its application to diverse data sets at multiple institutions. [ABSTRACT FROM AUTHOR]