Using Maximum Entropy to Extend a Consent Privacy Impact Quantification

Due to the progress of digitization in the medical sector digital consent becomes more and more common. While digital consent itself has a huge number of benefits for the researcher it can impose a lot of questions for the individual giving it. One of those questions is what impact the consent to sharing data with a research project has on the individual’s privacy. The Consent Privacy Impact Quantification (CPIQ) provides a quantification to help the user making a consent decision based on the potential data sharing risk and his individual acceptance preferences for a research project. While this quantification provides a good first estimation it has some limitations especially in the method the re-identification risk is calculated for a member of a dataset. This paper presents a method using the Maximum Entropy principle. This principle provides a way to measure the maximum unbiased distribution using limited background knowledge, which is provided by epidemiological data. This distribution can then be used to see how much higher the re-identification risk based on a sensitive attribute is compared to the uniform distribution. In addition, the first promising results of the method will be shown based on an experimental setting

Privacy and Utility of Private Synthetic Data for Medical Data Analyses

The increasing availability and use of sensitive personal data raises a set of issues regarding the privacy of the individuals behind the data. These concerns become even more important when health data are processed, as are considered sensitive (according to most global regulations). Privacy Enhancing Technologies (PETs) attempt to protect the privacy of individuals whilst preserving the utility of data. One of the most popular technologies recently is Differential Privacy (DP), which was used for the 2020 U.S. Census. Another trend is to combine synthetic data generators with DP to create so-called private synthetic data generators. The objective is to preserve statistical properties as accurately as possible, while the generated data should be as different as possible compared to the original data regarding private features. While these technologies seem promising, there is a gap between academic research on DP and synthetic data and the practical application and evaluation of these techniques for real-world use cases. In this paper, we evaluate three different private synthetic data generators (MWEM, DP-CTGAN, and PATE-CTGAN) on their use-case-specific privacy and utility. For the use case, continuous heart rate measurements from different individuals are analyzed. This work shows that private synthetic data generators have tremendous advantages over traditional techniques, but also require in-depth analysis depending on the use case. Furthermore, it can be seen that each technology has different strengths, so there is no clear winner. However, DP-CTGAN often performs slightly better than the other technologies, so it can be recommended for a continuous medical data use case

Sovereign Digital Consent through Privacy Impact Quantification and Dynamic Consent

Digitization is becoming more and more important in the medical sector. Through electronic health records and the growing amount of digital data of patients available, big data research finds an increasing amount of use cases. The rising amount of data and the imposing privacy risks can be overwhelming for patients, so they can have the feeling of being out of control of their data. Several previous studies on digital consent have tried to solve this problem and empower the patient. However, there are no complete solution for the arising questions yet. This paper presents the concept of Sovereign Digital Consent by the combination of a consent privacy impact quantification and a technology for proactive sovereign consent. The privacy impact quantification supports the patient to comprehend the potential risk when sharing the data and considers the personal preferences regarding acceptance for a research project. The proactive dynamic consent implementation provides an implementation for fine granular digital consent, using medical data categorization terminology. This gives patients the ability to control their consent decisions dynamically and is research friendly through the automatic enforcement of the patients’ consent decision. Both technologies are evaluated and implemented in a prototypical application. With the combination of those technologies, a promising step towards patient empowerment through Sovereign Digital Consent can be made

Sotorasib Shows Intracranial Activity in Patients with KRAS G12C-Mutated Adenocarcinoma of the Lung and Untreated Active Brain Metastases.

Treatment with sotorasib has shown intracranial complete responses and continued intracranial stabilization in KRAS G12C-mutated non-small-cell lung carcinoma (NSCLC) patients with previously treated, stable brain metastases in a post hoc analysis of the ongoing CodeBreaK 100 trial. We present the case of a patient with KRAS G12C-mutant adenocarcinoma of the lung with active untreated brain metastases with a nearly complete intracranial response only 6 weeks after start of sotorasib illustrating the benefit of sotorasib in patients with active, previously untreated brain metastases in KRAS G12C-mutated NSCLC

Diagnosing Overtraining Syndrome: A Scoping Review

Overtraining syndrome (OTS) is a condition characterized by a long-term performance decrement, which occurs after a persisting imbalance between training-related and nontraining-related load and recovery. Because of the lack of a gold standard diagnostic test, OTS remains a diagnosis of exclusion.; To systematically review and map biomarkers and tools reported in the literature as potentially diagnostic for OTS.; PubMed, Web of Science, and SPORTDiscus were searched from database inception to February 4, 2021, and results screened for eligibility. Backward and forward citation tracking on eligible records were used to complement results of database searching.; Studies including athletes with a likely OTS diagnosis, as defined by the European College of Sport Science and the American College of Sports Medicine, and reporting at least 1 biomarker or tool potentially diagnostic for OTS were deemed eligible.; Scoping review following the guidelines of the Joanna Briggs Institute and PRISMA Extension for Scoping Reviews (PRISMA-ScR).; Level 4.; Athletes' population, criteria used to diagnose OTS, potentially diagnostic biomarkers and tools, as well as miscellaneous study characteristics were extracted.; The search yielded 5561 results, of which 39 met the eligibility criteria. Three diagnostic scores, namely the EROS-CLINICAL, EROS-SIMPLIFIED, and EROS-COMPLETE scores (EROS = Endocrine and Metabolic Responses on Overtraining Syndrome study), were identified. Additionally, basal hormone, neurotransmitter and other metabolite levels, hormonal responses to stimuli, psychological questionnaires, exercise tests, heart rate variability, electroencephalography, immunological and redox parameters, muscle structure, and body composition were reported as potentially diagnostic for OTS.; Specific hormones, neurotransmitters, and metabolites, as well as psychological, electrocardiographic, electroencephalographic, and immunological patterns were identified as potentially diagnostic for OTS, reflecting its multisystemic nature. As exemplified by the EROS scores, combinations of these variables may be required to diagnose OTS. These scores must now be validated in larger samples and within female athletes

Towards a Privacy Compliant Research Interface for Multicenter Medical Data

Big Data analysis gains more and more interest in the processing of e-Healthdata. The potentially big benefit of those analyses comes with a set of newunknown impacts to an individual’s privacy. Still it is important to find a balancebetween privacy impact and utility of the medical data analysis. To achieve this,this technical report takes a look on different privacy preserving techniques,that could be used for a privacy preserving research interface for medical data.The three techniques Differential privacy,k-Anonymity and Secure multi-partyComputation are evaluated on their feasibility for a medical use-case. Withthose preliminaries some formal definitions are made for a privacy preservingresearch interface which implements an hybrid approach of the three techniquesand a consent based interface

Privacy and Utility of Private Synthetic Data for Medical Data Analyses

The increasing availability and use of sensitive personal data raises a set of issues regarding the privacy of the individuals behind the data. These concerns become even more important when health data are processed, as are considered sensitive (according to most global regulations). Privacy Enhancing Technologies (PETs) attempt to protect the privacy of individuals whilst preserving the utility of data. One of the most popular technologies recently is Differential Privacy (DP), which was used for the 2020 U.S. Census. Another trend is to combine synthetic data generators with DP to create so-called private synthetic data generators. The objective is to preserve statistical properties as accurately as possible, while the generated data should be as different as possible compared to the original data regarding private features. While these technologies seem promising, there is a gap between academic research on DP and synthetic data and the practical application and evaluation of these techniques for real-world use cases. In this paper, we evaluate three different private synthetic data generators (MWEM, DP-CTGAN, and PATE-CTGAN) on their use-case-specific privacy and utility. For the use case, continuous heart rate measurements from different individuals are analyzed. This work shows that private synthetic data generators have tremendous advantages over traditional techniques, but also require in-depth analysis depending on the use case. Furthermore, it can be seen that each technology has different strengths, so there is no clear winner. However, DP-CTGAN often performs slightly better than the other technologies, so it can be recommended for a continuous medical data use case

Impact of sedentary behavior on large artery structure and function in children and adolescents: a systematic review

Sedentary behavior contributes to increased atherosclerotic risk in adults. Whether or not this can be extended to pediatric populations is unclear. This systematic review assessed associations of sedentary behavior with large artery structure and function in pediatric populations. MEDLINE, EMBASE, CENTRAL, and Web of Science were searched from the earliest available date to 31st of December 2018. Analyses of associations of sedentary behavior with large artery structure or function in a pediatric (sub-)population were included, adhering to the PRISMA guidelines. The protocol was published in advance on PROSPERO (CRD42018112996). Study quality and quality of evidence were analyzed using NHLBI Study Quality assessment tools and GRADE. Six observational studies found no association of exposure and outcome variables, and one had contradicting results. One intervention found reduced flow-mediated dilation after 3 h of uninterrupted sitting. Exposure and outcome measures were highly heterogeneous. Study quality was low to moderate. Quality of evidence was very low or low in the observational studies and high in the intervention.Conclusion: In pediatric populations, current evidence is limited and of low quality about how acute effects of sedentary behavior translate into early vascular aging and the long-term development of vascular dysfunction and atherosclerotic risk. Future studies should emphasize a careful choice of the adequate type and measurement site of a biomarker for large artery structure and function as well as conduct a detailed assessment of sedentary behavior patterns.Trial registration: PROSPERO Registration Number: CRD42018112996What is known: - An independent association of sedentary behavior and biomarkers of large artery structure and function has been demonstrated in adults. - In children, sedentary behavior is directly associated with classical cardiovascular risk factors like elevated blood glucose levels, insulin resistance, high blood pressure, obesity, and elevated blood lipids.What is new: - Currently, only few studies of low quality in children and adolescents provide limited evidence about how acute effects of sedentary behavior translate into early vascular aging and the long-term development of atherosclerosis. - The type and measurement site of vascular biomarker need to be chosen carefully, and a detailed assessment of sedentary behavior patterns is important to minimize the methodological bias