Enabling analytics on sensitive medical data with secure multi-party computation

While there is a clear need to apply data analytics in the healthcare sector, this is often difficult because it requires combining sensitive data from multiple data sources. In this paper, we show how the cryptographic technique of secure multiparty computation can enable such data analytics by performing analytics without the need to share the underlying data. We discuss the issue of compliance to European privacy legislation; report on three pilots bringing these techniques closer to practice; and discuss the main challenges ahead to make fully privacy-preserving data analytics in the medical sector commonplace

Privacy-preserving dataset combination and Lasso regression for healthcare predictions

Background: Recent developments in machine learning have shown its potential impact for clinical use such as risk prediction, prognosis, and treatment selection. However, relevant data are often scattered across different stakeholders and their use is regulated, e.g. by GDPR or HIPAA. As a concrete use-case, hospital Erasmus MC and health insurance company Achmea have data on individuals in the city of Rotterdam, which would in theory enable them to train a regression model in order to identify high-impact lifestyle factors for heart failure. However, privacy and confdentiality concerns make it unfeasible to exchange these data. Methods: This article describes a solution where vertically-partitioned synthetic data of Achmea and of Erasmus MC are combined using Secure Multi-Party Computation. First, a secure inner join protocol takes place to securely determine the identifiers of the patients that are represented in both datasets. Then, a secure Lasso Regression model is trained on the securely combined data. The involved parties thus obtain the prediction model but no further information on the input data of the other parties. Results: We implement our secure solution and describe its performance and scalability: we can train a prediction model on two datasets with 5000 records each and a total of 30 features in less than one hour, with a minimal difference from the results of standard (non-secure) methods. Conclusions: This article shows that it is possible to combine datasets and train a Lasso regression model on this combination in a secure way. Such a solution thus further expands the potential of privacy-preserving data analysis in the medical domain

Graduation committee: Prof. dr. F.M.G. de Jong, promotor

ter verkrijging van de graad van doctor aan de Universiteit Twente, op gezag van de rector magnificus, prof. dr. F.A. van Vught, volgens besluit van het College voor Promoties in het openbaar te verdedigen op vrijdag 18 juni 2004 om 13.15 uu

Query interpretation : an application of semiotics in image retrieval

One of the challenges in the field of content-based image retrieval is to bridge the semantic gap that exists between the information extracted from visual data using classifiers, and the interpretation of this data made by the end users. The semantic gap is a cascade of 1) the transformation of image pixels into labelled objects and 2) the semantic distance between the label used to name the classifier and that what it refers to for the end-user. In this paper, we focus on the second part and specifically on (semantically) scalable solutions that are independent from domain-specific vocabularies. To this end, we propose a generic semantic reasoning approach that applies semiotics in its query interpretation. Semiotics is about how humans interpret signs, and we use its text analysis structures to guide the query expansion that we apply. We evaluated our approach using a general-purpose image search engine. In our experiments, we compared several semiotic structures to determine to what extent semiotic structures contribute to the semantic interpretation of user queries. From the results of the experiments we conclude that semiotic structures can contribute to a significantly higher semantic interpretation of user queries and significantly higher image retrieval performance, measured in quality and effectiveness and compared to a baseline with only synonym expansions

Lifestyle and health changes in wheelchair users with a chronic disability after 12 weeks of using the WHEELS mHealth application

PURPOSE: The aim of this study was to determine changes in physical activity, nutrition, sleep behaviour and body composition in wheelchair users with a chronic disability after 12 weeks of using the WHEELS mHealth application (app). METHODS: A 12-week pre-post intervention study was performed, starting with a 1-week control period. Physical activity and sleep behaviour were continuously measured with a Fitbit charge 3. Self-reported nutritional intake, body mass and waist circumference were collected. Pre-post outcomes were compared with a paired-sample t-test or Wilcoxon signed-rank test. Fitbit data were analysed with a mixed model or a panel linear model. Effect sizes were determined and significance was accepted at p < .05. RESULTS: Thirty participants completed the study. No significant changes in physical activity (+1.5 √steps) and sleep quality (-9.7 sleep minutes; -1.2% sleep efficiency) were found. Significant reduction in energy (-1022 kJ, d  = 0.71), protein (-8.3 g, d  = 0.61) and fat (-13.1 g, d  = 0.87) intake, body mass (-2.2 kg, d  = 0.61) and waist circumference (-3.3 cm, d  = 0.80) were found. CONCLUSION: Positive changes were found in nutritional behaviour and body composition, but not in physical activity and sleep quality. The WHEELS app seems to partly support healthy lifestyle behaviour.Implications for RehabilitationHealthy lifestyle promotion is crucial, especially for wheelchair users as they tend to show poorer lifestyle behaviour despite an increased risk of obesity and comorbidity.The WHEELS lifestyle app seems to be a valuable tool to support healthy nutrition choices and weight loss and to improve body satisfaction, mental health and vitality

A multi-stakeholder approach to eHealth development: Promoting sustained healthy living among cardiovascular patients

Background: Healthy living is key in the prevention and rehabilitation of cardiovascular disease (CVD). Yet, supporting and maintaining a healthy lifestyle is exceptionally difficult and people differ in their needs regarding optimal support for healthy lifestyle interventions. Objective: The goals of this study were threefold: to uncover stakeholders’ needs and preferences, to translate these to core values, and develop eHealth technology based on these core values. Our primary research question is: What type of eHealth application to support healthy living among people with (a high risk of) CVD would provide the greatest benefit for all stakeholders? Methods: User-centered design principles from the CeHRes roadmap for eHealth development were followed to guide the uncovering of important stakeholder values. Data were synthesized from various qualitative studies (i.e., literature studies, interviews, think-aloud sessions, focus groups) and usability tests (i.e., heuristic evaluation, cognitive walkthrough, think aloud study). We also developed an innovative application evaluation tool to perform a competitor analysis on 33 eHealth applications. Finally, to make sure to take into account all end-users needs and preferences in eHealth technology development, we created personas and a customer journey. Results: We uncovered 10 universal values to which eHealth-based initiatives to support healthy living in the context of CVD prevention and rehabilitation should adhere to (e.g., providing social support, stimulating intrinsic motivation, offering continuity of care). These values were translated to 14 desired core attributes and then prototype designs. Interestingly, we found that the primary attribute of good eHealth technology was not a single intervention principle, but rather that the technology should be in the form of a digital platform disseminating various interventions, i.e., a ‘one-stop-shop’. Conclusion: Various stakeholders in the field of cardiovascular prevention and rehabilitation may benefit most from utilizing one personalized eHealth platform that integrates a variety of evidence-based interventions, rather than a new tool. Instead of a one-size-fits-all approach, this digital platform should aid the matchmaking between patients and specific interventions based on personal characteristics and preferences