Contagious cooperation, temptation, and ecosystem collapse

AbstractReal world observations suggest that social norms of cooperation can be effective in overcoming social dilemmas such as the joint management of a common pool resource—but also that they can be subject to slow erosion and sudden collapse. We show that these patterns of erosion and collapse emerge endogenously in a model of a closed community harvesting a renewable natural resource in which individual agents face the temptation to overexploit the resource, while a cooperative harvesting norm spreads through the community via interpersonal relations. We analyze under what circumstances small changes in key parameters (including the size of the community, and the rate of technological progress) trigger catastrophic transitions from relatively high levels of cooperation to widespread norm violation—causing the social–ecological system to collapse

Analyze Decentralized Personal Health Data using Solid, Digital Consent, and Federated Learning

TIDAL is a Solid (SOcial Linked Data)-based, citizen-centric data platform that facilitates interactions by citizens and researchers for health research. In this demonstration, we will show how TIDAL 1) can store personal data in Solid pods as RDF with well-known health-related vocabularies (e.g., SNOMED CT), 2) controls access to query fine-grained subsets of personal data, 3) enables researchers to post a human- and machine-readable digital consent using Data Privacy Vocabulary, and 4) uses federated learning to analyze personal health data from multiple individuals using a Personal Health Train framework. TIDAL offers a new data paradigm of sharing and using personal data for research and ultimately increase the availability of personal data for societal relevant uses

A modular approach to knowledge graphs and FAIR data in healthcare

In healthcare, and more specifically cancer treatment, data sharing is essential yet difficult. 1 in 5 people diagnosed with cancer have a rare type of cancer, which means considerable time is needed to collect sufficient data for research. Combining data from multiple centres is therefore vital, unfortunately, linking this data is not straightforward. There are various ways healthcare centres store their data, due to for instance differences in treatment protocols and clinical systems. This means different variables and annotations are used. Consequently before we can solve any medical problems, we first need to solve this data integration challenge

ciTIzen-centric DatA pLatform (TIDAL): Sharing Distributed Personal Data in a Privacy-Preserving Manner for Health Research

Developing personal data sharing tools and standards in conformity with data protection regulations is essential to empower citizens to control and share their health data with authorized parties for any purpose they approve. This can be, among others, for primary use in healthcare, or secondary use for research to improve human health and well-being. Ensuring that citizens are able to make fine-grained decisions about how their personal health data can be used and shared will significantly encourage citizens to participate in more health-related research. In this paper, we propose a ciTIzen-centric DatA pLatform (TIDAL) to give individuals ownership of their own data, and connect them with researchers to donate the use of their personal data for research while being in control of the entire data life cycle, including data access, storage and analysis. We recognize that most existing technologies focus on one particular aspect such as personal data storage, or suffer from executing data analysis over a large number of participants, or face challenges of low data quality and insufficient data interoperability. To address these challenges, the TIDAL platform integrates a set of components for requesting subsets of RDF (Resource Description Framework) data stored in personal data vaults based on SOcial LInked Data (Solid) technology and analyzing them in a privacy-preserving manner. We demonstrate the feasibility and efficiency of the TIDAL platform by conducting a set of simulation experiments using three different pod providers (Inrupt, Solidcommunity, Self-hosted Server). On each pod provider, we evaluated the performance of TIDAL by querying and analyzing personal health data with varying scales of participants and configurations. The reasonable total time consumption and a linear correlation between the number of pods and variables on all pod providers show the feasibility and potential to implement and use the TIDAL platform in practice. TIDAL facilitates individuals to access their personal data in a fine-grained manner and to make their own decision on their data. Researchers are able to reach out to individuals and send them digital consent directly for using personal data for health-related research. TIDAL can play an important role to connect citizens, researchers, and data organizations to increase the trust placed by citizens in the processing of personal data.publishedVersio

Transformation and integration of heterogeneous health data in a privacy-preserving distributed learning infrastructure

Problem statement: A growing volume and variety of personal health data are being collected by different entities, such as healthcare providers, insurance companies, and wearable device manufacturers. Combining heterogeneous health data offers unprecedented opportunities to augment our understanding of human health and disease. However, a major challenge to research lies in the difficulty of accessing and analyzing health data that are dispersed in their format (e.g. CSV, XML), sources (e.g., medical records, laboratory data), representation (unstructured, structured), and governance (e.g., data collection and maintenance)[2]. Such considerations are crucial when we link and use personal health data across multiple legal entities with different data governance and privacy concerns

Annotation of existing databases using Semantic Web technologies:Making data more FAIR

Making data FAIR is an elaborate task. Hospitals and/or departments have to invest into technologies usually unknown and often do not have the resources to make data FAIR. Our work aims to provide a framework and tooling where users can easily make their data (more) FAIR. This framework uses RDF and OWL-based inferencing to annotate existing databases or comma-separated files. For every database, a custom ontology is build based on the database schema, which can be annotated to describe matching standardized terminologies. In this work, we describe the tooling developed, and the current implementation in an institutional datawarehouse pertaining over 3000 rectal cancer patients. We report on the performance (time) of the extraction and annotation process by the developed tooling. Furthermore, we do show that annotation of existing databases using OWL2-based reasoning is possible. Furthermore, we show that the ontology extracted from existing databases can provide a description framework to describe and annotate existing data sources. This would target mostly the “Interoperable” aspect of FAIR

Colorectal cancer health and care quality indicators in a federated setting using the Personal Health Train

Objective: Hospitals and healthcare providers should assess and compare the quality of care given to patients and based on this improve the care. In the Netherlands, hospitals provide data to national quality registries, which in return provide annual quality indicators. However, this process is time-consuming, resource intensive and risks patient privacy and confidentiality. In this paper, we presented a multicentric ‘Proof of Principle’ study for federated calculation of quality indicators in patients with colorectal cancer. The findings suggest that the proposed approach is highly time-efficient and consume significantly lesser resources. Materials and methods: Two quality indicators are calculated in an efficient and privacy presevering federated manner, by i) applying the Findable Accessible Interoperable and Reusable (FAIR) data principles and ii) using the Personal Health Train (PHT) infrastructure. Instead of sharing data to a centralized registry, PHT enables analysis by sending algorithms and sharing only insights from the data. Results: ETL process extracted data from the Electronic Health Record systems of the hospitals, converted them to FAIR data and hosted in RDF endpoints within each hospital. Finally, quality indicators from each center are calculated using PHT and the mean result along with the individual results plotted. Discussion and conclusion: PHT and FAIR data principles can efficiently calculate quality indicators in a privacy-preserving federated approach and the work can be scaled up both nationally and internationally. Despite this, application of the methodology was largely hampered by ELSI issues. However, the lessons learned from this study can provide other hospitals and researchers to adapt to the process easily and take effective measures in building quality of care infrastructures.</p

OCT-measured plaque free wall angle is indicative for plaque burden: overcoming the main limitation of OCT?

textabstractThe aim of this study was to investigate the relationship between the plaque free wall (PFW) measured by optical coherence tomography (OCT) and the plaque burden (PB) measured by intravascular ultrasound (IVUS). We hypothesize that measurement of the PFW could help to estimate the PB, thereby overcoming the limited ability of OCT to visualize the external elastic membrane in the presence of plaque. This could enable selection of the optimal stent-landing zone by OCT, which is traditionally defined by IVUS as a region with a PB < 40 %. PB (IVUS) and PFW angle (OCT and IVUS) were measured in 18 matched IVUS and OCT pullbacks acquired in the same coronary artery. We determined the relationship between OCT measured PFW (PFWOCT) and IVUS PB (PBIVUS) by non-linear regression analysis. An ROC-curve analysis was used to determine the optimal cut-off value of PFW angle for the detection of PB < 40 %. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated. There is a significant correlation between PFWOCT and PBIVUS (r2 = 0.59). The optimal cut-off value of the PFWOCT for the prediction of a PBIVUS < 40 % is ≥220° with a PPV of 78 % and an NPV of 84 %. This study shows that PFWOCT can be considered as a surrogate marker for PBIVUS, which is currently a common criterion to select an optimal stent-landing zone

Distributed learning: Developing a predictive model based on data from multiple hospitals without data leaving the hospital – A real life proof of concept

AbstractPurposeOne of the major hurdles in enabling personalized medicine is obtaining sufficient patient data to feed into predictive models. Combining data originating from multiple hospitals is difficult because of ethical, legal, political, and administrative barriers associated with data sharing. In order to avoid these issues, a distributed learning approach can be used. Distributed learning is defined as learning from data without the data leaving the hospital.Patients and methodsClinical data from 287 lung cancer patients, treated with curative intent with chemoradiation (CRT) or radiotherapy (RT) alone were collected from and stored in 5 different medical institutes (123 patients at MAASTRO (Netherlands, Dutch), 24 at Jessa (Belgium, Dutch), 34 at Liege (Belgium, Dutch and French), 48 at Aachen (Germany, German) and 58 at Eindhoven (Netherlands, Dutch)).A Bayesian network model is adapted for distributed learning (watch the animation: http://youtu.be/nQpqMIuHyOk). The model predicts dyspnea, which is a common side effect after radiotherapy treatment of lung cancer.ResultsWe show that it is possible to use the distributed learning approach to train a Bayesian network model on patient data originating from multiple hospitals without these data leaving the individual hospital. The AUC of the model is 0.61 (95%CI, 0.51–0.70) on a 5-fold cross-validation and ranges from 0.59 to 0.71 on external validation sets.ConclusionDistributed learning can allow the learning of predictive models on data originating from multiple hospitals while avoiding many of the data sharing barriers. Furthermore, the distributed learning approach can be used to extract and employ knowledge from routine patient data from multiple hospitals while being compliant to the various national and European privacy laws