PhD position in automated regulatory constraints and data governance for healthcare data
The System and Network Engineering Lab is one of the three largest research labs at the Informatics Institute (IvI) of the University of Amsterdam (UvA). The SNE Lab conducts research on leading-edge computer systems of all scales, ranging from global-scale systems and networks to embedded devices. Across these multiple scales our particular interest is on extra-functional properties of computer systems, such as performance, energy consumption, reliability, programmability, productivity, trustability, and security.
We invite applications for a fully funded PhD position in the area of regulatory automation and data governance for healthcare data. The PhD candidate will be embedded and contribute to the recently awarded EPI NWO project.
The research related to this position will take place in close collaboration with the Princess Máxima Center for Pediatric Oncology as partner in the project, an independent organization caring for children with particular types of cancer, and in particular with the team led by Dr Dannis van Vuurden.
Project description
All patient data is personal data, and naturally falls under data protection regulations as the GDPR, issued by the EU in 2018. Therefore, any healthcare data sharing infrastructure has to satisfy the data protection regulations in force, but also to promote the private arrangements made by the actors involved, as patients’ consent and agreements between the partners involved in the infrastructure. Besides, when data processing is embedded in a biomedical device, additional regulations apply (e.g. EU Regulation 2017/745). As a PhD student focusing on Regulatory Constraints and Data Governance, you will investigate how to create scalable solutions that meet legal requirements, consent, agreements or medical necessity-based access to data for allowing data processing and preventing breaches of these rules by embedded compliance, providing evidence trails and transparency. This is a necessary step for building trust in sensitive big data sharing infrastructure. Your investigation will be developed on a concrete use case, in close collaboration with physicians, postdocs and other PhD students of the Princess Maxima Center for Pediatric Oncology, and leveraging parallel research conducted within the EPI consortium (e.g. on machine-learning algorithms).
Use case
Diffuse Intrinsic Pontine Gliomas (DIPG) are largely fatal, rare brainstem tumors in children that need innovation in treatment. The team at the Princess Máxima Center for Pediatric Oncology led by Dr. Dannis van Vuurden has established, together with the International Society of Pediatric Oncology (SIOPE) a multinational DIPG Registry with a growing number of multidimensional, pseudonymized data from DIPG patients from 33 countries to overcome this lack of knowledge. The Registry currently contains data from more than 700 children in Europe and beyond. Data consist of MRI images, clinical features (history, symptoms, physical examination), treatment and outcome data, as well as biological data from tumor biopsies taken at diagnosis and/or from autopsies. In the near future, the registry will be extended with real-time quality of life (QoL) data during treatment and in the terminal phase of life of these patients.
The SIOPE DIPG Registry is in need of integration of different data platforms, as well as data-analysis using self-learning algorithms; furthermore, at the moment, there is no automated way to manage and process consent and to check whether legal requirements or specific agreements are met during an analysis or other operations.
Objectives
The aims of this project are:
- to research how the processing of legal and organizational requirements, consent and medical necessity-based access-to-data can be automatized within a data-sharing infrastructure, preventing breaches by embedded compliance. Adequate evidence trails need to be identified based on requirements of transparency, but also to ensure that, as possibilities of infringements (for misuses, abuses, etc.) exist, responsibility can be rightfully ascribed. The study aims the conception of an open authorization and governance solution, able to trace back any decision enabling operations to evidence and regulations, consents and agreements;
- to conceive a solution that enables data analytics processing and data governance in a distributed environment dealing with the various legal requirements, including issues as variability, ownership, data protection and privacy; the DIPG Registry use case will provide an example of a multi-platform, multi-domain, multi-data-source, big data sharing infrastructure where to apply the solution. As a particular data analytics application, you will consider, possibly exploiting parallel research conducted within the EPI consortium, a predictive analysis through machine learning and statistics from various heterogeneous data sources in order to dynamically group of patients according to disease state and/or treatments received.
Requirements
- MSc in computer science, computer or biomedical engineering, AI or related fields;
- fluency in oral and written English is required as well as good presentation skills, for scientific and business audiences;
- strong programming skills;
- capability to work with a team of researchers;
- well-developed analytical skills, creativity, precision and perseverance.
The candidate has experience with knowledge representation, logic, data analytics, statistics, and strong coding abilities. Knowledge of law and/or bio-medical sciences is considered a plus.
For more information click "LINK TO ORIGINAL" below.
This opportunity has expired. It was originally published here:
https://www.uva.nl/en/content/vacancies/2019/04/19-221-phd-position-in-automated-regulatory-constraints-and-data-governance-for-healthcare-data.html?origin=dxuSI3bDRY2CW7N9J3yPlw