Data Infrastructure for Medical Research

While we are witnessing rapid growth in data across the sciences and in many applications, this growth is particularly remarkable in the medical domain, be it because of higher resolution instruments and diagnostic tools (e.g. MRI), new sources of structured data like activity trackers, the wide-spread use of electronic health records and many others. The sheer volume of the data is not, however, the only challenge to be faced when using medical data for research. Other crucial challenges include data heterogeneity, data quality, data privacy and so on. In this article, we review solutions addressing these challenges by discussing the current state of the art in the areas of data integration, data cleaning, data privacy, scalable data access and processing in the context of medical data. The techniques and tools we present will give practitioners — computer scientists and medical researchers alike — a starting point to understand the challenges and solutions and ultimately to analyse medical data and gain better and quicker insights

The prospect of artificial intelligence to personalize assisted reproductive technology

The Department of Metabolism, Digestion, and Reproduction is funded by grants from the MRC and NIHR. S.H. is supported by the UKRI CDT in AI for Healthcare http://ai4health.io (EP/S023283/1). A.A. is supported by an NIHR Clinician Scientist Award (CS-2018-18-ST2-002). M.V. and K.T.A. are supported by the EPSRC (EP/T017856/1). W.S.D. is supported by an NIHR Senior Investigator Award (NIHR202371).Infertility affects 1-in-6 couples, with repeated intensive cycles of assisted reproductive technology (ART) required by many to achieve a desired live birth. In ART, typically, clinicians and laboratory staff consider patient characteristics, previous treatment responses, and ongoing monitoring to determine treatment decisions. However, the reproducibility, weighting, and interpretation of these characteristics are contentious, and highly operator-dependent, resulting in considerable reliance on clinical experience. Artificial intelligence (AI) is ideally suited to handle, process, and analyze large, dynamic, temporal datasets with multiple intermediary outcomes that are generated during an ART cycle. Here, we review how AI has demonstrated potential for optimization and personalization of key steps in a reproducible manner, including: drug selection and dosing, cycle monitoring, induction of oocyte maturation, and selection of the most competent gametes and embryos, to improve the overall efficacy and safety of ART.Peer reviewe

Proposal of a modern data infrastructure for medical research in Germany

At the particular detriment of patient-based medical research, commencement of the EU General Data Protection Regulation in May 2018 has not yet led to a simplification of the federal regulatory framework in Germany. Instead, the many convoluted, impractical and sometimes contradictory rules persisted in the field. Not least in the interest of Germany as a science location, this unfortunate situation calls for immediate remedial action, preferably by the establishment of a data infrastructure that meets the demands of privacy protection and medical research in equal measure. One meaningful step in this direction would be the conclusion of a Federal State Treaty ("Bund-Länder Staatsvertrag") to unify the legal conditions for the use of personal data in medical research. In the course of this reorganisation, it would also be most reasonable to upgrade any existing or newly established use and access committees managing personalized medical data so as to take on a function as official registration and authorization points. These institutions could form the core of a modern data infrastructure that not only provides more legal certainty to the researchers themselves but at the same time allows for more transparency towards patients, probands and the interested public.Die Hoffnung, dass nach Inkrafttreten der europäischen Datenschutz-Grundverordnung im Mai 2018 eine Bereinigung der Datenschutzklauseln in Deutschland stattfinden würde, erwies sich gerade im medizinischen Forschungsbereich als falsch. Vielmehr bestehen dort die unübersichtlichen, teilweise widersprüchlichen und nicht praktikablen Regelungen der Datennutzung fort und verlangen im Interesse des Wissenschaftsstandorts dringend nach Abhilfe. Ziel muss die Schaffung einer modernen Dateninfrastruktur sein, die den Anforderungen des Datenschutzes ebenso genügt wie den Erwartungen und Bedürfnissen der Wissenschaft. Ein entscheidender Beitrag hierzu könnte die Schließung eines Bund-Länder-Staatsvertrags sein, der die Rahmenbedingungen für die Verwendung personenbezogener Daten in der medizinischen Forschung vereinheitlicht. Im Zuge der Neuregelungen würde sich zudem eine rechtliche Aufwertung der vielerorts bereits etablierten oder neu entstehenden "Use and Access Committees" von Forschungsverbünden zu Melde- und Genehmigungsstellen anbieten. Diese Einrichtungen könnten den Kern einer Dateninfrastruktur bilden, die gleichzeitig für mehr Rechtssicherheit bei den Forschenden und für mehr Transparenz gegenüber Außenstehenden wie z.B. Patienten, Probanden und interessierter Öffentlichkeit sorgt