Martijn G. Kersloot

Amsterdam UMC · Medical Informatics

The recent rise in acute kidney injury (AKI) incidence, with approximately 30% attributed to potentially preventable adverse drug events (ADEs), poses challenges in evaluating drug-induced AKI due to polypharmacy and other risk factors. This study seeks to consolidate knowledge on the drugs with AKI potential from four distinct sources: (i) bio(med...

Since 2014, “Bring Your Own Data” workshops (BYODs) have been organised to inform people about the process and benefits of making resources Findable, Accessible, Interoperable, and Reusable (FAIR, and the FAIRification process). The BYOD workshops’ content and format differ depending on their goal, context, and the background and needs of participa...

The International Society for the Study of Vascular Anomalies (ISSVA) provides a classification for vascular anomalies that enables specialists to unambiguously classify diagnoses. This classification is only available in PDF format and is not machine-readable, nor does it provide unique identifiers that allow for structured registration. In this p...

The FAIR Data Principles are being rapidly adopted by many research institutes and funders worldwide. This study aimed to assess the awareness and attitudes of clinical researchers and research support staff regarding data FAIRification. A questionnaire was distributed to researchers and support staff in six Dutch University Medical Centers and Ele...

Background Patient data registries that are FAIR—Findable, Accessible, Interoperable, and Reusable for humans and computers—facilitate research across multiple resources. This is particularly relevant to rare diseases, where data often are scarce and scattered. Specific research questions can be asked across FAIR rare disease registries and other F...

The FAIR Principles are supported by various initiatives in the biomedical community. However, little is known about the knowledge and efforts of individual clinical researchers regarding data FAIRification. We distributed an online questionnaire to researchers from six Dutch University Medical Centers, as well as researchers using an Electronic Da...

Introduction: Existing methods to make data Findable, Accessible, Interoperable, and Reusable (FAIR) are usually carried out in a post-hoc manner: after the research project is conducted and data are collected. De-novo FAIRification, on the other hand, incorporates the FAIRification steps in the process of a research project. In medical research,...

Introduction Existing methods to make data Findable, Accessible, Interoperable, and Reusable (FAIR) are usually carried out in a post-hoc manner: after the research project is conducted and data are collected. De-novo FAIRification, on the other hand, incorporates the FAIRification steps in the process of a research project. In medical research, da...

Background Patient data registries that are FAIR - Findable, Accessible, Interoperable, and Reusable for humans and computers - facilitate research across multiple resources. This is particularly relevant to rare diseases, where data often are scarce and scattered. Specific research questions can be asked across FAIR rare disease registries and oth...

Background Free-text descriptions in electronic health records (EHRs) can be of interest for clinical research and care optimization. However, free text cannot be readily interpreted by a computer and, therefore, has limited value. Natural Language Processing (NLP) algorithms can make free text machine-interpretable by attaching ontology concepts t...

Background: Connecting currently existing, heterogeneous rare disease (RD) registries would greatly facilitate epidemiological and clinical research. To increase their interoperability, the European Union developed a set of Common Data Elements (CDEs) for RD registries. Objectives: To implement the CDEs and the FAIR data principles in the Regist...

The FAIR principles have been widely cited, endorsed and adopted by a broad range of stakeholders since their publication in 2016. By intention, the 15 FAIR guiding principles do not dictate specific technological implementations, but provide guidance for improving Findability, Accessibility, Interoperability and Reusability of digital resources. T...

The industry sector is a very large producer and consumer of data, and many companies traditionally focused on production or manufacturing are now relying on the analysis of large amounts of data to develop new products and services. As many of the data sources needed are distributed and outside the company, FAIR data will have a major impact, both...

Background: Information in Electronic Health Records is largely stored as unstructured free text. Natural language processing (NLP), or Medical Language Processing (MLP) in medicine, aims at extracting structured information from free text, and is less expensive and time-consuming than manual extraction. However, most algorithms in MLP are institu...