Exploiting Ontology Based Search and EHR Interoperability to Facilitate Clinical Trial Design

Abstract

Clinical trials often fail to demonstrate beneficial effects and might overestimate the unwanted effects, with their results having low external validity. They focus on single interventions, whereas the clinical practice environment comprises various features that affect the efficacy, feasibility, duration and costs of a clinical trial. In this chapter we discuss PONTE, a platform which effectively guides medical researchers through clinical trial protocol design and offers intelligent services that address clinical needs, such as effective inclusion/exclusion criteria specification, intelligent search through a wide range of databases, clinical findings and background knowledge, and automated estimation of eligible patient population at cooperating healthcare entities. To the best of our knowledge, and to date, the PONTE platform is the first paradigm of an automated system that can effectively guide clinical trials protocol design, by linking data with drug, target and disease knowledge databases, clinical care and clinical research information systems, and guiding the users automatically though the whole pipeline of the clinical trial protocol design.

Temporal Minimal-World Query Answering over Sparse ABoxes

“…The biomedical domain is a fruitful area for OMQA methods, due to the availability of large ontologies covering a multitude of topics 1 and the demand for managing large amounts of patient data, in the form of electronic health records (EHRs) (Cresswell and Sheikh 2017). For example, for the preparation of clinical trials, 2 a large number of patients need to be screened for eligibility, and an important area of current research is how to automate this process (Patel et al 2007;Besana et al 2010;Köpcke and Prokosch 2014;Tagaris et al 2014;Ni et al 2015). In particular, many clinical trials contain temporal eligibility criteria (Crowe and Tao 2015), such as: "type 1 diabetes with duration at least 12 months" 3 ; "known history of heart disease or heart rhythm abnormalities" 4 ; "CD4+ lymphocytes count ≥ 250/mm 3 , for at least 6 months" 5 ; or "symptomatic recurrent paroxysmal atrial fibrillation (PAF) (≥ 2 episodes in the last 6 months)" 6 .…”

Finding New Diamonds: Temporal Minimal-World Query Answering over Sparse ABoxes

“…The biomedical domain is a fruitful area for OMQA methods, due to the availability of large ontologies covering a multitude of topics 1 and the demand for managing large amounts of patient data, in the form of electronic health records (EHRs) (Cresswell and Sheikh 2017). For example, for the preparation of clinical trials 2 a large number of patients need to be screened for eligibility, and an important area of current research is how to automate this process (Patel et al 2007;Besana et al 2010;Köpcke and Prokosch 2014;Tagaris et al 2014;Ni et al 2015).…”