Natural language processing (NLP) is an effective tool for generating structured information from unstructured data, the one that is commonly found in clinical trial texts. Such interdisciplinary research has gradually grown into a flourishing research field with accumulated scientific outputs available. In this study, bibliographical data collected from Web of Science, PubMed, and Scopus databases from 2001 to 2018 had been investigated with the use of three prominent methods, including performance analysis, science mapping, and, particularly, an automatic text analysis approach named structural topic modeling. Topical trend visualization and test analysis were further employed to quantify the effects of the year of publication on topic proportions. Topical diverse distributions across prolific countries/regions and institutions were also visualized and compared. In addition, scientific collaborations between countries/regions, institutions, and authors were also explored using social network analysis. The findings obtained were essential for facilitating the development of the NLP-enhanced clinical trial texts processing, boosting scientific and technological NLP-enhanced clinical trial research, and facilitating inter-country/region and inter-institution collaborations.

https://www.mdpi.com/2076-3417/10/6/2157/pdf

Trends and Features of the Applications of Natural Language Processing Techniques for Clinical Trials Text Analysis

The INTEGRATE project

This paper describes a new Cohort Selection application implemented to support streamlining the definition phase of multi-centric clinical research in oncology. Our approach aims at both ease of use and precision in defining the selection filters expressing the characteristics of the desired population. The application leverages our standards-based Semantic Interoperability Solution and a Groovy DSL to provide high expressiveness in the definition of filters and flexibility in their composition into complex selection graphs including splits and merges. Widely-adopted ontologies such as SNOMED-CT are used to represent the semantics of the data and to express concepts in the application filters, facilitating data sharing and collaboration on joint research questions in large communities of clinical users. The application supports patient data exploration and efficient collaboration in multi-site, heterogeneous and distributed data environments.

Cohort Selection and Management Application Leveraging Standards-based Semantic Interoperability and a Groovy DSL.

Tool description The recruitment and feasibility tool (earlier named Yakobo) was developed in the EURECA project to assess protocol feasibility and to find eligible patients for clinical trials. Protocol feasibility functions analyze the feasibility of a trial protocol by assessing the expected patient enrollment rate of a selection of sites given the protocol’s eligibility criteria, based on “historical” data (existing patient data). It allows for answering questions such as whether inclusion and exclusion criteria are useful for defining the proper study population, whether it is likely that the necessary volume of patients can be recruited in time to collect data with sufficient statistical power and/or the expected duration of a trial. Criteria are expressed in a domain specific language [1]. A Trial metadata repository contains protocol definitions (Figure 1) and the SNAQL engine executes the DSL of the criteria to find patients belonging to the cohort. The SNAQL engine accesses the semantic integration services which provide a query interface [2] with reasoning abilities. Once a clinical protocol has been finalized, the tool is used to find patients eligible for enrollment.

/pdf/recruitment-and-feasibility-tool-cupcq8qpsy.pdf

Recruitment and feasibility tool

BRIDG-based Trial Metadata Repository - Need for Standardized Machine Interpretable Trial Descriptions.

A substantial amount of clinical trials are subject to failure because they cannot recruit sufficient patients within the foreseen time and budget. Computer assisted evaluation of eligibility criteria is envisaged to improve the recruitment process by increasing coverage (i.e. making sure no eligible patients are `missed') and speeding up the eligibility scanning process; and thus eventually reduce the overall failure rate of clinical trials. In this paper a new recruitment application is presented which assists in evaluating eligibility criteria based on available clinical patient data (e.g. Electronic Health Record data). The application leverages for the querying of clinical data on a generic semantic integration platform and a new Domain Specific Language (DSL) based on the Groovy programming language.

Raul Alonso-Calvo

Papers

BRIDG-based Trial Metadata Repository - Need for Standardized Machine Interpretable Trial Descriptions.

Implementing patient recruitment on EURECA semantic integration platform through a Groovy query engine