Artificial intelligence

Yewno is a pilot being undertaken by University Hospitals Derby and Burton.

A Deep Learning Network “reads” high-quality sources and projects concepts into a knowledge graph. This groups related concepts together and shows their interrelationship. It gives users a visual map of how the concepts of research included in search results relate to each other and shows connections that they may not otherwise think about.

User feedback suggests that Yewno better at handling broad subjects than precise, specific ones​ and may show unhelpful connections – prior knowledge is needed to be aware that these are not useful. 

EBSCO is also working on a similar product for inclusion within its EDS system. The new Concept Map provides a more visual and interactive approach to learning and exploring topics with Artificial Intelligence on top of a sophisticated knowledge graph and the ability to push results back into linear lists. 

The Knowledge Graph ensures an excellent search that can turn even inadequate queries into quality results. Therefore, users don’t need to start off as expert researchers to get expert information. The Knowledge Graph does this by mapping new datasets that incorporate natural language, extensive subject vocabularies, and a vast array of synonyms and concepts in more than 280 languages and dialects.

Find out more about EBSCO Discovery Service.

RobotAnalyst is currently used as a screening tool by analysts to develop systematic reviews and guidelines. Specifically, the system has been employed by National Institute for Health and Care Excellence (NICE) in developing new guidelines and surveillance reviews on benefits of exercise, behavioural change, fighting addictions, managing weight and others. 

The Population, Intervention, Control, and Outcomes (PICO) framework is conventionally used to structure pre-defined research questions matching clinical needs.  However, PICO is not well-suited to the needs of evidence-based public health reviews such as those conducted by NICE.  

Public health questions are always complex, involving behaviour, culture and organizations, and often are described using abstract, fuzzy terminology in ways that make binary definitions of all the parameters in PICO a priori extremely problematic.  

We thus developed novel research methods integrated in a system, the RobotAnalyst, which supports users to screen while searching. Its text mining based functionalities, allow users to search via different facets (terms, journals, authors), via clusters (based on descriptive clustering), via topics (based on topic modelling), and via similarities to a reference. It then supports screening and prioritises references using relevancy predictions based on an active learning model which learns from reviewers’ screening decisions. 

Find out more information about Case Study - Robot analyst..

Iris.ai in Mersey Care Evidence Service reads all Open Access papers. In reading papers Iris can understand keywords, concepts, context and relationships which it can then map against all the other papers it has read.  

This theoretically changes the nature of searching as the AI will be able to identify relevant papers that might not contain the keywords used in a more traditional search. 

Uses natural language processing (NLP) Iris allows searchers to type your question in a normal, fluent format. It identifies keywords and context, matches them to the information it has read, and creates a concept map.

See A glimpse of the future: IRIS AI in Mersey Care Evidence Service for more information.

The project will build an ‘Evidence Pipeline’ in which study citations identified through automated and enhanced centralised search activities, including Project Transform's Crowd platform, are ‘triaged’ to the most relevant review group or review using machine learning technologies. It will also assign suggested study type flags automatically: RCT only to begin with, and other types (for example, diagnostic test) in later releases. The objectives are to improve the efficiency by which study reports are identified and to support the inclusion of all relevant studies in Cochrane information systems.

Data Lens brings together information about multiple databases, providing a fast-access search in multiple languages. Using Natural Language Processing (NLP) and other AI technologies, the Data Lens project is creating a universal search engine for health and social care data catalogues and metadata. 

Data Lens currently joins up five data catalogues from across the NHS system including NHS England and Improvement, NHS Digital, and Public Health England.

Find out more about Data Lens.

R is a statistical programming language. It has the power to visualise data in new ways, helping analysts understand problems better and offering new insights to improve the quality of care. R can be used for a range of topics in health, such as reporting hospital mortality statistics and evaluating interventions.

Find out more about the power of R for the NHS and the R Project.

Metaverse is an R eco system for meta research currently in development. 

Evidence synthesis (ES) is the process of identifying, collating and synthesising primary scientific research (such as articles and reports) for the purposes of providing reliable, transparent summaries. The goal of the metaverse project is to collect, integrate and expand the universe of available functions for ES projects in R. 

Find out more about Metaverse.

Kindred Posts uses artificial intelligence to learn how your website visitors use your site and recommends content based on their interests. 

In Ireland's National Health Library and Knowledge Serivces plan to add a chatbot to the library’s website to answer some of the most common questions students come in with, as well as to help them get started with their research. The system can tackle things like “where can I print?” or “what databases do you have about biology?” Anything it can’t answer gets sent to a human librarian.