Projects

Our research projects are focused on developing techniques for large-scale extraction and management of un- and semi-structured textual resources.

We focus on:

  • Health-related information synthesis
    • Synthesis of information from unstructured electronic health-care records, patient narratives and literature to support clinical decision support
    • Clinical documentation, text mining and terminology management
    • Integration and analytics of health-care linked-data
    • Sentiment mining of health-related social media
    • Temporal text mining
  • Large-scale extraction and contextualization of biomolecular events
    • Identification of conflicting statements in scientific texts
    • Extraction of host-pathogen interactions
  • Mining of scientific methodologies from literature
    • Capturing and analysing the use of in-silico experimental methods in computational biology and bioinformatics

Our projects

  • Healthcare text mining projects: mining clinical narratives and patient-generated data

    We currently run a number of projects to extract various structured data from unstructured clinical narratives and electronic healthcare records (EHRs). In previous projects we have developed a combination of rule-based and machine-learning methods to identify diseses that a patient has or does not have (“disease status”), including identification of…

  • Integration of Dynamic Documentation Knowledge Services into Siemens Framework

    This project is carried out in collaboration with the Clinical Knowledge Management Research Team at Manchester. The team, originated by Professor Alan Rector, has a thirty-year history of leading innovations in clinical user interfaces, development of health terminology/ontologies, and medial records. The Clinical Knowledge Management Research Team and Siemens Medical…

  • Linked2Safety – a next-generation, secure linked-data medical information space for semantically-interconnecting electronic health records and clinical trials systems

    The main aim of the Linked2Safety project is to explore the Semantic Web and Linked Data to facilitate semantic interlinking of electronic health records (EHRs) and clinical trials systems for gathering and sharing knowledge to support decision making in medical and clinical research. The vision is to facilitate early detection…

  • Health eResearch Centre (HeRC) – harnessing electronic health data to improve care for patients and communities

    Our research on clinical text mining, processing patient generated data and building interoperable clinical data processing infrastructures is part of a new multimillion-pound centre of excellence in that has been awarded to a consortium led by The University of Manchester. The consortium brings together partners from academia, the NHS, local…

  • Clinical Temporal Expression Mining

    The aim of this project is to extract mentions of temporal expressions in clinical narratives (and patient-generated data) using a combination of rule-based and machine-learning methods. We also aim to normalise those mentions through mapping them to their value (using the ISO-8601 representation (e.g. “2012-10-31T09:00″) and type (e.g. Date, Duration,…

  • Mining molecular interaction data and its context

    The project involves extraction of the context of molecular interaction data from the scientific literature. So far, little attempt has been made to capture the context of molecular interaction, how reliable it is, what is the nature of interaction etc. The project aims to study the way findings, experiments and…

  • Logical modelling of molecular interactions in the development of thyroid cancer

    The main goal of the project is to generate new hypotheses for the understanding and treatment of thyroid cancer with the help of Text Mining and Logical Modelling. Text Mining is used to extract information related to the molecular interactions for thyroid cancer from the biomedical literature (based on BioContext).…

  • Contrasts and contradictions in scientific texts

    Detecting and analysing contrasts and contradictions in scientific texts is essential for suggesting further research potentials and discoveries. Finding contrasts and contradictions in text by means of automatic methods is a relatively new area in text mining. Specifically, most biological text mining research has so far focused on mining affirmative…

  • Investigating Data Quality Aspects of Question and Answer Reports

    As the quantity of available data increases, the level of “quality” varies significantly, and this becomes a critically important factor for the effectiveness of organisations and individuals. Most of the business and scientific data is represented in unstructured and semi-structured formats. However, most current data quality methodologies work solely on…

  • Text Analytics and Sentiment Analysis in Healthcare Web 2.0

    Sentiment analysis is a field in computational linguistics involving identification, extraction and classification of opinions and emotions expressed in natural language. The capture and analysis of such attitudes and opinions in an automated and structured fashion might offer a powerful technology to a number of problem domains, including business intelligence,…