gnTEAM » Projects

Veterinary text mining

gnenadic — Mon, 03 Sep 2018 12:57:08 +0000

The Small Animal Veterinary Surveillance Network (SAVSNET) is an initiative from the British Small Animal Veterinary Association and the University of Liverpool to collect and analyse companion animal data. SAVSNET collects pets health data in real time from diagnostic laboratories and veterinary consultations across the UK (from ~500 veterinary premises). Given that consultation notes are presented as free-text, we have been working with SAVSNET to develop text mining methods focusing on two goals:

Extraction of key symptoms and complaints along with additional context (e.g. diagnosis and medications/treatments); and
Automatic coding of consultation notes with standard veterinary terminologies like VetSCT (the veterinary extension for SNOMED CT) and VeNom.

The project is funded by BBSRC BRC fund.

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

admin — Thu, 02 Jul 2015 10:39:18 +0000

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 various co-morbidities, problems, tests and treatments. We also work on the extraction of medication-related information (such as medication name, dosage, reason for taking, frequency, duration etc.) and clinical temporal text mining. These tasks were assessed as part of an international text mining challenge in the clinical/health-care domain: for more detail, see here, where we have showed continuous success.

Another strand in healthcare text mining is the extraction of subjective information from patient-generated data, such as tweets, blogs or patient’s narratives. We have also done some work on analysis of suicide notes (as part of the i2b2 challenges).

In collaboration with The Christie Hospital and the University of Salford, we are running “A study using techniques from clinical text mining to compare the narrative experiences of patients with medulloblastoma with factors identified from their hospital records”. This project aims to capture the narrative experiences of patients and compare them to the themes that are identified by text mining of the Christie Hospital health records. The findings are intended to provide an evidence-base for clinical service development. This work is funded by The Christie Charity Fund (£25K), and is part of Azad’s PhD.

As a continuation of The Christie Hospital’s project, we are part of a project led by the University of Salford (Prof Tony Long) on “Systematic analysis of healthcare records and the narrative experiences of children with tumours of the central nervous system and their carers – informing the evolution of selfesteem and healthrelated outcomes for future targeted interventions”. This project is funded by the Kidscan Charity (£62K).

The NIHR-funded project on “Enhanced occupational therapy interventions for children and adolescents with central nervous system tumours” aims to use advanced text-mining techniques to support patient involvement and decision making in clinical practice. It is a £250K collaboration with Royal Manchester Children Hospital and University of Salford, due to start in 2013.

Our team currently inolves A. Dehghan, G. Karystianis, J. Keane, S. Stivaros, E. Estlin, A. Kovacevic (collaborator), M. Filannino, G. Nenadic. More information on healthcare text mining tools is available on our HECTA pages.

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Integration of Dynamic Documentation Knowledge Services into Siemens Framework

admin — Thu, 02 Jul 2015 10:35:50 +0000

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 Solutions have an established collaboration to develop the next generation of context-sensitive clinical systems and associated knowledge management technologies. The core of the methodology is built around the Web Ontology Language (OWL) and related Semantic technologies, which are used to manage context, merge content, and implement associations and rules.

An initial version of the Knowledge Management Environment has been designed and developed in the previous phase of the project, and the current project is focused on integration with Siemens’ Hospital Information System, Sorian Clinicals. The project will also explore the role of data analytics, including text/association mining, statistics or probability modelling for knowledge acquisition.

In addition to Prof Rector, the project involves Drs Bijan Parsia (PI) and Goran Nenadic (CoI), with a team of six clinical knowledge engineers and software developers.

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

admin — Thu, 02 Jul 2015 10:29:26 +0000

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 of patients’ safety issues, the identification of adverse events and the identification of a suitable critical mass of patients to participate in small (Phases II and III) or larger scale (Phase IV) clinical trials.

Our role is focused on the design of an interoperable EHR data space and development of bio-marker data mining techniques for adverse events early detection. We will also provide several clinical trials showcases and organise the Clinical research and patients safety Special Interest Group.

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

admin — Thu, 02 Jul 2015 10:19:03 +0000

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 authorities and industry in a five to 10-year programme. The Medical Research Council (MRC), along with nine other government and charity funders, are investing £4.5 million in HeRC over the next five years, and the total activity with investments from industry and academia will be around £18 million.

One of the main objectives is to enable different research teams to collaborate across different organisations to produce more powerful and timely analyses of anonymised healthcare records. The aim is to combine clinical, social and research data to identify more effective treatments, improve drug safety, assess risks to public health and study the causes of diseases and disability. The Centre will make use of patient data sets available through the Clinical Practice Research Datalink, a £60 million service recently announced by the Medicines and Healthcare Products Regulatory Agency and the National Institute for Health Research. The centre is led by Prof Iain Buchan, and Dr Goran Nenadic is one of CI, co-leading the development of CHIP-SET (Community Health Intelligence Partnership – Semantic Epidemiology Toolkit) along with Prof Carole Goble and John Ainsworth, in particular in the area of text mining and overall bio-health informatics input.

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Clinical Temporal Expression Mining

admin — Thu, 02 Jul 2015 10:15:20 +0000

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, Frequency). This work is part of Michele’s and Azad’s PhDs.

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Mining molecular interaction data and its context

admin — Thu, 02 Jul 2015 10:12:44 +0000

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 knowledge about molecular interactions is presented in the literature, and in particular how contextual information that details molecular interactions are encoded and presented. The project implements a text mining framework to extract (from full-text articles) contextual information and link it with data in other resources to support informed decisions for understanding the complexity of interactions. The project is collaboration with Pfizer and thus the focus is placed on pharmaceutically relevant data sets including various pathogens such as HIV, hepatitis viruses, malaria etc.
In previous work (with M. Gerner, S. Farzaneh, C. Bergman) we have developed BioContext, a system for extracting and integrating information about molecular processes in biomedical articles. Using the data extracted by BioContext, it is possible to get an overview of a range of biomolecular processes relating to a particular gene or anatomical location. The current project is part of Dan’s PhD, with Prof D. Robertson (Bioinformatics) and Drs B. Sidders (Pfizer) and G. Nenadic as supervisors.

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Logical modelling of molecular interactions in the development of thyroid cancer

admin — Thu, 02 Jul 2015 10:09:54 +0000

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). The second step is to use the extracted information to construct a logical model for thyroid cancer and use it to make predictions about medically useful pathways or drug targets; the hypotheses formed in the second phase will be validated experimentally in the third year. This work is part of Chengkun’s PhD, with J-M. Schwartz, G. Brabant and G. Nenadic as supervisors.

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Contrasts and contradictions in scientific texts

admin — Thu, 02 Jul 2015 10:07:07 +0000

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 statements about the relations amongst entities, although it is of growing interest to find reports on weak or negative relations, or lack there of. Negation detection is a middle step to finding contrasts and contradictions, and has been of special interest in medical text mining, because of the abundance of negative patterns in medical descriptions. The aim of this research is to develop text mining methods to detect and analyse contrasting facts in the biomedical literature and specifically in molecular interactions.

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Investigating Data Quality Aspects of Question and Answer Reports

admin — Fri, 26 Jun 2015 14:25:26 +0000

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 structured data from conceptual perspective. Furthermore, question and answer reports are gaining momentum as a way to collect responses that can be used for data brokers, for instance, in business (customer satisfaction reports and FAQ). However these reports suffer from many data quality issues that affect their performance and efficiency in use. Therefore, we have been working on developing a data quality methodology with an associated data quality assistant tool that can improve the data quality of these reports by linguistically analysing them in order to track and identify the data quality problems found in such reports before they are deployed into a data store system or used for data analysis. This is Mona’s PhD work, with G. Nenadic and B. Theodoulidis as supervisors.

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