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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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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The current research post-graduate themes include:

• Integrated and Contrastive Text and Data Mining
• Text Analytics and Blog/Forum Sentiment Analysis
• Extracting negations, contrasts and contradiction from biomedical literature
• Clinical text mining
• Text mining in engineering

More specific post-graduate information is available here. For PhD funding opportunities see CDT in Computer Science.

Selected completed student projects

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