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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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Dr Michele Filannino

admin — Thu, 25 Jun 2015 09:51:03 +0000

I am obsessed by the following question: Can computers understand time?
My research attempts to positively answer that question by designing a software that exhibits such peculiar human mind’s characteristic. I am interested in measuring, in an objective way, what is the performance difference between such software and a person. Finally, I sometimes challenge myself in figuring out some interesting new applications of such technology.

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Projects

admin — Mon, 22 Jun 2015 12:04:33 +0000

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 please doing paper for me
- 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

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