King's College London

Research portal

Identification of Adverse Drug Events from Free Text Electronic Patient Records and Information in a Large Mental Health Case Register

Research output: Contribution to journalArticlepeer-review

Ehtesham Iqbal, Robbie Mallah, Richard George Jackson, Michael Ball, Zina M. Ibrahim, Matthew Broadbent, Olubanke Dzahini, Robert Stewart, Caroline Johnston, Richard J. B. Dobson, Christos A. Ouzounis (Editor)

Original languageEnglish
Article numbere0134208
Number of pages14
JournalPL o S One
Issue number8
Early online date14 Aug 2015
Accepted/In press8 Jul 2015
E-pub ahead of print14 Aug 2015
Published14 Aug 2015


King's Authors


Objectives Electronic healthcare records (EHRs) are a rich source of information, with huge potential for secondary research use. The aim of this study was to develop an application to identify instances of Adverse Drug Events (ADEs) from free text psychiatric EHRs. Methods We used the GATE Natural Language Processing (NLP) software to mine instances of ADEs from free text content within the Clinical Record Interactive Search (CRIS) system, a de-identified psychiatric case register developed at the South London and Maudsley NHS Foundation Trust, UK. The tool was built around a set of four movement disorders (extrapyramidal side effects [EPSEs]) related to antipsychotic therapy and rules were then generalised such that the tool could be applied to additional ADEs. We report the frequencies of recorded EPSEs in patients diagnosed with a Severe Mental Illness (SMI) and then report performance in identifying eight other unrelated ADEs. Results The tool identified EPSEs with >0.85 precision and >0.86 recall during testing. Akathisia was found to be the most prevalent EPSE overall and occurred in the Asian ethnic group with a frequency of 8.13%. The tool performed well when applied to most of the non-EPSEs but least well when applied to rare conditions such as myocarditis, a condition that appears frequently in the text as a side effect warning to patients. Conclusions The developed tool allows us to accurately identify instances of a potential ADE from psychiatric EHRs. As such, we were able to study the prevalence of ADEs within subgroups of patients stratified by SMI diagnosis, gender, age and ethnicity. In addition we demonstrated the generalisability of the application to other ADE types by producing a high precision rate on a non-EPSE related set of ADE containing documents. Availability The application can be found at

Download statistics

No data available

View graph of relations

© 2020 King's College London | Strand | London WC2R 2LS | England | United Kingdom | Tel +44 (0)20 7836 5454