Objective:
Mining the data contained within Electronic Health Records (EHRs) can potentially generate
a greater understanding of medication effects in the real world, complementing what we
know from Randomised control trials (RCTs). We Propose a text mining approach to detect
adverse events and medication episodes from the clinical text to enhance our understanding
of adverse effects related to Clozapine, the most effective antipsychotic drug for the management of treatment-resistant schizophrenia, but underutilised due to concerns over its
side effects.
Material and methods:
We used data from de-identified EHRs of three mental health trusts in the UK (>50 million
documents, over 500,000 patients, 2835 of which were prescribed Clozapine). We explored
the prevalence of 33 adverse effects by age, gender, ethnicity, smoking status and admission type three months before and after the patients started Clozapine treatment. Where
possible, we compared the prevalence of adverse effects with those reported in the Side
Effects Resource (SIDER).
Results:
Sedation, fatigue, agitation, dizziness, hypersalivation, weight gain, tachycardia, headache,
constipation and confusion were amongst the highest recorded Clozapine adverse effect in
the three months following the start of treatment. Higher percentages of all adverse effects
were found in the first month of Clozapine therapy. Using a significance level of (p< 0.05)
our chi-square tests show a significant association between most of the ADRs and smoking
status and hospital admission, and some in gender, ethnicity and age groups in all trusts
hospitals. Later we combined the data from the three trusts hospitals to estimate the average effect of ADRs in each monthly interval. In gender and ethnicity, the results show significant association in 7 out of 33 ADRs, smoking status shows significant association in 21 out
of 33 ADRs and hospital admission shows the significant association in 30 out of 33 ADRs.
Conclusion:
A better understanding of how drugs work in the real world can complement clinical trials
