Abstract
BackgroundMost structured clinical data, such as diagnosis codes, are not sufficient to obtain precise phenotypes and assess disease burden. Text mining of clinical notes could provide a basis for detailed profiles of phenotypic traits. The objective of the current study was to determine whether drug dose, regardless of polypharmacy, is associated with the length of clinical notes, and to determine the frequency of adverse events per word in clinical notes.MethodsIn this observational study, we utilized restricted-access data from an electronic patient record system. Using three methods (defined daily dose, olanzapine equivalents, and chlorpromazine equivalents) we calculated antipsychotic dose equivalents and compared these with the number of words recorded per treatment day. For each normalization method, the frequencies of adverse events per word in manually curated samples were compared to dose intervals.ResultsThe length of clinical notes per treatment day was positively associated with the prescribed dose for all normalization methods. The number of adverse events per word was stable over the analyzed dose spectrum.ConclusionsAssuming that drug dose increases with the severity of disease, the length of clinical notes can serve as a proxy for disease severity. Due to the near-linear relationship, correction of daily word count is unnecessary when text mining for potential adverse drug reactions.
Highlights
Most structured clinical data, such as diagnosis codes, are not sufficient to obtain precise phenotypes and assess disease burden
Safety monitoring is further complicated by polypharmacy and the fact that drugs may be used in higher doses than recommended in guidelines [6], both of which are associated with adverse drug reactions as well as disease severity [7, 8]
Consistently across normalization methods, we found a near-linear relationship between number of words in clinical notes and potential adverse events
Summary
Most structured clinical data, such as diagnosis codes, are not sufficient to obtain precise phenotypes and assess disease burden. Drug safety surveillance efforts rely heavily on spontaneous reporting systems for post-approval monitoring [1]. Such spontaneous reports suffer from a variety of issues, including massive underreporting [2], and alternative real-world data approaches are being developed. One of these approaches is to monitor adverse events extracted from clinical narratives by text mining [3] and we have. By converting all antipsychotic drugs to equivalents, polypharmacy can be converted to one single equivalent dose and enable comparisons
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