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Complexities, variations, and errors of numbering within clinical notes: the potential impact on information extraction and cohort-identification

BACKGROUND: Numbers and numerical concepts appear frequently in free text clinical notes from electronic health records. Knowledge of the frequent lexical variations of these numerical concepts, and their accurate identification, is important for many information extraction tasks. This paper describ...

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Detalles Bibliográficos
Autores principales: Hanauer, David A., Mei, Qiaozhu, Vydiswaran, V. G. Vinod, Singh, Karandeep, Landis-Lewis, Zach, Weng, Chunhua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6448181/
https://www.ncbi.nlm.nih.gov/pubmed/30944012
http://dx.doi.org/10.1186/s12911-019-0784-1
Descripción
Sumario:BACKGROUND: Numbers and numerical concepts appear frequently in free text clinical notes from electronic health records. Knowledge of the frequent lexical variations of these numerical concepts, and their accurate identification, is important for many information extraction tasks. This paper describes an analysis of the variation in how numbers and numerical concepts are represented in clinical notes. METHODS: We used an inverted index of approximately 100 million notes to obtain the frequency of various permutations of numbers and numerical concepts, including the use of Roman numerals, numbers spelled as English words, and invalid dates, among others. Overall, twelve types of lexical variants were analyzed. RESULTS: We found substantial variation in how these concepts were represented in the notes, including multiple data quality issues. We also demonstrate that not considering these variations could have substantial real-world implications for cohort identification tasks, with one case missing > 80% of potential patients. CONCLUSIONS: Numbering within clinical notes can be variable, and not taking these variations into account could result in missing or inaccurate information for natural language processing and information retrieval tasks.