Detecting the presence of an indwelling urinary catheter and urinary symptoms in hospitalized patients using natural language processing

Abstract

We have shown that it is possible to identify the presence of an indwelling urinary catheter and urinary symptoms from the free text of electronic medical notes from inpatients using natural language processing. These are two key steps in developing automated protocols to assist humans in large-scale review of patient charts for catheter-associated urinary tract infection. The challenges associated with extracting indwelling urinary catheter-related concepts also inform the design of electronic medical record templates to reliably and consistently capture data on indwelling urinary catheters.

Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review

“…The main objectives of studies included in this review (Table 2) were to capture or detect symptoms (n ¼ 10) 20,23,27,30,31,35,[37][38][39]42 ; identify, classify, or characterize disease (n ¼ 8) 21,22,24,25,33,43,45,46 ; study adverse drug (n ¼ 5) 32,34,36,41,44 or vaccine (n ¼ 1) 29 events; and identify or detect readmission (n ¼ 1), 26 presence of a device (n ¼ 1), 28 or unplanned clinical encounters (n ¼ 1). 40 Approximately 52% (n ¼ 14) of studies presented symptom-related information as a primary outcome.…”

“…Free-text narratives were primarily from EHRs (n ¼ 13) 20,22,24,26,29,31,35,37,38,41,42,45,46 and data repositories (n ¼ 12). 21,23,25,27,28,32,33,36,39,40,43,44 Free-text narratives used in the 2 remaining studies were obtained from paper records converted into electronic free text 30 and Informatics for Integrating Biology & the Bedside Challenge datasets. 34 Narratives represented both inpatient (eg, admission documents, discharge summaries, emergency department documents, progress notes, nursing narratives) and outpatient (eg, primary care and specialty clinic documents, mental health encounters) settings and were written by various members of the clinical care team (eg, physicians, nurses).…”

Predicting Infections Using Computational Intelligence – A Systematic Review

“…Those were used in two studies for the prediction of Sepsis in an expert system setup [4], [5]. Five of the studies customized their rules with respect to the infection type considered [14], [32], [42], [104], [106]. Two studies deployed fuzzy-logic-based reasoning to soften the boundaries for decision [18], [107].…”

A Review of Automatic Phenotyping Approaches using Electronic Health Records

“…Abhyankar et al [16] adopted a similar strategy to the one described above, with the additional step of expanding the keyword list by using synonyms obtained from exploring domain knowledge. Gundlapalli et al [59] obtained the keywords from catheter-associated urinary tract infection (CAUTI) related published work, and a list of terms relevant to CAUTI from Centers for Disease Control and Prevention. The resulting list has been expanded using words from domain experts.…”