Advertisement

A natural language–based tool for diagnosis of serrated polyposis syndrome

      Background and Aims

      Serrated polyposis syndrome (SPS) is common but under-recognized and is associated with an increased risk of colorectal cancer. The diagnosis is based on the World Health Organization (WHO) criteria and is inclusive of the cumulative number of lifetime serrated polyps. We used natural language processing (NLP) to extract colonoscopy and pathology data from the electronic medical record (EMR). The aim of this study was to assess feasibility of using an NLP-based SPS tool to identify patients with SPS.

      Methods

      NLP was used to extract data from 323,494 colonoscopies performed in 255,074 distinct patients between August 1998 and March 2016 to identify individuals who met SPS criteria. The accuracy of diagnosis of SPS was assessed by manual review of the EMR.

      Results

      Of 255,074 patients, 71 were identified as meeting 1 WHO criteria for SPS. Manual review confirmed the diagnosis of SPS to be accurate in 66 cases (93%). Erroneous diagnosis in the remaining 5 cases occurred because of duplicate polyp data by NLP extraction. Only 25 of 66 patients (38%) were diagnosed with SPS by a clinician in the EMR. Of these, SPS was diagnosed by NLP at least 2 years before the clinician in 5 of 25 patients (20%).

      Conclusions

      In a large cohort, NLP accurately identified SPS in over 90% of cases, most of which were not previously recognized. NLP can assist in collating colonoscopy and pathology data across multiple procedures in the same patient to make an accurate and earlier diagnosis of SPS.

      Abbreviations:

      CRC (colorectal cancer), EMR (electronic medical record), NLP (natural language processing), SP (serrated polyp), SPS (serrated polyposis syndrome), WHO (World Health Organization)
      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Gastrointestinal Endoscopy
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Vemulapalli K.C.
        • Rex D.K.
        Failure to recognize serrated polyposis syndrome in a cohort with large sessile colorectal polyps.
        Gastrointest Endosc. 2012; 75: 1206-1210
        • Kahi C.J.
        • Li X.
        • Eckert G.J.
        • et al.
        High colonoscopic prevalence of proximal colon serrated polyps in average-risk men and women.
        Gastrointest Endosc. 2012; 75: 515-520
        • IJspeert J.E.G.
        • Rana S.A.
        • Atkinson N.S.
        • et al.
        Clinical risk factors of colorectal cancer in patients with serrated polyposis syndrome: a multicentre cohort analysis.
        Gut. 2017; 66: 278-284
        • Bleijenberg A.G.
        • IJspeert J.E.G.
        • van Herwaarden Y.J.
        • et al.
        Personalised surveillance for serrated polyposis syndrome: results from a prospective 5-year international cohort study.
        Gut. 2020; 69: 112-121
        • Edelstein D.L.
        • Cruz-Correa M.
        • Soto-Salgado M.
        • et al.
        Risk of colorectal and other cancers in patients with serrated polyposis.
        Clin Gastroenterol Hepatol. 2015; 13: 1697-1699
        • Wang Y.
        • Wang L.
        • Rastegar-Mojarad M.
        • et al.
        Clinical information extraction applications: a literature review.
        J Biomed Inform. 2018; 77: 34-49
        • Hou J.K.
        • Imler T.D.
        • Imperiale T.F.
        Current and future applications of natural language processing in the field of digestive diseases.
        Clin Gastroenterol Hepatol. 2014; 12: 1257-1261
        • Karwa A.
        • Patell R.
        • Parthasarathy G.
        • et al.
        Development of an automated algorithm to generate guideline-based recommendations for follow-up colonoscopy.
        Clin Gastroenterol Hepatol. 2019 Oct 10;
        • Raju G.S.
        • Lum P.J.
        • Slack R.S.
        • et al.
        Natural language processing as an alternative to manual reporting of colonoscopy quality metrics.
        Gastrointest Endosc. 2015; 82: 512-519
        • Denny J.C.
        • Choma N.N.
        • Peterson J.F.
        • et al.
        Natural language processing improves identification of colorectal cancer testing in the electronic medical record.
        Med Decis Making. 2012; 32: 188-197
        • Nayor J.
        • Borges L.F.
        • Goryachev S.
        • et al.
        Natural language processing accurately calculates adenoma and sessile serrated polyp detection rates.
        Dig Dis Sci. 2018; 63: 1794-1800
        • Crockett S.D.
        • Gourevitch R.A.
        • Morris M.
        • et al.
        Endoscopist factors that influence serrated polyp detection: a multicenter study.
        Endoscopy. 2018; 50: 984-992
        • Hui V.W.
        • Steinhagen E.
        • Levy R.A.
        • et al.
        Utilization of colonoscopy and pathology reports for identifying patients meeting the world health organization criteria for serrated polyposis syndrome.
        Dis Colon Rectum. 2014; 57: 846-850
        • IJspeert J.E.G.
        • Bevan R.
        • Senore C.
        • et al.
        Detection rate of serrated polyps and serrated polyposis syndrome in colorectal cancer screening cohorts: a European overview.
        Gut. 2017; 66: 1225-1232
        • van Herwaarden Y.J.
        • Pape S.
        • Vink-Borger E.
        • et al.
        Reasons why the diagnosis of serrated polyposis syndrome is missed.
        Eur J Gastroenterol Hepatol. 2019; 31: 340-344
        • Boparai K.S.
        • Mathus-Vliegen E.M.
        • Koornstra J.J.
        • et al.
        Increased colorectal cancer risk during follow-up in patients with hyperplastic polyposis syndrome: a multicentre cohort study.
        Gut. 2010; 59: 1094-1100
        • Garg A.X.
        • Adhikari N.K.
        • McDonald H.
        • et al.
        Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review.
        JAMA. 2005; 293: 1223-1238
        • Nerminathan A.
        • Harrison A.
        • Phelps M.
        • et al.
        Doctors' use of mobile devices in the clinical setting: a mixed methods study.
        Intern Med J. 2017; 47: 291-298
        • Bates D.W.
        • Landman A.
        • Levine D.M.
        Health apps and health policy: What is needed?.
        JAMA. 2018; 320: 1975-1976