Advertisement

Obesity Cohorts Based on Comorbidities Extracted from Clinical Notes

  • Ruth Reátegui
  • Sylvie Ratté
  • Estefanía Bautista-Valarezo
  • Victor Duque
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 746)

Abstract

Clinical notes provide a comprehensive and overall impression of the patient’s health. However, the automatic extraction of information within these notes is challenging due to their narrative style. In this context, our goal was two-fold: first, extracting fourteen comorbidities related to obesity automatically from i2b2 Obesity Challenge data using the MetaMap tool; and second, identify patients’ cohorts applying sparse K-means algorithms on the extracted data. The results showed an average of 0.86 for recall, 0.94 for precision, and 0.89 for F-score. Also, three types of cohorts were found. The results showed that MetaMap can represent a good strategy for automatically extracting medical entities such as diseases or syndromes. Moreover, three types of cohorts could be identified based on the number of comorbidities and the percentage of patients suffering from them. These results show that hypertension, diabetes, CAD, CHF, HCL, OSA, asthma, and GERD were the most prevalent diseases.

Keywords

Obesity Clinical notes MetaMap Patient cohorts Cluster analysis 

References

  1. 1.
    Shivade, C., Raghavan, P., Fosler-Lussier, E., Embi, P.J., Elhadad, N., Johnson, S.B., Lai, A.M.: A review of approaches to identifying patient phenotype cohorts using electronic health records. JAMIA 21, 221–230 (2014)Google Scholar
  2. 2.
    Zhang, P., Wang, F., Hu, J., Sorrentino, R.: Towards personalized medicine: leveraging patient similarity and drug similarity analytics. AMIA Summits Transl. Sci. Proc. 2014, 132–136 (2014)Google Scholar
  3. 3.
    Lyalina, S., Percha, B., LePendu, P., Iyer, S.V., Altman, R.B., Shah, N.H.: Identifying phenotypic signatures of neuropsychiatric disorders from electronic medical records. JAMIA 20, e297–e305 (2013)Google Scholar
  4. 4.
    Vavougios, G.D., Natsios, G., Pastaka, C., Zarogiannis, S.G., Gourgoulianis, K.I.: Phenotypes of comorbidity in OSAS patients: combining categorical principal component analysis with cluster analysis. J. Sleep Res. 25, 31–38 (2016)CrossRefGoogle Scholar
  5. 5.
    Serrano-Pariente, J., Rodrigo, G., Fiz, J.A., Crespo, A., Plaza, V., High Risk Asthma Research Group: Identification and characterization of near-fatal asthma phenotypes by cluster analysis. Allergy 70, 1139–1147 (2015)CrossRefGoogle Scholar
  6. 6.
    van der Esch, M., Knoop, J., van der Leeden, M., Roorda, L.D., Lems, W.F., Knol, D.L., Dekker, J.: Clinical phenotypes in patients with knee osteoarthritis: a study in the Amsterdam osteoarthritis cohort. Osteoarthr. Cartilage 23, 544–549 (2015)CrossRefGoogle Scholar
  7. 7.
    Ahmad, T., Pencina, M.J., Schulte, P.J., O’Brien, E., Whellan, D.J., Pina, I.L., Kitzman, D.W., Lee, K.L., O’Connor, C.M., Felker, G.M.: Clinical Implications of chronic heart failure phenotypes defined by cluster analysis. J. Am. Coll. Cardiol. 64, 1765–1774 (2014)CrossRefGoogle Scholar
  8. 8.
    Guh, D.P., Zhang, W., Bansback, N., Amarsi, Z., Birmingham, C.L., Anis, A.H.: The incidence of co-morbidities related to obesity and overweight: a systematic review and meta-analysis. BMC Publ. Health 9, 88 (2009)CrossRefGoogle Scholar
  9. 9.
    Sutherland, E.R., Goleva, E., King, T.S., Lehman, E., Stevens, A.D., Jackson, L.P., Stream, A.R., Fahy, J.V., Leung, D.Y.M., Asthma Clinical Research Network: Cluster analysis of obesity and asthma phenotypes. PLoS ONE 7, e36631 (2012)CrossRefGoogle Scholar
  10. 10.
    Laing, S.T., Smulevitz, B., Vatcheva, K.P., Rahbar, M.H., Reininger, B., McPherson, D.D., McCormick, J.B., Fisher-Hoch, S.P.: Subclinical atherosclerosis and obesity phenotypes among Mexican Americans. J. Am. Heart Assoc. 4, e001540 (2015)CrossRefGoogle Scholar
  11. 11.
    LaGrotte, C., Fernandez-Mendoza, J., Calhoun, S.L., Liao, D., Bixler, E.O., Vgontzas, A.N.: The relative association of obstructive sleep apnea, obesity, and excessive daytime sleepiness with incident depression: a longitudinal, population-based study. Int J Obes. 40, 1397 (2016)CrossRefGoogle Scholar
  12. 12.
    Uzuner, Ö.: Recognizing obesity and comorbidities in sparse data. JAMIA 16, 561–570 (2009)Google Scholar
  13. 13.
    Aronson, A.R., Lang, F.-M.: An overview of MetaMap: historical perspective and recent advances. JAMIA 17, 229–236 (2010)Google Scholar
  14. 14.
    Witten, D.M., Tibshirani, R.: A framework for feature selection in clustering. J. Am. Stat. Assoc. 105, 713–726 (2010)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Tibshirani, R., Walther, G., Hastie, T.: Estimating the number of clusters in a data set via the gap statistic. J. Roy. Stat. Soc. Ser. B 63, 411–423 (2001)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Willett, W.C., Dietz, W.H., Colditz, G.A.: Guidelines for healthy weight. N. Engl. J. Med. 341, 427–434 (1999)CrossRefGoogle Scholar
  17. 17.
    Peppard, P.E., Young, T., Barnet, J.H., Palta, M., Hagen, E., Hla, K.M.: Increased prevalence of sleep-disordered breathing in adults. Am. J. Epidemiol. 177, 1006–1014 (2013)CrossRefGoogle Scholar
  18. 18.
    Wolf, J., Lewicka, J., Narkiewicz, K.: Obstructive sleep apnea: An update on mechanisms and cardiovascular consequences. Nutr. Metab. Cardiovas. 17, 233–240 (2007)CrossRefGoogle Scholar
  19. 19.
    Somers, V.K., White, D.P., Amin, R., Abraham, W.T., Costa, F., Culebras, A., Daniels, S., Floras, J.S., Hunt, C.E., Olson, L.J., Pickering, T.G., Russell, R., Woo, M., Young, T.: Sleep apnea and cardiovascular disease - an American heart association/American college of cardiology foundation scientific statement from the American heart association council for high blood pressure research professional education committee, council on clinical cardiology, stroke council, and council on cardiovascular nursing. Circulation 118, 1080–1111 (2008)CrossRefGoogle Scholar
  20. 20.
    Poirier, P., Giles, T.D., Bray, G.A., Hong, Y., Stern, J.S., Pi-Sunyer, F.X., Eckel, R.H.: Obesity and cardiovascular disease: pathophysiology, evaluation, and effect of weight loss. Arterioscler. Thromb. Vasc. Biol. 26, 968–976 (2006)CrossRefGoogle Scholar
  21. 21.
    Kramer, C.K., Zinman, B., Retnakaran, R.: Are metabolically healthy overweight and obesity benign conditions? A systematic review and meta-analysis. Ann. Intern. Med. 159, 758–769 (2013)CrossRefGoogle Scholar
  22. 22.
    Dixon, J.B., Dixon, M.E., O’Brien, P.E.: Depression in association with severe obesity - changes with weight loss. Arch. Intern. Med. 163, 2058–2065 (2003)CrossRefGoogle Scholar
  23. 23.
    Roberts, R.E., Deleger, S., Strawbridge, W.J., Kaplan, G.A.: Prospective association between obesity and depression: evidence from the Alameda county study. Int. J. Obes. 27, 514–521 (2003)CrossRefGoogle Scholar
  24. 24.
    Luppino, F.S., de Wit, L.M., Bouvy, P.F., Stijnen, T., Cuijpers, P., Penninx, B.W., Zitman, F.G.: Overweight, obesity, and depression: a systematic review and meta-analysis of longitudinal studies. Arch. Gen. Psychiatry 67, 220–229 (2010)CrossRefGoogle Scholar
  25. 25.
    Rocha, A., Rocha, B.: Adopting nursing health record standards. Inf. Health Soc. Care 39, 1–14 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Ruth Reátegui
    • 1
    • 2
  • Sylvie Ratté
    • 1
  • Estefanía Bautista-Valarezo
    • 2
  • Victor Duque
    • 2
  1. 1.École de technologie supérieureMontrealCanada
  2. 2.Universidad Técnica Particular de LojaLojaEcuador

Personalised recommendations