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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Uzuner, Ö.: Recognizing obesity and comorbidities in sparse data. JAMIA 16, 561–570 (2009)
Aronson, A.R., Lang, F.-M.: An overview of MetaMap: historical perspective and recent advances. JAMIA 17, 229–236 (2010)
Witten, D.M., Tibshirani, R.: A framework for feature selection in clustering. J. Am. Stat. Assoc. 105, 713–726 (2010)
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)
Willett, W.C., Dietz, W.H., Colditz, G.A.: Guidelines for healthy weight. N. Engl. J. Med. 341, 427–434 (1999)
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)
Wolf, J., Lewicka, J., Narkiewicz, K.: Obstructive sleep apnea: An update on mechanisms and cardiovascular consequences. Nutr. Metab. Cardiovas. 17, 233–240 (2007)
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)
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)
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)
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)
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)
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)
Rocha, A., Rocha, B.: Adopting nursing health record standards. Inf. Health Soc. Care 39, 1–14 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Reátegui, R., Ratté, S., Bautista-Valarezo, E., Duque, V. (2018). Obesity Cohorts Based on Comorbidities Extracted from Clinical Notes. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S. (eds) Trends and Advances in Information Systems and Technologies. WorldCIST'18 2018. Advances in Intelligent Systems and Computing, vol 746. Springer, Cham. https://doi.org/10.1007/978-3-319-77712-2_107
Download citation
DOI: https://doi.org/10.1007/978-3-319-77712-2_107
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-77711-5
Online ISBN: 978-3-319-77712-2
eBook Packages: EngineeringEngineering (R0)