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Journal of Medical Systems

Erschienen in:

01.03.2019 | Transactional Processing Systems

Cluster Analysis of Obesity Disease Based on Comorbidities Extracted from Clinical Notes

verfasst von: Ruth Reátegui, Sylvie Ratté, Estefanía Bautista-Valarezo, Víctor Duque

Erschienen in: Journal of Medical Systems | Ausgabe 3/2019

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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 to identify clusters of patients based on fourteen comorbidities related to obesity, automatically extracted with the cTAKES tool from the i2b2 Obesity Challenge data. Furthermore, results were compared with clusters obtained from experts’ annotated data. The sparse K-means algorithms were used in both experiment at two levels: at the first level, three clusters were found, and at the second, new clusters were found by applying the same algorithm to each of the clusters from the former level. The results show that three types of clusters could be identified based on the number of comorbidities and the percentage of patients suffering from them. Diabetes, hypercholesterolemia, atherosclerotic cardiovascular diseases, congestive heart failure, obstructive sleep apnea, and depression were the diseases with the highest weights contributing to the cluster distribution.

Bukhanov, N., Balakhontceva, M., Krikunov, A., Sabirov, A., Semakova, A., Zvartau, N., and Konradi, A., Clustering of comorbidities based on conditional probabilities of diseases in hypertensive patients. Proc. Comput. Sci. 108:2478–2487, 2017. https://doi.org/10.1016/j.procs.2017.05.073.CrossRef

Shivade, C., Raghavan, P., Fosler-Lussier, E., Embi, P. J., Elhadad, N., Johnson, S. B., and Lai, A. M., A review of approaches to identifying patient phenotype cohorts using electronic health records. JAMIA 21(2):221–230, 2014. https://doi.org/10.1136/amiajnl-2013-001935.CrossRefPubMed

National Library of Medicine (US), UMLS® Reference Manual, 2009. http://www.ncbi.nlm.nih.gov/books/NBK9676/. Accessed 20 Mar 2018.

National Library of Medicine (US), Overview of SNOMED CT, 2016. https://www.nlm.nih.gov/healthit/snomedct/snomed_overview.html. Accessed 20 Mar 2018.

Chen, C.-Z., Wang, L.-Y., Ou, C.-Y., Lee, C.-H., Lin, C.-C., and Hsiue, T.-R., Using cluster analysis to identify phenotypes and validation of mortality in men with COPD. Lung 192(6):889–896, 2014. https://doi.org/10.1007/s00408-014-9646-x.CrossRefPubMed

Bourdin, A., Molinari, N., Vachier, I., Varrin, M., Marin, G., Gamez, A.-S., Paganin, F., and Chanez, P., Prognostic value of cluster analysis of severe asthma phenotypes. J. Allerg. Clin. Immunol. 134(5):1043–1050, 2014. https://doi.org/10.1016/j.jaci.2014.04.038.CrossRef

Rocha, A., and Rocha, B., Adopting nursing health record standards. Inform. Health Soc. Care 39(1):1–14, 2014. https://doi.org/10.3109/17538157.2013.827200.CrossRefPubMed

van der Esch, M., Knoop, J., van der Leeden, M., Roorda, L. D., Lems, W. F., Knol, D. L., and Dekker, J., Clinical phenotypes in patients with knee osteoarthritis: A study in the Amsterdam osteoarthritis cohort. Osteoarthr. Cartil. 23(4):544–549, 2015. https://doi.org/10.1016/j.joca.2015.01.006.CrossRefPubMed

Vavougios, G. D., Natsios, G., Pastaka, C., Zarogiannis, S. G., and Gourgoulianis, K. I., Phenotypes of comorbidity in OSAS patients: Combining categorical principal component analysis with cluster analysis. J. Sleep Res. 25(1):31–38, 2016. https://doi.org/10.1111/jsr.12344.CrossRefPubMed

10.

Joosten, S. A., Hamza, K., Sands, S., Turton, A., Berger, P., and Hamilton, G., Phenotypes of patients with mild to moderate obstructive sleep apnoea as confirmed by cluster analysis. Respirology 17(1):99–107, 2012. https://doi.org/10.1111/j.1440-1843.2011.02037.x.CrossRefPubMed

11.

Figueroa, R. L., and Flores, C. A., Extracting information from electronic medical records to identify the obesity status of a patient based on comorbidities and bodyweight measures. J. Med. Syst. 40(8):1–9, 2016.CrossRef

12.

Serrano-Pariente, J., Rodrigo, G., Fiz, J. A., Crespo, A., Plaza, V., and High Risk Asthma Res G, Identification and characterization of near-fatal asthma phenotypes by cluster analysis. Allergy 70(9):1139–1147, 2015. https://doi.org/10.1111/all.12654.CrossRefPubMed

13.

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., and Felker, G. M., Clinical implications of chronic heart failure phenotypes defined by cluster analysis. J. Am. Coll. Cardiol. 64(17):1765–1774, 2014. https://doi.org/10.1016/j.jacc.2014.07.979.CrossRefPubMedPubMedCentral

14.

Poirier, P., Giles, T. D., Bray, G. A., Hong, Y., Stern, J. S., Pi-Sunyer, F. X., and Eckel, R. H., Obesity and cardiovascular disease: Pathophysiology, evaluation, and effect of weight loss. Arterioscler. Thromb. Vasc. Biol. 26(5):968–976, 2006. https://doi.org/10.1161/01.ATV.0000216787.85457.f3.CrossRefPubMed

15.

Guh, D. P., Zhang, W., Bansback, N., Amarsi, Z., Birmingham, C. L., and Anis, A. H., The incidence of co-morbidities related to obesity and overweight: A systematic review and meta-analysis. BMC Pub. Health 9:1–20, 2009. https://doi.org/10.1186/1471-2458-9-88.CrossRef

16.

Foster, M. C., Hwang, S. J., Larson, M. G., Lichtman, J. H., Parikh, N. I., Vasan, R. S., Levy, D., and Fox, C. S., Overweight, obesity, and the development of stage 3 CKD: The Framingham heart study. Am. J. Kidney Dis. : Off. J. Natl. Kidney Found 52(1):39–48, 2008. https://doi.org/10.1053/j.ajkd.2008.03.003.CrossRef

17.

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., and Asthma Clin Res, N., Cluster analysis of obesity and Asthma phenotypes. Plos One 7(5):1–7, 2012. https://doi.org/10.1371/journal.pone.0036631.CrossRef

18.

Laing, S. T., Smulevitz, B., Vatcheva, K. P., Rahbar, M. H., Reininger, B., McPherson, D. D., McCormick, J. B., and Fisher-Hoch, S. P., Subclinical atherosclerosis and obesity phenotypes among Mexican Americans. J. Am. Heart Assoc. 4(3):e001540, 2015. https://doi.org/10.1161/jaha.114.001540.CrossRefPubMedPubMedCentral

19.

LaGrotte, C., Fernandez-Mendoza, J., Calhoun, S. L., Liao, D., Bixler, E. O., and 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.:1–8, 2016. doi:https://doi.org/10.1038/ijo.2016.87.

20.

Uzuner, Ö., Recognizing obesity and comorbidities in sparse data. JAMIA 16(4):561–570, 2009.PubMed

21.

Reategui, R., and Ratte, S., Comparison of MetaMap and cTAKES for entity extraction in clinical notes. BMC Med. Inform. Dec. Mak. 18(Suppl 3):74, 2018. https://doi.org/10.1186/s12911-018-0654-2.CrossRef

22.

Witten, D. M., and Tibshirani, R., A framework for feature selection in clustering. J. Am. Stat. Assoc. 105(490):713–726, 2010. https://doi.org/10.1198/jasa.2010.tm09415.CrossRefPubMedPubMedCentral

23.

Tibshirani, R., Walther, G., and Hastie, T., Estimating the number of clusters in a data set via the gap statistic. J. Roy. Stat. Soc. B 63:411–423, 2001. https://doi.org/10.1111/1467-9868.00293.CrossRef

24.

Bruce, S. G., Riediger, N. D., Zacharias, J. M., and Young, T. K., Obesity and obesity-related comorbidities in a Canadian first nation population. Prevent. Chron. Dis. 8(1):A03, 2011.

25.

Willett, W. C., Dietz, W. H., and Colditz, G. A., Guidelines for healthy weight. N. Engl. J. Med. 341(6):427–434, 1999. https://doi.org/10.1056/NEJM199908053410607.CrossRefPubMed

26.

Leslie, W. S., Hankey, C. R., and Lean, M. E. J., Weight gain as an adverse effect of some commonly prescribed drugs: A systematic review. Qjm-Int J. Med. 100(7):395–404, 2007. https://doi.org/10.1093/qjmed/hcm044.CrossRef

27.

Peppard, P. E., Young, T., Barnet, J. H., Palta, M., Hagen, E., and Hla, K. M., Increased prevalence of sleep-disordered breathing in adults. Am. J. Epidemiol. 177(9):1006–1014, 2013. https://doi.org/10.1093/aje/kws342.CrossRefPubMedPubMedCentral

28.

Wolf, J., Lewicka, J., and Narkiewicz, K., Obstructive sleep apnea: An update on mechanisms and cardiovascular consequences. Nutr. Metab. Cardiovas. 17(3):233–240, 2007. https://doi.org/10.1016/j.numecd.2006.12.005.CrossRef

29.

Canto, J. G., Kiefe, C. I., Rogers, W. J., Peterson, E. D., Frederick, P. D., French, W. J., Gibson, C. M., Pollack, C. V., Ornato, J. P., Zalenski, R. J., Penney, J., Tiefenbrunn, A. J., Greenland, P., and Investigators, N., Number of coronary heart disease risk factors and mortality in patients with first myocardial infarction. Jama J. Am. Med. Assoc. 306(19):2120–2127, 2011. https://doi.org/10.1001/jama.2011.1654.CrossRef

30.

Mamudu, H. M., Paul, T. K., Wang, L., Veeranki, S. P., Panchal, H. B., Alamian, A., Sarnosky, K., and Budoff, M., The effects of multiple coronary artery disease risk factors on subclinical atherosclerosis in a rural population in the United States. Prevent. Med. 88:140–146, 2016. https://doi.org/10.1016/j.ypmed.2016.04.003.CrossRef

31.

Kramer, C. K., Zinman, B., and Retnakaran, R., Are metabolically healthy overweight and obesity benign conditions?: A systematic review and meta-analysis. Ann. Intern. Med. 159(11):758–769, 2013. https://doi.org/10.7326/0003-4819-159-11-201312030-00008.CrossRefPubMed

32.

Dixon, J. B., Dixon, M. E., and O'Brien, P. E., Depression in association with severe obesity - Changes with weight loss. Arch. Intern. Med. 163(17):2058–2065, 2003. https://doi.org/10.1001/archinte.163.17.2058.CrossRefPubMed

33.

Roberts, R. E., Deleger, S., Strawbridge, W. J., and Kaplan, G. A., Prospective association between obesity and depression: Evidence from the Alameda County study. Int. J. Obes. 27(4):514–521, 2003. https://doi.org/10.1038/sj.ijo.08022204.CrossRef

34.

Luppino, F. S., de Wit, L. M., Bouvy, P. F., Stijnen, T., Cuijpers, P., Penninx, B. W., and Zitman, F. G., Overweight, obesity, and depression: A systematic review and meta-analysis of longitudinal studies. Arch. Gen. Psychiat. 67(3):220–229, 2010. https://doi.org/10.1001/archgenpsychiatry.2010.2.CrossRefPubMed

35.

Gao, Y. H., Zhao, H. S., Zhang, F. R., Gao, Y., Shen, P., Chen, R. C., and Zhang, G. J., The relationship between depression and Asthma: A meta-analysis of prospective studies. Plos One 10(7):1–12, 2015. https://doi.org/10.1371/journal.pone.0132424.CrossRef

Titel: Cluster Analysis of Obesity Disease Based on Comorbidities Extracted from Clinical Notes
verfasst von: Ruth Reátegui
Sylvie Ratté
Estefanía Bautista-Valarezo
Víctor Duque
Publikationsdatum: 01.03.2019
Verlag: Springer US
Erschienen in: Journal of Medical Systems / Ausgabe 3/2019
Print ISSN: 0148-5598
Elektronische ISSN: 1573-689X
DOI: https://doi.org/10.1007/s10916-019-1172-1

Springer Medizin

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