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2019 | OriginalPaper | Buchkapitel

Deep Learning Architectures for Vector Representations of Patients and Exploring Predictors of 30-Day Hospital Readmissions in Patients with Multiple Chronic Conditions

verfasst von : Muhammad Rafiq, George Keel, Pamela Mazzocato, Jonas Spaak, Carl Savage, Christian Guttmann

Erschienen in: Artificial Intelligence in Health

Verlag: Springer International Publishing

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Abstract

This empirical study of a complex group of patients with multiple chronic concurrent conditions (diabetes, cardiovascular and kidney diseases) explores the use of deep learning architectures to identify patient segments and contributing factors to 30-day hospital readmissions. We implemented Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) on sequential Electronic Health Records data at the Danderyd Hospital in Stockholm, Sweden. Three distinct sub-types of patient groups were identified: chronic obstructive pulmonary disease, kidney transplant, and paroxysmal ventricular tachycardia. The CNN learned about vector representations of patients, but the RNN was better able to identify and quantify key contributors to readmission such as myocardial infarction and echocardiography. We suggest that vector representations of patients with deep learning should precede predictive modeling of complex patients. The approach also has potential implications for supporting care delivery, care design and clinical decision-making.

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Vorheriges Kapitel Generating Reward Functions Using IRL Towards Individualized Cancer Screening

Pache, B., Vollenweider, P., Waeber, G., Marques-Vidal, P.: Prevalence of measured and reported multimorbidity in a representative sample of the Swiss population. BMC Public Health. 15(1), 164 (2015). https://doi.org/10.1186/s12889-015-1515-xCrossRef

Marengoni, A., Winblad, B., Karp, A., Fratiglioni, L.: Prevalence of chronic diseases and multimorbidity among the elderly population in Sweden. Am. J. Publ. Health 98(7), 1198–1200 (2008). https://doi.org/10.2105/AJPH.2007.121137CrossRef

Melis, R., Marengoni, A., Angleman, S., Fratiglioni, L.: Incidence and predictors of multimorbidity in the elderly: a population-based longitudinal study. PLoS ONE. 9(7), e103120 (2014). https://doi.org/10.1371/journal.pone.0103120. (Ed. by, A. Scuteri)CrossRef

Ward, B.W., Schiller, J.S.: Prevalence of multiple chronic conditions among US adults: estimates from the National Health Interview Survey, 2010. Prev. Chronic Dis. 25(10), 120203 (2013). https://doi.org/10.5888/pcd10.120203CrossRef

Sambamoorthi, U., Tan, X., Deb, A.: Multiple chronic conditions and healthcare costs among adults. Expert Rev. Pharmacoecon. Outcomes Res. 15(5), 823–832 (2015). https://doi.org/10.1586/14737167.2015.1091730CrossRef

Schneider, K.M., O’Donnell, B.E., Dean, D.: Prevalence of multiple chronic conditions in the United States’ Medicare population. Health Qual. Life Outcomes 7(1), 82 (2009). https://doi.org/10.1186/1477-7525-7-82CrossRef

Suckling, R., Gallagher, H.: Chronic kidney disease, diabetes mellitus and cardiovascular disease: Risks and commonalities. J. Ren. Care 38, 4–11 (2012). https://doi.org/10.1111/j.1755-6686.2012.00274.xCrossRef

Johnson, T.L., et al.: For many patients who use large amounts of health care services, the need is intense yet temporary. Health Aff. (Millwood) 34(8), 1312–1319 (2015). https://doi.org/10.1377/hlthaff.2014.1186CrossRef

Marengoni, A., et al.: Aging with multimorbidity: a systematic review of the literature. Ageing Res. Rev. 10(4), 430–439 (2011). https://doi.org/10.1016/j.arr.2011.03.003CrossRef

10.

Fortin, M., Lapointe, L., Hudon, C., Vanasse, A.: Multimorbidity is common to family practice: is it commonly researched? Can. Fam. Physician 51, 244–245 (2005). PMID 16926936

11.

Baytas, I.M., Xiao, C., Zhang, X., Wang, F., Jain, A.K., Zhou, J.: Patient subtyping via time-aware LSTM networks. In: Proceedings of 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD 2017, pp. 65–74 (2017). https://doi.org/10.1145/3097983.3097997

12.

Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., Elhadad, N.: Intelligible models for healthcare. In: Proceedings of 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD 2015, pp. 1721–30 (2015). https://doi.org/10.1145/2783258.2788613

13.

Guttmann, C., Sun, X.Z.: Balancing provenance and accuracy tradeoffs in data modeling 2016. United States Patent No: US 9.275.425 B2

14.

Bosco, J.A., Karkenny, A.J., Hutzler, L.H., Slover, J.D., Iorio, R.: Cost burden of 30-day readmissions following medicare total hip and knee arthroplasty. J. Arthroplasty 29(5), 903–905 (2014). https://doi.org/10.1016/j.arth.2013.11.006CrossRef

15.

Stefan, M.S., et al.: Hospital performance measures and 30-day readmission rates. J. Gen. Intern. Med. 28(3), 377–385 (2013). https://doi.org/10.1007/s11606-012-2229-8CrossRef

16.

Wish, J.B.: The role of 30-day readmission as a measure of quality. Clin. J. Am. Soc. Nephrol. 9(3), 440–442 (2014). https://doi.org/10.2215/CJN.00240114CrossRef

17.

Basu Roy, S., et al.: Dynamic hierarchical classification for patient risk-of-readmission. In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD 2015, pp. 1691–700. ACM Press, New York (2015). https://doi.org/10.1145/2783258.2788585

18.

McIlvennan, C.K., Eapen, Z.J., Allen, L.A.: Hospital readmissions reduction program. Circulation 131(20), 1796–1803 (2015). https://doi.org/10.1161/CIRCULATIONAHA.114.010270CrossRef

19.

Jencks, S.F., Williams, M.V., Coleman, E.A.: Rehospitalizations among patients in the medicare fee-for-service program. N. Engl. J. Med. 360(14), 1418–1428 (2009). https://doi.org/10.1056/NEJMsa0803563CrossRef

20.

Xie, J., Zhang, B., Ma, J., Zeng, D.D., Ciganic, J.L.: Readmission prediction for patients with heterogeneous hazard: a trajectory-based deep learning approach. SSRN Electron J. 1–41 (2018). https://doi.org/10.2139/ssrn.3144798

21.

Zhou, H., Della, P.R., Roberts, P., Goh, L., Dhaliwal, S.S.: Utility of models to predict 28-day or 30-day unplanned hospital readmissions: an updated systematic review. BMJ Open 6(6), e011060 (2016). https://doi.org/10.1136/bmjopen-2016-011060CrossRef

22.

Keyhani, S., Myers, L.J., Cheng, E., Hebert, P., Williams, L.S., Bravata, D.M.: Effect of clinical and social risk factors on hospital profiling for stroke readmission. Ann. Intern. Med. 161(11), 775 (2014). https://doi.org/10.7326/M14-0361CrossRef

23.

Rana, S., Tran, T., Luo, W., Phung, D., Kennedy, R.L., Venkatesh, S.: Predicting unplanned readmission after myocardial infarction from routinely collected administrative hospital data. Aust. Health Rev. 38(4), 377 (2014). https://doi.org/10.1071/AH14059CrossRef

24.

Donzé, J., Lipsitz, S., Schnipper, J.L.: Risk factors for potentially avoidable readmissions due to end-of-life care issues. J. Hosp. Med. 9(5), 310–314 (2014). https://doi.org/10.1002/jhm.2173CrossRef

25.

Taha, M., Pal, A., Mahnken, J.D., Rigler, S.K.: Derivation and validation of a formula to estimate risk for 30-day readmission in medical patients. Int. J. Qual. Health Care 26(3), 271–277 (2014). https://doi.org/10.1093/intqhc/mzu038CrossRef

26.

Hebert, C., et al.: Diagnosis-specific readmission risk prediction using electronic health data: a retrospective cohort study. BMC Med. Inform. Decis. Mak. 14(1), 65 (2014). https://doi.org/10.1186/1472-6947-14-65CrossRef

27.

Iannuzzi, J.C., Chandra, A., Kelly, K.N., Rickles, A.S., Monson, J.R.T., Fleming, F.J.: Risk score for unplanned vascular readmissions. J. Vasc. Surg. 59(5), 1340–1347.e1 (2014). https://doi.org/10.1016/j.jvs.2013.11.089CrossRef

28.

Lucas, D.J., et al.: Assessing readmission after general, vascular, and thoracic surgery using ACS-NSQIP. Ann. Surg. 258(3), 430–439 (2013). https://doi.org/10.1097/SLA.0b013e3182a18fccCrossRef

29.

Wallmann, R., Llorca, J., Gómez-Acebo, I., Ortega, Á.C., Roldan, F.R., Dierssen-Sotos, T.: Prediction of 30-day cardiac-related-emergency-readmissions using simple administrative hospital data. Int. J. Cardiol. 164(2), 193–200 (2013). https://doi.org/10.1016/j.ijcard.2011.06.119CrossRef

30.

Wasfy, J.H., et al.: A prediction model to identify patients at high risk for 30-day readmission after percutaneous coronary intervention. Circ. Cardiovasc. Qual. Outcomes 6(4), 429–435 (2013). https://doi.org/10.1161/CIRCOUTCOMES.111.000093CrossRef

31.

Raposeiras-Roubín, S., et al.: Mortality and cardiovascular morbidity within 30 days of discharge following acute coronary syndrome in a contemporary European cohort of patients: How can early risk prediction be improved? The six-month GRACE risk score. Rev. Port. Cardiol. 34(6), 383–391 (2015). https://doi.org/10.1016/j.repc.2014.11.020CrossRef

32.

Sudhakar, S., Zhang, W., Kuo, Y.-F., Alghrouz, M., Barbajelata, A., Sharma, G.: Validation of the readmission risk score in heart failure patients at a tertiary hospital. J. Card. Fail. 21(11), 885–891 (2015). https://doi.org/10.1016/j.cardfail.2015.07.010CrossRef

33.

van Walraven, C., Wong, J., Forster, A.J., Hawken, S.: Predicting post-discharge death or readmission: deterioration of model performance in population having multiple admissions per patient. J. Eval. Clin. Pract. 19(6), 1012–1018 (2013). https://doi.org/10.1111/jep.12012CrossRef

34.

van Walraven, C., Wong, J., Forster, A.J.: LACE+index: extension of a validated index to predict early death or urgent readmission after hospital discharge using administrative data. Open Med. 6(3), e80–e90 (2012). PMID 23696773

35.

Choi, E., Bahadori, M.T., Kulas, J.A., et al.: RETAIN: an interpretable predictive model for healthcare using reverse time attention mechanism. In: NIPS (2016). arXiv:1608.05745

36.

Aczon, M., et al.: Dynamic mortality risk predictions in pediatric critical care using recurrent neural networks, 1–18 (2017). arXiv:1701.06675

37.

Lipton, Z.C., Kale, D.C., Elkan, C., Wetzel, R.: Learning to diagnose with LSTM recurrent neural networks, 1–18 (2015). https://doi.org/10.14722/ndss.2015.23268

38.

Razavian, N., Marcus, J., Sontag, D.: Multi-task prediction of disease onsets from longitudinal lab test, 1–27 (2016). arXiv:1608.00647

39.

Suresh, H., Hunt, N., Johnson, A., Celi, L.A., Szolovits, P., Ghassemi, M.: Clinical intervention prediction and understanding using deep networks, 1–16 (2017). arXiv:1705.08498

40.

Graves, A., Mohamed, A., Hinton, G.: Speech recognition with deep recurrent neural networks, (3) (2013). https://doi.org/10.1109/icassp.2013.6638947

41.

Wen, T.-H., Gasic, M., Mrksic, N., Su, P.-H., Vandyke, D., Young, S.: Semantically conditioned LSTM-based natural language generation for spoken dialogue systems (2015). arXiv:1508.01745

42.

Lai, S., Xu, L., Liu, K., Zhao, J.: Recurrent convolutional neural networks for text classification. In: Twenty-Ninth AAAI Conference on Artificial Intelligence, pp. 2267–2273 (2015). ISBN 9781577357018

43.

Xiao, C., Ma, T., Dieng, A.B., Blei, D.M., Wang, F.: Readmission prediction via deep contextual embedding of clinical concepts. PLoS ONE. 13(4), e0195024 (2018). (Ed. by, C. Hou). https://doi.org/10.1371/journal.pone.0195024CrossRef

44.

Futoma, J., Sendak, M., Cameron, C.B., Heller, K.: Predicting disease progression with a model for multivariate longitudinal clinical data. J. Mach. Learn. Res. 56, 42–54 (2016)

45.

Joynt, K.E., Jha, A.K.: Thirty-day readmissions—truth and consequences. N. Engl. J. Med. 366(15), 1366–1369 (2012). https://doi.org/10.1056/NEJMp1201598CrossRef

46.

Zhao, C., Shen, Y.: Convolutional neural network-based model for patient representation learning to uncover temporal phenotypes for heart failure (2017)

47.

Che, Z., Purushotham, S., Khemani, R., Liu, Y.: Distilling knowledge from deep networks with applications to healthcare domain, 1–13 (2015). arXiv:1512.03542

48.

Parr, D.G.: Patient phenotyping and early disease detection in chronic obstructive pulmonary disease. Proc. Am. Thorac. Soc. 8(4), 338–349 (2011). https://doi.org/10.1513/pats.201101-014RMCrossRef

49.

Shickel, B., Tighe, P.J., Bihorac, A., Rashidi, P.: Deep EHR: a survey of recent advances in deep learning techniques for Electronic Health Record (EHR) analysis. IEEE J. Biomed. Health Inform. 22(5), 1589–1604 (2018). https://doi.org/10.1109/JBHI.2017.2767063CrossRef

50.

Miotto, R., Li, L., Kidd, B.A., Dudley, J.T.: Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci. Rep. 6(1), 26094 (2016). https://doi.org/10.1038/srep26094CrossRef

51.

Chatterjee, S., Davies, M.J.: Accurate diagnosis of diabetes mellitus and new paradigms of classification. Nat. Rev. Endocrinol. 14(7), 383–384 (2018). https://doi.org/10.1038/s41574-018-0025-1CrossRef

52.

Bohmer, R.M.J.: Designing Care: Aligning the Nature and Management of Health Care. Harvard Business School Press, Brighton (2009). ISBN 142217560X

Titel: Deep Learning Architectures for Vector Representations of Patients and Exploring Predictors of 30-Day Hospital Readmissions in Patients with Multiple Chronic Conditions
verfasst von: Muhammad Rafiq
George Keel
Pamela Mazzocato
Jonas Spaak
Carl Savage
Christian Guttmann
Verlag: Springer International Publishing
Buch: Artificial Intelligence in Health
Print ISBN: 978-3-030-12737-4

Electronic ISBN: 978-3-030-12738-1

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-12738-1_17

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