Diagnosis Prediction from Electronic Health Records Using the Binary Diagnosis History Vector Representation

Journal of Computational Biology

Volumn 24, Issue 8, 2017, Pages 767-786

  Vasiljeva, Ieva ^a   Arandjelović, Ognjen ^a  

^a UNIVERSITY OF ST ANDREWS   (United Kingdom)

Author keywords

Bayesian; disease; electronic medical records; EMRs; epidemiology; risk

Indexed keywords

COMPUTER ASSISTED DIAGNOSIS; ELECTRONIC HEALTH RECORD; FACTUAL DATABASE; HOSPITAL ADMISSION; HUMAN; PROCEDURES; PROGNOSIS; THEORETICAL MODEL;

DATABASES, FACTUAL; DIAGNOSIS, COMPUTER-ASSISTED; ELECTRONIC HEALTH RECORDS; HUMANS; MODELS, THEORETICAL; PATIENT ADMISSION; PROGNOSIS;

EID: 85032199900     PISSN: 10665277     EISSN: None     Source Type: Journal    
DOI: 10.1089/cmb.2017.0023     Document Type: Article

References (34)

1. Allaudeen, N., Vidyarthi, A., Maselli, J., et al. 2011. Redefining readmission risk factors for general medicine patients. J. Hosp. Med. 6, 54-60.
2. Andrei, V., and Arandjelović, O. 2016. Identification of promising research directions using machine learning aided medical literature analysis. In Proc. International Conference of the IEEE Engineering in Medicine and Biology Society, 2471-2474. IEEE, Orlando, FL.
3. Arandjelović, O. 2011. Contextually learnt detection of unusual motion-based behaviour in crowded public spaces. In Proc. International Symposium on Computer and Information Sciences, 403-410. Springer, London, UK.
4. Arandjelović, O. 2015a. Prediction of health outcomes using big (health) data. In Proc. International Conference of the IEEE Engineering in Medicine and Biology Society, 2543-2546. IEEE, Milan, Italy.
5. Arandjelović, O. 2015b. Modelling disease progression using electronic hospital records. In Proc. IJCAI Workshop on Bioinformatics and Artificial Intelligence, 10-16. AAAI, Buenos Aires, Argentina.
6. Arandjelović, O. 2016. On the discovery of hospital admission patterns-A clarification. Bioinformatics 32, 2078.
7. Barracliffe, L., Arandjelović, O., and Humphris, G. 2017. Can machine learning predict healthcare professionals' responses to patient emotions? In Proc. International Conference on Bioinformatics and Computational Biology. ISCA, Honolulu, HI.
8. Bartolomeo, N., Trerotoli, P., Moretti, A., et al. 2008. A Markov model to evaluate hospital readmission. BMC Med. Res. Methodol. 8, 23.
9. Berwick, D.M., and Hackbarth, A.D. 2012. Eliminating waste in US health care. JAMA 307, 1513-1516.
10. Bessou, A., Guelfucci, F., Aballea, S., et al. 2015. Comparison of comorbidity measures to predict economic outcomes in a large UK primary care database. Value Health 18, A691.
11. Beykikhoshk, A., Arandjelović, O., Phung, D., et al. 2014. Data-mining Twitter and the autism spectrum disorder: A pilot study. In Proc. IEEE/ACM International Conference on Advances in Social Network Analysis and Mining, 349-356. IEEE, Beijing, China.
12. Beykikhoshk, A., Arandjelović, O., Phung, D., et al. 2015a. Hierarchical Dirichlet process for tracking complex topical structure evolution and its application to autism research literature. In Proc. Pacific-Asia Conference on Knowledge Discovery and Data Mining, 1, 550-562. IEEE, Ho Chi Minh City, Viet Nam.
13. Beykikhoshk, A., Arandjelović, O., Phung, D., et al. 2015b. Using Twitter to learn about the autism community. Soc. Netw. Anal. Min. 5, 5-22. Springer.
14. Beykikhoshk, A., Phung, D., Arandjelović, O., et al. 2016. Analysing the history of autism spectrum disorder using topic models. In Proc. IEEE International Conference on Data Science and Advanced Analytics, 762-771. IEEE, Montreal, Canada.
15. Bhatnagar, P., Wickramasinghe, K., Williams, J., et al. 2015. The epidemiology of cardiovascular disease in the UK 2014. Heart 101, 1182-1189.
16. Birkhead, G.S., Klompas, M., and Shah, N.R. 2015. Uses of electronic health records for public health surveillance to advance public health. Annu. Rev. Public Health 36, 345-359.
17. Bolle, R.M., Connell, J.H., Pankanti, S., et al. 2005. The relation between the ROC curve and the CMC. In Proc. IEEE Workshop on Automatic Identification Advanced Technologies, 15-20. IEEE, Washington, USA.
18. Butler, J., and Kalogeropoulos, A. 2012. Hospital strategies to reduce heart failure readmissions. J. Am. Coll. Cardiol. 60, 615-617.
19. Canavan, C., West, J., and Card, T. 2015. Calculating total health service utilisation and costs from routinely collected electronic health records using the example of patients with irritable bowel syndrome before and after their first gastroenterology appointment. Pharmacoeconomics 34, 181-194.
20. Christensen, B., and Ellingsen, G. 2016. Evaluating model-driven development for large-scale EHRs through the openEHR approach. Int. J. Med. Inform. 89, 43-54.
21. Coloma, P.M., Trifiro, G., Patadia, V., et al. 2013. Postmarketing safety surveillance: Where does signal detection using electronic healthcare records fit into the big picture? Drug Saf. 36, 183-197.
22. Crawford, A.G., Cote, C., Couto, J., et al. 2010. Comparison of GE Centricity electronic medical record database and National Ambulatory Medical Care Survey findings on the prevalence of major conditions in the United States. Popul. Health Manag. 13, 139-150.
23. De Gaetano, A., Hardy, T., Beck, B., et al. 2008. Mathematical models of diabetes progression. Am. J. Physiol. Endocrinol. Metab. 295, E1462-E1479.
24. Dharmarajan, K., Hsieh, A.F., Lin, Z., et al. 2013. Diagnoses and timing of 30-day readmissions after hospitalization for heart failure, acute myocardial infarction, or pneumonia. JAMA 309, 355-363.
25. Duffy, N.D., and Yau, J.F.S. 1995. Estimation of mean sojourn time in breast cancer screening using a Markov chain model of both entry to and exit from the preclinical detectable phase. Stat. Med. 14, 1531-1543.
26. Fan, K., Aiello, A.E., and Heller, K.A. 2016. Bayesian models for heterogeneous personalized health data. arXiv preprint: https://arxiv.org/abs/1509.00110
27. Folino, F., and Pizzuti, C. 2011. Combining Markov models and association analysis for disease prediction. Information Technology in Bio- and Medical Informatics. 39-52. ACM, Toulouse, France.
28. Friedman, B., Jiang, H.J., and Elixhauser, A. 2008-2009. Costly hospital readmissions and complex chronic illness. Inquiry 45, 408-421.
29. Gabriel, K.R., and Neumann, J.1962. A Markov chain model for daily rainfall occurrence at Tel Aviv. Q. J. R. Meteorol. Soc. 88, 90-95.
30. Jackson, C.H., Sharples, L.D., Thompson, S.G., et al. 2003. Multistate Markov models for disease progression with classification error. J. R. Stat. Soc. Series D 52, 193-209.
31. Kilkenny, M.F., Longworth, M., Pollack, M., et al. 2013. Factors associated with 28-day hospital readmission after stroke in Australia. Stroke 44, 2260-2268.
32. Kukafka, R., Ancker, J.S., Chan, C., et al. 2007. Redesigning electronic health record systems to support public health. J. Biomed. Inform. 40, 398-409.
33. Lau, E.C., Mowat, F.S., Kelsh, M.A., et al. 2011. Use of electronic medical records (EMR) for oncology outcomes research: Assessing the comparability of EMR information to patient registry and health claims data. Clin. Epidemiol. 3, 259-272.
34. Lee, K., Ho, M., Yang, J., et al. 2005. Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans. Pattern Anal. Mach. Intell. 27, 684-698.