Automatic summarization of risk factors preceding disease progression an insight-driven healthcare service case study on using medical records of diabetic patients

World Wide Web

Volumn 18, Issue 4, 2015, Pages 1163-1175

  Hsueh, Pei Yun S ^a   Zhu, Xin Xin ^a   Hsiao, Mark J H ^b   Lee, Selina Y F ^b   Deng, Vincent ^b   Ramakrishnan, Sreeram ^a  

^a IBM T J WATSON RESEARCH CENTER   (United States)

^b IBM RESEARCH   (United States)

Author keywords

Case study; Data driven analytics; Disease management; Healthcare service design; Medical record summarization

Indexed keywords

DISEASE CONTROL; DISEASES; HEALTH CARE; HEALTH RISKS;

AUTOMATIC SUMMARIZATION; CARDIOVASCULAR DISEASE; DATA DRIVEN; DISEASE MANAGEMENT; HEALTH INFORMATIONS; HEALTHCARE SERVICES; MEDICAL RECORD; TRADITIONAL MANAGEMENT;

QUALITY CONTROL;

EID: 84932624118     PISSN: 1386145X     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11280-014-0304-2     Document Type: Article

References (22)

1. Adibhatla, R.M., Hatcher, J.F.: Altered Lipid Metabolism in Brain Injury and Disorders. Subcell. Biochem. 49, 241–68 (2008)
2. Brindle, P.M., Beswick, A.D., Fahey, T., Ebrahim, S.B.: The accuracy and impact of risk assessment in the primary prevention of cardiovascular disease: A systematic review. Heart 92(12), 1752–9 (2006)
3. Brindle, P., Emberson, J., Lampe, F., Walker, M., Whincup, P., Fahey, T., Ebrahim, S.: Predictive accuracy of the Framingham coronary risk score in British men: prospective cohort study. BMJ 327(7426), 1267 (2003)
4. Brown, A.F., et al.: Race, Ethnicity, Socioeconomic Position and Quality of Care for Adults with Diabetes Enrolled in Managed Care: the Translating Research Into Action for Diabetes (TRIAD) study. Diabetes Care 28(12), 2864–2870 (2005)
5. Cairncross, J.G., Ueki, K., Zlatescu, M.C., Lisle, D.K., Finkelstein, D.M., Hammond, R.R., Silver, J.S., Stark, P.C., Macdonald, D.R., Ino, Y., Ramsay, D.A., Louis, D.N.: Specific Genetic Predictors of Chemotherapeutic Response and Survival in Patients with Anaplastic Oligodendrogliomas. J. Natl. Cancer Inst. 90(19), 1473–9 (1998)
6. Carr VL, Sangiorgi D, Büscher M, Junginger S, Cooper R. Integrating evidence-based design and experience-based approaches in healthcare service design. HERD. 2011 Summer; 4 (4):12–33
7. Chobanian, A.V., et al.: The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. JAMA 289(19), 2560–71 (2003)
8. Concaro S, Sacchi L, Cerra C, Stefanelli M. Temporal Data Mining for the Assessment of the Costs Related to Diabetes Mellitus Pharmacological Treatment. Proc. AMIA 2009 Symposium Proceedings 2009; 119–123.
9. D’Agostino Sr., R.B., Vasan, R.S., Pencina, M.J., Wolf, P.A., Cobain, M., Massaro, J.M., Kannel, W.B.: General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation 117(6), 743–53 (2008)
10. de Ruijter, W., Westendorp, R.G.J., Assendelft, W.J.J., den Elzen, W.P.J., de Craen, A.J.M., le Cessie, S., Gussekloo, J.: Use of Framingham risk score and new biomarkers to predict cardiovascular mortality in older people: population based observational cohort study. BMJ 338(7688), a3083 (2008)
11. Genest, J., Libby, P.: Lipoprotein disorders and cardiovascular disease. In: Bonow, R.O., Mann, D.L., Zipes, D.P., Libby, P. (eds.) Braunwald’s Heart Disease: A Textbook of Cardiovascular Medicine, 9th edn. Elsevier, Philadelphia (2011). chap 47
12. Henry N. Ginsberg. the ACCORD (Action to Control Cardiovascular Risk in Diabetes) Lipid Trial: What we learn from subgroup analyses. Diabetes Care May 2011; 34 (2): 107–108.
13. Inzucchi, S.E., Bergenstal, R.M., Buse, J.B., Diamant, M., Ferrannini, E., Nauck, M., Peters, A.L., Tsapas, A., Wender, R., Matthews, D.R.: Management of hyperglycemia in type 2 diabetes: a patient-centered approach: position statement of the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetes Care 35(6), 1364–79 (2012)
14. Johansson, B.B.: Hypertension Mechanisms Causing Stroke. Clin. Exp. Pharmacol. Physiol. 26, 563–565 (1999)
15. Klausen, K., Borch-Johnsen, K., Feldt-Rasmussen, B., Jensen, G., Clausen, P., Scharling, H., Appleyard, M., Jensen, J.S.: Very Low Levels of Microalbuminuria Are Associated With Increased Risk of Coronary Heart Disease and Death Independently of Renal Function, Hypertension, and Diabetes. Circulation 110(1), 32–5 (2004)
16. Klein, T.E., Altman, R.B., et al.: Estimation of the warfarin dose with clinical and pharmacogenetic data. N. Engl. J. Med. 360, 753–764 (2009)
17. Lindstrom J. and Tuomilehto J. The Diabetes Risk Score: A practical tool to predict type 2 diabetes risk Diabetes Care 2003; 26:3: 725–731.
18. Liu, J., Hong, Y., D’Agostino Sr., R.B., Wu, Z., Wang, W., Sun, J., Wilson, P.W., Kannel, W.B., Zhao, D.: Predictive value for the Chinese population of the Framingham CHD risk assessment tool compared with the Chinese Multi-Provincial Cohort Study. JAMA 291(21), 2591–9 (2004)
19. Owens, D.K.: Improving practice guidelines with Patient-Specific Recommendations. Ann. Intern. Med. 154(9), 638–9 (2011)
20. Park, J., Edington, D.W.: A Sequential Neural Network Model for Diabetes Prediction. Artif. Intell. Med. 23, 277–293 (2001)
21. The Look AHEAD Research Group: Effect of the Look AHEAD Study Intervention on Medication Use and Related Cost to Treat Cardiovascular Disease Risk Factors in Individuals With Type 2 Diabetes. Diabetes Care 33(6), 1153–1158 (2010)
22. Victor, R.G.: Arterial hypertension. In: Goldman, L., Schafer, A.I. (eds.) Cecil Medicine, 24th edn. Elsevier, Philadelphia (2011). chap 67