References
- Berkman, L. F., Leo-Summers, L., & Horwitz, R. I. (1992). Emotional support and survival after myocardial infarction. Annals of Internal Medicine, 117,1003-1009 https://doi.org/10.7326/0003-4819-117-12-1003
- Blewitt, D. K, & Jones, K. R. (1996). Using elements of the nursing minimum data set for determining outcomes. J Nurs Adm, 26(6), 48-56 https://doi.org/10.1097/00005110-199606000-00014
- Brossette, S. E., Sprague, A. P., Hardin, J. M., Waites, K B., Jones, W. T., & Moser, S. A. (1998). Association rules and data mining in hospital infection control and public health surveillance, J Am Med Inform Assoc, 5(4), 372-381
- Burn-Thornton, K. E., & Edenbrandt, L. (1998). Myocardial infarction-pinpointing the key indicators in the 12-Lead ECG using data mining. Comput Biomed Res, 31, 293-303 https://doi.org/10.1006/cbmr.1998.1482
- Delaney, C., Ruiz, M. E., Clarke, M., & Srinivasan, P. (2000). Knowledge discovery in databases: data mining NMOS. Proceedings of the 7th IMIA International Conference on Nursing Use of Computers and Information Science. Aukland, New Zealand, 61-65
- Eriksen, L. R., Turley, J. P., Denton, D., & Manning, S. (1997). Data mining: A strategy for knowledge development and structure in nursing practice. In U. Gerdin, M. Tallberg, & Wainwright. Nursing informatics: the impact of nursing knowledge on health care informatics .. proceedings of Nf 97, Sixth Triennial International Congress of IMIA-NI, Nursing Informatics of International Medical Informatics. Amsterdam, Netherlands: IOS Press
- Fayyad, U. M., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery: An overview. In U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, & R. Uthurusamy (Ed.). Advances in knowledge discovery and data mining. (pp. 1-31). Menlo Park, CA: AAAI Press/MIT Press
- Gliksman, M. D., Lazarus, R., Wilson, A., & Leeder, S. R. (1995). Social support, marital status and living arrangement correlates of cardiovascular disease risk factors in the elderly. Social Science & Medicine, 40(6), 811-814 https://doi.org/10.1016/0277-9536(94)00149-N
- Goossen, W. T. F., Eppng, P. J. M., Feuth, T., van den Heuvel, W. J. A., Hasman, A., & Dassen, T. W. N. (2001). Using the nursing minimum data set for the Netherlands (NMDSN) to illustrate differences in patient populations and variations in nursing activities. Int J Nurs Stud, 38(3), 243-257 https://doi.org/10.1016/S0020-7489(00)00075-4
- Goodwin, L., Saville, J., Jasion, B., Turner, B., Prather, J., Dobousek, T., & Egger, S. (1997). A collaborative international nursing informatics research project: Predicting ARDS risk in critically ill patients. In U. Gerdin, M. Tallberg, & Wainwright. Nursing informatics: the impact of nursing knowledge on health care informatics. proceedings of NI 97, Sixth Triennial International Congress of IMIA-NI, Nursing Informatics of International Medical Informatics. Amsterdam, Netherlands: lOS Press
- Goodwin, L., & Iannacchione, M. A. (2002). Data mining methods for improving birth outcomes prediction. Outcomes Manage, 6(2), 80-85
- Goodwin, L., Prather, J., Schlitz, K., Iannacchione, M. A., Hage, M., Hammond, W. E., & Grzymala-Busse, J. (1998). Data mining issues for improved birth outcomes. Biomed Sci Instrum, 34, 291-298
- Harris, M. R., Graves, J. R. Solbrig, H. R., Elkin, P. L., & Chute, C. G. (2000). Embedded structures and representation of nursing knowledge. J Am Med Inform Assoc, 7(6),539-549 https://doi.org/10.1136/jamia.2000.0070539
- Kraft, M.R. (2003). Mining a spinal cord injury clinical database for nursing information: A source of nursing knowledge. Unpublished Doctoral Dissertation, Loyola Univerity of Chicago
- Kusiak, A. (2000). Computational Intelligence in Design and Manufacturing. New York: John Wiley
- Lee, T. (1998). An Analysis of the Relationship among Patient Profile Variables in Predicting Home Care Resource Utilization and Outcomes. Unpublished Doctoral Dissertation, University of Maryland, Baltimore
- McCloskey, J. C., & Bulechek, G. M. (1996). Nursing Interventions Classification (NIC) (2nd ed). St. Louis, MO: Mosby Year Book
- McDonald, J. M., Brossette, S., & Moser, S. A. (1998). Pathology information systems: Data mining leads to knowledge discovery. Archive of Pathology Laboratory Medicine, 122,409-411
- Moser, S. A., Jones, W. T., & Brossette, S. E. (1999). Application of data mining to intensive care unit microbiologic data. Emerging Infection Disease, 5(3),454-457 https://doi.org/10.3201/eid0503.990320
- Newhouse, R. P., Johantgen, M., Pronovost, P. J., & Jonhnson, E. (2005). Perioperative nurses and patient outcomes: Mortality, complications, and length of stay. AORN J, 81(3), 508-518 https://doi.org/10.1016/S0001-2092(06)60438-9
- North American Nursing Diagnosis Association. (2000). Nursing diagnoses: Definitions & classification. Philadelphia, PA: Authors
- Ohm, A., & Rowland, T. (2000). Rough sets: A knowledge discovery technique for multifactorial medical outcomes. Am J Phys Med Rehabil, 79, 100-108 https://doi.org/10.1097/00002060-200001000-00022
- Park, M., Delaney, C., Maas, M., & Reed, D. (2004). Using a nursing minimum data set with older patients with dementia in an acute care setting. J Advan Nurs, 47(3). 329-339 https://doi.org/10.1111/j.1365-2648.2004.03097.x
- Ryan, P., & Delaney, C. (1995). Nursing Minimum Data Set. In J.J. Fitzpatrick & J .S. Stevenson (Ed.), Annual review of nursing research, (pp. 169-194). New York: Springer Publishing Company
- Podraza, W., & Podraza, H. (1999). Childhood leukemia relapse risk factors. A rough sets approach. Medical Informatics, 24(2), 91-108 https://doi.org/10.1080/146392399298447
- Tam, S., Cheing, G. L. Y., & Hui-Chan, C. W. Y. (2004). Predicting osteoarthritic knee rehabilitation outcome by using a prediction model devleoped by data mining techiniques. Int J Rehabil Re, 27(1), 65-69 https://doi.org/10.1097/00004356-200403000-00009
- Werley, H. H., & Lang, N. M. (1988). Identification of the nursing minimum data set. New York: Springer
- Windle, P. E. (2004). Data mining: An excellent research tool. J Peri Anesth Nurs, 19(5),355-356 https://doi.org/10.1016/S1089-9472(04)00216-3
- Woolery, L. K., & Grzymla-Busse, J. (1994). Machine learning for an expert system to predict preterm birth risk. J Am Med Inform Assoc, 1(6),439-445 https://doi.org/10.1136/jamia.1994.95153433