Use of an artificial neural network (ANN) for classifying nursing care needed, using incomplete input data

Medical Informatics and the Internet in Medicine

Volumn 25, Issue 2, 2000, Pages 147-158

  Michel, E ^a,b,d   Zernikow, B ^c   Wichert, S A ^a  

^a Medizinischer Dienst K   (Germany)

^b UNIVERSITY OF AMSTERDAM   (Netherlands)

^c UNIVERSITY OF WITTEN HERDECKE   (Germany)

^d NONE   (Germany)

Author keywords

Artificial neural network; Classification; Missing data; Nursing data; Pattern recognition; Quality control

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; CLASSIFICATION; DISABILITY; GERMANY; NURSING;

EID: 84860403316     PISSN: 14639238     EISSN: None     Source Type: Journal    
DOI: 10.1080/14639230050058329     Document Type: Article

References (28)

1. ANONYMOUS, 1994, Nursing insurance law from May 26th, 1994 (in German: 'Gesetz zur sozialen Absicherung des Risikos der Pflegebedürftigkeit (Pflegeversicherungsgesetz)'), Bundesgesetzblatt, I, 1014 [revised on June 14th, 1996. Bundesgesetzblatt, (1996) I, 830].
2. ANONYMOUS, 1994, Nursing insurance law from May 26th, 1994 (in German: 'Gesetz zur sozialen Absicherung des Risikos der Pflegebedürftigkeit (Pflegeversicherungsgesetz)'), Bundesgesetzblatt, I, 1014 [revised on June 14th, 1996. Bundesgesetzblatt, (1996) I, 830].
3. MEDIZINISCHER DIENST DER SPITZENVERBÄNDE 1995, 1996, Guidelines for the expert assessment in the nursing insurance scheme according to German legislation (in German: 'Begutachtungsanleitung "Pflegebedürftigkeit gemäß SGB XI"), (Essen, Germany: Medizinischer Dienst der Spitzenverbände).
4. MICHEL, E., ZERNIKOW, B., HEINE, U., and BERG, H., 1998, Bestimmen "Hilfebedarf" oder "Aktivitäten des täglichen Lebens" die Pflegestufe? Eine multivariate Analyse. Gesundheitswesen, 60, 385-391.
5. LAWRENCE, J., 1992, Neuronal Networks. Computer simulation of intelligence in biology (in German: 'Neuronale Netze. Computersimulation biologischer Intelligenz'), (Munich, Germany: Systhema).
6. ANONYMOUS, 1995, Predict™ Manual, (Pittsburgh, USA: NeuralWare Inc.).
7. SMITH, M., 1993, Neural Networks for statistical modelling, (New York, USA: van Nostrand Reinhold) pp. 88-91.
8. ALTMAN, D. G., 1991, Practical statistics for medical research, (London, UK: Chapman & Hall).
9. BAXT, W. G., 1996, Prospective validation of artificial neural network trained to identify acute myocardial infarction. Lancet 347, 12-15.
10. BRUNAK, S., ENGELBRECHT, J., and KNUDSEN, S., 1990, Cleaning up database. Nature 342, 123.
11. MAREN, A., HARSTON, C., and PAP R., 1990, Handbook of neural computing applications, (San Diego, USA: Academic Press Inc.).
12. O'NEIL, M. C., 1991, Training back-propagation neural networks to define and detect DNA-binding sites. Nucleic Acids Research 19, 313-318.
13. ZERNIKOW, B., HOLTMANNSPÖTTER, K., MICHEL, E., Pielemeier, W., Hornschuh, F., Westermann, A., Hennecke, K. H., 1998, Artificial Neural Network for risk assessment in preterm neonates. Archives of Diseases in Childhood, 79, F129-F134.
14. ZERNIKOW, B., HOLTMANNSPÖTTER, K., MICHEL, E., PIELEMEIER, W., THEILHABER, M., and HEINECKE, K. H., 1998, Artificial neural network for predicting intracranial hemorrhage in preterm neonates. Acta Paed Scand, 87, 969-975.
15. ZERNIKOW, B., HOLTMANNSPÖTTER, K., MICHEL, E., HORNSCHUH, F., GROOTE, K., and HENNECKE, K. H., 1999, Predicting length of stay of preterm neonates. European Journal of Pediatrics, 158, 59-62.
16. HERTZ, J., KROGH, A., and PALMER, R., 1991, Introduction to the theory of neural computing, (Massachusetts, USA: Addison Wesley).
17. RUMELHART, D. E., HINTON, G. E., and WILLIAMS, R. J., 1986, Learning internal representations by error propagation. In D. Rumelhart and J. L. McClelland (eds) Parallel distributed processing: Exploration in the microstructure of cognition, (Massachusetts, USA: MIT Press) pp. 318-364.
18. NELSON-MCCORD, M., and ILLINGWORTH, W. T., 1991, A practical guide to neural nets, (Massachusetts, USA: Addison-Wesley).
19. LESAGE, A. D., FOUHNIER, L., CYR, M., TOUPIN, J., et al., 1996, The reliability of the community version of the MRC Needs for Care Assessment. Psychological Medicine, 26, 237-243.
20. MCPHERSON, K.M., PENTLAND, B., CUDMORE, S. F., and PRESCOTT, R. J., 1996, An inter-rater reliability study of the Functional Assessment Measure (FIM+FAM). Disability and Rehabilitation, 18, 341-347.
21. NORDMARK, E., HAGGLUND, G., and JARNLO, G. B.,1997, Reliability of the gross motor function measure in cerebral palsy. Scandinavian Journal of Rehabilitation Medicine, 29, 25-28.
22. O'KEEFFE, S. T., SMITH, T., VALACIO, R., JACK, C. I., PLAYFER, J. R., and LYE, M., 1994, A comparison of two techniques for ankle jerk assessment in elderly subjects. Lancet, 344, 1619-1620.
23. OLSEN, L. H., 1989, Inter-observer variation in assessment of undescended testis. Analysis of kappa statistics as a coefficient of reliability. British Journal of Urology, 64, 44-48.
24. ROSENDAHL, K., ASLAKSEN, A., LIE, R. T., and MARKESTAD, T., 1995, Reliability of ultrasound in the early diagnosis of developmental dysplasia of the hip. Pediatric Radiology, 25, 219-224.
25. KLIEBSCH, U., REISER, K., and BRENNER, H., 1997, Reliability of the assessment procedure in the nursing insurance scheme (in German: 'Reliabilitätsstudie zum Begutachtungsverfahren der Pflegebedürftigkeit im Rahmen der Pflegeversicherung'). Gesundheitswesen, 59 Suppl. 1, 34-41.
26. REED, R., 1993, Pruning algorithms - a survey. IEEE Trans Neural Networks, 4, 740-747.
27. MICHEL, E., ZERNIKOW, B., HEINE, U., and BERG, H., 1998, Statistical basis of a modular system complying with the requirements of the new German statutory nursing insurance legislation (in German: 'Statistische Basis eines Modulsystems in der Pflegeversicherung nach SGB XI'). Gesundheitsökonomie Qualitätsmanagement, 3, 75-80.
28. MICHEL, E., and ZERNIKOW, B., 1997, TOSCA, a neural network-based quality management system in the nursing insurance sector (in German: 'TOSCA, ein künstliches neuronales Netz zur Qualitätssicherung in der Pflegebegutachtung'). Gesundheitsökonomie Qualitätsmanagement, 2, 187-192.