목차
1)서 론
2)전문가 시스템
1.연구동향
2. 전문가 시스템의 개념
3. 전문가 시스템의 구조
4. 전문가 시스템 도구
3)인공신경망
1. 연구 동향
2. 인공신경망의 구조
3. 역전파 학습모델
4)뉴로컴퓨터(인공신경망)과 인공지능(전문가시스템)의 비교
5)결론
2)전문가 시스템
1.연구동향
2. 전문가 시스템의 개념
3. 전문가 시스템의 구조
4. 전문가 시스템 도구
3)인공신경망
1. 연구 동향
2. 인공신경망의 구조
3. 역전파 학습모델
4)뉴로컴퓨터(인공신경망)과 인공지능(전문가시스템)의 비교
5)결론
본문내용
5). 병원전산화 과정중 간호사의 참여. 대한의료정보학회 제 9차 춘계학술대회 초록.
10. 성영희(1995). 사용자입장에서 본 병원 전산화 사례. 대한의료정보학회 제 9차 춘계학술 대회 초록
11. 유지수(1996). 간호진단을 위한 신경망모델의 도구화. 대한의료정보학회지, 2(2), 55-64.
12. 이은옥 외 3인(1996). 인공지능을 도입한 간호정보시스템 개발. 간호학회지, 26(2), 281-289.
13. 최영희 외 4인(1996). 간호진단의 임상적용 활성화를 위한 기초조사연구. 간호학회지, 26(4), 930-945.
14. Astion M. A. & Wilding P.(1992). The Application of backpropagation neural netwotks to problem in pathology and laboratory medicine. Arch Pathol Lab Med, 116,995-1001.
15. Caudill, M.(1990). Using Neural Nets : Hybrid Expert Networks. AI Expert, November, 43-47
16. Dayhoff, J.(1990). Neural Network Architectures. Van Nostrand Reinhold.
17. Fonteyn, M. E., & Grobe, S. J.(1994). Expert System Development in Nursing : Implications for Critical are Nursing Practice. Heart & Lung, 23(1), 80-87.
18. Hilliam, D. V.(1990). Intergratin Neural Nets and Expert Systems. AI Expert, June, 54-59
19. Jiang Y. etc (1996). Malignant and Benign Clustered Microcalcifications : Automated Feature Analysis and Classfication, Radiology, 198(3), 671-678.
20. Johbson, J. E.(1992). Computers in Nursing in the Year 2000. Computers in Nursing, 10(4), 143-144.
21. Jones, B. T.(1991). Building Nursing Expert Systems Using Automated Rule Induction. Computers in Nursing, 9(2), 52-60.
22. Khanna, T.(1990). Foundations of Neural Networks. Addition-Wesley Pub.
23. Kim, J. A.(1997). A Comparative Study on the Nursing Diagnosis Systems Using Neural Network and Expert System. NI'97 구연예정.
24. Koch, B., & McGovern, J.(1993). EXTEND : A Prototype Expert System for Teaching Nursing Diagnosis. Computers in Nursing, 11(1), 35-41.
25. Nelon, M. M. & Illingworth W. T.(1991). A Partical Guide to Neural Nets. Addison-Wesley Pub.
26. Patterson, D. W.(1990). Introduction to Artificial Intelligence and Expert Systems Prentice-Hall International, INC.
27. Poli, R., Cagnoni, S., Livi, R. Coppini, G., & Valli, Guido(1991). A Neural Network Expert System for Diagnosing and Treating Hypertension. IEEE, March, 64-71.
28. Saleem, N., & Moses, B.(1994). Expert Systems as Computer Assisted Instruction Systems for Nursing Education and Tranning. Computers in Nursing, 12 (1), 35-39.
29. Waterman, D. A.(1986). A Guido to Expert Systems. Addison-Wesley Pub.
Abstract
Cognitive Information Proessing in the Clinical Practice Using the Computer
Jung Ae Kim
There are two development trends of nursing information system-one as a part of the hospital informational system and the other as decision support system in clinical practice. This study introduces two fields of computer related to cognitive information processing in order to support the nurses' decision-making.
One is the Expert System as a part of Artificial Intelligence, and the other is the Neurocomputer-Artificial Neural Network. Through the reviews of literature, strength and weakness of two systems are investigated, and it presents the possibilities of building the more suitable decision support systems with the integration of two computer fields.
10. 성영희(1995). 사용자입장에서 본 병원 전산화 사례. 대한의료정보학회 제 9차 춘계학술 대회 초록
11. 유지수(1996). 간호진단을 위한 신경망모델의 도구화. 대한의료정보학회지, 2(2), 55-64.
12. 이은옥 외 3인(1996). 인공지능을 도입한 간호정보시스템 개발. 간호학회지, 26(2), 281-289.
13. 최영희 외 4인(1996). 간호진단의 임상적용 활성화를 위한 기초조사연구. 간호학회지, 26(4), 930-945.
14. Astion M. A. & Wilding P.(1992). The Application of backpropagation neural netwotks to problem in pathology and laboratory medicine. Arch Pathol Lab Med, 116,995-1001.
15. Caudill, M.(1990). Using Neural Nets : Hybrid Expert Networks. AI Expert, November, 43-47
16. Dayhoff, J.(1990). Neural Network Architectures. Van Nostrand Reinhold.
17. Fonteyn, M. E., & Grobe, S. J.(1994). Expert System Development in Nursing : Implications for Critical are Nursing Practice. Heart & Lung, 23(1), 80-87.
18. Hilliam, D. V.(1990). Intergratin Neural Nets and Expert Systems. AI Expert, June, 54-59
19. Jiang Y. etc (1996). Malignant and Benign Clustered Microcalcifications : Automated Feature Analysis and Classfication, Radiology, 198(3), 671-678.
20. Johbson, J. E.(1992). Computers in Nursing in the Year 2000. Computers in Nursing, 10(4), 143-144.
21. Jones, B. T.(1991). Building Nursing Expert Systems Using Automated Rule Induction. Computers in Nursing, 9(2), 52-60.
22. Khanna, T.(1990). Foundations of Neural Networks. Addition-Wesley Pub.
23. Kim, J. A.(1997). A Comparative Study on the Nursing Diagnosis Systems Using Neural Network and Expert System. NI'97 구연예정.
24. Koch, B., & McGovern, J.(1993). EXTEND : A Prototype Expert System for Teaching Nursing Diagnosis. Computers in Nursing, 11(1), 35-41.
25. Nelon, M. M. & Illingworth W. T.(1991). A Partical Guide to Neural Nets. Addison-Wesley Pub.
26. Patterson, D. W.(1990). Introduction to Artificial Intelligence and Expert Systems Prentice-Hall International, INC.
27. Poli, R., Cagnoni, S., Livi, R. Coppini, G., & Valli, Guido(1991). A Neural Network Expert System for Diagnosing and Treating Hypertension. IEEE, March, 64-71.
28. Saleem, N., & Moses, B.(1994). Expert Systems as Computer Assisted Instruction Systems for Nursing Education and Tranning. Computers in Nursing, 12 (1), 35-39.
29. Waterman, D. A.(1986). A Guido to Expert Systems. Addison-Wesley Pub.
Abstract
Cognitive Information Proessing in the Clinical Practice Using the Computer
Jung Ae Kim
There are two development trends of nursing information system-one as a part of the hospital informational system and the other as decision support system in clinical practice. This study introduces two fields of computer related to cognitive information processing in order to support the nurses' decision-making.
One is the Expert System as a part of Artificial Intelligence, and the other is the Neurocomputer-Artificial Neural Network. Through the reviews of literature, strength and weakness of two systems are investigated, and it presents the possibilities of building the more suitable decision support systems with the integration of two computer fields.
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