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Volumn , Issue , 2015, Pages 379-402

Temporal data mining for healthcare data

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATA MINING; DECISION SUPPORT SYSTEMS; HEALTH CARE; HOSPITALS;

EID: 85016225083     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1201/b18588     Document Type: Chapter
Times cited : (7)

References (59)
  • 3
    • 0021466185 scopus 로고
    • Towards a General Theory of Action and Time
    • F. Allen. Towards a General Theory of Action and Time. Artificial Intelligence, 23:123-154, 1984.
    • (1984) Artificial Intelligence , vol.23 , pp. 123-154
    • Allen, F.1
  • 11
    • 84875271177 scopus 로고    scopus 로고
    • Artificial Intelligence Framework for Simulating Clinical Decision-Making:AMarkov Decision Process Approach
    • C. Bennett and K. Hauser. Artificial Intelligence Framework for Simulating Clinical Decision-Making:AMarkov Decision Process Approach. Artificial Intelligence in Medicine, 2013.
    • (2013) Artificial Intelligence in Medicine
    • Bennett, C.1    Hauser, K.2
  • 13
    • 0017216776 scopus 로고
    • Testing for the Consecutive Ones Property, Interval Graphs, and Graph Planarity Using PQ-Tree Algorithms
    • K. Booth and George Lueker. Testing for the Consecutive Ones Property, Interval Graphs, and Graph Planarity Using PQ-Tree Algorithms. Journal of Computer and System Sciences, 13(3):335-379, 1976.
    • (1976) Journal of Computer and System Sciences , vol.13 , Issue.3 , pp. 335-379
    • Booth, K.1    Lueker, G.2
  • 15
    • 0038931405 scopus 로고    scopus 로고
    • Bayesian DataMining in Large Frequency Tables with an Application to the FDA Spontaneous Reporting System
    • W. DuMouchel. Bayesian DataMining in Large Frequency Tables with an Application to the FDA Spontaneous Reporting System. The American Statistician, 53(3):177-190, 1999.
    • (1999) The American Statistician , vol.53 , Issue.3 , pp. 177-190
    • DuMouchel, W.1
  • 17
    • 0035700740 scopus 로고    scopus 로고
    • Use of Proportional Reporting Ratios (PRRs) for Signal Generation from Spontaneous Adverse Drug Reaction Reports
    • S. J. Evans, P. C. Waller, and S. Davis. Use of Proportional Reporting Ratios (PRRs) for Signal Generation from Spontaneous Adverse Drug Reaction Reports. Pharmacoepidemiology and Drug Safety, 10(6):483-486, 2001.
    • (2001) Pharmacoepidemiology and Drug Safety , vol.10 , Issue.6 , pp. 483-486
    • Evans, S.J.1    Waller, P.C.2    Davis, S.3
  • 18
    • 33749319347 scopus 로고    scopus 로고
    • Interestingness Measures for Data Mining: A Survey
    • Article 9
    • L. Geng and H. J. Hamilton. Interestingness Measures for Data Mining: A Survey. ACM Computing Surveys, 38(3): Article 9, 2006.
    • (2006) ACM Computing Surveys , vol.38 , Issue.3
    • Geng, L.1    Hamilton, H.J.2
  • 29
    • 37849185018 scopus 로고    scopus 로고
    • Mining Sequential Patterns from Data Streams: A Centroid Approach
    • A. Marascu and F. Masseglia. Mining Sequential Patterns from Data Streams: A Centroid Approach. Journal of Intelligent Information Systems, 27(3):291-307, 2006.
    • (2006) Journal of Intelligent Information Systems , vol.27 , Issue.3 , pp. 291-307
    • Marascu, A.1    Masseglia, F.2
  • 30
    • 16244408617 scopus 로고    scopus 로고
    • A Method for Detection of Alzheimer’s Disease Using ICA-enhanced EEG Measurements
    • C. Melissant, A. Ypma, E. E. Frietman, and C. J. Stam. A Method for Detection of Alzheimer’s Disease Using ICA-enhanced EEG Measurements. Artificial Intelligence in Medicine, 33(3):209-222, 2005.
    • (2005) Artificial Intelligence in Medicine , vol.33 , Issue.3 , pp. 209-222
    • Melissant, C.1    Ypma, A.2    Frietman, E.E.3    Stam, C.J.4
  • 41
    • 33947584673 scopus 로고    scopus 로고
    • Constraint-based Sequential PatternMining: The Pattern-growth Methods
    • J. Pei, J. Han, and W.Wang. Constraint-based Sequential PatternMining: The Pattern-growth Methods. Journal of Intelligent Information Systems, 28:133-160, 2007.
    • (2007) Journal of Intelligent Information Systems , vol.28 , pp. 133-160
    • Pei, J.1    Han, J.2    Wang, W.3
  • 43
    • 0031069719 scopus 로고    scopus 로고
    • A Framework for Knowledge-Based Temporal Abstraction
    • Y. Shahar. A Framework for Knowledge-Based Temporal Abstraction. Artificial Intelligence, 90:79-133, 1997.
    • (1997) Artificial Intelligence , vol.90 , pp. 79-133
    • Shahar, Y.1
  • 45
    • 84949178860 scopus 로고    scopus 로고
    • Mining of Sensor Data in Healthcare: A Survey
    • Springer
    • D. Sow, D. Turaga, and M. Schmidt. Mining of Sensor Data in Healthcare: A Survey. In Managing and Mining Sensor Data, pages 459-504. Springer, 2013.
    • (2013) Managing and Mining Sensor Data , pp. 459-504
    • Sow, D.1    Turaga, D.2    Schmidt, M.3
  • 47
    • 33845455091 scopus 로고    scopus 로고
    • Temporal Abstraction in Intelligent Clinical Data Analysis: A Survey
    • M. Stacey and C. McGregor. Temporal Abstraction in Intelligent Clinical Data Analysis: A Survey. Artificial Intelligence in Medicine, 39(1):1-24, 2007.
    • (2007) Artificial Intelligence in Medicine , vol.39 , Issue.1 , pp. 1-24
    • Stacey, M.1    McGregor, C.2
  • 48
    • 84871542589 scopus 로고    scopus 로고
    • Signal Detection and Monitoring Based on Longitudinal Healthcare Data
    • M. Suling and I. Pigeot. Signal Detection and Monitoring Based on Longitudinal Healthcare Data. Pharmaceutics, 4(4):607-640, 2012.
    • (2012) Pharmaceutics , vol.4 , Issue.4 , pp. 607-640
    • Suling, M.1    Pigeot, I.2
  • 50
    • 54049104046 scopus 로고    scopus 로고
    • Maximum Entropy Based Significance of Itemsets
    • N. Tatti. Maximum Entropy Based Significance of Itemsets. Knowledge and Information Systems, 17(1):57-77, 2008.
    • (2008) Knowledge and Information Systems , vol.17 , Issue.1 , pp. 57-77
    • Tatti, N.1
  • 51
    • 0036210975 scopus 로고    scopus 로고
    • A Comparison of Measures of Disproportionality for Signal Detection in Spontaneous Reporting Systems for Adverse Drug Reactions
    • E. P. van Puijenbroek, A. Bate, H. G. Leufkens, M. Lindquist, R. Orre, and A. C. Egberts. A Comparison of Measures of Disproportionality for Signal Detection in Spontaneous Reporting Systems for Adverse Drug Reactions. Pharmacoepidemiology and Drug Safety, 11(1):3-10, 2002.
    • (2002) Pharmacoepidemiology and Drug Safety , vol.11 , Issue.1 , pp. 3-10
    • van Puijenbroek, E.P.1    Bate, A.2    Leufkens, H.G.3    Lindquist, M.4    Orre, R.5    Egberts, A.C.6
  • 53
    • 0038392539 scopus 로고    scopus 로고
    • Heparin-Induced Thrombocytopenia: Pathogenesis and Management
    • T. Warkentin. Heparin-Induced Thrombocytopenia: Pathogenesis and Management. British Journal of Haematology, 121:535-555, 2000.
    • (2000) British Journal of Haematology , vol.121 , pp. 535-555
    • Warkentin, T.1
  • 54
    • 34250202878 scopus 로고    scopus 로고
    • ARMADA-An Algorithm for Discovering Richer Relative Temporal Association Rules from Interval-based Data
    • E. Winarko and J. F. Roddick. ARMADA-An Algorithm for Discovering Richer Relative Temporal Association Rules from Interval-based Data. Data and Knowledge Engineering, 63:76-90, 2007.
    • (2007) Data and Knowledge Engineering , vol.63 , pp. 76-90
    • Winarko, E.1    Roddick, J.F.2
  • 57
    • 0034826102 scopus 로고    scopus 로고
    • SPADE: An Efficient AlgorithmforMining Frequent Sequences
    • M. J. Zaki. SPADE: An Efficient AlgorithmforMining Frequent Sequences. Machine Learning, 42:31-60, 2001.
    • (2001) Machine Learning , vol.42 , pp. 31-60
    • Zaki, M.J.1
  • 58
    • 84863351725 scopus 로고    scopus 로고
    • Predicting Future Clinical Changes of MCI Patients Using Longitudinal and Multimodal Biomarkers
    • D. Zhang and D. Shen. Predicting Future Clinical Changes of MCI Patients Using Longitudinal and Multimodal Biomarkers. PLoS One, 7, 2012.
    • (2012) PLoS One , vol.7
    • Zhang, D.1    Shen, D.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.