Data mining issues and opportunities for building nursing knowledge

Health care information systems tend to capture data for nursing tasks, and have little basis in nursing knowledge. Opportunity lies in an important issue where the knowledge used by expert nurses (nursing knowledge workers) in caring for patients is undervalued in the health care system. The complexity of nursing's knowledge base remains poorly articulated and inadequately represented in contemporary information systems. There is opportunity for data mining methods to assist with discovering important linkages between clinical data, nursing interventions, and patient outcomes. Following a brief overview of relevant data mining techniques, a preterm risk prediction case study illustrates the opportunities and describes typical data mining issues in the nontrivial task of building knowledge. Building knowledge in nursing, using data mining or any other method, will make progress only if important data that capture expert nurses' contributions are available in clinical information systems configurations.

[1]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[2]  Jaime G. Carbonell,et al.  Introduction: Paradigms for Machine Learning , 1989, Artif. Intell..

[3]  John W. Tukey,et al.  Exploratory Data Analysis. , 1979 .

[4]  Creasy Rk,et al.  Prevention of preterm birth. , 1981 .

[5]  R. P. Carver The Case Against Statistical Significance Testing , 1978 .

[6]  Edward B. Fowlkes,et al.  Risk analysis of the space shuttle: Pre-Challenger prediction of failure , 1989 .

[7]  Linda K Goodwin,et al.  Protecting patient privacy in clinical data mining. , 2002, Journal of healthcare information management : JHIM.

[8]  Jaime G. Carbonell,et al.  Machine learning: paradigms and methods , 1990 .

[9]  W O Thompson,et al.  Validity of the Creasy Risk Appraisal Instrument for Prediction of Preterm Labor , 1995, Nursing research.

[10]  K. A. Ericsson,et al.  Protocol Analysis: Verbal Reports as Data , 1984 .

[11]  M. Grier,et al.  Information Processing in Nursing Practice , 1984, Annual Review of Nursing Research.

[12]  Geoffrey E. Hinton Connectionist Learning Procedures , 1989, Artif. Intell..

[13]  W. E. Hammond,et al.  Data Mining Methods Find Demographic Predictors of Preterm Birth , 2001, Nursing research.

[14]  Joseph L. Breault,et al.  Data mining a diabetic data warehouse , 2002, Artif. Intell. Medicine.

[15]  Gregory Piatetsky-Shapiro,et al.  Advances in Knowledge Discovery and Data Mining , 2004, Lecture Notes in Computer Science.

[16]  P E Johnson,et al.  What kind of expert should a system be? , 1983, The Journal of medicine and philosophy.

[17]  J. Iams,et al.  Prevention of preterm birth. , 1988, Seminars in perinatology.

[18]  Peter C Albertsen,et al.  Lessons learnt about early prostate cancer from large scale databases: population-based pearls of wisdom. , 2002, Surgical oncology.

[19]  Alan Stuart,et al.  Data-Dredging Procedures in Survey Analysis , 1966 .

[20]  J J Warren,et al.  Toward comparable nursing data: American Nurses Association criteria for data sets, classification systems, and nomenclatures. , 2001, Computers in nursing.

[21]  S A Finkler,et al.  A randomized trial of nurse specialist home care for women with high-risk pregnancies: outcomes and costs. , 2001, The American journal of managed care.

[22]  R. D'Agostino,et al.  A comparison of performance of mathematical predictive methods for medical diagnosis: identifying acute cardiac ischemia among emergency department patients. , 1995, Journal of investigative medicine : the official publication of the American Federation for Clinical Research.

[23]  John R. Anderson,et al.  MACHINE LEARNING An Artificial Intelligence Approach , 2009 .

[24]  Jerzy W. Grzymala-Busse,et al.  A comparison of three closest fit approaches to missing attribute values in preterm birth data , 2002, Int. J. Intell. Syst..

[25]  D.E. Goldberg,et al.  Classifier Systems and Genetic Algorithms , 1989, Artif. Intell..

[26]  J. Berger,et al.  Testing a Point Null Hypothesis: The Irreconcilability of P Values and Evidence , 1987 .

[27]  Klaus Jung,et al.  Multicenter evaluation of an artificial neural network to increase the prostate cancer detection rate and reduce unnecessary biopsies. , 2002, Clinical chemistry.

[28]  R. Creasy,et al.  Preterm birth prevention: where are we? , 1993, American journal of obstetrics and gynecology.

[29]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[30]  J W Grzymala-Busse,et al.  Improving prediction of preterm birth using a new classification scheme and rule induction. , 1994, Proceedings. Symposium on Computer Applications in Medical Care.