Next »
International Journal of Medical Informatics
Volume 77, Issue 2
, Pages 81-97
, February 2008
Predictive data mining in clinical medicine: Current issues and guidelines
References
- . Applied Data Mining Statistical Methods for Business and Industry. Wiley & Sons; 2003;
- . Data mining and knowledge discovery in databases. Commun. ACM. 1996;39:24–26
- . Predicting patient's long-term clinical status after hip arthroplasty using hierarchical decision modelling and data mining. Meth. Inf. Med. 2001;40:25–31
- . Orange: from experimental machine learning to interactive data mining. In: European Conference of Machine Learning. Pisa, Italy: Springer Verlag; 2004;537-539
- . Inductive and Bayesian learning in medical diagnosis. Appl. Artif. Intelligen. 1993;7:317–337
- . A practical device for the application of a diagnostic or prognostic function. Meth. Inf. Med. 1978;17:127–129
- . Nomograms for visualization of naive bayesian classifier. In: Proceedings of the Principles Practice of Knowledge Discovery in Databases (PKDD-04). Pisa, Italy. 2004;p. 337–348
- . Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis. New York: Springer; 2001;
- . A preoperative nomogram for disease recurrence following radical prostatectomy for prostate cancer. J. Natl. Cancer Inst. 1998;90:766–771
- International validation of a preoperative nomogram for prostate cancer recurrence after radical prostatectomy. J. Clin. Oncol. 2002;20:3206–3212
- . C4.5: Programs for Machine Learning. San Mateo, Calif: Morgan Kaufmann Publishers; 1993;
- . Classification and Regression Trees. New York, London: Chapman & Hall; 1993;
- . The CN2 Induction Algorithm. Mach. Learn. 1989;3:261–283
- . Learning patterns in noisy data: the AQ approach. In: Paliouras G, Karkaletsis V, Spyropoulos C editor. Machine Learning and its Applications. Berlin: Springer-Verlag; 2001;p. 22–38
- . Intelligent data analysis for medical diagnosis: using machine learning and temporal abstraction. AI Commun. 1998;11:191–218
- . Applied Logistic Regression. 2nd ed.. New York: Wiley; 2000;
- . The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer; 2001;
- . On the misuses of artificial neural networks for prognostic and diagnostic classification in oncology. Stat. Med. 2000;19:541–561
- . An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge, UK, New York: Cambridge University Press; 2000;
- . Statistical Learning Theory. New York: Wiley; 1998;
- . Support-vectors networks. Mach. Learn. 1995;20:273–297
- . Machine learning for medical diagnosis: history, state of the art and perspective. Artif. Intell. Med. 2001;23:89–109
- . Medical expert systems based on causal probabilistic networks. Int. J. Biomed. Comput. 1991;28:1–30
- . Clinical applications of Bayesian belief networks in pathology. Pathologica. 1995;87:237–245
- . NasoNet, modeling the spread of nasopharyngeal cancer with networks of probabilistic events in discrete time. Artif. Intell. Med. 2002;25:247–264
- . The role of Bayesian Networks in the diagnosis of pulmonary embolism. J. Thromb. Haemost. 2003;1:698–707
- . Sequential updating of conditional probabilities on directed graphical structures. Networks. 1990;20:579–605
- . A guide to the literature on learning probabilistic networks from data. IEEE Trans. Know. Data Eng. 1996;8:195–210
- . A Bayesian method for the induction of probabilistic networks from data. Mach. Learn. 1992;9:309–347
- . Robust learning with missing data. Mach. Learn. 2001;45:147–170
- . Application of a data-mining method based on Bayesian networks to lesion-deficit analysis. Neuroimage. 2003;19:1664–1673
- . Genetic dissection and prognostic modeling of overt stroke in sickle cell anemia. Nat. Genet. 2005;37:435–440
- . Learning Gaussian networks. In: de Mantaras RL, Poole D editor. Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence. San Francisco, CA/Seattle, WA: Morgan Kaufmann; 1994;p. 235–243
- . P J M. Learning Bayesian networks by genetic algorithms: a case study in the prediction of survival in malignant skin melanoma. In: Keravnou E, Garbay C, Baud R, Wyatt CJ editor. Artificial Intelligence in Medicine Europe. France: Grenoble; 1997;p. 261–272
- . Using prior knowledge to improve genetic network reconstruction from microarray data. In. Silico. Biol. 2004;4:335–353
- . CRISP-DM 1. 0: Step-by-Step Data Mining Guide: The CRISP-DM Consortium. 2000;
- . Anatomic pathology data mining. In: Cios KJ editors. Medical Data Mining and Knowledge Discovery. Berlin/Heidelberg: Springer-Verlag; 2001;p. 61–108
- . Supporting discovery in medicine by association rule mining in Medline and UMLS. Medinfo. 2001;10:1344–1348
- . Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. Proc. AMIA Symp. 2001;17–21
- . Data mining: statistics and more?. Am. Statist. 1998;52:112–118
- . Principles of Data Mining. Cambridge, Mass: MIT Press; 2001;
- . Data-driven constructive induction. IEEE Intell. Syst. 1998;13:30–37
- . Attribute interactions in medical data analysis. In: Dojad M, Keravnou E, Barahona P editor. Proceedings of the Ninth Conference on Artificial Intelligence in Medicine in Europe (AIME 2003). Protaras, Cyprus: Springer. 2003;p. 229–238
- . Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. U. S. A. 1998;95:14863–14868
- . Ovarian cancer identification based on dimensionality reduction for high-throughput mass spectrometry data. Bioinformatics. 2005;21:2200–2209
- . Data integration and genomic medicine. J. Biomed. Inform. 2007;40:5–16
- . Using molecular information to guide brain tumor therapy. Nat. Clin. Pract. Neurol. 2006;2:232–233
- Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. 1999;286:531–537
- Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002;415:530–536
- Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature. 2002;415:436–442
- Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nat. Med. 2002;8:68–74
- . Towards integrated clinico-genomic models for personalized medicine: combining gene expression signatures and clinical factors in breast cancer outcomes prediction. Hum. Mol. Genet. 2003;R153–R15712 Spec No 2:
- . Prediction of clinical behaviour and treatment for cancers. Appl. Bioinform. 2003;2:S53–S58
- . Combining gene expression profiles and clinical parameters for risk stratification in medulloblastomas. J. Clin. Oncol. 2004;22:994–998
- . Molecular classification and molecular forecasting of breast cancer: ready for clinical application?. J. Clin. Oncol. 2005;23:7350–7360
- . Avoiding model selection bias in small-sample genomic datasets. Bioinformatics. 2006;22:1245–1250
- . Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. J. Natl. Cancer Inst. 2003;95:14–18
- . Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer. Proc. Natl. Acad. Sci. U. S. A. 2006;103:5923–5928
- The molecular portraits of breast tumors are conserved across microarray platforms. BMC Genom. 2006;7:96
- Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men. Cancer Res. 2002;62:3609–3614
- Use of proteomic patterns in serum to identify ovarian cancer. Lancet. 2002;359:572–577
- . A new approach for the analysis of mass spectrometry data for biomarker discovery. AMIA Annu Symp. Proc. 2006;26–30
- . Class prediction and discovery using gene microarray and proteomics mass spectroscopy data: curses, caveats, cautions. Bioinformatics. 2003;19:1484–1491
- . Statistical methods for analyzing tissue microarray data. J. Biopharm. Stat. 2004;14:671–685
- Defining aggressive prostate cancer using a 12-gene model. Neoplasia. 2006;8:59–68
- . Machine learning for detecting gene-gene interactions: a review. Appl. Bioinform. 2006;5:77–88
- . A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. J. Theor. Biol. 2006;241:252–261
- . Prognostic models: clinically useful or quickly forgotten?. BMJ. 1995;311
- . Pretreatment nomogram for predicting the outcome of three-dimensional conformal radiotherapy in prostate cancer. J. Clin. Oncol. 2000;18:3352–3359
- . Computer-based consultations in clinical therapeutics: explanation and rule acquisition capabilities of the MYCIN system. Comput. Biomed. Res. 1975;8:303–320
- . Internist-1, an experimental computer-based diagnostic consultant for general internal medicine. N. Engl. J. Med. 1982;307:468–476
- . Predicting recovery in patients suffering from traumatic brain injury by using admission variables and physiological data: a comparison between decision tree analysis and logistic regression. J. Neurosurg. 2002;97:326–336
- . A classification tree for predicting recurrent falling in community-dwelling older persons. J. Am. Geriatr. Soc. 2003;51:1356–1364
- . Patients with hip fracture: subgroups and their outcomes. J. Am. Geriatr. Soc. 2002;50:1240–1249
- . Predicting survival in malignant skin melanoma using Bayesian networks automatically induced by genetic algorithms An empirical comparison between different approaches. Artif. Intell. Med. 1998;14:215–230
- . WordNet An Electronic Lexical Database. MIT Press; 1998;
- . Knowledge-based data analysis and interpretation. Artif. Intell. Med. 2006;37:163–165
- . The utility of background knowledge in learning medical diagnostic rules. Appl. Artif. Intelligen. 1993;7:273–293
- . An influence diagram for assessing GVHD prophylaxis after bone marrow transplantation in children. Med. Decis. Mak. 1994;14:223–235
- . Knowledge-based and data-driven models in arrhythmia fuzzy classification. Meth. Inf. Med. 2001;40:397–402
- . Two-stage machine learning model for guideline development. Artif. Intell. Med. 1999;16:51–71
- . Expert knowledge and its role in learning Bayesian Networks in medicine: an appraisal. In: Quaglini S, Barahona P, Andreassen S editor. Artificial Intelligence in Medicine. Berlin: Springer; 2001;p. 156–166
- . Building probabilistic networks: “Where do the numbers come from?”. IEEE Transn. Knowl. Data Eng. 2000;12:481–486
- . Sensitivity analysis: an aid for belief-network quantification. Knowl. Eng. Rev. 2000;15:215–232
- . The utility of background knowledge in inductive learning. Mach. Learn. 1992;9:57–94
- . Acceptance of rules generated by machine learning among medical experts. Meth. Inf. Med. 2001;40:380–385
- . Estimating attributes: analysis and extensions of RELIEF. In: European Conference on Machine Learning (ECML). 1994;p. 171–182
- . Wrappers for feature subset selection. Artif. Intell. 1997;97:273–324
- . Machine learning for survival analysis: a case study on recurrence of prostate cancer. Artif. Intell. Med. 2000;20:59–75
- . Intelligent data analysis in medicine and pharmacology: a position statement. In: Workshop on Intelligent Data Analysis in Medicine and Pharmacology (IDAMAP). Brighton, UK. 1998;p. 2–5
- . Data preparation for data mining. San Francisco, CA: Morgan Kaufmann Publishers; 1999;
- . Rule evaluation measures: a unifying view. In: Workshop on Inductive Logic Programming. 1999;p. 174–185
- . The use of relative operating characteristic (ROC) curves in test performance evaluation. Arch. Pathol. Lab. Med. 1986;110:13–20
- . Verification of forecasts expressed in terms of probability. Month. Weather Rev. 1950;78:1–3
- . Data Mining: Practical Machine Learning Tools and Techniques With Java Implementations. San Francisco, CA: Morgan Kaufmann; 1999;
- . Construction and assessment of classification rules. Chichester; New York: Wiley; 1997;
- . Integrating Decision Support and Data Mining by Hierarchical Multi-Attribute Decision Models. In: Intl. Workshop on Integration and Collaboration Aspects of Data Mining, Decision Support and Meta-Learning. Helsinki, Finland. 2001;p. 25–36
- . Factors that predict outcome of intensive care treatment in very elderly patients: a review. Crit. Care. 2005;9:R307–R314
- . Handbook of Medical Informatics. Heidelberg, Germany: Springer Verlag; 1997;
- . Decisions at hand: a decision support system on handhelds. Medinfo. 2001;10:566–570
- . Orange and Decisions-at-Hand: bridging predictive data mining and decision support.. In: Intlerationa Workshop on Integration and Collaboration Aspects of Data Mining, Decision Support and Meta-Learning. Helsinki, Finland. 2001;p. 151–162
- . Knowledge management in healthcare: towards ‘knowledge-driven’ decision-support services. Int. J. Med. Inf. 2001;63:5–18
- . Knowledge discovery from data?. IEEE Intell. Syst. March–April 2000;10–13
- . Uniqueness of medical data mining. Artif. Intell. Med. 2002;26:1–24
- . Safe and Sound: Artificial Intelligence In Hazardous Applications. Cambridge, Mass: MIT Press; 2000;
- . Intelligent data analysis. Meth. Inf. Med. 2001;40:362–364
- . Health care in the information society A prognosis for the year 2013. Int. J. Med. Inf. 2002;66:3–21
- Towards 2020 Science, Available at http://research.microsoft.com/towards2020science.
PII: S1386-5056(06)00274-7
doi: 10.1016/j.ijmedinf.2006.11.006
© 2006 Elsevier Ireland Ltd. All rights reserved.
Next »
International Journal of Medical Informatics
Volume 77, Issue 2
, Pages 81-97
, February 2008
