800
Views
37
CrossRef citations to date
0
Altmetric
Original Articles

Neuropsychological test selection for cognitive impairment classification: A machine learning approach

, , &
Pages 899-916 | Received 17 Mar 2014, Accepted 24 Jun 2015, Published online: 02 Sep 2015

REFERENCES

  • Abdi, H., & Williams, L. J. (2010). Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 2, 433–459.
  • Albert, M. S., DeKosky, S. T., Dickson, D., Dubois, B., Feldman, H. H., Fox, N. C., … Phelps, C. H. (2011). The diagnosis of mild cognitive impairment due to Alzheimer’s disease: Recommendations from the National Institute on Aging Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s and Dementia: The Journal of the Alzheimer’s Association, 7, 270–279.
  • American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed., text rev.). Washington, DC: Author.
  • Banich, M. T., & Compton, R. (2010). Cognitive neuroscience. Belmont, CA: Cengage Learning.
  • Barbizet, J., & Cany, E. (1968). Clinical and psychometrical study of a patient with memory disturbances. International Journal of Neurology, 7, 44.
  • Benedict, R. H. B. (1997). Brief Visuospatial Memory Test–Revised. Odessa, FL: Psychological Assessment Resources.
  • Brandt, J., & Folstein, M. (2003). Telephone Interview for Cognitive Status. Lutz, FL: Psychological Assessment Resources.
  • Breiman, L. (2001). Statistical modeling: The two cultures. Statistical Science, 16, 199–231.
  • Burke, W. J., Miller, J. P., Rubin, E. H., Morris, J. C., Coben, L. A., Duchek, J., … Berg, L. (1988). Reliability of the Washington University Clinical Dementia Rating. Archives of Neurology, 45, 31–32.
  • Cahn, D. A., Salmon, D. P., Butters, N., Wiederholt, W. C., Corey-Bloom, J., Edelstein, S. L., & Barrett-Connor, E. (1995). Detection of dementia of the Alzheimer type in a population-based sample: Neuropsychological test performance. Journal of the International Neuropsychological Society, 1, 252–260.
  • Caruana, R., & Niculescu-Mizil, A. (2006, June). An empirical comparison of supervised learning algorithms. In Proceedings of the 23rd International Conference on Machine Learning (pp. 161–168). Pittsburgh, PA: ACM.
  • Cho, S. B., & Won, H. H. (2003, January). Machine learning in DNA microarray analysis for cancer classification. In Y.-P. P. Chen (Ed.), Proceedings of the first Asia-Pacific bioinformatics conference on research and practice in information technology (Vol. 19, pp. 189–198). Adelaide: Australian Computer Society.
  • Cook, D. J., & Schmitter-Edgecombe, M. (2009). Assessing the quality of activities in a smart environment. Methods of Information in Medicine, 48, 480–485.
  • Cox, D. R., & Snell, E. J. (1989). Analysis of binary data (Vol. 32). Boca Raton, FL: CRC Press.
  • Curk, T., Demsar, J., Xu, Q., Leban, G., Petrovic, U., Bratko, I., … Zupan, B. (2005). Microarray data mining with visual programming. Bioinformatics, 21, 396–398.
  • Datta, P., Shankle, W. R., & Pazzani, M. (1996). Applying machine learning to an Alzheimer’s database. In proceedings of Artificial Intelligence in Medicine: AAAI-96 Spring Symposium, Portland, OR (pp. 26–30).
  • Dawadi, P. N., Cook, D. J., Schmitter-Edgecombe, M., & Parsey, C. (2013). Automated assessment of cognitive health using smart home technologies. Technology and Health Care, 21, 323–343.
  • Delis, D. C., Kaplan, E., & Kramer, J. H. (2001). Delis–Kaplan Executive Function System: Examiner’s manual. San Antonio, TX: The Psychological Corporation.
  • Entezari-Maleki, R., Rezaei, A., & Minaei-Bidgoli, B. (2009). Comparison of classification methods based on the type of attributes and sample size. Journal of Convergence Information Technology, 4, 94–102.
  • Farias, S. T., Mungas, D., Reed, B. R., Harvey, D., & DeCarli, C. (2009). Progression of mild cognitive impairment to dementia in clinic- vs community-based cohorts. Archives of Neurology, 66, 1151–1157.
  • Flack, V. F., & Chang, P. C. (1987). Frequency of selecting noise variables in subset regression analysis: A simulation study. The American Statistician, 41, 84–86.
  • Fraser, K. C., Meltzer, J. A., Graham, N. L., Leonard, C., Hirst, G., Black, S. E., & Rochon, E. (2014). Automated classification of primary progressive aphasia subtypes from narrative speech transcripts. Cortex, 55, 43–60.
  • Gauthier, S. G. (2002). Alzheimer’s disease: The benefits of early treatment. European Journal of Neurology, 12, 11–16.
  • Geisser, S. (1993). Predictive inference: An introduction. New York, NY: Chapman & Hall.
  • Griffiths, W. E., Hill, R. C., & Pope, P. J. (1987). Small sample properties of probit model estimators. Journal of the American Statistical Association, 82, 929–937.
  • Grundman, M., Petersen, R. C., Ferris, S. H., Thomas, R. G., Aisen, P. S., Bennett, D. A., … Thal, L. J. (2004). Mild cognitive impairment can be distinguished from Alzheimer disease and normal aging for clinical trials. Archives of Neurology, 61, 59–66.
  • Gütlein, M., Frank, E., Hall, M., & Karwath, A. (2009). Large-scale variable selection using wrappers. In S. Geisser (Ed.), Proceedings of the computational intelligence and data mining, IEEE symposium (pp. 332–339). New York, NY: Chapman & Hall.
  • Hosmer, D. W., & Lemeshow, S. (2000). Applied logistic regression. New York, NY: Wiley.
  • IBM. (2012). IBM SPSS Statistics Version 21 [Computer software]. Boston, MA: International Business Machines Corporation.
  • Kaplan, E. F., Goodglass, H., & Weintraub, S. (1983). The Boston Naming Test (2nd ed.). Philadelphia, PA: Lea & Febiger.
  • Kononenko, I. (2001). Machine learning for medical diagnosis: History, state of the art and perspective. Artificial Intelligence in Medicine, 23, 89–109.
  • Kubat, M., & Matwin, S. (1997). Addressing the curse of imbalanced training set: One-sided selection. In M. Kaufmann (Ed.), Proceedings of the fourteenth international conference on machine learning (pp. 179–186). Nashville, TN.
  • Kukar, M., Kononenko, I., & Groselj, C. (2011). Modern parameterization and explanation techniques in diagnostic decision support system: A case study in diagnostics of coronary artery disease. Artificial Intelligence in Medicine, 52, 77–90.
  • Lawton, M. P., & Brody, E. M. (1969). Assessment of older people: Self-maintaining and instrumental activities of daily living. The Gerontologist, 9, 179–186.
  • Lemon, S. C., Roy, J., Clark, M. A., Friedmann, P. D., & Rakowski, W. (2003). Classification and regression tree analysis in public health: Methodological review and comparison with logistic regression. Annals of Behavioral Medicine, 26, 172–181.
  • Lemsky, C. M., Smith, G., Malec, J. F., & Ivnik, R. J. (1996). Identifying risk for functional impairment using cognitive measures: An application of CART modeling. Neuropsychology, 10, 368–375.
  • Magnin, B., Mesrob, L., Kinkingnéhun, S., Pélégrini-Issac, M., Colliot, O., Sarazin, M., … Benali, H. (2009). Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI. Neuroradiology, 51, 73–83.
  • Magoulas, G. D., & Pentza, A. (2001). Machine learning in medical applications. In G. Paliouras, V. Karkaletsis, & C. Spyropoulos (Eds.), Machine learning and its applications (pp. 300–307). Berlin: Springer.
  • McCulla, M. M., Coats, M., Van Fleet, N., Duchek, J., Grant, E., & Morris, J. (1989). Reliability of clinical nurse specialists in the staging of dementia. Archives of Neurology, 46, 1210–1211.
  • McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Stadlan, E. M. (1984). Clinical diagnosis of Alzheimer’s disease report of the NINCDS‐ADRDA work group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology, 34, 939–944.
  • Mickey, R. M., & Greenland, S. (1989). The impact of confounder selection criteria on effect estimation. American Journal of Epidemiology, 129, 125–137.
  • Morris, J. C. (1993). The Clinical Dementia Rating (CDR): Current version and scoring rules. Neurology, 43, 2412–2414.
  • Morris, J. C. (1997). Clinical dementia rating: A reliable and valid diagnostic and staging measure for dementia of the Alzheimer type. International Psychogeriatrics, 9, 173–176.
  • Nasreddine, Z. S., Phillips, N. A., Bédirian, V., Charbonneau, S., Whitehead, V., Collin, I., … Chertkow, H. (2005). The Montreal Cognitive Assessment, MoCA: A brief screening tool for mild cognitive impairment. Journal of the American Geriatrics Society, 53, 695–699.
  • Orimaye, S. O., Wong, J. S. M., & Golden, K. J. (2014). Learning predictive linguistic variables for Alzheimer’s disease and related dementias using verbal utterances. In Workshop on computational linguistics and clinical psychology: From linguistic signal to clinical reality (pp. 78–87). Baltimore, MD.
  • Pedhazur, E. J. (1997). Multiple regression in behavioral research (3rd ed.). Orlando, FL: Harcourt Brace.
  • Peduzzi, P., Concato, J., Kemper, E., Holford, T. R., & Feinstein, A. (1996). A simulation of the numbers of events per variable in logistic regression analysis. Journal of Clinical Epidemiology, 99, 1373–1379.
  • Petersen, R. C., Doody, R., Kurz, A., Mohs, R. C., Morris, J. C., Rabins, P. V., … Winblad, B. (2001). Current concepts in mild cognitive impairment. Archives of Neurology, 58, 1985–1992.
  • Petersen, R. C., & Morris, J. C. (2005). Mild cognitive impairment as a clinical entity and treatment target. Archives of Neurology, 62, 1160–1163.
  • Provost, F., & Fawcett, T. (2013). Data science for business: What you need to know about data mining and data-analytic thinking. Sebastopol, CA: O’Reilly Media.
  • Quinlan, J. R. (1993). C4.5: Programs for machine learning (Vol. 1). San Mateo, CA: Organ Kaufmann Publishers.
  • Rashidi, P., Cook, D., Holder, L., & Schmitter-Edgecombe, M. (2011). Discovering activities to recognize and track in a smart environment. IEEE Transactions on Knowledge Data Engineering, 23, 527–539.
  • Reitan, R. M. (1958). Validity of the Trail Making Test as an indicator of organic brain damage. Perceptual and Motor Skills, 8, 271–276.
  • Roderick, J. A., & Rubin, B. (2002). Statistical analysis with missing data (2nd ed.). Hoboken, NJ: Wiley.
  • Royall, D. R., Cordes, J. A., & Polk, M. (1998). CLOX: An executive clock drawing task. Journal of Neurology, Neurosurgery & Psychiatry, 64, 588–594.
  • Saeys, Y., Inza, I., & Larranaga, P. (2007). A review of variable selection techniques in bioinformatics. Bioinformatics, 23, 2507–2517.
  • Salmon, D. P., Thomas, R. G., Pay, M. M., Booth, A., Hofstetter, C. R., Thal, L. J., & Katzman, R. (2002). Alzheimer’s disease can be accurately diagnosed in very mildly impaired individuals. Neurology, 59, 1022–1028.
  • Schmidt, M. (1996). Rey Auditory Verbal Learning Test: A handbook. Los Angeles, CA: Western Psychological Services.
  • Schmitter-Edgecombe, M., Parsey, C., & Cook, D. (2011). Cognitive correlates of functional performance in older adults: Comparison of self-report, direct observation and performance-based measures. Journal of the International Neuropsychological Society, 17, 853–864.
  • Schmitter-Edgecombe, M., Parsey, C., & Lamb, R. (2014). Development and psychometric properties of the Instrumental Activities of Daily Living: Compensation scale. Archives of Clinical Neuropsychology, 29, 776–792.
  • Schmitter-Edgecombe, M., Woo, E., & Greeley, D. (2009). Characterizing multiple memory deficits and their relation to everyday functioning in individuals with mild cognitive impairment. Neuropsychology, 23, 168–177.
  • Shankle, W. R., Mani, S., Dick, M. B., & Pazzani, M. J. (1998). Simple models for estimating dementia severity using machine learning. Studies in Health Technology and Informatics, 1, 472–476.
  • Shankle, W. R., Mani, S., Pazzani, M. J., & Smyth, P. (1997). Detecting very early stages of dementia from normal aging with machine learning methods. In E. Keravnou, C. Garbay, R. Baud, & Y. Wyatt (Eds.), Artificial intelligence in medicine (pp. 71–85). Berlin: Springer.
  • Smith, A. (1991). Symbol Digit Modalities Test. Los Angeles, CA: Western Psychological Services.
  • Spreen, O., & Strauss, E. (1998). A compendium of neuropsychological tests: Administration, norms, and commentary. New York, NY: Oxford University Press.
  • Swearer, J. M., O’Donnell, B. F., Kane, K. J., Hoople, N. E., & Lavoie, M. (1998). Delayed recall in dementia: Sensitivity and specificity in patients with higher than average general intellectual abilities. Cognitive and Behavioral Neurology, 11, 200–206.
  • Tierney, M. C., Yao, C., Kiss, A., & McDowell, I. (2005). Neuropsychological tests accurately predict incident Alzheimer disease after 5 and 10 years. Neurology, 64, 1853–1859.
  • Tsien, C. L., Fraser, H. S., Long, W. J., & Kennedy, R. L. (1998). Using classification tree and logistic regression methods to diagnose myocardial infarction. Studies in Health Technology and Informatics, 1, 493–497.
  • Wang, Y., Tetko, I. V., Hall, M. A., Frank, E., Facius, A., Mayer, K. F., & Mewes, H. W. (2005). Gene selection from microarray data for cancer classification: A machine learning approach. Computational Biology and Chemistry, 29, 37–46.
  • Wechsler, D. (1997). WAIS–III: Wechsler Adult Intelligence Scale. San Antonio, TX: Psychological Corporation.
  • Wilder, D., Cross, P., Chen, J., Gurland, B., Lantigua, R. A., Teresi, J., … Encarnacion, P. (1995). Operating characteristics of brief screens for dementia in a multicultural population. The American Journal of Geriatric Psychiatry, 3, 96–107.
  • Williams, J. A., Weakley, A., Cook, D. J., & Schmitter-Edgecombe, M. (2013). Machine learning techniques for diagnostic differentiation of mild cognitive impairment and dementia. In Proceedings of the twenty-seventh AAAI conference on artificial Intelligence (pp. 71–76). Bellevue, WA.
  • Williams, J. M. (1991). Memory assessment scales. Odessa, FL: Psychological Assessment Resources.
  • Yesavage, J. A., Brink, T. L., Rose, T. L., Lum, O., Huang, V., Adey, M., & Leirer, V. O. (1983). Development and validation of a Geriatric Depression Screening Scale: A preliminary report. Journal of Psychiatric Research, 17, 37–49.
  • Zachary, R. A. (1991). Shipley Institute of Living Scale–Revised manual. Los Angeles, CA: Western Psychological Services.
  • Zadeh, L. H. (1973). Outline of a new approach to the analysis of complex systems and decision processes. IEEE Transactions on Systems, Man, and Cybernetics, 3, 28–44.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.