Clicca qui per scaricare

Decision-making odontoiatrico: un modello basato su alberi decisionali
Titolo Rivista: MECOSAN 
Autori/Curatori: Ernesto D’Avanzo, Paola Adinolfi, Ambrosina Michelotti 
Anno di pubblicazione:  2018 Fascicolo: 105 Lingua: Italiano 
Numero pagine:  16 P. 9-24 Dimensione file:  428 KB
DOI:  10.3280/MESA2018-105002
Il DOI è il codice a barre della proprietà intellettuale: per saperne di più:  clicca qui   qui 

Il lavoro propone un’implementazione di albero decisionale per la modellizzazione dei processi sanitari e il supporto al decision-making in ambito ortodontico. La sperimentazione, che impiega un dataset costituito da 290 cartelle cliniche (costituite da 39 attributi) di pazienti della scuola di Odontoiatria dell’Università d Napoli Federico II, ha il duplice obiettivo di migliorare il grado di within e between agreeement tra gli ortodontisti e validare, da un punto di vista clinico, le euristiche generate tramite l’albero decisionale. La procedura, oltre a soddisfare i criteri di accuratezza, facilità di utilizzo e rapidità proposti da Bodemer et al. (2015), migliora il livello di agreement fra gli ortodontisti e, allo stesso tempo, riduce il numero di attributi impiegati per arrivare alla decisione ortodontica. Tutto ciò, sul piano dell’efficienza gestionale, implica una riduzione dei costi delle prestazioni, sia per gli esami diagnostici sia per quelli clinici.

Keywords: Euristiche, processi sanitari, alberi decisionali, odontoiatria, efficienza.

  1. Anderson D.R., Sweeney D.J., Williams T.A., Camm J.D., Cochran J.J. (2015). An introduction to management science: quantitative approaches to decision making. Boston, MA: Cengage Learning.
  2. Bennett C.C., Hauser K. (2013). Artificial intelligence framework for simulating clinical decision-making: A Markov decision process approach. Artificial intelligence in medicine, 57(1): 9-19.
  3. Bodemer N., Hanoch Y., Katsikopoulos K.V. (2015). Heuristics: foundations for a novel approach to medical decision making. Internal and emergency medicine, 10(2): 195-203.
  4. Bornstein B.H., Emler A.C. (2001). Rationality in medical decision making: a review of the literature on doctors’ decision making biases. Journal of evaluation in clinical practice, 7(2): 97-107.
  5. Chiang H.J., Tseng C.C., Torng C.C. (2013). A retrospective analysis of prognostic indicators in dental implant therapy using the C5. 0 decision tree algorithm. Journal of Dental Sciences, 8(3); 248-255.
  6. D’Avanzo E., Lytras M.D. (2016). From Big Data to Smart Business. Some Philosophical Remarks –Revealing the new Era of Smart Data, Deep Learning and Cognitive Computing. International Journal of Knowledge Society Research, 7(2): 125-135.
  7. Edwards R., Alsufyani N., Heo G., Flores-Mir C. (2015). Agreement among orthodontists experienced with cone-beam computed tomography on the need for follow-up and the clinical impact of craniofacial findings from multiplanar and 3-dimensional reconstructed views. American Journal of Orthodontics and Dentofacial Orthopedics, 148(2): 264-273.
  8. Feigenbaum E.A., Buchanan B.G., Lederberg J. (1970). On generality and problem solving: A case study using the DENDRAL program. Stanford, CA: Stanford University.
  9. Fawcett T. (2006). An introduction to ROC analysis. Pattern recognition letters, 27(8): 861-874.
  10. Flach P.A. (2003). An analysis of rule evaluation metrics. Proceedings of the 20th International Conference on Machine Learning (pp. 202-209). August 21-24, 2003, Washington, DC.
  11. Goldstein D. G., & Gigerenzer G. (2002). Models of ecological rationality: the recognition heuristic. Psychological review, 109(1), 75.
  12. Green L., Mehr D.R. (1997). What alters physicians’ decisions to admit to the coronary care unit?. Journal of Family Practice, 45(3): 219-226.
  13. Hogarth R. (1987). Judgement and Choice. New York: John Wiley & Sons.
  14. Hu Z., Yin X., Liao J., Zhou C., Yang Z., Zou S. (2015). The effect of teeth extraction for orthodontic treatment on the upper airway: a systematic review. Sleep and Breathing, 19(2): 441-451.
  15. Hunink M.M., Weinstein M.C., Wittenberg E., Drummond M.F., Pliskin J.S., Wong J.B., Glasziou
  16. P.P. (2014). Decision making in health and medicine: integrating evidence and values. Cambridge: Cambridge University Press.
  17. Langley P., Bradshaw G.L., Simon H.A. (1983). Rediscovering chemistry with the BACON system. In: J.G. Carbonell., R. S. Michalski., T. M. Mitchell (Eds.). Machine learning (pp. 307-329). Berlin: Springer.
  18. Lusted L.B. (1970). Introduction to Medical Decision Making. American Journal of Physical Medicine & Rehabilitation, 49(5): 322.
  19. Mitchell T.M. (1997). Machine Learning (1 ed.). New York: McGraw-Hill.
  20. Musen M.A., Middleton B., Greenes R.A. (2014). Clinical decision-support systems. In: E.H.
  21. Shortliffe, J.J. Cimino (Eds.) Biomedical informatics (pp. 643-674). London: Springer.
  22. Pizzini M. (2006). The relation between cost-system design, managers evaluations of the relevance and usefulness of cost data, and financial performance: an empirical study of US hospitals. Accounting, Organizations and Society, 31(2): 179-210
  23. Polya G. (2014). How to solve it: A new aspect of mathematical method. Princeton: Princeton University Press.
  24. Power D.J., Sharda R., Burstein F. (2015). Decision support systems. New York: John Wiley & Sons.
  25. Quinlan R.J. (1993). C4.5: Programs for Machine Learning. San Francisco: Morgan Kaufmann Publishers.
  26. Redelmeier D.A., Shafir E. (1995). Medical decision making in situations that offer multiple alternatives. Jama, 273(4): 302-305.
  27. Sadegh-Zadeh K. (2015). Medical Decision-Making.
  28. In K. Sadegh-Zadeh (Ed.) Handbook of Analytic Philosophy of Medicine (pp. 699-703). Rotterdam: Springer.
  29. Sackett D.L., Rosenberg W.M.C., Gray J.A.M., Haynes R.B. (1996). Evidence based medicine: what it is and what it isn’t. BMJ, 312: 71-72.
  30. Shortliffe E.H., Davis R., Axline S.G., Buchanan B.G., Green C.C., Cohen S.N. (1975). Computer-based consultations in clinical therapeutics: explanation and rule acquisition capabilities of the MYCIN system. Computers and biomedical research, 8(4): 303-320.
  31. Stoean R., Stoean C. (2013). Modeling medical decision making by support vector machines, explaining by rules of evolutionary algorithms with feature selection. Expert Systems with Applications, 40(7): 2677-2686.
  32. Sujatha G., Rani K.U. (2013). Evaluation of decision tree classifiers on tumor datasets. International Journal of Emerging Trends and Technology in Computer Science, 2(4): 418-23.
  33. Turpin D.L., Huang G. (2016). The Role of Evidence in Orthodontics. Orthodontics: Current Principles and Techniques, Vancouver: Elsevier.
  34. Wolf M., Krause J., Carney P.A., Bogart A., Kurvers
  35. R.H. (2015). Collective intelligence meets medical decision-making: The collective outperforms the best radiologist. PloS one, 10(8): e0134269.

Ernesto D’Avanzo, Paola Adinolfi, Ambrosina Michelotti, in "MECOSAN" 105/2018, pp. 9-24, DOI:10.3280/MESA2018-105002


FrancoAngeli è membro della Publishers International Linking Association associazione indipendente e no profit per facilitare l'accesso degli studiosi ai contenuti digitali nelle pubblicazioni professionali e scientifiche