Argument based machine learning in a medical domain

Frontiers in Artificial Intelligence and Applications

Volumn 144, Issue , 2006, Pages 59-70

  Žabkar, Jure ^a   Možina, Martin ^a   Videčnik, Jerneja ^b   Bratko, Ivan ^a  

^a UNIVERSITY OF LJUBLJANA   (Slovenia)

^b Clinic for Infectious Diseases   (Slovenia)

Author keywords

Argument Based Machine Learning; Bacterial infections; Geriatric population; Rule learning

Indexed keywords

MACHINE LEARNING;

ARGUMENT-BASED MACHINE LEARNING; BACTERIAL INFECTIONS; EXPERT KNOWLEDGE; GERIATRIC POPULATION; LEARNING PROCESS; MACHINE-LEARNING; MEDICAL DOMAINS; NEW APPROACHES; RULE LEARNING; RULE LEARNING ALGORITHMS;

LEARNING ALGORITHMS;

EID: 84940517521     PISSN: 09226389     EISSN: 18798314     Source Type: Book Series    
DOI: None     Document Type: Conference Paper

References (22)

1. Ben-Yehuda A,WekslerME. Host resistance and the immune system. Clin GeriatrMed 1992; 8: 701-11.
2. Marco CA et al. Fever in geriatric emergency patients: clinical features associated with serious illness. Ann Emerg Med 1995; 26:18-24.
3. Castle SC et al. Fever response in elderly nursing home residents: are the older truly colder? JAGS 1991; 39: 853-7.
4. Gleckman R, Hibert D. Afebrile bacteriemia: a phenomena in geriatric patients. JAMA 1982; 248: 1478-81.
5. Mellors JW et al. A simple index to identify occult bacterial infection in adults with acute unexplained fever. Arch Intern Med 1987; 147: 666-71.
6. Rockwood K. Acute confusion in elderly medical patients. J Am Geriatr Soc 1989; 37: 150-4.
7. Fontanarosa PB, Kaeberlein FJ, Gerson LW, Thompson RB. Difficulty in predicting bacteriemia in elderly emergency patients. Ann Emerg Med 1992; 21: 842-8.
8. Pfitzenmeyer P, Decrey H, Auckenthaler R, Michel JP. Predicting bacteriemia in older patients. JAGS 1995; 43: 230-5.
9. Fine MJ, Auble TE, Yeay DM, et al. A prediction rule to identify low-risk patients with community acquired pneumonia. NEJM 1997; 336: 243-50.
10. Farr BM, Sloman AJ, Fisch MJ. Predicting death in patients hospitalized for community acquired pneumonia. Ann Int Med 1991; 115:428-36.
11. Fine MJ, Smith MA, Carson CA, et al. Prognosis and outcomes of patients with community acquired pneumonia. JAMA 1996; 275: 134-41.
12. Prakken H, Vreeswijk G. Handbook of Philosophical Logic, second edition, volume 4, chapter Logics for Defeasible Argumentation, pages 218-319. Kluwer Academic Publishers, Dordrecht etc, 2002.
13. Bratko I, Možina M: Argumentation and Machine Learning. In: Deliverable 2.1 for the ASPIC project 2004.
14. Možina M, Žabkar J, Bratko I: Implementation of and experiments with ABML and MLBA, preliminary version. ASPIC deliverable D3.4, 2006.
15. Možina M, Žabkar J, Bratko I: Argument Based Rule Learning. Accepted for publication in Proceedings of ECAI, Riva del Garda, 2006.
16. Clark P, Niblett T: The CN2 induction algorithm. Machine Learning Journal, 4: 261-283, 1989.
17. Clark P, Boswell R. Rule Induction with CN2: Some Recent Improvements. In Machine Learning-Proceedings of the Fifth European Conference (ESWL-91), pages 151-163, Berlin, 1991.
18. Možina M, Žabkar J, Bench-Capon T, Bratko I: Argument Based Machine Learning Applied to Law. Artificial Intelligence and Law, 2005; In press.
19. Možina M, Demšar J, Žabkar J, Bratko I: Why is Rule Learning Optimistic and How To Correct It. Accepted for publication in Proceedings of ECML conference, Berlin, 2006.
20. John von Neumann. Zur Theorie der gessellshaftsspiele. Mathematische Annalen, 100:295-320, 1928. English Translation Fin
21. Tucker AW, Luce RD, Contributions to the Theory of Games IV, Annals of Mathematics Studies 40, 1959.
22. Demšar J, Zupan B, Leban G (2004) Orange: From Experimental Machine Learning to Interactive Data Mining, White Paper (www.ailab.si/orange), Faculty of Computer and Information Science, University of Ljubljana.