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Items: 1 to 20 of 105

1.

Intelligent judgements over health risks in a spatial agent-based model.

Abdulkareem SA, Augustijn EW, Mustafa YT, Filatova T.

Int J Health Geogr. 2018 Mar 20;17(1):8. doi: 10.1186/s12942-018-0128-x.

2.

Bayesian structured additive regression modeling of epidemic data: application to cholera.

Osei FB, Duker AA, Stein A.

BMC Med Res Methodol. 2012 Aug 6;12:118. doi: 10.1186/1471-2288-12-118.

3.

Spatial and demographic patterns of cholera in Ashanti region - Ghana.

Osei FB, Duker AA.

Int J Health Geogr. 2008 Aug 12;7:44. doi: 10.1186/1476-072X-7-44.

4.

The role of soft computing in intelligent machines.

de Silva CW.

Philos Trans A Math Phys Eng Sci. 2003 Aug 15;361(1809):1749-80.

PMID:
12952684
5.

An agent-based approach for modeling dynamics of contagious disease spread.

Perez L, Dragicevic S.

Int J Health Geogr. 2009 Aug 5;8:50. doi: 10.1186/1476-072X-8-50.

6.

Supervised learning and prediction of spatial epidemics.

Pokharel G, Deardon R.

Spat Spatiotemporal Epidemiol. 2014 Oct;11:59-77. doi: 10.1016/j.sste.2014.08.003. Epub 2014 Sep 16.

PMID:
25457597
7.
8.

High-resolution spatial analysis of cholera patients reported in Artibonite department, Haiti in 2010-2011.

Allan M, Grandesso F, Pierre R, Magloire R, Coldiron M, Martinez-Pino I, Goffeau T, Gitenet R, François G, Olson D, Porten K, Luquero FJ.

Epidemics. 2016 Mar;14:1-10. doi: 10.1016/j.epidem.2015.08.001. Epub 2015 Sep 3.

9.

Assessment of the response to cholera outbreaks in two districts in Ghana.

Ohene SA, Klenyuie W, Sarpeh M.

Infect Dis Poverty. 2016 Nov 2;5(1):99.

10.

Adaptive user displays for intelligent tutoring software.

Beal CR.

Cyberpsychol Behav. 2004 Dec;7(6):689-93.

PMID:
15687804
11.

Learning and exploration in action-perception loops.

Little DY, Sommer FT.

Front Neural Circuits. 2013 Mar 22;7:37. doi: 10.3389/fncir.2013.00037. eCollection 2013.

12.

Intelligent diagnosis of jaundice with dynamic uncertain causality graph model.

Hao SR, Geng SC, Fan LX, Chen JJ, Zhang Q, Li LJ.

J Zhejiang Univ Sci B. 2017 May;18(5):393-401. doi: 10.1631/jzus.B1600273.

13.

AIDS-related health behavior: coping, protection motivation, and previous behavior.

Van der Velde FW, Van der Pligt J.

J Behav Med. 1991 Oct;14(5):429-51.

PMID:
1744908
14.

Impact of censoring on learning Bayesian networks in survival modelling.

Stajduhar I, Dalbelo-Basić B, Bogunović N.

Artif Intell Med. 2009 Nov;47(3):199-217. doi: 10.1016/j.artmed.2009.08.001. Epub 2009 Oct 14.

PMID:
19833488
15.

Seminal quality prediction using data mining methods.

Sahoo AJ, Kumar Y.

Technol Health Care. 2014;22(4):531-45. doi: 10.3233/THC-140816.

PMID:
24898862
16.

BELM: Bayesian extreme learning machine.

Soria-Olivas E, Gómez-Sanchis J, Martín JD, Vila-Francés J, Martínez M, Magdalena JR, Serrano AJ.

IEEE Trans Neural Netw. 2011 Mar;22(3):505-9. doi: 10.1109/TNN.2010.2103956. Epub 2011 Jan 20.

PMID:
21257373
17.
18.

Artificial intelligence framework for simulating clinical decision-making: a Markov decision process approach.

Bennett CC, Hauser K.

Artif Intell Med. 2013 Jan;57(1):9-19. doi: 10.1016/j.artmed.2012.12.003. Epub 2012 Dec 31.

PMID:
23287490
19.

Macromolecular crowding: chemistry and physics meet biology (Ascona, Switzerland, 10-14 June 2012).

Foffi G, Pastore A, Piazza F, Temussi PA.

Phys Biol. 2013 Aug;10(4):040301. Epub 2013 Aug 2.

PMID:
23912807
20.

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