Discriminating between true-positive and false-positive clinical mastitis alerts from automatic milking systems

Barkema, 1998, Incidence of clinical mastitis in dairy herds grouped in three categories by bulk milk somatic cell counts, J. Dairy Sci., 81, 411, 10.3168/jds.S0022-0302(98)75591-2 Cavero, 2008, Mastitis detection in dairy cows by application of neural networks, Livest. Sci., 114, 280, 10.1016/j.livsci.2007.05.012 Chagunda, 2006, A model for detection of individual cow mastitis based on an indicator measured in milk, J. Dairy Sci., 89, 2980, 10.3168/jds.S0022-0302(06)72571-1 Chapman, 2005, Classification of emergency department chief complaints into 7 syndromes: A retrospective analysis of 527,228 patients, Ann. Emerg. Med., 46, 445, 10.1016/j.annemergmed.2005.04.012 Claycomb, 2009, An automated in-line clinical mastitis detection system using measurement of conductivity from foremilk of individual udder quarters, N. Z. Vet. J., 57, 208, 10.1080/00480169.2009.36903 de Mol, 2001, Detection model for mastitis in cows milked in an automatic milking system, Prev. Vet. Med., 49, 71, 10.1016/S0167-5877(01)00176-3 Detilleux, 1999, Methods for estimating areas under receiver-operating characteristic curves: Illustration with somatic-cell scores in subclinical intramammary infections, Prev. Vet. Med., 41, 75, 10.1016/S0167-5877(99)00054-9 Dohoo, 2003 Espada, 2002, Milk colour analysis as a tool for the detection of abnormal milk, 28 Friedman, 1997, Bayesian network classifiers, Mach. Learn., 29, 131, 10.1023/A:1007465528199 Friggens, 2007, Estimating degree of mastitis from time-series measurements in milk: A test of a model based on lactate dehydrogenase measurements, J. Dairy Sci., 90, 5415, 10.3168/jds.2007-0148 Halasa, 2007, Economic effects of bovine mastitis and mastitis management: A review, Vet. Q., 29, 18, 10.1080/01652176.2007.9695224 Hogeveen, 2003, Sensors and management support in high-technology milking, J. Anim. Sci., 81, 1, 10.2527/2003.81suppl_31x Jensen, 2001 Jensen, 2009, An object-oriented Bayesian network modeling the causes of leg disorders in finisher herds, Prev. Vet. Med., 89, 237, 10.1016/j.prevetmed.2009.02.009 Kamphuis, 2010, Decision-tree induction to detect clinical mastitis with automatic milking, Comput. Electron. Agric., 70, 60, 10.1016/j.compag.2009.08.012 Kohavi, 1997, Wrappers for feature subset selection, Artificial Intelligence J., 97, 273, 10.1016/S0004-3702(97)00043-X Langley, 1994, Induction of selective Bayesian classifiers, 199 Mein, 2008, Performance evaluation of systems for automated monitoring of udder health: Would the real gold standard please stand up?, 259 Neijenhuis, 2009 Otto, 2004, A biological network describing infection with Mycoplasma hyopneumoniae in swine herds, Prev. Vet. Med., 66, 141, 10.1016/j.prevetmed.2004.09.005 Schrage, 2005, Haskell ready to Dazzle the real world, 17 Sherlock, 2008, Performance evaluation of systems for automated monitoring of udder health: Analytical issues and guidelines, 275 Steeneveld, 2008, The influence of cow factors on the incidence of clinical mastitis in dairy cows, J. Dairy Sci., 91, 1391, 10.3168/jds.2007-0705 Steeneveld, 2009, Providing probability distributions for the causal pathogen of clinical mastitis using naive Bayesian networks, J. Dairy Sci., 92, 2598, 10.3168/jds.2008-1694 Steeneveld, 2010, Simplify the interpretation of alert lists for clinical mastitis in automatic milking systems, Comput. Electron. Agric., 71, 50, 10.1016/j.compag.2009.12.011