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Among the dairy sector's current concerns, the as-sessment of global animal health status is a complex challenge. Its multidimensionality means that global monitoring tools are rarely considered. Instead, specific disease detection is often studied separately and, due to financial and ethical issues, uses small-scale data sets focusing on few biomarkers. Several studies have already been conducted using milk Fourier transform mid-infrared (FT-MIR) spectroscopy to detect mastitis and lameness or to quantify health-related biomarkers in milk or blood. Those studies are relevant but they focus mainly on one biomarker or disease. To solve this issue and the small-scale data set, in this study, we proposed a holistic approach using big data obtained from milk recording, including milk yield, somatic cell count, and 27 FT-MIR-based predictors related to milk composition and animal health status. Using 740,454 records collected from 114,536 first-parity Holstein cows in southern Belgium, we performed repeated unsupervised learning algorithms based on Ward's agglomerative hierarchical clustering method to find potential interesting patterns. A divide-and-conquer approach was used to overcome the limitation of computational resources in clustering a relatively large data set. Five groups of records were identified. Differences observed in the fourth group suggested a relationship to metabolic disorders. The fifth group seemed to be related to mastitis. In a second step, we performed a partial least squares discriminant analysis (PLS-DA) to predict the probability of belonging to those specific groups for the entire data set. The obtained global ac-curacy was 0.77 and the balanced accuracy (i.e., the mean between sensitivity and specificity) of discrimi-nating the fourth and fifth groups was 0.88 and 0.96, respectively. Then, a validation of the interpretation of those groups was performed using 204 milk and blood reference records. The predicted probability associated with the metabolic disorders issue had significant cor-relations of 0.54 with blood beta-hydroxybutyrate, 0.44 with blood nonesterified fatty acids, -0.32 with blood glucose, -0.23 with milk glucose-6-phosphate, and 0.38 with milk isocitrate. In contrast, the predicted prob-ability of belonging to the mastitis group had correla-tions of 0.69 with milk lactate dehydrogenase, 0.46 with milk N-acetyl-beta-D-glucosaminidase, -0.18 with milk free glucose, and 0.16 with milk glucose-6-phosphate. Consequently, these results suggest that the obtained quantitative traits indirectly reflect some of the main health disorders in dairy farming and could be used to monitor dairy cows on a large scale. By using unsuper-vised learning on large-scale milk recording data and then validating the pattern using reference laboratory measures, we propose a new approach to quickly assess dairy cow health status.

Nowadays, data is stored on farm with a focus on reproduction, production, and health parameters. Recently, this has been increased with a vast amount of data coming from new so called precision livestock farming (PLF) technologies. These PLF technologies includes rectal, vaginal, and ruminal temperature data loggers, accelerometers, automated weight scales, 3D-imaging technologies and many others. However, they commonly create large data sets, which are stored in on-farm databases that do not reach the veterinary community. The objective of the current initiative is to help re-establish that veterinary link to on-farm data by including large data set analysis in the veterinary curriculum., American Association of Bovine Practitioners Proceedings of the Annual Conference, 2015