{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T13:00:33Z","timestamp":1792155633084,"version":"4.4.0"},"reference-count":65,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,6,5]],"date-time":"2025-06-05T00:00:00Z","timestamp":1749081600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Animals"],"abstract":"<jats:p>Metritis is one of the most common postpartum diseases in dairy cows, associated with impaired reproductive performance and substantial economic losses. In this study, we investigated the potential of machine learning (ML) techniques applied to physiological, behavioural, and milk quality parameters for the early detection of metritis in dairy cows during the postpartum period. A total of 2707 daily observations were collected from 94 cows in early lactation, of which 11 cows (275 records) were diagnosed with metritis. The dataset included daily measurements of body weight, rumination time, milk yield, milk composition (fat, protein, lactose), somatic cell count (SCC), and feed intake. Five classification models\u2014partial least squares discriminant analysis (PLS-DA), random forest (RF), support vector machine (SVM), neural network (NN), and an Ensemble model\u2014were developed using standardised features and stratified 80\/20 training\/test splits. To address class imbalance, model loss functions were adjusted using class weights. Models were evaluated based on accuracy, sensitivity, specificity, positive and negative predictive values (PPV, NPV), area under the receiver operating characteristic (ROC) area under the curve (AUC), and Matthews correlation coefficient (MCC). The NN model demonstrated the highest overall performance (accuracy = 96.1%, AUC = 96.3%, MCC = 0.79), indicating strong capability in distinguishing both healthy and diseased animals. The SVM achieved the highest sensitivity (90.9%), while RF and Ensemble models showed high specificity (&gt;98%) and PPV. This study provides novel evidence that ML methods can effectively detect metritis using routinely collected, non-invasive on-farm data. Our findings support the integration of neural and Ensemble learning models into automated health monitoring systems to enable earlier disease detection and improved animal welfare. Although external validation was not performed, internal cross-validation demonstrated consistent performance across models, suggesting suitability for application in multi-farm settings. To the best of our knowledge, this is among the first studies to apply ML for early metritis detection based exclusively only automated herd data.<\/jats:p>","DOI":"10.3390\/ani15111674","type":"journal-article","created":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T03:52:28Z","timestamp":1749181948000},"page":"1674","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Application of Machine Learning Models for the Early Detection of Metritis in Dairy Cows Based on Physiological, Behavioural and Milk Quality Indicators"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5343-6590","authenticated-orcid":false,"given":"Karina","family":"D\u017eermeikait\u0117","sequence":"first","affiliation":[{"name":"Animal Clinic, Veterinary Academy, Lithuania University of Health Sciences, Til\u017e\u0117s Str. 18, LT-47181 Kaunas, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0462-1463","authenticated-orcid":false,"given":"Justina","family":"Kri\u0161tolaityt\u0117","sequence":"additional","affiliation":[{"name":"Animal Clinic, Veterinary Academy, Lithuania University of Health Sciences, Til\u017e\u0117s Str. 18, LT-47181 Kaunas, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9993-2224","authenticated-orcid":false,"given":"Ram\u016bnas","family":"Antanaitis","sequence":"additional","affiliation":[{"name":"Animal Clinic, Veterinary Academy, Lithuania University of Health Sciences, Til\u017e\u0117s Str. 18, LT-47181 Kaunas, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Vidal, G., Sharpnack, J., Pinedo, P., Tsai, I.C., Lee, A.R., and Mart\u00ednez-L\u00f3pez, B. (2023). Comparative Performance Analysis of Three Machine Learning Algorithms Applied to Sensor Data Registered by a Leg-Attached Accelerometer to Predict Metritis Events in Dairy Cattle. Front. Anim. Sci., 4.","DOI":"10.3389\/fanim.2023.1157090"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"12887","DOI":"10.3168\/jds.2021-20262","article-title":"Integration of Statistical Inferences and Machine Learning Algorithms for Prediction of Metritis Cure in Dairy Cows","volume":"104","author":"Ferreira","year":"2021","journal-title":"J. Dairy Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4881","DOI":"10.3168\/jds.2023-23611","article-title":"Application of Behavior Data to Predictive Exploratory Models of Metritis Self-Cure and Treatment Failure in Dairy Cows","volume":"107","author":"Prim","year":"2024","journal-title":"J. Dairy Sci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Kelly, E. (2022). Reproductive Tract Disease and Estrus Detection Inaccuracy in Irish Seasonal Calving Pasture-Based Dairy Cows, School of Veterinary Medicine, University College Dublin.","DOI":"10.1016\/j.theriogenology.2020.11.011"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3440","DOI":"10.3168\/jds.2021-21203","article-title":"Defining Clinical Diagnosis and Treatment of Puerperal Metritis in Dairy Cows: A Scoping Review","volume":"105","author":"Garzon","year":"2022","journal-title":"J. Dairy Sci."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"D\u017eermeikait\u0117, K., Ba\u010d\u0117ninait\u0117, D., and Antanaitis, R. (2023). Innovations in Cattle Farming: Application of Innovative Technologies and Sensors in the Diagnosis of Diseases. Animals, 13.","DOI":"10.3390\/ani13050780"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.compag.2010.09.001","article-title":"Detection of Cows with Insemination Problems Using Selected Classification Models","volume":"74","author":"Grzesiak","year":"2010","journal-title":"Comput. Electron. Agric."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhou, X., Xu, C., Wang, H., Xu, W., Zhao, Z., Chen, M., Jia, B., and Huang, B. (2022). The Early Prediction of Common Disorders in Dairy Cows Monitored by Automatic Systems with Machine Learning Algorithms. Animals, 12.","DOI":"10.3390\/ani12101251"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Palma, O., Pl\u00e0-Aragon\u00e9s, L.M., Mac Cawley, A., and Albornoz, V.M. (2025). AI and Data Analytics in the Dairy Farms: A Scoping Review. Animals, 15.","DOI":"10.3390\/ani15091291"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Bobbo, T., Biffani, S., Taccioli, C., Penasa, M., and Cassandro, M. (2021). Comparison of Machine Learning Methods to Predict Udder Health Status Based on Somatic Cell Counts in Dairy Cows. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-93056-4"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Cockburn, M. (2020). Review: Application and Prospective Discussion of Machine Learning for the Management of Dairy Farms. Animals, 10.","DOI":"10.3390\/ani10091690"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"106143","DOI":"10.1016\/j.compag.2021.106143","article-title":"Intelligent Perception for Cattle Monitoring: A Review for Cattle Identification, Body Condition Score Evaluation, and Weight Estimation","volume":"185","author":"Qiao","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Antanaitis, R., Anskien\u0117, L., Palubinskas, G., D\u017eermeikait\u0117, K., Ba\u010d\u0117ninait\u0117, D., Viora, L., and Rutkauskas, A. (2023). Ruminating, Eating, and Locomotion Behavior Registered by Innovative Technologies around Calving in Dairy Cows. Animals, 13.","DOI":"10.3390\/ani13071257"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Shaikh, N.I., Sethi, R.S., and Bhardwaj, A. (2024). Application of Biosensors and Artificial Learning in Disease Diagnoses. Periparturient Diseases of Cattle, John Wiley & Sons, Ltd.","DOI":"10.1002\/9781394204007.ch36"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"105903","DOI":"10.1016\/j.prevetmed.2023.105903","article-title":"Impact of Sensor Data Pre-Processing Strategies and Selection of Machine Learning Algorithm on the Prediction of Metritis Events in Dairy Cattle","volume":"215","author":"Vidal","year":"2023","journal-title":"Prev. Vet. Med."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kalkan, A., Tepeli, M., and G\u00f6de, A. (2025). Mastitis Diagnosis with Machine Learning Algorithms. Neural Comput. Appl., 1\u201322.","DOI":"10.1007\/s00521-025-11176-8"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"105431","DOI":"10.1016\/j.prevetmed.2021.105431","article-title":"Predictive Models to Identify Holstein Cows at Risk of Metritis and Clinical Cure and Reproductive\/Productive Failure Following Antimicrobial Treatment","volume":"194","author":"Merenda","year":"2021","journal-title":"Prev. Vet. Med."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"9604","DOI":"10.3168\/jds.2020-18661","article-title":"Discovery of Different Metabotypes in Overconditioned Dairy Cows by Means of Machine Learning","volume":"103","author":"Ghaffari","year":"2020","journal-title":"J. Dairy Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"105193","DOI":"10.1016\/j.compag.2019.105193","article-title":"A Machine Learning Based Decision Aid for Lameness in Dairy Herds Using Farm-Based Records","volume":"169","author":"Warner","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_20","first-page":"233","article-title":"Vision-Based Measuring Method for Individual Cow Feed Intake Using Depth Images and a Siamese Network","volume":"16","author":"Wang","year":"2023","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"108877","DOI":"10.1016\/j.compag.2024.108877","article-title":"A Predictive Model for Hypocalcaemia in Dairy Cows Utilizing Behavioural Sensor Data Combined with Deep Learning","volume":"220","author":"Hut","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lee, J.G., Lee, S.S., Alam, M., Lee, S.M., Seong, H.-S., Park, M.N., Han, S., Nguyen, H.-P., Baek, M.K., and Phan, A.T. (2024). Utilizing 3D Point Cloud Technology with Deep Learning for Automated Measurement and Analysis of Dairy Cows. Sensors, 24.","DOI":"10.3390\/s24030987"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Jia, Z., Zhao, Y., Mu, X., Liu, D., Wang, Z., Yao, J., and Yang, X. (2025). Intelligent Deep Learning and Keypoint Tracking-Based Detection of Lameness in Dairy Cows. Vet. Sci., 12.","DOI":"10.3390\/vetsci12030218"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.theriogenology.2024.05.002","article-title":"Metrisor: A Novel Diagnostic Method for Metritis Detection in Cattle Based on Machine Learning and Sensors","volume":"223","author":"Risvanli","year":"2024","journal-title":"Theriogenology"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Marzougui, A., McConnel, C.S., Adams-Progar, A., Biggs, T.D., Ficklin, S.P., and Sankaran, S. (2025). Machine Learning-Derived Cumulative Health Measure for Assessing Disease Impact in Dairy Cattle. Front. Anim. Sci., 6.","DOI":"10.3389\/fanim.2025.1532385"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"105069","DOI":"10.1016\/j.idairyj.2021.105069","article-title":"Future of Dairy Farming from the Dairy Brain Perspective: Data Integration, Analytics, and Applications","volume":"121","author":"Cabrera","year":"2021","journal-title":"Int. Dairy. J."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Neethirajan, S. (2020). Transforming the Adaptation Physiology of Farm Animals through Sensors. Animals, 10.","DOI":"10.20944\/preprints202007.0417.v1"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.biosystemseng.2024.01.010","article-title":"A Comparative Study of Machine Learning Models for Respiration Rate Prediction in Dairy Cows: Exploring Algorithms, Feature Engineering, and Model Interpretation","volume":"239","author":"Yan","year":"2024","journal-title":"Biosyst. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"731","DOI":"10.3168\/jds.2013-6693","article-title":"Prediction of Insemination Outcomes in Holstein Dairy Cattle Using Alternative Machine Learning Algorithms","volume":"97","author":"Shahinfar","year":"2014","journal-title":"J. Dairy Sci."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Li, D., Yan, G., Li, F., Lin, H., Jiao, H., Han, H., and Liu, W. (2024). Optimized Machine Learning Models for Predicting Core Body Temperature in Dairy Cows: Enhancing Accuracy and Interpretability for Practical Livestock Management. Animals, 14.","DOI":"10.3390\/ani14182724"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"100391","DOI":"10.1016\/j.animal.2021.100391","article-title":"Machine Learning Approaches for the Prediction of Lameness in Dairy Cows","volume":"15","author":"Shahinfar","year":"2021","journal-title":"Animal"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"10234","DOI":"10.3168\/jds.2017-12954","article-title":"A 100-Year Review: Methods and Impact of Genetic Selection in Dairy Cattle\u2014From Daughter\u2013Dam Comparisons to Deep Learning Algorithms","volume":"100","author":"Weigel","year":"2017","journal-title":"J. Dairy Sci."},{"key":"ref_33","unstructured":"(2021). Nutrient Requirements of Dairy Cattle: Eighth Revised Edition, National Academies Press."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Mensching, A., Zschiesche, M., Hummel, J., Schmitt, A.O., Grelet, C., and Sharifi, A.R. (2020). An Innovative Concept for a Multivariate Plausibility Assessment of Simultaneously Recorded Data. Animals, 10.","DOI":"10.3390\/ani10081412"},{"key":"ref_35","unstructured":"Rossum, G.V., and Drake, F.L. (2009). Python 3 Reference Manual, Python Software Foundation. Available online: https:\/\/docs.python.org\/3\/."},{"key":"ref_36","first-page":"55","article-title":"Pipeline Design to Identify Key Features and Classify the Chemotherapy Response on Lung Cancer Patients Using Large-Scale Genetic Data","volume":"12","author":"Ripoll","year":"2018","journal-title":"BMC Syst. Biol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"429","DOI":"10.3233\/IDA-2002-6504","article-title":"The Class Imbalance Problem: A Systematic Study1","volume":"6","author":"Japkowicz","year":"2002","journal-title":"Intell. Data Anal."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1002\/cem.785","article-title":"Partial Least Squares for Discrimination","volume":"17","author":"Barker","year":"2003","journal-title":"J. Chemom."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Chicco, D., and Jurman, G. (2020). The Advantages of the Matthews Correlation Coefficient (MCC) over F1 Score and Accuracy in Binary Classification Evaluation. BMC Genomics, 21.","DOI":"10.1186\/s12864-019-6413-7"},{"key":"ref_40","first-page":"2825","article-title":"Scikit-Learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_41","unstructured":"The Pandas Development Team (2025, June 03). Pandas: Powerful Python Data Analysis Toolkit; Pandas-dev\/Pandas: 2024. Available online: https:\/\/pandas.pydata.org\/."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/MCSE.2007.55","article-title":"Matplotlib: A 2D Graphics Environment","volume":"9","author":"Hunter","year":"2007","journal-title":"Comput. Sci. Eng."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","article-title":"SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python","volume":"17","author":"Virtanen","year":"2020","journal-title":"Nat. Methods"},{"key":"ref_44","unstructured":"(2025, May 03). Keras: Deep Learning for Humans. Available online: https:\/\/keras.io\/."},{"key":"ref_45","unstructured":"(2025, May 03). TensorFlow. Available online: https:\/\/www.tensorflow.org\/."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"105051","DOI":"10.1016\/j.idairyj.2021.105051","article-title":"Exploring Machine Learning Algorithms for Early Prediction of Clinical Mastitis","volume":"119","author":"Delgado","year":"2021","journal-title":"Int. Dairy. J."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1493","DOI":"10.1017\/S1751731116000744","article-title":"A Decision-Tree Model to Detect Post-Calving Diseases Based on Rumination, Activity, Milk Yield, BW and Voluntary Visits to the Milking Robot","volume":"10","author":"Steensels","year":"2016","journal-title":"Animal"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"10186","DOI":"10.3168\/jds.2018-15791","article-title":"Prediction of Metabolic Status of Dairy Cows in Early Lactation with On-Farm Cow Data and Machine Learning Algorithms","volume":"102","author":"Xu","year":"2019","journal-title":"J. Dairy Sci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.theriogenology.2020.02.006","article-title":"Metabolic Biomarkers, Body Condition, Uterine Inflammation and Response to Superovulation in Lactating Holstein Cows","volume":"146","author":"Kasimanickam","year":"2020","journal-title":"Theriogenology"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Machado, T.F., Neto, F.d.C.B., Gon\u00e7alves, M.d.S., Barbosa, C.G., and Barreto, M.E. (2024). Exploring Machine Learning Algorithms in Sickle Cell Disease Patient Data: A Systematic Review. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0313315"},{"key":"ref_51","unstructured":"CDC, U.S. (2025, May 04). Actions & Events to Combat Antimicrobial Resistance, Available online: https:\/\/www.cdc.gov\/antimicrobial-resistance\/programs\/AR-actions-events.html."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2360","DOI":"10.3168\/jds.2016-12001","article-title":"Glucose Requirements of an Activated Immune System in Lactating Holstein Cows","volume":"100","author":"Kvidera","year":"2017","journal-title":"J. Dairy Sci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"S29","DOI":"10.1262\/jrd.1056S29","article-title":"Monitoring Metabolic Health of Dairy Cattle in the Transition Period","volume":"56","author":"LeBlanc","year":"2010","journal-title":"J. Reprod. Dev."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2820","DOI":"10.2527\/1995.7392820x","article-title":"Impact of Changes in Organic Nutrient Metabolism on Feeding the Transition Dairy Cow","volume":"73","author":"Grummer","year":"1995","journal-title":"J. Anim. Sci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"880","DOI":"10.3168\/jds.2014-8362","article-title":"Risk Factors for Subclinical and Clinical Ketosis and Association with Production Parameters in Dairy Cows in the Netherlands","volume":"98","author":"Vanholder","year":"2015","journal-title":"J. Dairy Sci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.anifeedsci.2005.08.003","article-title":"Feeding- and Management-Related Diseases in the Transition Cow: Physiological Adaptations around Calving and Strategies to Reduce Feeding-Related Diseases","volume":"126","author":"Ingvartsen","year":"2006","journal-title":"Anim. Feed. Sci. Technol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"8248","DOI":"10.3168\/jds.2018-14510","article-title":"Assessment of Daily Activity Patterns and Biomarkers of Pain, Inflammation, and Stress in Lactating Dairy Cows Diagnosed with Clinical Metritis","volume":"101","author":"Barragan","year":"2018","journal-title":"J. Dairy Sci."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"4544","DOI":"10.2527\/jas.2011-5064","article-title":"Relationships between Rumination Time, Metabolic Conditions, and Health Status in Dairy Cows during the Transition Period1","volume":"90","author":"Soriani","year":"2012","journal-title":"J. Anim. Sci."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"7411","DOI":"10.3168\/jds.2016-10908","article-title":"Use of Rumination and Activity Monitoring for the Identification of Dairy Cows with Health Disorders: Part II. Mastitis","volume":"99","author":"Stangaferro","year":"2016","journal-title":"J. Dairy Sci."},{"key":"ref_60","first-page":"115","article-title":"Uterine Diseases in Cattle after Parturition","volume":"176","author":"Sheldon","year":"2008","journal-title":"Vet. J. Lond. Engl. 1997"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"s75","DOI":"10.1017\/S175173111800349X","article-title":"Review: Metabolic Challenges in Lactating Dairy Cows and Their Assessment via Established and Novel Indicators in Milk","volume":"13","author":"Gross","year":"2019","journal-title":"Animal"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Rajagopal, A.R., Kumar, P., Choudhury, D., and Archangel, J.A. (2025). Automated Tracking of Individual Cattle within a Group. Smart Technologies for Sustainable Livestock Systems, CRC Press.","DOI":"10.1201\/9781003527114-7"},{"key":"ref_63","unstructured":"Riboni, V., and Emilio, M.P. (2019). Management During the Dry Period and Its Effect on Hepatic and Adipose Tissue Molecular Biomarkers of Metabolism and Health in Grazing Dairy Cows, University of Illinois at Urbana-Champaign."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Collignon, P.J., and McEwen, S.A. (2019). One Health\u2014Its Importance in Helping to Better Control Antimicrobial Resistance. Trop. Med. Infect. Dis., 4.","DOI":"10.3390\/tropicalmed4010022"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"766","DOI":"10.1111\/jvim.16664","article-title":"A Novel Machine Learning-Based Web Application for Field Identification of Infectious and Inflammatory Disorders of the Central Nervous System in Cattle","volume":"37","author":"Ferrini","year":"2023","journal-title":"J. Vet. Intern. Med."}],"container-title":["Animals"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-2615\/15\/11\/1674\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:47:22Z","timestamp":1760032042000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-2615\/15\/11\/1674"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,5]]},"references-count":65,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["ani15111674"],"URL":"https:\/\/doi.org\/10.3390\/ani15111674","relation":{},"ISSN":["2076-2615"],"issn-type":[{"value":"2076-2615","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,5]]}}}