Machine-learning based prediction of Cushing's syndrome in dogs attending UK primary-care veterinary practice
| Autor: | Imogen Schofield, David Brodbelt, David B. Church, Dan G. O’Neill, R.F. Geddes, Noel Kennedy, Stijn J. M. Niessen |
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| Rok vydání: | 2020 |
| Předmět: |
Male
Pediatrics medicine.medical_specialty 040301 veterinary sciences Science MEDLINE Primary care Article 0403 veterinary science Machine Learning 03 medical and health sciences 0302 clinical medicine Dogs Animal physiology medicine Animals 030212 general & internal medicine Overall performance Diagnosis Computer-Assisted Dog Diseases Medical diagnosis Cushing Syndrome Multidisciplinary S syndrome Endocrine disease business.industry 04 agricultural and veterinary sciences medicine.disease United Kingdom Medicine Female business Clinical record Algorithms |
| Zdroj: | Scientific Reports, Vol 11, Iss 1, Pp 1-12 (2021) Scientific Reports |
| ISSN: | 2045-2322 |
| Popis: | Cushing’s syndrome is an endocrine disease in dogs that negatively impacts upon the quality-of-life of affected animals. Cushing’s syndrome can be a challenging diagnosis to confirm, therefore new methods to aid diagnosis are warranted. Four machine-learning algorithms were applied to predict a future diagnosis of Cushing's syndrome, using structured clinical data from the VetCompass programme in the UK. Dogs suspected of having Cushing's syndrome were included in the analysis and classified based on their final reported diagnosis within their clinical records. Demographic and clinical features available at the point of first suspicion by the attending veterinarian were included within the models. The machine-learning methods were able to classify the recorded Cushing’s syndrome diagnoses, with good predictive performance. The LASSO penalised regression model indicated the best overall performance when applied to the test set with an AUROC = 0.85 (95% CI 0.80–0.89), sensitivity = 0.71, specificity = 0.82, PPV = 0.75 and NPV = 0.78. The findings of our study indicate that machine-learning methods could predict the future diagnosis of a practicing veterinarian. New approaches using these methods could support clinical decision-making and contribute to improved diagnosis of Cushing’s syndrome in dogs. |
| Databáze: | OpenAIRE |
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