Dataset Information

Predicting early risk of chronic kidney disease in cats using routine clinical laboratory tests and machine learning.

ABSTRACT:

Background

Advanced machine learning methods combined with large sets of health screening data provide opportunities for diagnostic value in human and veterinary medicine.

Hypothesis/objectives

To derive a model to predict the risk of cats developing chronic kidney disease (CKD) using data from electronic health records (EHRs) collected during routine veterinary practice.

Animals

A total of 106?251 cats that attended Banfield Pet Hospitals between January 1, 1995, and December 31, 2017.

Methods

Longitudinal EHRs from Banfield Pet Hospitals were extracted and randomly split into 2 parts. The first 67% of the data were used to build a prediction model, which included feature selection and identification of the optimal neural network type and architecture. The remaining unseen EHRs were used to evaluate the model performance.

Results

The final model was a recurrent neural network (RNN) with 4 features (creatinine, blood urea nitrogen, urine specific gravity, and age). When predicting CKD near the point of diagnosis, the model displayed a sensitivity of 90.7% and a specificity of 98.9%. Model sensitivity decreased when predicting the risk of CKD with a longer horizon, having 63.0% sensitivity 1 year before diagnosis and 44.2% 2?years before diagnosis, but with specificity remaining around 99%.

Conclusions and clinical importance

The use of models based on machine learning can support veterinary decision making by improving early identification of CKD.

SUBMITTER: Bradley R

PROVIDER: S-EPMC6872623 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Publications

Predicting early risk of chronic kidney disease in cats using routine clinical laboratory tests and machine learning.

Bradley Richard R Tagkopoulos Ilias I Kim Minseung M Kokkinos Yiannis Y Panagiotakos Theodoros T Kennedy James J De Meyer Geert G Watson Phillip P Elliott Jonathan J

Journal of veterinary internal medicine 20190926 6

<h4>Background</h4>Advanced machine learning methods combined with large sets of health screening data provide opportunities for diagnostic value in human and veterinary medicine.<h4>Hypothesis/objectives</h4>To derive a model to predict the risk of cats developing chronic kidney disease (CKD) using data from electronic health records (EHRs) collected during routine veterinary practice.<h4>Animals</h4>A total of 106 251 cats that attended Banfield Pet Hospitals between January 1, 1995, and Decem ...[more]

PMID: 31557361

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Project description:BackgroundThe recent use of artificial intelligence (AI) in medical research is noteworthy. However, most research has focused on medical imaging. Although the importance of laboratory tests in the clinical field is acknowledged by clinicians, they are undervalued in medical AI research. Our study aims to develop an early prediction AI model for pneumonia mortality, primarily using laboratory test results.Materials and methodsWe developed a mortality prediction model using initial laboratory results and basic clinical information of patients with pneumonia. Several machine learning (ML) models and a deep learning method-multilayer perceptron (MLP)-were selected for model development. The area under the receiver operating characteristic curve (AUROC) and F1-score were optimized to improve model performance. In addition, an ensemble model was developed by blending several models to improve the prediction performance. We used 80,940 data instances for model development.ResultsAmong the ML models, XGBoost exhibited the best performance (AUROC = 0.8989, accuracy = 0.88, F1-score = 0.80). MLP achieved an AUROC of 0.8498, accuracy of 0.86, and F1-score of 0.75. The performance of the ensemble model was the best among the developed models, with an AUROC of 0.9006, accuracy of 0.90, and F1-score of 0.81. Several laboratory tests were conducted to identify risk factors that affect pneumonia mortality using the "Feature importance" technique and SHapley Additive exPlanations. We identified several laboratory results, including systolic blood pressure, serum glucose level, age, aspartate aminotransferase-to-alanine aminotransferase ratio, and monocyte-to-lymphocyte ratio, as significant predictors of mortality in patients with pneumonia.ConclusionsOur study demonstrates that the ensemble model, incorporating XGBoost, CatBoost, and LGBM techniques, outperforms individual ML and deep learning models in predicting pneumonia mortality. Our findings emphasize the importance of integrating AI techniques to leverage laboratory test data effectively, offering a promising direction for advancing AI applications in medical research and clinical decision-making.

Dataset Information

Predicting early risk of chronic kidney disease in cats using routine clinical laboratory tests and machine learning.

Background

Hypothesis/objectives

Animals

Methods

Results

Conclusions and clinical importance

Publications

Predicting early risk of chronic kidney disease in cats using routine clinical laboratory tests and machine learning.

Similar Datasets

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