Flegel Thomas, Neumann Anja, Holst Anna-Lena, Kretzschmann Olivia, Loderstedt Shenja, Tästensen Carina, Gutmann Sarah, Dietzel Josephine, Becker Lisa Franziska, Kalliwoda Theresa, Weiß Vivian, Kowarik Madlene, Böttcher Irene Christine, Martin Christian
Department for Small Animals, Veterinary Faculty, Leipzig University, Leipzig, Germany.
Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Leipzig University, Leipzig, Germany.
Front Vet Sci. 2024 Jul 22;11:1406107. doi: 10.3389/fvets.2024.1406107. eCollection 2024.
Clinical reasoning in veterinary medicine is often based on clinicians' personal experience in combination with information derived from publications describing cohorts of patients. Studies on the use of scientific methods for patient individual decision making are largely lacking. This applies to the prediction of the individual underlying pathology in seizuring dogs as well. The aim of this study was to apply machine learning to the prediction of the risk of structural epilepsy in dogs with seizures.
Dogs with a history of seizures were retrospectively as well as prospectively included. Data about clinical history, neurological examination, diagnostic tests performed as well as the final diagnosis were collected. For data analysis, the Bayesian Network and Random Forest algorithms were used. A total of 33 features for Random Forest and 17 for Bayesian Network were available for analysis. The following four feature selection methods were applied to select features for further analysis: Permutation Importance, Forward Selection, Random Selection and Expert Opinion. The two algorithms Bayesian Network and Random Forest were trained to predict structural epilepsy using the selected features.
A total of 328 dogs of 119 different breeds were identified retrospectively between January 2017 and June 2021, of which 33.2% were diagnosed with structural epilepsy. An overall of 89,848 models were trained. The Bayesian Network in combination with the Random feature selection performed best. It was able to predict structural epilepsy with an accuracy of 0.969 (sensitivity: 0.857, specificity: 1.000) among all dogs with seizures using the following features: age at first seizure, cluster seizures, seizure in last 24 h, seizure in last 6 month, and seizure in last year.
Machine learning algorithms such as Bayesian Networks and Random Forests identify dogs with structural epilepsy with a high sensitivity and specificity. This information could provide some guidance to clinicians and pet owners in their clinical decision-making process.
兽医学中的临床推理通常基于临床医生的个人经验以及从描述患者队列的出版物中获取的信息。关于使用科学方法进行患者个体决策的研究在很大程度上是缺乏的。这同样适用于癫痫发作犬潜在个体病理的预测。本研究的目的是将机器学习应用于癫痫发作犬结构性癫痫风险的预测。
回顾性和前瞻性纳入有癫痫发作病史的犬。收集有关临床病史、神经学检查、进行的诊断测试以及最终诊断的数据。对于数据分析，使用了贝叶斯网络和随机森林算法。随机森林有33个特征、贝叶斯网络有17个特征可用于分析。应用以下四种特征选择方法来选择特征以进行进一步分析：排列重要性、前向选择、随机选择和专家意见。使用选定的特征对贝叶斯网络和随机森林这两种算法进行训练以预测结构性癫痫。
在2017年1月至2021年6月期间回顾性鉴定出119个不同品种的328只犬，其中33.2%被诊断为结构性癫痫。总共训练了89,848个模型。结合随机特征选择的贝叶斯网络表现最佳。使用以下特征，它能够在所有癫痫发作犬中以0.969的准确率（敏感性：0.857，特异性：1.000）预测结构性癫痫：首次癫痫发作年龄、成簇癫痫发作、过去24小时内癫痫发作、过去6个月内癫痫发作以及过去一年内癫痫发作。
贝叶斯网络和随机森林等机器学习算法能够以高敏感性和特异性识别患有结构性癫痫的犬。这些信息可为临床医生和宠物主人的临床决策过程提供一些指导。
