NLP advances to better understand drivers of antibiotic use in veterinary care of companion animal electronic health records.

抗微生物药物耐药性（AMR）是人类和动物健康面临的重大挑战，需要临床和数据科学各学科的协调努力。该项目是与利物浦大学感染、兽医和生态科学研究所合作的，特别是与SAVSNET小组合作，该小组是一个兽医生物信息学实验室，自2014年以来一直在整理电子健康记录。如今，该数据集已经积累了超过700万条记录，每条记录都包含年龄、性别、物种、药理学处方等条目，最重要的是，它还包含一个免费的文本叙述，供兽医从业者进一步详细介绍咨询情况。这个小组以前的工作是从使用正则表达式的监督策略中产生的，尽管这对于单一疾病或病症的高度特异性靶向是成功的，但它不适合更广泛的分析。Sean Farrell先生加入了杜伦大学创新计算小组的Noura Al Moubayed博士实验室，实现了这种新颖的自然语言处理解决方案的整合，并将其提取到新兴的兽医生物信息学领域。该项目旨在体现该接口，以识别可能在大型伴侣动物临床记录数据集中可用的特征和信号，并开发和应用尖端的机器学习方法来获得重要的见解。为了对抗抗生素耐药性，我们需要了解影响兽医临床医生使用抗菌药物处方的因素。该项目旨在利用自然语言过程来利用这个庞大的数据集，这可能是减少抗菌素使用并最终停滞AMR发展的关键。该项目从监督学习方法开始，使用兽医临床医生在咨询期间应用的通用标签来制定一个经过调整的BERT模型，该模型具有特定的专业知识，可以为任何兽医临床叙述识别广泛的疾病分类器，并有可能不再要求兽医从业人员自我标记。依赖于成功，这些分类器的分辨率可以增加到这些简单标签的范围之外，以指示特定的疾病和条件。因此，模型的可解释性对于理解是什么临床特征导致模型选择一个标签而不是另一个标签很重要，并且理论上可以发现以前与之无关的疾病的新症状。重要的是要理解为什么兽医从业者认为有必要开抗生素，以及他们的决定是否合理，如果某些疾病不必要地过度开抗生素，这将变得很明显，并可能影响未来关于何时需要抗生素或何时应该避免抗生素的指导方针。这种更广泛的分析还可能发现导致需要抗微生物药物的疾病的新风险因素，并可以将疾病视为导致某一事件的步骤汇编，而不是将所有感染视为急性感染。在这个抗菌素耐药性日益增加的世界里，任何抗生素处方都是必要的，而且没有其他手段，这一点非常重要。这里产生的模型可能会形成未来的工具，为兽医提供第二种意见，以确定病情是否证明处方是合理的，或者是否有替代解决方案更合适。抗微生物药物是一种改变历史进程的宝贵工具，然而，回到抗微生物药物出现之前的世界已不是那么遥远的现实。兽医从业人员在保持良好的抗菌药物管理方面与人类药物同样负有责任，我们希望这个项目能够提高认识并减少不必要的处方。

Antimicrobial resistance (AMR) is a critical challenge for human and animal health that requires a coordinated endeavour across the disciplines of clinical and data science. The project is a collaboration with the Institute of Infection, Veterinary and Ecological Sciences at the University of Liverpool specifically with the SAVSNET group, a veterinary bioinformatics lab who have been collating electronic health records at the point of consultation since 2014. Today, the dataset has amassed over 7 million records with each record containing entries such as age, sex, species, pharmacological prescriptions and crucially a free text narrative for the veterinary practitioners to further detail the consultation. Previous works from this group have arisen from supervised strategies using regular expressions, although this has been successful for highly specific targeting of a singular disease or condition it is not appropriate for a wider analysis. Mr Sean Farrell has joined Dr Noura Al Moubayed lab within the Innovative Computing Group at Durham University to achieve this integration of novel Natural Language Processing solutions and distilling them into the emerging field of veterinary bioinformatics. The project seeks to embody this interface to recognise the features and signals that might be available in a large companion animal clinical records dataset and to develop and apply cutting-edge machine-learning methodologies to derive important insights.To combat AMR, we need to understand the factors that influence antimicrobial prescription by veterinary clinicians. The project aims to use Natural Language Processes in capitalising on this large dataset which may hold the key to reducing antimicrobial usage and ultimately to stagnate the development of AMR. The project begins with supervised learning approaches, using a generic label applied by veterinary clinicians during the consultations to formulate a tuned BERT model with specific speciality in identifying broad disease classifiers for any veterinary clinical narrative with a potential possibility to no longer requiring veterinary practitioners to self-label. Dependent on the success, the resolution of these classifiers could be increased beyond the scope of these simple labels into indicating specific diseases and conditions. Explainability of the models is therefore important to understand what clinical features resulted in the model selecting a label over another, and theoretically could uncover new symptoms of diseases not previously associated with them. It is important to understand why a veterinary practitioner felt it were necessary to prescribe an antibiotic and whether their decision is justified, it will become apparent if certain diseases are unnecessarily overprescribed over others and may shape future guidelines on when antibiotics are necessary or when they should be avoided. This broader analysis may also uncover new risk factors resulting in disease requiring antimicrobials and can view diseases as a compilation of steps leading to an event rather than considering all infections as being acute.It is highly important in this increasingly antimicrobial resistance world that any prescription of an antibiotic is both necessary and without other means, it is a possibility that the models produced here may formulate into future tools to provide a second opinion to vets to whether the condition justifies the prescription or whether an alternative solution is more appropriate. Antimicrobials are an invaluable tool that has changed the course of history, however the return to a pre antimicrobial world is a not-so-distant reality. Veterinary practitioners are as equally responsible in maintaining good antimicrobial stewardship as human medicine and we would hope this project can increase awareness and reduce unnecessary prescriptions.