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

抗菌素耐药性(AMR)是人类和动物健康面临的一项重大挑战，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，特别是与SAVSNET小组合作，SAVSNET是一家兽医生物信息学实验室，自2014年以来一直在咨询点整理电子健康记录。今天，该数据集已经积累了700多万条记录，每条记录都包含年龄、性别、物种、药理处方等条目，更重要的是，还提供了免费的文本说明，供兽医从业者进一步详细说明咨询情况。该小组之前的工作源于使用正则表达式的监督策略，尽管这对于针对单一疾病或疾病的高度特异性目标是成功的，但不适合更广泛的分析。Sean Farrell先生加入了Durham大学创新计算组的Noura Al Moubayed博士实验室，实现了这种新型自然语言处理解决方案的集成，并将其提炼成新兴的兽医生物信息学领域。该项目旨在体现这一接口，以识别可能在大型伴随动物临床记录数据集中可用的特征和信号，并开发和应用尖端机器学习方法来获得重要见解。为了对抗AMR，我们需要了解影响兽医临床医生抗菌药物处方的因素。该项目旨在使用自然语言过程来利用这个庞大的数据集，它可能是减少抗菌素使用的关键，并最终停滞不前的AMR的发展。该项目从监督学习方法开始，使用兽医临床医生在会诊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长，即为任何兽医临床叙述确定广泛的疾病分类器，并有可能不再要求兽医从业者自行标记。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，以指示具体的疾病和状况。因此，模型的可解释性对于理解模型选择一个标签而不是另一个标签的临床特征是重要的，并且理论上可以发现以前与它们无关的疾病的新症状。重要的是要了解为什么兽医认为有必要开抗生素，他们的决定是否合理，如果某些疾病被不必要地过度开出而不是其他疾病，这一点将变得明显，并可能决定未来何时需要或何时应该避免抗生素的指导方针。这一更广泛的分析还可能发现导致疾病需要抗菌药物的新风险因素，并可以将疾病视为导致某一事件的步骤的集合，而不是将所有感染视为急性感染。在这个日益增长的抗菌素耐药性世界中，任何抗生素处方都是必要的，而且没有其他手段，这里产生的模型可能会形成未来的工具，为兽医提供第二意见，以确定这种情况是否证明处方是合理的，或者替代解决方案是否更合适。抗菌剂是一种改变了历史进程的无价工具，然而，回到抗菌素之前的世界是一个并不遥远的现实。兽医在保持良好的抗菌药物管理方面与人类药物具有同等的责任，我们希望这个项目能够提高人们的意识，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.