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

NLP 的进步有助于更好地了解伴侣动物电子健康记录兽医护理中抗生素使用的驱动因素。

• 批准号: 2611611
• 金额: --
• 依托单位: Durham University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2611611
• 关键词: NLP; advances; better; understand; drivers

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.

抗菌素耐药性（AMR）是人类和动物健康面临的一个关键挑战，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的合作，特别是与SAVSNET小组的合作，SAVSNET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，该数据集已积累了超过700万条记录，每条记录包含年龄、性别、物种、药理学处方等条目，至关重要的是，兽医从业人员还可以使用自由文本叙述来进一步详细说明咨询情况。这个小组以前的工作来自于使用正则表达式的监督策略，尽管这对于高度特异性地针对单一疾病或病症是成功的，但它不适合更广泛的分析。Sean Farrell先生加入了达勒姆大学创新计算小组内的Noura Al Mackayed博士实验室，以实现这种新颖的自然语言处理解决方案的整合，并将其提炼到兽医生物信息学的新兴领域。该项目旨在体现这一接口，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。该项目旨在利用自然语言过程来利用这个大型数据集，这可能是减少抗菌药物使用并最终停滞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调整的BERT模型，该模型具有为任何兽医临床叙述确定广泛疾病分类的特定专业，有可能不再需要兽医从业者自我标签。取决于成功，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状况。因此，模型的可解释性对于理解哪些临床特征导致模型选择一个标签而不是另一个标签非常重要，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，以及他们的决定是否合理，如果某些疾病不必要地超过其他疾病，这将变得很明显，并可能塑造未来的抗生素使用指南。这种更广泛的分析还可能发现导致需要抗菌药物的疾病的新风险因素，并且可以将疾病视为导致事件的步骤的汇编，而不是将所有感染视为急性感染。在这个抗菌药物耐药性日益严重的世界中，任何抗生素处方都是必要的，并且没有其他手段，这一点非常重要，这里产生的模型有可能成为未来的工具，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，或者是否有其他解决方案更合适。抗菌剂是改变历史进程的宝贵工具，然而回到抗菌剂之前的世界是一个不那么遥远的现实。兽医在保持良好的抗菌药物管理方面与人类医学一样负有同等责任，我们希望该项目能够提高认识并减少不必要的处方。