P109 Early diagnosis of inflammatory arthritis (IA) using machine learning analysis of GP referral letters and blood tests to improve pre-hospital referral triage
P109 Early diagnosis of inflammatory arthritis (IA) using machine learning analysis of GP referral letters and blood tests to improve pre-hospital referral triage
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P109 使用全科医生转诊信和血液检测的机器学习分析来早期诊断炎症性关节炎 (IA)，以改善院前转诊分诊
DOI：
10.1093/rheumatology/keac133.108
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发表时间：
2022
期刊：
影响因子：
5.5
通讯作者：
Bradlow A
中科院分区：
文献类型：
--
作者：
Bradlow A
背景/目的通过转诊信诊断炎症性关节炎（IA）具有挑战性。我们的目的是开发一个模型，整合GP转诊信数据和转诊前血液检测结果（BTR），以确定患者表型的集群在转诊时预测患者将患有三种类型IA中的任何一种的（特征）（类风湿性关节炎，血清阴性炎性关节炎和银屑病关节炎）在我们部门的DMARD监测数据库中，将IA与同期输入同一数据库的其他炎性流变学疾病（OIC）患者的转诊信进行比较。我们开发和使用新的自然语言处理（NLP）方法的基础上，双向编码器表示变压器（BERT），以确定患者IA和OICsfor triage purposes.ResultsWe分析了867 OIC患者和267 IJD字母使用我们的NLP方法。数据增强用于解决此实际应用中的不平衡和小数据挑战。我们的方法只使用GP转介信实现了80%的整体准确性（使用确诊为结果）在确定IJD和OCI，达到86%的灵敏度和84%的精度（再现性）在检测OIC，与71%的灵敏度和68%的精度在检测IA patients.ConclusionOur NLP方法单独确定OIC与>80%的灵敏度和精度。在数据科学方面，我们基于NLP的方法因此具有高灵敏度，可用于识别可能需要DMARD治疗的各种OIC。我们的方法可能有很大的实用价值，如果GP转介信可以在源头分析。为了获得高特异性和精确度，需要非常大量的数据来训练NLP模型;我们有更多的OIC字母，而不是来自我们部门其他数据库的IA字母。目前难以获得初级保健转诊信的可分析文本，对电子病历（EPR）中转诊信的照片进行NLP是一项重大挑战。必须消除NHS数据系统中的这些重大瓶颈，以便能够以兼容格式直接访问和分析全科医生转诊信。BTR将更容易在EPR中进行分析。转诊前NLP和BTR的联合分析可能会提高对疑似早期IA的GP转诊的自动分诊效率。对全科医生转诊的机器学习分析可能允许在第一时间将正确的患者分诊到正确的诊所，协助需求管理和紧张的风湿病诊所的能力，并通过确保正确的诊所预约来改善患者体验。目前正在进一步开展工作，以提高该系统的准确性，以期将其纳入我们先进的建议和指导分诊程序。布拉德洛：没有。王：没有。李康：没有。没有，A.T.Y.陈：发言人主席团成员; UCB、诺华、赛诺菲、艾伯维、赛尔金和杨森。
Background/AimsThe diagnosis of inflammatory arthritis (IA) from referral letters is challenging. We aimed to develop a model integrating GP referral letter data and pre-referral blood test results (BTR) to identify clusters of patient phenotypes (characteristics) that predict at referral that patients will have any of three types of IA (rheumatoid arthritis, seronegative inflammatory arthritis and psoriatic arthritis) requiring early DMARD treatment.MethodsThe anonymised text of original GP referral letters of patients on our departmental DMARD monitoring database with diagnoses of IA was compared with referral letters of patients with other inflammatory rheumatological conditions (OICs) entered into the same database over the same period. We developed and used novel natural language processing (NLP) methods based on bidirectional encoder representations from transformers (BERT) to identify patients with IA and OICs for triage purposes.ResultsWe have analysed 867 OIC patients and 267 IJD letters using our NLP methods. Data augmentation was used to address imbalance and small data challenges in this real-world application. Our method using only GP referral letters achieved overall accuracy (using confirmed diagnosis as the outcome) of 80% in identifying IJD and OCIs, reaching 86% sensitivity and 84% precision (reproducibility) at detecting OICs, compared with 71% sensitivity and 68% precision in detecting IA patients.ConclusionOur NLP methods alone identified OIC with >80% sensitivity and precision. In data science terms our NLP based method thus has high sensitivity for identifying a wide variety of OICs likely to need DMARD treatment. Our methodology may have great practical value if GP referral letters can be analysed at source. For high specificity and precision very large amounts of data are required to train the NLP model; we had many more OIC letters than IA letters available in analysable formats from other databases in our department. Analysable text of referral letters from primary care is currently difficult to obtain and performing NLP on photographs of referral letters in electronic patient records (EPRs) is a major challenge. These significant bottlenecks in NHS data systems must be removed to allow direct access and analysis of GP referral letters in the compatible format. BTR will be easier to analyse in the EPRs. Combined analysis of pre-referral NLP and BTR may increase the efficiency of automated triage of GP referrals with suspected early IA. Machine learning analysis of GP referrals potentially allows triage of the right patient into the right clinic first time, assisting demand management and capacity in stretched rheumatology clinics and improving patient experience by ensuring the right clinic appointment. Further work is underway to improve the precision of the system with a view to embedding this into our advanced Advice and Guidance triage process.DisclosureA. Bradlow:None.B. Wang:None.W. Li:None.E. Bazuaye:None.A.T.Y. Chan:Member of speakers’ bureau; UCB, Novartis, Sanofi, AbbVie, Celgene and Janssen.