CanDetect: AI based early cancer detection using unstructured data

英国的癌症存活率落后于其他发达国家(1)，相比之下，每年有多达10,000例额外死亡(2)。延迟诊断被认为是造成这种情况的原因(3)。目前，将近一半的癌症在晚期才被诊断出来(4)。大约20%的患者在癌症诊断前看GP 3次或更多(7)，大约12%的可避免的诊断延误发生在这种情况下(6)。为了诊断癌症，必须考虑多种特征，从细微的发现到非特异性症状，这些特征记录在电子健康记录的不同部分。此外，在多达59%的记录中，这些数据是“隐藏的”：医生经常在严格的编码框架之外将它们写在自由文本记录中（10），无法通过常规手段识别。随着需求和复杂性的增加，初级保健临床医生在资源紧张的NHS中挣扎：全科医生平均每名患者9分钟(9)。在这种情况下，准确而快速地识别相关数据有可能出现错误：事实上，在病人最终被诊断出来之前，癌症的预测模式往往早就出现在记录中了。(8)从新的诊断中心、对初级保健的激励和区域癌症联盟，全国都在推动癌症的早期检测。与这些利益相关者的讨论表明，人们对支持初级保健癌症检测的智能方法有着强烈的兴趣和需求。在英国癌症研究2020年报告中，临床决策支持系统（CDSS）已被主要的国家癌症利益相关者确定为早期诊断的关键工具（11）。我们的目标是开发一个CDSS，通过使用健康记录中的关键信息来早期诊断癌症，减少延误或漏诊。我们将学习机器学习和自然语言处理，人工智能方法，使分析大型，复杂的数据集。我们将对CDSS进行评估，以确保它能够准确地检测癌症，然后评估其在实际临床实践中的表现，以评估其对全科医生癌症诊断的影响。通过利用强大的人工智能方法和现有工具无法获得的数据类型，我们相信我们可以在为临床医生和卫生服务提供支持的同时，为癌症诊断做出改变。最终目标是让患者得到更早的诊断，更有效的治疗，活得更长、更健康，通过降低发病率和医疗保健系统的压力，对经济产生下游影响。

UK cancer survival rates lag behind other developed countries(1), up to 10,000 excess deaths occur annually in comparison(2). Delayed diagnosis is thought to contribute to this(3). Currently nearly half of all cancers are diagnosed at a late stage(4).Approximately 20% of patients see their GP 3 or more times before their cancer diagnosis(7) with approximately 12% of avoidable diagnosis delay occurring in in this setting(6).To diagnose cancer, multiple features ranging from subtle findings to non-specific symptoms must be considered - these are recorded across different sections of the electronic health record. In addition, in up to 59% of records this data is 'hidden': doctors often write them in the free-text records(10) outside of rigid coding frameworks, where they cannot be identified by routine means.With increasing demand and complexity, primary care clinicians struggle in a resource-strained NHS: GPs have on average 9 minutes per patient(9). In this time, identifying this relevant data accurately and with speed has potential for error: indeed, often tell-tale patterns predictive of cancer are already present in the records long before a patient is finally diagnosed(8)There is a national drive towards earlier detection of cancer, ranging from new diagnostic centres, incentives for primary care and regional Cancer Alliances. Discussions with these stakeholders have shown us that there is a strong appetite and need for an intelligent method of supporting primary care cancer detection. Clinical decision support systems (CDSS) have been identified as a key tool for early diagnosis by major national cancer stakeholders in a Cancer Research UK 2020 report(11).Our aim is to develop a CDSS to diagnose cancer earlier, and reduce delayed or missed diagnoses, by using key information in health records. We will machine learning and natural language processing, AI methods which enable analysis of large, complex datasets.We will evaluate the CDSS to ensure it is able to detect cancers accurately, and then assess its performance in real-world clinical practice to assess its impact on cancer diagnosis by GPs.By leveraging powerful AI methods and the type of data unreachable by existing tools, we believe we can make a difference to cancer diagnosis whilst supporting clinicians and the health service. The end goal is for patients to be diagnoses earlier, treated more effectively, live longer, healthier lives with downstream impacts on the economy through reduced morbidity and pressures on the healthcare system.