Prostate cancer is a heterogeneous disease, displaying a multitude of genetic alterations, histological patterns and clinical outcomes. This heterogen

前列腺癌是一种异质性疾病，表现出多种基因改变、组织学模式和临床结果。

• 批准号: 2432020
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2432020
• 关键词: Prostate; cancer; heterogeneous; disease; displaying

The main aims of this project is to leverage recent advances in Natural Language Processing (NLP) to develop end-to-end clinical support systems which can utilise longitudinal free text documents within Electronic Health Records (EHRs). EHRs will often contain historic records, pertaining to all interactions between a patient and the healthcare service, including freetext documents, such as referral letters and discharge notes. A notable challenge is being able to adequately capturing longitudinal representations of clinical texts. Common state-of-the-art models such as the Bidirectional Encoder Representations fromTransformers (BERT) can only process sequences of 512 tokens (Devlin et al., 2018), but a years worth of clinical text for a single patient can consist of more than 10, 000 tokens. Another more general problem relates to the transparency, interpretability and algorithmic fairness of large language models. Therefore this project aims to develop methods and protocol to enhance these aspects.One proposed approach to representing sequential free-textbuilds upon the signature of a path, a non-parametric approach to extracting features from data in the form of tensors(Chevyrev and Kormilitzin, 2016). Loosely speaking, a signature is a collection of statistics about a stream of data that are time invariant, and has universal non-linearity, whereby it is sufficient to capture all possibly nonlinear functions of the original data: allowing aunique approach to representing complex sequential data. Combining signature techniques with strategies to address the limited ability of attention mechanisms in common transformer basedmodels, such as spare-attention mechanisms (Zaheer et al., 2020). This hybrid approach should allow efficient computation and representations of patients clinical text history, usable in a numberof relevant downstream tasks.Another approach will embrace a new paradigm shift in NLP research, named prompt-learning. Traditional approaches tomany downstream tasks involved taking a model such as BERT pre-trained on masked language modelling (MLM) and next sentence prediction (NSP) followed by a fine-tuning process on downstream tasks. Prompt-learning instead reconstructs the pretraining to embed the downstream task, encouraging the model to implicitly learn the desired task. The use of prompt-learning in a clinical domain has not been documented yet, thus provides a great opportunity.The proposed new methodologies will be developed and implemented in consultation with clinicians and will address real clinical use-cases. Specifically, the language models will be trained on a large collection of free-text notes from secondary care UKCRIS database to help triage patients to specialist teams. Other strands will explore the feasibility of identifying patients for clinical trials and identification of self-harm. The feasibility of translation of the developed methodology and models will be tested beyond the scope of mental health under the support provided by the EPSRC CDT in Health Data Science.This project falls within the EPSRC healthcare technologies research area.

该项目的主要目的是利用自然语言处理（NLP）的最新进展来开发端到端的临床支持系统，该系统可以利用电子健康记录（EHR）中的纵向自由文本文档。EHR通常包含与患者和医疗服务之间的所有交互有关的历史记录，包括自由文本文档，例如转诊信和出院记录。一个值得注意的挑战是能够充分捕获临床文本的纵向表示。常见的最先进的模型，诸如来自变压器的双向编码器表示（BERT），只能处理512个令牌的序列（Devlin等人，2018），但单个患者一年的临床文本可能包含超过10，000个代币。另一个更普遍的问题涉及大型语言模型的透明性、可解释性和算法公平性。因此，该项目旨在开发方法和协议来增强这些方面。一种提出的表示顺序自由文本的方法建立在路径的签名上，这是一种以张量形式从数据中提取特征的非参数方法（Chevyrev和Kormilitzin，2016）。不严格地说，签名是关于数据流的统计数据的集合，这些数据是时不变的，并且具有普遍的非线性，因此足以捕获原始数据的所有可能的非线性函数：允许一种独特的方法来表示复杂的序列数据。将签名技术与策略相结合，以解决常见的基于Transformer的模型中注意力机制的有限能力，例如备用注意力机制（Zaheer等人，2020年）。这种混合方法应该允许有效的计算和表示患者的临床文本历史，可用于许多相关的下游tasks.Another方法将拥抱一个新的范式转变，在NLP研究，命名为自动学习。传统的下游任务处理方法包括采用一个模型，例如在掩蔽语言模型（MLM）和下一句预测（NSP）上预先训练的BERT，然后对下游任务进行微调。相反，主动学习重构预训练以嵌入下游任务，鼓励模型隐式学习所需的任务。在临床领域中使用自动学习还没有记录，因此提供了一个很好的机会。拟议的新方法将在与临床医生协商后开发和实施，并将解决真实的临床用例。具体来说，语言模型将在二级护理UKCRIS数据库的大量自由文本笔记上进行训练，以帮助将患者分类到专家团队。其他股将探讨确定临床试验和自我伤害的识别患者的可行性。在EPSRC健康数据科学CDT的支持下，将在心理健康范围之外测试所开发的方法和模型的可行性。该项目属于EPSRC医疗保健技术研究领域的福尔斯。