Automatic identification of clinical trials for which a patient is eligible
is complicated by the fact that trial eligibility is stated in natural
language. A potential solution to this problem is to employ text classification
methods for common types of eligibility criteria. In this study, we focus on
seven common exclusion criteria in cancer trials: prior malignancy, human
immunodeficiency virus, hepatitis B, hepatitis C, psychiatric illness,
drug/substance abuse, and autoimmune illness. Our dataset consists of 764 phase
III cancer trials with these exclusions annotated at the trial level. We
experiment with common transformer models as well as a new pre-trained clinical
trial BERT model. Our results demonstrate the feasibility of automatically
classifying common exclusion criteria. Additionally, we demonstrate the value
of a pre-trained language model specifically for clinical trials, which yields
the highest average performance across all criteria.

通过使用文本分类方法，我们研究了肿瘤试验中常见的七种排除标准，即先前恶性肿瘤、人免疫缺陷病毒、乙型肝炎、丙型肝炎、精神疾病、药物 / 物质滥用和自身免疫性疾病，结果显示自动分类常见排除标准的可行性，并展示了针对临床试验的预训练语言模型在所有标准上取得的最优性能。

癌症临床试验资格标准的文本分类

Text Classification of Cancer Clinical Trial Eligibility Criteria

Interventional cancer clinical trials are generally too restrictive, and some
patients are often excluded on the basis of comorbidity, past or concomitant
treatments, or the fact that they are over a certain age. The efficacy and
safety of new treatments for patients with these characteristics are,
therefore, not defined. In this work, we built a model to automatically predict
whether short clinical statements were considered inclusion or exclusion
criteria. We used protocols from cancer clinical trials that were available in
public registries from the last 18 years to train word-embeddings, and we
constructed a~dataset of 6M short free-texts labeled as eligible or not
eligible. A text classifier was trained using deep neural networks, with
pre-trained word-embeddings as inputs, to predict whether or not short
free-text statements describing clinical information were considered eligible.
We additionally analyzed the semantic reasoning of the word-embedding
representations obtained and were able to identify equivalent treatments for a
type of tumor analogous with the drugs used to treat other tumors. We show that
representation learning using {deep} neural networks can be successfully
leveraged to extract the medical knowledge from clinical trial protocols for
potentially assisting practitioners when prescribing treatments.

本文旨在构建一个自动预测短临床语句是否被视为纳入或排除标准的模型，为此我们使用癌症临床试验协议训练词嵌入，构建了一个标记为合格或不合格的 6M 短文本数据集，并使用深度神经网络对文字分类器进行训练，结果表明，利用深度神经网络的表示学习从临床试验协议中提取医学知识可为医师开方治疗提供帮助。同时，我们还能够识别出一种肿瘤的等效治疗方法。