目标为可以处理多答题问题的临床问答（QA）系统的开发和评估创建数据集。我们利用2018年国家NLP临床挑战（N2C2）语料库的注释关系来产生QA数据集。 1-0和1-o-n药物 - 理性关系形成了不可批售和多答案的条目，它代表了现有临床QA数据集缺乏的具有挑战性的情景。结果结果rxwhyqa dataSet包含91,440个QA条目，其中一半是未签发的，并且应答的21％（n = 19,269）需要多个答案。数据集符合社区审查的斯坦福问题应答DataSet（Squad）格式。讨论RXWhyQA对于比较需要处理零和多答案挑战的不同系统非常有用，要求对误报和假阴性答案的双重缓解。结论我们创建并共用了一个临床QA数据集，重点是多答题问题，以代表真实世界的情景。

在过去的几年中，临床笔记中的问题回答（QA）引起了很多关注。临床领域中现有的机器阅读理解方法只能处理有关单个临床文本的问题，并且无法检索有关多个患者及其临床笔记的信息。为了处理更复杂的问题，我们旨在从临床注释中创建知识库，以将不同的患者和临床笔记联系起来，并进行知识基础问题答案（KBQA）。根据N2C2数据集中可用的专家注释，我们首先创建了ClinicalKBQA数据集，其中包括大约9K QA对，并使用300多个问题模板涵盖了有关七个医学主题的问题。然后，我们研究了KBQA的一种基于注意力的方面推理（AAR）方法，并分析了答案的不同方面（例如，实体，类型，路径和上下文）对预测的影响。由于设计精良的编码器和注意力机制，AAR方法可实现更好的性能。从我们的实验中，我们发现这两个方面，类型和路径都使模型能够识别满足一般条件的答案，并产生较低的精度和更高的回忆。另一方面，各个方面，实体和上下文通过特定于节点的信息限制答案，并导致更高的精度和较低的回忆。

自动问题应答（QA）系统的目的是以时间有效的方式向用户查询提供答案。通常在数据库（或知识库）或通常被称为语料库的文件集合中找到答案。在过去的几十年里，收购知识的扩散，因此生物医学领域的新科学文章一直是指数增长。因此，即使对于领域专家，也难以跟踪域中的所有信息。随着商业搜索引擎的改进，用户可以在某些情况下键入其查询并获得最相关的一小组文档，以及在某些情况下从文档中的相关片段。但是，手动查找所需信息或答案可能仍然令人疑惑和耗时。这需要开发高效的QA系统，该系统旨在为用户提供精确和精确的答案提供了生物医学领域的自然语言问题。在本文中，我们介绍了用于开发普通域QA系统的基本方法，然后彻底调查生物医学QA系统的不同方面，包括使用结构化数据库和文本集合的基准数据集和几种提出的方​​法。我们还探讨了当前系统的局限性，并探索潜在的途径以获得进一步的进步。

临床问题应答（QA）旨在根据临床文本自动回答医疗专业人员的问题。研究表明，在一个语料库上培训的神经QA模型可能对来自不同研究所或不同患者组的新临床文本概括，其中大规模的QA对不容易获得模型再培训。为了解决这一挑战，我们提出了一个简单但有效的框架CliniQG4QA，它利用问题生成（QG）在新的临床环境中综合QA对，并在不需要手动注释的情况下提升QA模型。为了生成对训练QA模型至关重要的不同类型的问题，我们进一步引入了基于SEQ2SEQ的问题短语预测（QPP）模块，可以与大多数现有的QG模型一起使用以使生成多样化。我们的综合实验结果表明，我们的框架产生的QA​​语料库可以改善新上下文的QA模型（在完全匹配方面最高8％的绝对增益），QPP模块在实现增益方面发挥着至关重要的作用。

There is an increasing interest in developing artificial intelligence (AI) systems to process and interpret electronic health records (EHRs). Natural language processing (NLP) powered by pretrained language models is the key technology for medical AI systems utilizing clinical narratives. However, there are few clinical language models, the largest of which trained in the clinical domain is comparatively small at 110 million parameters (compared with billions of parameters in the general domain). It is not clear how large clinical language models with billions of parameters can help medical AI systems utilize unstructured EHRs. In this study, we develop from scratch a large clinical language model - GatorTron - using >90 billion words of text (including >82 billion words of de-identified clinical text) and systematically evaluate it on 5 clinical NLP tasks including clinical concept extraction, medical relation extraction, semantic textual similarity, natural language inference (NLI), and medical question answering (MQA). We examine how (1) scaling up the number of parameters and (2) scaling up the size of the training data could benefit these NLP tasks. GatorTron models scale up the clinical language model from 110 million to 8.9 billion parameters and improve 5 clinical NLP tasks (e.g., 9.6% and 9.5% improvement in accuracy for NLI and MQA), which can be applied to medical AI systems to improve healthcare delivery. The GatorTron models are publicly available at: https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/models/gatortron_og.

虽然通过简单的因素问题回答，文本理解的大量进展，但更加全面理解话语仍然存在重大挑战。批判性地反映出文本的人将造成好奇心驱动，通常是开放的问题，这反映了对内容的深刻理解，并要求复杂的推理来回答。建立和评估这种类型的话语理解模型的关键挑战是缺乏注释数据，特别是因为找到了这些问题的答案（可能根本不回答），需要高度的注释载荷的高认知负荷。本文提出了一种新的范式，使可扩展的数据收集能够针对新闻文件的理解，通过话语镜头查看这些问题。由此产生的语料库DCQA（疑问回答的话语理解）包括在607名英语文件中的22,430个问题答案对组成。 DCQA以自由形式，开放式问题的形式捕获句子之间的话语和语义链接。在评估集中，我们向问题上的问题提交了来自好奇数据集的问题，我们表明DCQA提供了有价值的监督，以回答开放式问题。我们还在使用现有的问答资源设计预训练方法，并使用合成数据来适应不可批售的问题。

自然语言处理（NLP）是一个人工智能领域，它应用信息技术来处理人类语言，在一定程度上理解并在各种应用中使用它。在过去的几年中，该领域已经迅速发展，现在采用了深层神经网络的现代变体来从大型文本语料库中提取相关模式。这项工作的主要目的是调查NLP在药理学领域的最新使用。正如我们的工作所表明的那样，NLP是药理学高度相关的信息提取和处理方法。它已被广泛使用，从智能搜索到成千上万的医疗文件到在社交媒体中找到对抗性药物相互作用的痕迹。我们将覆盖范围分为五个类别，以调查现代NLP方法论，常见的任务，相关的文本数据，知识库和有用的编程库。我们将这五个类别分为适当的子类别，描述其主要属性和想法，并以表格形式进行总结。最终的调查介绍了该领域的全面概述，对从业者和感兴趣的观察者有用。

在本文中，我们介绍了西班牙语中的第一个系统，能够回答有关人类使用药物的问题，称为Meqa（药物问题回答），由西班牙药品和健康产品（AEMPS，以西班牙语的首字母缩略词为本）创建的项目。提供医疗帮助的在线服务大大增殖，主要是由于Covid-19由于目前的大流行情况。例如，诸如Doctoralia，Savia或Saludonnet等网站提供医生答案类型咨询，其中患者或用户可以向医生和专家发送问题，并在不到24小时内接收答案。收到的许多问题与人类使用的药物有关，大多数都可以通过传单回答。因此，能够自动回答这些类型问题的MEQA等系统可以减轻这些网站的负担，并且对这些患者来说是很好的用途。

The meaningful use of electronic health records (EHR) continues to progress in the digital era with clinical decision support systems augmented by artificial intelligence. A priority in improving provider experience is to overcome information overload and reduce the cognitive burden so fewer medical errors and cognitive biases are introduced during patient care. One major type of medical error is diagnostic error due to systematic or predictable errors in judgment that rely on heuristics. The potential for clinical natural language processing (cNLP) to model diagnostic reasoning in humans with forward reasoning from data to diagnosis and potentially reduce the cognitive burden and medical error has not been investigated. Existing tasks to advance the science in cNLP have largely focused on information extraction and named entity recognition through classification tasks. We introduce a novel suite of tasks coined as Diagnostic Reasoning Benchmarks, DR.BENCH, as a new benchmark for developing and evaluating cNLP models with clinical diagnostic reasoning ability. The suite includes six tasks from ten publicly available datasets addressing clinical text understanding, medical knowledge reasoning, and diagnosis generation. DR.BENCH is the first clinical suite of tasks designed to be a natural language generation framework to evaluate pre-trained language models. Experiments with state-of-the-art pre-trained generative language models using large general domain models and models that were continually trained on a medical corpus demonstrate opportunities for improvement when evaluated in DR. BENCH. We share DR. BENCH as a publicly available GitLab repository with a systematic approach to load and evaluate models for the cNLP community.

Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation, but the quality bar for medical and clinical applications is high. Today, attempts to assess models' clinical knowledge typically rely on automated evaluations on limited benchmarks. There is no standard to evaluate model predictions and reasoning across a breadth of tasks. To address this, we present MultiMedQA, a benchmark combining six existing open question answering datasets spanning professional medical exams, research, and consumer queries; and HealthSearchQA, a new free-response dataset of medical questions searched online. We propose a framework for human evaluation of model answers along multiple axes including factuality, precision, possible harm, and bias. In addition, we evaluate PaLM (a 540-billion parameter LLM) and its instruction-tuned variant, Flan-PaLM, on MultiMedQA. Using a combination of prompting strategies, Flan-PaLM achieves state-of-the-art accuracy on every MultiMedQA multiple-choice dataset (MedQA, MedMCQA, PubMedQA, MMLU clinical topics), including 67.6% accuracy on MedQA (US Medical License Exam questions), surpassing prior state-of-the-art by over 17%. However, human evaluation reveals key gaps in Flan-PaLM responses. To resolve this we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars. The resulting model, Med-PaLM, performs encouragingly, but remains inferior to clinicians. We show that comprehension, recall of knowledge, and medical reasoning improve with model scale and instruction prompt tuning, suggesting the potential utility of LLMs in medicine. Our human evaluations reveal important limitations of today's models, reinforcing the importance of both evaluation frameworks and method development in creating safe, helpful LLM models for clinical applications.

培训和评估语言模型越来越多地要求构建元数据 - 多样化的策划数据收集，并具有清晰的出处。自然语言提示最近通过将现有的，有监督的数据集转换为多种新颖的预处理任务，突出了元数据策划的好处，从而改善了零击的概括。尽管将这些以数据为中心的方法转化为生物医学语言建模的通用域文本成功，但由于标记的生物医学数据集在流行的数据中心中的代表性大大不足，因此仍然具有挑战性。为了应对这一挑战，我们介绍了BigBio一个由126个以上的生物医学NLP数据集的社区库，目前涵盖12个任务类别和10多种语言。 BigBio通过对数据集及其元数据进行程序化访问来促进可再现的元数据策划，并与当前的平台兼容，以及时工程和端到端的几个/零射击语言模型评估。我们讨论了我们的任务架构协调，数据审核，贡献指南的过程，并概述了两个说明性用例：生物医学提示和大规模，多任务学习的零射门评估。 BigBio是一项持续的社区努力，可在https://github.com/bigscience-workshop/biomedical上获得。

We present TriviaQA, a challenging reading comprehension dataset containing over 650K question-answer-evidence triples. TriviaQA includes 95K questionanswer pairs authored by trivia enthusiasts and independently gathered evidence documents, six per question on average, that provide high quality distant supervision for answering the questions. We show that, in comparison to other recently introduced large-scale datasets, TriviaQA (1) has relatively complex, compositional questions, (2) has considerable syntactic and lexical variability between questions and corresponding answer-evidence sentences, and (3) requires more cross sentence reasoning to find answers. We also present two baseline algorithms: a featurebased classifier and a state-of-the-art neural network, that performs well on SQuAD reading comprehension. Neither approach comes close to human performance (23% and 40% vs. 80%), suggesting that Trivi-aQA is a challenging testbed that is worth significant future study. 1

The need for Question Answering datasets in low resource languages is the motivation of this research, leading to the development of Kencorpus Swahili Question Answering Dataset, KenSwQuAD. This dataset is annotated from raw story texts of Swahili low resource language, which is a predominantly spoken in Eastern African and in other parts of the world. Question Answering (QA) datasets are important for machine comprehension of natural language for tasks such as internet search and dialog systems. Machine learning systems need training data such as the gold standard Question Answering set developed in this research. The research engaged annotators to formulate QA pairs from Swahili texts collected by the Kencorpus project, a Kenyan languages corpus. The project annotated 1,445 texts from the total 2,585 texts with at least 5 QA pairs each, resulting into a final dataset of 7,526 QA pairs. A quality assurance set of 12.5% of the annotated texts confirmed that the QA pairs were all correctly annotated. A proof of concept on applying the set to the QA task confirmed that the dataset can be usable for such tasks. KenSwQuAD has also contributed to resourcing of the Swahili language.

The internet has had a dramatic effect on the healthcare industry, allowing documents to be saved, shared, and managed digitally. This has made it easier to locate and share important data, improving patient care and providing more opportunities for medical studies. As there is so much data accessible to doctors and patients alike, summarizing it has become increasingly necessary - this has been supported through the introduction of deep learning and transformer-based networks, which have boosted the sector significantly in recent years. This paper gives a comprehensive survey of the current techniques and trends in medical summarization

医学视觉问题应答（VQA）是医疗人工智能和流行的VQA挑战的组合。鉴于医学形象和在自然语言中的临床相关问题，预计医疗VQA系统将预测符号和令人信服的答案。虽然一般域VQA已被广泛研究，但医疗VQA仍然需要特定的调查和探索，因为它的任务特征是。在本调查的第一部分，我们涵盖并讨论了关于数据源，数据数量和任务功能的公开可用的医疗VQA数据集。在第二部分中，我们审查了医疗VQA任务中使用的方法。在最后，我们分析了该领域的一些有效的挑战，并讨论了未来的研究方向。

在该职位论文中，我们提出了一种新方法，以基于问题的产生和实体链接来生成文本的知识库（KB）。我们认为，所提出的KB类型具有传统符号KB的许多关键优势：尤其是由小型模块化组件组成，可以在组合上合并以回答复杂的查询，包括涉及“多跳跃”的关系查询和查询。“推论。但是，与传统的KB不同，该信息商店与常见的用户信息需求相符。

Objective: Social Determinants of Health (SDOH) influence personal health outcomes and health systems interactions. Health systems capture SDOH information through structured data and unstructured clinical notes; however, clinical notes often contain a more comprehensive representation of several key SDOH. The objective of this work is to assess the SDOH information gain achievable by extracting structured semantic representations of SDOH from the clinical narrative and combining these extracted representations with available structured data. Materials and Methods: We developed a natural language processing (NLP) information extraction model for SDOH that utilizes a deep learning entity and relation extraction architecture. In an electronic health record (EHR) case study, we applied the SDOH extractor to a large existing clinical data set with over 200,000 patients and 400,000 notes and compared the extracted information with available structured data. Results: The SDOH extractor achieved 0.86 F1 on a withheld test set. In the EHR case study, we found 19\% of current tobacco users, 10\% of drug users, and 32\% of homeless patients only include documentation of these risk factors in the clinical narrative. Conclusions: Patients who are at-risk for negative health outcomes due to SDOH may be better served if health systems are able to identify SDOH risk factors and associated social needs. Structured semantic representations of text-encoded SDOH information can augment existing structured, and this more comprehensive SDOH representation can assist health systems in identifying and addressing social needs.

在商业航空域中，有大量文件，例如事故报告（NTSB，ASRS）和监管指令（ADS）。有必要有效地访问这些多样化的存储库，以便在航空业中的服务需求，例如维护，合规性和安全性。在本文中，我们提出了一个基于深度学习的知识图（kg）基于深度学习（DL）的问题答案（QA）航空安全系统。我们从飞机事故报告中构建了知识图，并向研究人员社区贡献了这一资源。该资源的功效由上述质量保证系统测试和证明。根据上述文档构建的自然语言查询将转换为SPARQL（RDF图数据库的接口语言）查询并回答。在DL方面，我们有两个不同的质量检查模型：（i）BERT QA，它是通道检索（基于句子的）和问题答案（基于BERT）的管道，以及（ii）最近发布的GPT-3。我们根据事故报告创建的一系列查询评估系统。我们组合的QA系统在GPT-3上的准确性增长了9.3％，比Bert QA增加了40.3％。因此，我们推断出KG-DL的性能比单一表现更好。

We introduce a large scale MAchine Reading COmprehension dataset, which we name MS MARCO. The dataset comprises of 1,010,916 anonymized questionssampled from Bing's search query logs-each with a human generated answer and 182,669 completely human rewritten generated answers. In addition, the dataset contains 8,841,823 passages-extracted from 3,563,535 web documents retrieved by Bing-that provide the information necessary for curating the natural language answers. A question in the MS MARCO dataset may have multiple answers or no answers at all. Using this dataset, we propose three different tasks with varying levels of difficulty: (i) predict if a question is answerable given a set of context passages, and extract and synthesize the answer as a human would (ii) generate a well-formed answer (if possible) based on the context passages that can be understood with the question and passage context, and finally (iii) rank a set of retrieved passages given a question. The size of the dataset and the fact that the questions are derived from real user search queries distinguishes MS MARCO from other well-known publicly available datasets for machine reading comprehension and question-answering. We believe that the scale and the real-world nature of this dataset makes it attractive for benchmarking machine reading comprehension and question-answering models.

Multi-hop Machine reading comprehension is a challenging task with aim of answering a question based on disjoint pieces of information across the different passages. The evaluation metrics and datasets are a vital part of multi-hop MRC because it is not possible to train and evaluate models without them, also, the proposed challenges by datasets often are an important motivation for improving the existing models. Due to increasing attention to this field, it is necessary and worth reviewing them in detail. This study aims to present a comprehensive survey on recent advances in multi-hop MRC evaluation metrics and datasets. In this regard, first, the multi-hop MRC problem definition will be presented, then the evaluation metrics based on their multi-hop aspect will be investigated. Also, 15 multi-hop datasets have been reviewed in detail from 2017 to 2022, and a comprehensive analysis has been prepared at the end. Finally, open issues in this field have been discussed.