介绍 (Introduction)

The ubiquitous adoption of electronic health records in hospitals and other healthcare facilities generates vast real-world information, which is very valuable for conducting clinical research. Over the past many years, electronic health records (EHR) systems are been widely adopted across clinics and hospitals. Analysis of this huge data is the foundation to provide improved healthcare to the patients However, manual review of this vast amount of data generated from multiple sources is costly and very time-consuming. It brings huge challenges while attempting to review this data meaningfully. Hence, the role of artificial intelligence (AI) techniques are becoming important in enhancing clinical research and care. As a large number of EHR are locked in clinical narratives, Natural Language Processing (NLP) and Deep Learning (DL) techniques have been leveraged to extract information. While computer vision techniques can be used for medical imaging, NLP can be used for analysing unstructured information in the EHR, reinforcement learning techniques can be used in the context of robotics-assisted operations. NLP algorithms can be used to identify clinically relevant phenotypes while analysing text and determining the grammatical relationships between phrases. Rule-based NLP techniques can be used to get high sensitivity (identification of a large proportion of true cases) and high positive predictive value in clinical records.

电子病历在医院和其他医疗机构中的普遍采用产生了大量的现实世界信息，这对于进行临床研究非常有价值。 在过去的多年中，电子健康记录(EHR)系统在诊所和医院中得到广泛采用。 分析这些巨大的数据是为患者提供更好医疗保健的基础。但是，手动检查从多个来源生成的大量数据非常昂贵且非常耗时。 在试图有意义地审查这些数据时，它带来了巨大的挑战。 因此，人工智能(AI)技术的作用在增强临床研究和护理方面变得越来越重要。 由于大量的EHR被锁定在临床叙述中，因此自然语言处理(NLP)和深度学习(DL)技术已被用来提取信息。 虽然计算机视觉技术可以用于医学成像，但是NLP可以用于分析EHR中的非结构化信息，而强化学习技术可以用于机器人辅助操作的环境。 NLP算法可用于识别临床相关的表型，同时分析文本并确定短语之间的语法关系。 基于规则的NLP技术可用于在临床记录中获得较高的敏感性(鉴定大量真实病例)和较高的阳性预测价值。

Healthcare is one of the domains where computer science is becoming very supportive of varied tasks. AI is increasingly being adopted across the healthcare industry ranging from basic level practices to specialization, and lots of the foremost exciting AI applications leverage language processing (NLP). The capabilities of these AI techniques potentially enable the identification of distinctive clinical characteristics among patients which further helps in clinical care and minimizing methodological heterogeneity in medical research concerning various health diseases.

医疗保健是计算机科学非常支持各种任务的领域之一。 从基础水平实践到专业化，整个医疗行业都越来越多地采用AI，并且许多最令人兴奋的AI应用都利用语言处理(NLP)。 这些AI技术的功能可能使人们能够识别出患者中独特的临床特征，从而进一步帮助临床护理并最大程度地减少有关各种健康疾病的医学研究中的方法异质性。

抽象 (Abstract)

Day by day the health care domain is generating millions of records of patients in a structured and unstructured way. By applying deep learning techniques, it can be converted into a well-structured form i.e. Electronic Health Record (EHR). Decision making is one of the key parts in the Healthcare domain. In the decision making process doctors must refer to many data like laboratory reports, diagnosis reports, medical images, demographic information about the patient, clinical notes, and map it collectively with the concepts of medical science. Here AI especially Natural Language Processing and deep learning can be helpful in many ways.

卫生保健领域每天都以结构化和非结构化的方式生成数百万患者的记录。 通过应用深度学习技术，可以将其转换为结构良好的形式，即电子健康记录(EHR)。 决策是医疗保健领域的关键部分之一。 在决策过程中，医生必须参考许多数据，例如实验室报告，诊断报告，医学图像，有关患者的人口统计学信息，临床记录，并与医学概念一起进行映射。 在这里，人工智能(尤其是自然语言处理和深度学习)可以在许多方面提供帮助。

The objective of this article is to depict the importance of Deep Learning and Natural Language processing EHRs. Based on EHR, the doctor can take quick decisions in case of an emergency. Apart from that, it can also be effective in clinical predictions, detect disease at an earlier stage, forecasting future need of regular check-ups, predictions of hospitalization soon if required. The provision of an enormous amount of clinical information particularly EHR has stimulated the expansion of deep learning techniques that assist within the rapid analysis of patient data.

本文的目的是描述深度学习和自然语言处理EHR的重要性。 基于EHR，在紧急情况下，医生可以Swift做出决定。 除此之外，它还可以有效地用于临床预测，早期发现疾病，预测未来定期检查的需求以及在需要时尽快预测住院的预测。 提供大量的临床信息(尤其是EHR)刺激了深度学习技术的发展，这些技术有助于对患者数据进行快速分析。

什么是自然语言处理？ (What is Natural Language Processing?)

Natural language processing (NLP) focuses on analyzing text and speech to infer meaning from words. Recurrent Neural Networks (RNNs) — deep learning algorithms play a key role in processing sequential inputs like language, speech, and time-series data. (Andre Esteva et al., 2019). Deep learning is a subset of machine learning having the capacity of learning unsupervised data from unstructured or unlabeled data. On the other side deep learning will be able to learn optimal features from available data without human intervention.

自然语言处理(NLP)专注于分析文本和语音以推断单词的含义。 递归神经网络(RNN)-深度学习算法在处理诸如语言，语音和时间序列数据之类的顺序输入中起着关键作用。 (Andre Esteva等人，2019)。 深度学习是机器学习的一个子集，能够从非结构化或未标记的数据中学习非监督数据。 另一方面，深度学习将能够从可用数据中学习最佳功能，而无需人工干预。

Natural language processing is used to describe the process of using computer algorithms to identify key elements in everyday language and extract meaning from unstructured spoken or written input. NLP requires skills in artificial intelligence, computational linguistics, and other machine learning disciplines. (Jennifer Bresnick et al., 2019)

自然语言处理用于描述使用计算机算法识别日常语言中的关键元素并从非结构化的口头或书面输入中提取含义的过程。 NLP要求具备人工智能，计算语言学和其他机器学习学科的技能。 (詹妮弗·布雷斯尼克(Jennifer Bresnick)等人，2019)

There are two approaches to NLP.

NLP有两种方法。

1) A rule-based approach where computer follows predefined rules in the program

1)一种基于规则的方法，其中计算机遵循程序中的预定义规则

2) Machine Learning-based approach where we can have supervised and unsupervised learning methods. In supervised learning, the computer learns latent rules through human guidance called annotation while in unsupervised learning there is no human interaction.

2)基于机器学习的方法，我们可以有监督和无监督的学习方法。 在监督学习中，计算机通过称为“注释”的人工指导学习潜在规则，而在非监督学习中，则没有人与人的交互。

NLP algorithms, first extract information or concepts from EHRs, then process extracted information, and finally classify patients into a subgroup as per rules and learners. Procedures for NLP are complex because it consists of multiple techniques together. NLP will map phrases or words to concepts of interest, and it needs careful pre-processing of text and it will be converted to document from the natural form. (Young Juhn et al., 2019)

NLP算法首先从EHR中提取信息或概念，然后处理提取的信息，最后根据规则和学习者将患者分类为一个亚组。 NLP的过程很复杂，因为它包含多种技术。 NLP会将短语或单词映射到您感兴趣的概念，并且需要对文本进行仔细的预处理，并将其从自然形式转换为文档。 (Young Juhn等人，2019)

A few examples of low-level NLP tasks (text pre-processing) include the following:

低级NLP任务(文本预处理)的一些示例包括：

(1) Sentence boundary detection, which is usually defined by a period

(1)句子边界检测，通常由句点定义

(2) Tokenization (breaking a sentence into individual tokens)

(2)标记化(将句子分解为单个标记)

(3) Stemming (reducing word into a root form)

(3)词干(将词还原成词根形式)

(4) Lemmatization (process mapping a token)

(4)合法化(映射令牌的过程)

A few examples of higher-level NLP tasks include the following:

更高级别的NLP任务的一些示例包括：

(1) Named entity recognition

(1)命名实体识别

(2) Setting up negation rules

(2)设定否定规则

The below figure depicts the text preprocessing and classification of an asthma patient.

下图描述了哮喘患者的文本预处理和分类。

深度学习/ NLP在医疗保健中的作用 (Role of Deep Learning/NLP in Healthcare)

Basically, in terms of users, we can categorize the healthcare information coming from below four sources -

基本上，就用户而言，我们可以对来自以下四个来源的医疗保健信息进行分类-

1) Doctors

1)医生

2) Patient

2)病人

3) Paramedical staff

3)医护人员

4) Pharmaceuticals

4)药品

Every post-process depends on the diagnosis of a disease. If the disease is identified properly then one can get proper treatment. Sometimes the situation of patients gets serious due to delays in decision making.

每个后期处理都取决于疾病的诊断。 如果疾病被正确识别，则可以得到适当的治疗。 有时由于决策延迟而使患者的情况变得严重。

Below figure represents, deep learning is an approach where there is a large scale network that is going to accept a variety of input data types like text, image, audio, time-series data, etc. for each data type learns a useful features in its lower level towers. The data from each pillar is then merged and flows through higher levels, allowing the Deep Neural Network to reach to conclusion based on reasoning and evidence across data types.

下图表示，深度学习是一种方法，其中存在一个大规模网络，该网络将接受各种输入数据类型，例如文本，图像，音频，时间序列数据等。每种数据类型在下层的塔楼 然后，来自各个Struts的数据将合并并流经更高的层次，从而使深度神经网络可以根据各种数据类型的推理和证据得出结论。

Natural language processing can help healthcare in information extraction, unstructured data to structured data conversion, Document categorization, and summarization. Ultimately it will reduce administrative cost by efficient billing and accurate prior authorization approval. It also going to create Medical Value by providing support for ineffective clinical decisions and streamlined medical policy assessment etc. (Suresh Rangasamy et al., 2018). Apart from that it will help in improving patient interactions with the provider and the EHR, increase patient health awareness, improve care quality, and identify patients with critical care needed.

自然语言处理可以帮助医疗保健进行信息提取，非结构化数据到结构化数据的转换，文档分类和汇总。 最终，它将通过有效的计费和准确的事先授权批准来减少管理成本。 它还将通过为无效的临床决策和简化的医疗政策评估等提供支持来创造医疗价值(Suresh Rangasamy等，2018)。 除此之外，它将有助于改善患者与提供者和EHR的互动，提高患者的健康意识，改善护理质量，并确定需要重症监护的患者。

In healthcare, sequential deep learning and Natural language technologies boosts applications within domains like electronic health records (EHRs). (Andre Esteva et al., 2019)

在医疗保健中，顺序深度学习和自然语言技术在电子健康记录(EHR)等领域中促进了应用。 (安德烈·埃斯特瓦(Andre Esteva)等人，2019)

An outsized medical organization can capture 10 million patients’ medical transactions in a decade. One hospital alone generates 150,000 pieces of data. Aggregation of all EHRs of this scale exemplifies 100 million years of patient data and 200,000 years of doctors’ practice, and it covers excess of rare conditions and diseases. (Andre Esteva et al., 2019)

大型医疗机构可​​以在十年内捕获1000万患者的医疗交易。 仅一家医院就产生15万条数据。 如此规模的所有EHR的汇总，代表了1亿年的患者数据和200,000年的医生执业时间，涵盖了罕见的疾病和疾病。 (安德烈·埃斯特瓦(Andre Esteva)等人，2019)

The below figure depicts various stages of processing

下图描述了处理的各个阶段

A. Unstructured EHR data. Medical records are stored in heterogeneous formats as per the working mechanism and data structures of a specific hospital. So, it will vary from one hospital to another.

A. 非结构化EHR数据 。 根据特定医院的工作机制和数据结构，病历以不同的格式存储。 因此，它会因一家医院而异。

B. Data standardization. Based on FHIR, data will be standardized into the same or we can say homogeneous format by mapping data from multiple sources

B. 数据标准化。 基于FHIR，数据将被标准化为相同格式，或者我们可以通过映射来自多个来源的数据来说同质格式

C. Sequencing. By temporally sequencing all data into a patient timeline, time-based deep-learning techniques can be applied to the entirety of EHR datasets for making predictions about single patients.

C. 测序。 通过将所有数据按时间顺序排序到患者的时间轴中，基于时间的深度学习技术可以应用于整个EHR数据集，以便对单个患者进行预测。

NLP can be helpful in the neurology domain too. The below figure depicts how the iterative process of the NLP algorithm which can be helpful in identifying 24 brain imaging phenotypes in two areas of NHS Scotland which had excellent positive predictive value for cerebrovascular and neuro degenerative phenotypes.

NLP在神经病学领域也有帮助。 下图描述了NLP算法的迭代过程如何有助于识别NHS苏格兰两个地区的24种脑成像表型，这些表型对脑血管和神经退行性表型具有出色的阳性预测价值。

The NLP system, Edinburgh Information Extraction for Radiology reports (EdIE-R), is a staged pipeline process as depicted in the below figure, with XML rule-based text mining software at its core.

NLP系统，爱丁堡放射学信息提取报告(EdIE-R)，是一个分阶段的流水线过程，如下图所示，其核心是基于XML规则的文本挖掘软件。

结论 (Conclusion)

Deep Learning and NLP will play a significant role in accelerating the decision-making process in the healthcare area. However, the real rewards of developing good algorithms will depend heavily on the quality of the data that they acquire and maintain. The faster decision-making process will allow physicians to focus on the value-added care of patients. NLP with Deep Learning and Computer Vision can process a variety of data together to take precise decisions. Collaborative research can lead to a higher level of treatment in Healthcare. Considering the impact of AI techniques, these systems need to be designed and built very carefully in a larger socio-ecological context of clinical care settings to provide better healthcare to society.

深度学习和NLP将在加速医疗保健领域的决策过程中发挥重要作用。 但是，开发好的算法的真正好处将在很大程度上取决于它们获取和维护的数据的质量。 更快的决策过程将使医生专注于患者的增值护理。 具有深度学习和计算机视觉的NLP可以一起处理各种数据，以做出精确的决策。 协作研究可以提高医疗保健水平。 考虑到AI技术的影响，需要在临床护理环境的更大社会生态环境中精心设计和构建这些系统，以为社会提供更好的医疗服务。

参考书目 (Bibliography)

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2. A guide to deep learning in healthcare, Andre Esteva, Alexandre Robicquet, Bharath Ramsundar, Volodymyr Kuleshov, Mark DePristo, Katherine Chou, Claire Cui, Greg Corrado, Sebastian Thrun and Jeff Dean, Nature Medicine, VOL 25, JANUARY 2019, PP 24–29

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https://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/natural-language-processing-in-healthcare#:~:text=Artificial%20intelligence%20(AI)%20is% 20越来越多地生成％20语音％20或％20书面％20数据。

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4.医疗保健中自然语言处理的前12个用例

https://marutitech.com/use-cases-of-natural-language-processing-in-healthcare/

https://marutitech.com/use-cases-of-natural-language-processing-in-healthcare/

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作者 (Authors)

Kamal Jain — He is a dynamic, result-oriented digital technocrat with over 19 years of experience and a proven track record of building futuristic products using digital technologies — Artificial Intelligence (AI), neural networks and deep learning, Machine Learning (ML), Natural Language Processing (NLP), Virtual Desktop Infrastructure (VDI), ‘Bring Your Own Device’ (BYOD), Cloud based Software as a Service (SaaS) solution stack. He has rich experience in building complex technical product solutions in matrix organizations with international experience across different countries — US, the UK, Spain, South Korea, UAE, and India. As a part of giving back to the society and tech sector, he has been voluntary mentoring and providing knowledge sessions at various universities, tech start-ups in the areas of Artificial Intelligence, Machine Learning, Deep Learning & Neural Networks, Cloud based technologies, etc.

卡马尔·贾因 ( Kamal Jain) —他是一位动态的，注重结果的数字技术专家，拥有超过19年的经验，并拥有使用数字技术(人工智能(AI)，神经网络和深度学习，机器学习(ML)，自然语言处理(NLP)，虚拟桌面基础结构(VDI)，“自带设备”(BYOD)，基于云的软件即服务(SaaS)解决方案堆栈。 他在矩阵组织中构建复杂的技术产品解决方案方面拥有丰富的经验，并在不同国家(美国，英国，西班牙，韩国，阿联酋和印度)具有国际经验。 作为回馈社会和科技领域的一部分，他一直自愿指导并在各种大学，人工智能，机器学习，深度学习和神经网络，基于云的技术，等等

https://www.linkedin.com/in/kjainiimb/

https://www.linkedin.com/in/kjainiimb/

Vishal Prajapati — He is a young and enthusiastic person with sound teaching-learning and soft skills, working as an Assistant Professor in the Department of Information Technology, G H Patel College of Engineering & Technology, V. V. Nagar, Anand, Gujarat. He has more than 9 years of teaching experience. He obtained a post-graduate degree in Computer Engineering from Birla Vishwakarma Mahavidyalaya (BVM), GTU. He has published 9 Research papers in various National/International Conferences and Journals. His research interests include Artificial Intelligence, Machine Learning, Deep Learning, Data Science, SQL.

Vishal Prajapati —他是一个年轻而热情的人，具有良好的教学和软技能，曾在古吉拉特邦阿纳德市VV Nagar的GH Patel工程技术学院信息技术系担任助理教授。 他拥有9年以上的教学经验。 他从GTU的Birla Vishwakarma Mahavidyalaya(BVM)获得了计算机工程的研究生学位。 他在各种国家/国际会议和期刊上发表了9篇研究论文。 他的研究兴趣包括人工智能，机器学习，深度学习，数据科学，SQL。

https://www.linkedin.com/in/vishal-prajapati-b3242812/

https://www.linkedin.com/in/vishal-prajapati-b3242812/