In this work, we propose a novel problem formulation for de-identification of unstructured clinical text. We formulate the de-identification problem as a sequence to sequence learning problem instead of a token classification problem. Our approach is inspired by the recent state-of -the-art performance of sequence to sequence learning models for named entity recognition. Early experimentation of our proposed approach achieved 98.91% recall rate on i2b2 dataset. This performance is comparable to current state-of-the-art models for unstructured clinical text de-identification.
Electronic health records capture patient information using structured controlled vocabularies and unstructured narrative text. While structured data typically encodes lab values, encounters and medication lists, unstructured data captures the physician's interpretation of the patient's condition, prognosis, and response to therapeutic intervention. In this paper, we demonstrate that information extraction from unstructured clinical narratives is essential to most clinical applications. We perform an empirical study to validate the argument and show that structured data alone is insufficient i…
The problem of named entity recognition in the medical/clinical domain has gained increasing attention do to its vital role in a wide range of clinical decision support applications. The identification of complete and correct term span is vital for further knowledge synthesis (e.g., coding/mapping concepts thesauruses and classification standards). This paper investigates boundary adjustment by sequence labeling representations models and post-processing techniques in the problem of clinical named entity recognition (recognition of clinical events). Using current state-of-the-art sequence labe…
Centro de Tecnología Biomédica, Universidad Politécnica de Madrid（马德里理工大学生物医学技术中心）；Universidad Politécnica de Madrid（马德里理工大学）；ETS de Ingenieros Informáticos, Universidad Politécnica de Madrid（马德里理工大学计算机工程高等技术学院）
Recent rapid increase in the generation of clinical data and rapid development of computational science make us able to extract new insights from massive datasets in healthcare industry. Oncological clinical notes are creating rich databases for documenting patients history and they potentially contain lots of patterns that could help in better management of the disease. However, these patterns are locked within free text (unstructured) portions of clinical documents and consequence in limiting health professionals to extract useful information from them and to finally perform Query and Answer…
Saw Swee Hock School of Public Health, National University of Singapore and National University Health System（苏瑞福公共卫生学院、新加坡国立大学及新加坡国立大学健康系统）；Singapore Civil Defence Force（新加坡民防部队）；National University Hospital（国立大学医院）；Home Team Science & Technology Agency（Home Team科技局）；Singapore General Hospital（新加坡中央医院）
Clinical performance audits are routinely performed in Emergency Medical Services (EMS) to ensure adherence to treatment protocols, to identify individual areas of weakness for remediation, and to discover systemic deficiencies to guide the development of the training syllabus. At present, these audits are performed by manual chart review which is time-consuming and laborious. In this paper, we present an automatic audit system based on both the structured and unstructured ambulance case records and clinical notes with a deep neural network-based named entities recognition model. The dataset u…
Northwestern University Feinberg School of Medicine（西北大学范伯格医学院）
To reduce the large amount of time spent screening, identifying, and recruiting patients into clinical trials, we need prescreening systems that are able to automate the data extraction and decision-making tasks that are typically relegated to clinical research study coordinators. However, a major obstacle is the vast amount of patient data available as unstructured free-form text in electronic health records. Here we propose an information extraction-based approach that first automatically converts unstructured text into a structured form. The structured data are then compared against a list …
University of Maryland（马里兰大学）；IBM Research（IBM研究院）
There is a wealth of information about financial systems that is embedded in document collections. In this paper, we focus on a specialized text extraction task for this domain. The objective is to extract mentions of names of financial institutions, or FI names, from financial prospectus documents, and to identify the corresponding real world entities, e.g., by matching against a corpus of such entities. The tasks are Named Entity Recognition (NER) and Entity Resolution (ER); both are well studied in the literature. Our contribution is to develop a rule-based approach that will exploit lists …
University of Manchester（曼彻斯特大学）；Institute for Clinical Evaluative Studies（临床评估研究所）；Evenset（伊文塞特）
Medical health records and clinical summaries contain a vast amount of important information in textual form that can help advancing research on treatments, drugs and public health. However, the majority of these information is not shared because they contain private information about patients, their families, or medical staff treating them. Regulations such as HIPPA in the US, PHIPPA in Canada and GDPR regulate the protection, processing and distribution of this information. In case this information is de-identified and personal information are replaced or redacted, they could be distributed …
University of Edinburgh（爱丁堡大学）；University College London（伦敦大学学院）；Health Data Research UK（英国健康数据研究所）
The identification of rare diseases from clinical notes with Natural Language Processing (NLP) is challenging due to the few cases available for machine learning and the need of data annotation from clinical experts. We propose a method using ontologies and weak supervision. The approach includes two steps: (i) Text-to-UMLS, linking text mentions to concepts in Unified Medical Language System (UMLS), with a named entity linking tool (e.g. SemEHR) and weak supervision based on customised rules and Bidirectional Encoder Representations from Transformers (BERT) based contextual representations, a…
Clinical text provides essential information to estimate the acuity of a patient during hospital stays in addition to structured clinical data. In this study, we explore how clinical text can complement a clinical predictive learning task. We leverage an internal medical natural language processing service to perform named entity extraction and negation detection on clinical notes and compose selected entities into a new text corpus to train document representations. We then propose a multimodal neural network to jointly train time series signals and unstructured clinical text representations …
University of Massachusetts（马萨诸塞大学）；Bedford VAMC and CHOIR（贝德福德退伍军人医疗中心与CHOIR）
Sequence labeling is a widely used method for named entity recognition and information extraction from unstructured natural language data. In clinical domain one major application of sequence labeling involves extraction of medical entities such as medication, indication, and side-effects from Electronic Health Record narratives. Sequence labeling in this domain, presents its own set of challenges and objectives. In this work we experimented with various CRF based structured learning models with Recurrent Neural Networks. We extend the previously studied LSTM-CRF models with explicit modeling …
Question-Answering System Extracts Information on Injection Drug Use from Clinical Notes
Oak Ridge National Laboratory（橡树岭国家实验室）；Vanderbilt University（范德堡大学）；Department of Veterans Affairs（美国退伍军人事务部）；Stanford University School of Medicine（斯坦福大学医学院）；Veterans Affairs Great Lakes Health Care System（退伍军人事务部大湖区医疗系统）
Unstructured clinical text in EHRs contains crucial information for applications including decision support, trial matching, and retrospective research. Recent work has applied BERT-based models to clinical information extraction and text classification, given these models' state-of-the-art performance in other NLP domains. However, BERT is difficult to apply to clinical notes because it doesn't scale well to long sequences of text. In this work, we propose a novel fine-tuning approach called SnipBERT. Instead of using entire notes, SnipBERT identifies crucial snippets and then feeds them into…
Clinical trials predicate subject eligibility on a diversity of criteria ranging from patient demographics to food allergies. Trials post their requirements as semantically complex, unstructured free-text. Formalizing trial criteria to a computer-interpretable syntax would facilitate eligibility determination. In this paper, we investigate an information extraction (IE) approach for grounding criteria from trials in ClinicalTrials(dot)gov to a shared knowledge base. We frame the problem as a novel knowledge base population task, and implement a solution combining machine learning and context f…
Northeastern University（东北大学）；University of Pennsylvania（宾夕法尼亚大学）
The best evidence concerning comparative treatment effectiveness comes from clinical trials, the results of which are reported in unstructured articles. Medical experts must manually extract information from articles to inform decision-making, which is time-consuming and expensive. Here we consider the end-to-end task of both (a) extracting treatments and outcomes from full-text articles describing clinical trials (entity identification) and, (b) inferring the reported results for the former with respect to the latter (relation extraction). We introduce new data for this task, and evaluate mod…
Mila（米拉研究所）；Université de Montréal（蒙特利尔大学）；University of Tennessee, Knoxville（田纳西大学诺克斯维尔分校）
Healthcare data continues to flourish yet a relatively small portion, mostly structured, is being utilized effectively for predicting clinical outcomes. The rich subjective information available in unstructured clinical notes can possibly facilitate higher discrimination but tends to be under-utilized in mortality prediction. This work attempts to assess the gain in performance when multiple notes that have been minimally preprocessed are used as an input for prediction. A hierarchical architecture consisting of both convolutional and recurrent layers is used to concurrently model the differen…
Northwestern University Feinberg School of Medicine（西北大学范伯格医学院）
Background: Clinical guidelines and recommendations are the driving wheels of the evidence-based medicine (EBM) paradigm, but these are available primarily as unstructured text and are generally highly heterogeneous in nature. This significantly reduces the dissemination and automatic application of these recommendations at the point of care. A comprehensive structured representation of these recommendations is highly beneficial in this regard. Objective: The objective of this paper to present Clinical Recommendation Type System (CRTS), a common type system that can effectively represent a cli…
Healthcare providers usually record detailed notes of the clinical care delivered to each patient for clinical, research, and billing purposes. Due to the unstructured nature of these narratives, providers employ dedicated staff to assign diagnostic codes to patients' diagnoses using the International Classification of Diseases (ICD) coding system. This manual process is not only time-consuming but also costly and error-prone. Prior work demonstrated potential utility of Machine Learning (ML) methodology in automating this process, but it has relied on large quantities of manually labeled data…
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…
National Institute of Technology Karnataka Surathkal（卡纳塔克苏拉特卡尔国家理工学院）
Existing Clinical Decision Support Systems (CDSSs) largely depend on the availability of structured patient data and Electronic Health Records (EHRs) to aid caregivers. However, in case of hospitals in developing countries, structured patient data formats are not widely adopted, where medical professionals still rely on clinical notes in the form of unstructured text. Such unstructured clinical notes recorded by medical personnel can also be a potential source of rich patient-specific information which can be leveraged to build CDSSs, even for hospitals in developing countries. If such unstruc…
Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Peng Cheng Laboratory（鹏城实验室）；Peking University（北京大学）
As two important textual modalities in electronic health records (EHR), both structured data (clinical codes) and unstructured data (clinical narratives) have recently been increasingly applied to the healthcare domain. Most existing EHR-oriented studies, however, either focus on a particular modality or integrate data from different modalities in a straightforward manner, which usually treats structured and unstructured data as two independent sources of information about patient admission and ignore the intrinsic interactions between them. In fact, the two modalities are documented during th…
Dementia-R1: Reinforced Pretraining and Reasoning from Unstructured Clinical Notes for Real-World Dementia Prognosis
Graduate School of AI, KAIST, Republic of Korea（韩国科学技术院人工智能研究生院）；Haeundae Paik Hospital, Inje University, Republic of Korea（韩国延世大学海云台医院）；Asan Medical Center, University of Ulsan College of Medicine, Republic of Korea（韩国釜山大学医学院阿桑医院）
Imperial College London（帝国理工学院）；Pangaea Data Limited；Queen Mary University of London（伦敦玛丽女王大学）；Hong Kong Baptist University（香港浸会大学）
Objective: Clinical notes contain information not present elsewhere, including drug response and symptoms, all of which are highly important when predicting key outcomes in acute care patients. We propose the automatic annotation of phenotypes from clinical notes as a method to capture essential information, which is complementary to typically used vital signs and laboratory test results, to predict outcomes in the Intensive Care Unit (ICU). Methods: We develop a novel phenotype annotation model to annotate phenotypic features of patients which are then used as input features of predictive mod…
SemioLLM: Evaluating Large Language Models for Diagnostic Reasoning from Unstructured Clinical Narratives in Epilepsy
University of Tübingen（蒂宾根大学）；Technical University of Munich（慕尼黑工业大学）；University Clinic Tübingen（蒂宾根大学医院）；Hertie Institute for Clinical Brain Research（赫蒂临床脑研究所）；Excellence Cluster Machine Learning, Tübingen University（蒂宾根大学机器学习卓越集群）；Hertie Institute for AI in Brain Health (Hertie AI)（赫蒂人工智能脑健康研究所）
University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Amazon（亚马逊）；Northwestern University（西北大学）
Predicting patient mortality is an important and challenging problem in the healthcare domain, especially for intensive care unit (ICU) patients. Electronic health notes serve as a rich source for learning patient representations, that can facilitate effective risk assessment. However, a large portion of clinical notes are unstructured and also contain domain specific terminologies, from which we need to extract structured information. In this paper, we introduce an embedding framework to learn semantically-plausible distributed representations of clinical notes that exploits the semantic corr…
Saarland University（萨尔大学）；DFKI（德国人工智能研究中心）；Institut Guttmann（古特曼研究所）；Universidad de Murcia（穆尔西亚大学）
Despite the advances in digital healthcare systems offering curated structured knowledge, much of the critical information still lies in large volumes of unlabeled and unstructured clinical texts. These texts, which often contain protected health information (PHI), are exposed to information extraction tools for downstream applications, risking patient identification. Existing works in de-identification rely on using large-scale annotated corpora in English, which often are not suitable in real-world multilingual settings. Pre-trained language models (LM) have shown great potential for cross-l…
Bosch Center for Artificial Intelligence（博世人工智能中心）；Saarland University（萨尔大学）；Saarbrücken Graduate School of Computer Science（萨尔布吕肯计算机科学研究生院）
Exploiting natural language processing in the clinical domain requires de-identification, i.e., anonymization of personal information in texts. However, current research considers de-identification and downstream tasks, such as concept extraction, only in isolation and does not study the effects of de-identification on other tasks. In this paper, we close this gap by reporting concept extraction performance on automatically anonymized data and investigating joint models for de-identification and concept extraction. In particular, we propose a stacked model with restricted access to privacy-sen…
George Mason University（乔治梅森大学）；University of Washington（华盛顿大学）；Virginia Commonwealth University（弗吉尼亚联邦大学）
Objective: Neural network de-identification studies have focused on individual datasets. These studies assume the availability of a sufficient amount of human-annotated data to train models that can generalize to corresponding test data. In real-world situations, however, researchers often have limited or no in-house training data. Existing systems and external data can help jump-start de-identification on in-house data; however, the most efficient way of utilizing existing systems and external data is unclear. This article investigates the transferability of a state-of-the-art neural clinical…
Tokyo Metropolitan University（东京都立大学）；RIKEN（理化学研究所）；National Hospital Organization（国立医院机构）；Kitami Institute of Technology（北见工业大学）
Automated summarization of clinical texts can reduce the burden of medical professionals. "Discharge summaries" are one promising application of the summarization, because they can be generated from daily inpatient records. Our preliminary experiment suggests that 20-31% of the descriptions in discharge summaries overlap with the content of the inpatient records. However, it remains unclear how the summaries should be generated from the unstructured source. To decompose the physician's summarization process, this study aimed to identify the optimal granularity in summarization. We first define…
Electronic Health Records (EHRs) have been heavily used to predict various downstream clinical tasks such as readmission or mortality. One of the modalities in EHRs, clinical notes, has not been fully explored for these tasks due to its unstructured and inexplicable nature. Although recent advances in deep learning (DL) enables models to extract interpretable features from unstructured data, they often require a large amount of training data. However, many tasks in medical domains inherently consist of small sample data with lengthy documents; for a kidney transplant as an example, data from o…
University College London（伦敦大学学院）；University of Edinburgh（爱丁堡大学）
Adverse Childhood Experiences (ACEs) are defined as a collection of highly stressful, and potentially traumatic, events or circumstances that occur throughout childhood and/or adolescence. They have been shown to be associated with increased risks of mental health diseases or other abnormal behaviours in later lives. However, the identification of ACEs from free-text Electronic Health Records (EHRs) with Natural Language Processing (NLP) is challenging because (a) there is no NLP ready ACE ontologies; (b) there are limited cases available for machine learning, necessitating the data annotation…
In the era of clinical information explosion, a good strategy for clinical text summarization is helpful to improve the clinical workflow. The ideal summarization strategy can preserve important information in the informative but less organized, ill-structured clinical narrative texts. Instead of using pure statistical learning approaches, which are difficult to interpret and explain, we utilized knowledge of computational linguistics with human experts-curated biomedical knowledge base to achieve the interpretable and meaningful clinical text summarization. Our research objective is to use th…
The Ohio State University（俄亥俄州立大学）；The Hong Kong Polytechnic University（香港理工大学）
De-identification is the task of identifying protected health information (PHI) in the clinical text. Existing neural de-identification models often fail to generalize to a new dataset. We propose a simple yet effective data augmentation method PHICON to alleviate the generalization issue. PHICON consists of PHI augmentation and Context augmentation, which creates augmented training corpora by replacing PHI entities with named-entities sampled from external sources, and by changing background context with synonym replacement or random word insertion, respectively. Experimental results on the i…
Developing predictive modeling in medicine requires additional features from unstructured clinical texts. In Russia, there are no instruments for natural language processing to cope with problems of medical records. This paper is devoted to a module of negation detection. The corpus-free machine learning method is based on gradient boosting classifier is used to detect whether a disease is denied, not mentioned or presented in the text. The detector classifies negations for five diseases and shows average F-score from 0.81 to 0.93. The benefits of negation detection have been demonstrated by p…
Petuum Inc.（Petuum 公司）；Department of Psychiatry, University of Pittsburgh Medical Center（匹兹堡大学医学中心精神科）
$\textbf{Objective}$ Develop an automatic diagnostic system which only uses textual admission information from Electronic Health Records (EHRs) and assist clinicians with a timely and statistically proved decision tool. The hope is that the tool can be used to reduce mis-diagnosis. $\textbf{Materials and Methods}$ We use the real-world clinical notes from MIMIC-III, a freely available dataset consisting of clinical data of more than forty thousand patients who stayed in intensive care units of the Beth Israel Deaconess Medical Center between 2001 and 2012. We proposed a Convolutional Neural Ne…
DeIDClinic: A Risk-Aware Pseudonymization Framework for Clinical Text De-identification and Re-identification Risk Assessment
University of Manchester（曼彻斯特大学）；Leiden Institute of Advanced Computer Science (LIACS)（莱顿先进计算机科学研究所）；Leiden University（莱顿大学）；Biomedical Data Sciences, Leiden University Medical Center（莱顿大学医学中心生物医学数据科学）
Reliable Automated Triage in Spanish Clinical Notes: A Hybrid Framework for Risk-Aware HIV Suspicion Identification
Dept. of Lenguajes y Sistemas Informáticos, Escuela Técnica Superior de Ingeniería Informática, Universidad Nacional de Educación a Distancia (UNED)（语言与系统信息学系，信息工程技术学校，国家远程教育大学（UNED））；Dept. Informática y Estadística, Escuela Técnica Superior de Ingeniería Informática, Universidad Rey Juan Carlos (URJC)（信息与统计学系，信息工程技术学校，皇家胡安·卡洛斯大学（URJC））
