CREATe: Clinical Report Extraction and Annotation Technology

Zhou, Yichao; Zhang, Bowen; Lee, David; Caufield, J. Harry; Chang, Kai-Wei; Sun, Yizhou; Ping, Peipei; Wang, Wei

doi:10.48550/arxiv.2103.00562

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…Domain-specific Pre-trained Language Model. To tackle domainspecific tasks, such as Clinical information extraction [82] and Bioinformatics knowledge acquisition [46], recent studies pre-train new language models with large-scale corpora collected from those domains [2,47] to learn customized token and sequence representations. Motivated by these approaches, we leverage all COVID-19 relevant text corpora together with the social media data to pretrain a CoronaBERT language model with 12 layers of Transformers and over 110 million parameters, in order to equip our models with powerful input embeddings.…”

Section: Constructing Dynamic Knowledge Graphs From Social Media Datamentioning

confidence: 99%

#StayHome or #Marathon? Social Media Enhanced Pandemic Surveillance on Spatial-temporal Dynamic Graphs

Zhou,

Jiang,

Chen

et al. 2021

Preprint

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COVID-19 has caused lasting damage to almost every domain in public health, society, and economy. To monitor the pandemic trend, existing studies rely on the aggregation of traditional statistical models and epidemic spread theory. In other words, historical statistics of COVID-19, as well as the population mobility data, become the essential knowledge for monitoring the pandemic trend. However, these solutions can barely provide precise prediction and satisfactory explanations on the long-term disease surveillance while the ubiquitous social media resources can be the key enabler for solving this problem. For example, serious discussions may occur on social media before and after some breaking events take place. These events, such as marathon and parade, may impact the spread of the virus. To take advantage of the social media data, we propose a novel framework, Social Media enhAnced pandemic suRveillance Technique (SMART), which is composed of two modules: (i) information extraction module to construct heterogeneous knowledge graphs based on the extracted events and relationships among them; (ii) time series prediction module to provide both short-term and long-term forecasts of the confirmed cases and fatality at the state-level in the United States and to discover risk factors for COVID-19 interventions. Extensive experiments show that our method largely outperforms the state-of-the-art baselines by 7.3% and 7.4% in confirmed case/fatality prediction, respectively. CCS CONCEPTS• Information systems → Data mining.

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Section: Constructing Dynamic Knowledge Graphs From Social Media Datamentioning

confidence: 99%

#StayHome or #Marathon? Social Media Enhanced Pandemic Surveillance on Spatial-temporal Dynamic Graphs

Zhou,

Jiang,

Chen

et al. 2021

Preprint

Self Cite

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“…Clinical case reports (CCRs) are written descriptions of the unique aspects of a particular clinical case (Cabán-Martinez and García-Beltrán, 2012). They are intended to serve as educational aids to science and medicine, as they play an essential role in sharing clinical experiences about atypical disease phenotypes and new therapies (Caufield et al, 2018, Zhou et al, 2021a. Unlike other types of clinical documents (e.g., electronic medical records, or EMRs), CCRs generally describe single clinical narratives at a time: these are stories of diseases as they were observed and treated, written in language requiring domain familiarity but otherwise generally interpretable.…”

Section: Introductionmentioning

confidence: 99%

Clinical Named Entity Recognition using Contextualized Token Representations

Zhou¹,

Ju²,

Caufield³

et al. 2021

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The clinical named entity recognition (CNER) task seeks to locate and classify clinical terminologies into predefined categories, such as diagnostic procedure, disease disorder, severity, medication, medication dosage, and sign symptom. CNER facilitates the study of side-effect on medications including identification of novel phenomena and human-focused information extraction. Existing approaches in extracting the entities of interests focus on using static word embeddings to represent each word. However, one word can have different interpretations that depend on the context of the sentences. Evidently, static word embeddings are insufficient to integrate the diverse interpretation of a word. To overcome this challenge, the technique of contextualized word embedding has been introduced to better capture the semantic meaning of each word based on its context. Two of these language models, ELMo and Flair, have been widely used in the field of Natural Language Processing to generate the contextualized word embeddings on domain-generic documents. However, these embeddings are usually too general to capture the proximity among vocabularies of specific domains. To facilitate various downstream applications using clinical case reports (CCRs), we pre-train two deep contextualized language models, Clinical Embeddings from Language Model (C-ELMo) and Clinical Contextual String Embeddings (C-Flair) using the clinical-related corpus from the PubMed Central. Explicit experiments show that our models gain dramatic improvements compared to both static word embeddings and domain-generic language models. The pre-trained embeddings of these two models will be available soon.

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CREATe: Clinical Report Extraction and Annotation Technology

Cited by 2 publications

References 7 publications

#StayHome or #Marathon? Social Media Enhanced Pandemic Surveillance on Spatial-temporal Dynamic Graphs

#StayHome or #Marathon? Social Media Enhanced Pandemic Surveillance on Spatial-temporal Dynamic Graphs

Clinical Named Entity Recognition using Contextualized Token Representations

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