scholarly journals An Ensemble Learning Strategy for Eligibility Criteria Text Classification for Clinical Trial Recruitment: Algorithm Development and Validation

10.2196/17832 ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. e17832
Author(s):  
Kun Zeng ◽  
Zhiwei Pan ◽  
Yibin Xu ◽  
Yingying Qu
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Natural Language Processing ◽  
Ensemble Learning ◽  
Language Processing ◽  
Text Classification ◽  
State Of The Art ◽  
Shared Task ◽  
Eligibility Criteria ◽  
Short Text

Background Eligibility criteria are the main strategy for screening appropriate participants for clinical trials. Automatic analysis of clinical trial eligibility criteria by digital screening, leveraging natural language processing techniques, can improve recruitment efficiency and reduce the costs involved in promoting clinical research. Objective We aimed to create a natural language processing model to automatically classify clinical trial eligibility criteria. Methods We proposed a classifier for short text eligibility criteria based on ensemble learning, where a set of pretrained models was integrated. The pretrained models included state-of-the-art deep learning methods for training and classification, including Bidirectional Encoder Representations from Transformers (BERT), XLNet, and A Robustly Optimized BERT Pretraining Approach (RoBERTa). The classification results by the integrated models were combined as new features for training a Light Gradient Boosting Machine (LightGBM) model for eligibility criteria classification. Results Our proposed method obtained an accuracy of 0.846, a precision of 0.803, and a recall of 0.817 on a standard data set from a shared task of an international conference. The macro F1 value was 0.807, outperforming the state-of-the-art baseline methods on the shared task. Conclusions We designed a model for screening short text classification criteria for clinical trials based on multimodel ensemble learning. Through experiments, we concluded that performance was improved significantly with a model ensemble compared to a single model. The introduction of focal loss could reduce the impact of class imbalance to achieve better performance.

scholarly journals An Ensemble Learning Strategy for Eligibility Criteria Text Classification for Clinical Trial Recruitment: Algorithm Development and Validation (Preprint)

2020 ◽  
Author(s):  
Kun Zeng ◽  
Zhiwei Pan ◽  
Yibin Xu ◽  
Yingying Qu
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Natural Language Processing ◽  
Ensemble Learning ◽  
Language Processing ◽  
Text Classification ◽  
State Of The Art ◽  
Shared Task ◽  
Eligibility Criteria ◽  
Short Text

BACKGROUND Eligibility criteria are the main strategy for screening appropriate participants for clinical trials. Automatic analysis of clinical trial eligibility criteria by digital screening, leveraging natural language processing techniques, can improve recruitment efficiency and reduce the costs involved in promoting clinical research. OBJECTIVE We aimed to create a natural language processing model to automatically classify clinical trial eligibility criteria. METHODS We proposed a classifier for short text eligibility criteria based on ensemble learning, where a set of pretrained models was integrated. The pretrained models included state-of-the-art deep learning methods for training and classification, including Bidirectional Encoder Representations from Transformers (BERT), XLNet, and A Robustly Optimized BERT Pretraining Approach (RoBERTa). The classification results by the integrated models were combined as new features for training a Light Gradient Boosting Machine (LightGBM) model for eligibility criteria classification. RESULTS Our proposed method obtained an accuracy of 0.846, a precision of 0.803, and a recall of 0.817 on a standard data set from a shared task of an international conference. The macro F1 value was 0.807, outperforming the state-of-the-art baseline methods on the shared task. CONCLUSIONS We designed a model for screening short text classification criteria for clinical trials based on multimodel ensemble learning. Through experiments, we concluded that performance was improved significantly with a model ensemble compared to a single model. The introduction of focal loss could reduce the impact of class imbalance to achieve better performance.

scholarly journals Participatory Design of a Clinical Trial Eligibility Criteria Simplification Method

2021 ◽  
Author(s):  
Yilu Fang ◽  
Jae Hyun Kim ◽  
Betina Ross Idnay ◽  
Rebeca Aragon Garcia ◽  
Carmen E. Castillo ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Participatory Design ◽  
Eligibility Criteria ◽  
Database Querying ◽  
The Right

Clinical trial eligibility criteria are important for selecting the right participants for clinical trials. However, they are often complex and not computable. This paper presents the participatory design of a human-computer collaboration method for criteria simplification that includes natural language processing followed by user-centered eligibility criteria simplification. A case study on the ARCADIA trial shows how criteria were simplified for structured database querying by clinical researchers and identifies rules for criteria simplification and concept normalization.

Report on the 4th Joint Workshop on Bibliometric-Enhanced Information Retrieval and Natural Language Processing for Digital Libraries at SIGIR 2019

2019 ◽  
Vol 53 (2) ◽  
pp. 3-10
Author(s):  
Muthu Kumar Chandrasekaran ◽  
Philipp Mayr
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Natural Language ◽  
Research And Development ◽  
Language Processing ◽  
Digital Libraries ◽  
State Of The Art ◽  
Shared Task ◽  
Processing Information ◽  
Joint Workshop

The 4 th joint BIRNDL workshop was held at the 42nd ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019) in Paris, France. BIRNDL 2019 intended to stimulate IR researchers and digital library professionals to elaborate on new approaches in natural language processing, information retrieval, scientometrics, and recommendation techniques that can advance the state-of-the-art in scholarly document understanding, analysis, and retrieval at scale. The workshop incorporated different paper sessions and the 5 th edition of the CL-SciSumm Shared Task.

scholarly journals The 2019 n2c2/OHNLP Track on Clinical Semantic Textual Similarity: Overview (Preprint)

2020 ◽  
Author(s):  
Yanshan Wang ◽  
Sunyang Fu ◽  
Feichen Shen ◽  
Sam Henry ◽  
Ozlem Uzuner ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
State Of The Art ◽  
Shared Task ◽  
Data Set ◽  
Clinical Text ◽  
Clinical Notes ◽  
Clinical Domain ◽  
Semantic Textual Similarity

BACKGROUND Semantic textual similarity is a common task in the general English domain to assess the degree to which the underlying semantics of 2 text segments are equivalent to each other. Clinical Semantic Textual Similarity (ClinicalSTS) is the semantic textual similarity task in the clinical domain that attempts to measure the degree of semantic equivalence between 2 snippets of clinical text. Due to the frequent use of templates in the Electronic Health Record system, a large amount of redundant text exists in clinical notes, making ClinicalSTS crucial for the secondary use of clinical text in downstream clinical natural language processing applications, such as clinical text summarization, clinical semantics extraction, and clinical information retrieval. OBJECTIVE Our objective was to release ClinicalSTS data sets and to motivate natural language processing and biomedical informatics communities to tackle semantic text similarity tasks in the clinical domain. METHODS We organized the first BioCreative/OHNLP ClinicalSTS shared task in 2018 by making available a real-world ClinicalSTS data set. We continued the shared task in 2019 in collaboration with National NLP Clinical Challenges (n2c2) and the Open Health Natural Language Processing (OHNLP) consortium and organized the 2019 n2c2/OHNLP ClinicalSTS track. We released a larger ClinicalSTS data set comprising 1642 clinical sentence pairs, including 1068 pairs from the 2018 shared task and 1006 new pairs from 2 electronic health record systems, GE and Epic. We released 80% (1642/2054) of the data to participating teams to develop and fine-tune the semantic textual similarity systems and used the remaining 20% (412/2054) as blind testing to evaluate their systems. The workshop was held in conjunction with the American Medical Informatics Association 2019 Annual Symposium. RESULTS Of the 78 international teams that signed on to the n2c2/OHNLP ClinicalSTS shared task, 33 produced a total of 87 valid system submissions. The top 3 systems were generated by IBM Research, the National Center for Biotechnology Information, and the University of Florida, with Pearson correlations of <i>r</i>=.9010, <i>r</i>=.8967, and <i>r</i>=.8864, respectively. Most top-performing systems used state-of-the-art neural language models, such as BERT and XLNet, and state-of-the-art training schemas in deep learning, such as pretraining and fine-tuning schema, and multitask learning. Overall, the participating systems performed better on the Epic sentence pairs than on the GE sentence pairs, despite a much larger portion of the training data being GE sentence pairs. CONCLUSIONS The 2019 n2c2/OHNLP ClinicalSTS shared task focused on computing semantic similarity for clinical text sentences generated from clinical notes in the real world. It attracted a large number of international teams. The ClinicalSTS shared task could continue to serve as a venue for researchers in natural language processing and medical informatics communities to develop and improve semantic textual similarity techniques for clinical text.

A natural language processing tool for automatic identification of new disease and disease progression: Parsing text in multi-institutional radiology reports to facilitate clinical trial eligibility screening.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 1555-1555
Author(s):  
Eric J. Clayton ◽  
Imon Banerjee ◽  
Patrick J. Ward ◽  
Maggie D Howell ◽  
Beth Lohmueller ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Natural Language Processing ◽  
Natural Language ◽  
Disease Progression ◽  
Language Processing ◽  
Free Text ◽  
New Disease ◽  
Radiology Reports ◽  
Precision And Accuracy

1555 Background: Screening every patient for clinical trials is time-consuming, costly and inefficient. Developing an automated method for identifying patients who have potential disease progression, at the point where the practice first receives their radiology reports, but prior to the patient’s office visit, would greatly increase the efficiency of clinical trial operations and likely result in more patients being offered trial opportunities. Methods: Using Natural Language Processing (NLP) methodology, we developed a text parsing algorithm to automatically extract information about potential new disease or disease progression from multi-institutional, free-text radiology reports (CT, PET, bone scan, MRI or x-ray). We combined semantic dictionary mapping and machine learning techniques to normalize the linguistic and formatting variations in the text, training the XGBoost model particularly to achieve a high precision and accuracy to satisfy clinical trial screening requirements. In order to be comprehensive, we enhanced the model vocabulary using a multi-institutional dataset which includes reports from two academic institutions. Results: A dataset of 732 de-identified radiology reports were curated (two MDs agreed on potential new disease/dz progression vs stable) and the model was repeatedly re-trained for each fold where the folds were randomly selected. The final model achieved consistent precision (>0.87 precision) and accuracy (>0.87 accuracy). See the table for a summary of the results, by radiology report type. We are continuing work on the model to validate accuracy and precision using a new and unique set of reports. Conclusions: NLP systems can be used to identify patients who potentially have suffered new disease or disease progression and reduce the human effort in screening or clinical trials. Efforts are ongoing to integrate the NLP process into existing EHR reporting. New imaging reports sent via interface to the EHR will be extracted daily using a database query and will be provided via secure electronic transport to the NLP system. Patients with higher likelihood of disease progression will be automatically identified, and their reports routed to the clinical trials office for clinical trial screening parallel to physician EHR mailbox reporting. The over-arching goal of the project is to increase clinical trial enrollment. 5-fold cross-validation performance of the NLP model in terms of accuracy, precision and recall averaged across all the folds.[Table: see text]

scholarly journals Short Text Classification with Tolerance Near Sets

2021 ◽  
Author(s):  
◽  
Vrushang Patel
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Text Classification ◽  
Sentiment Classification ◽  
Data Sets ◽  
Short Text ◽  
Near Sets ◽  
Textual Features

Text classification is a classical machine learning application in Natural Language Processing, which aims to assign labels to textual units such as documents, sentences, paragraphs, and queries. Applications of text classification include sentiment classification and news categorization. Sentiment classification identifies the polarity of text such as positive, negative or neutral based on textual features. In this thesis, we implemented a modified form of a tolerance-based algorithm (TSC) to classify sentiment polarities of tweets as well as news categories from text. The TSC algorithm is a supervised algorithm that was designed to perform short text classification with tolerance near sets (TNS). The proposed TSC algorithm uses pre-trained SBERT algorithm vectors for creating tolerance classes. The effectiveness of the TSC algorithm has been demonstrated by testing it on ten well-researched data sets. One of the datasets (Covid-Sentiment) was hand-crafted with tweets from Twitter of opinions related to COVID. Experiments demonstrate that TSC outperforms five classical ML algorithms with one dataset, and is comparable with all other datasets using a weighted F1-score measure.

scholarly journals Evaluating Multilingual BERT for Estonian

2020 ◽  
Author(s):  
Claudia Kittask ◽  
Kirill Milintsevich ◽  
Kairit Sirts
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Text Classification ◽  
State Of The Art ◽  
Language Models ◽  
Neural Models ◽  
Comparable Level ◽  
Art Performance ◽  
Multiple Languages

Recently, large pre-trained language models, such as BERT, have reached state-of-the-art performance in many natural language processing tasks, but for many languages, including Estonian, BERT models are not yet available. However, there exist several multilingual BERT models that can handle multiple languages simultaneously and that have been trained also on Estonian data. In this paper, we evaluate four multilingual models—multilingual BERT, multilingual distilled BERT, XLM and XLM-RoBERTa—on several NLP tasks including POS and morphological tagging, NER and text classification. Our aim is to establish a comparison between these multilingual BERT models and the existing baseline neural models for these tasks. Our results show that multilingual BERT models can generalise well on different Estonian NLP tasks outperforming all baselines models for POS and morphological tagging and text classification, and reaching the comparable level with the best baseline for NER, with XLM-RoBERTa achieving the highest results compared with other multilingual models.

scholarly journals Transformable Convolutional Neural Network for Text Classification

2018 ◽  
Author(s):  
Liqiang Xiao ◽  
Honglun Zhang ◽  
Wenqing Chen ◽  
Yongkun Wang ◽  
Yaohui Jin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Natural Language Processing ◽  
Language Processing ◽  
Text Classification ◽  
State Of The Art ◽  
Feature Transformation ◽  
Sampling Locations ◽  
N Gram ◽  
Complex Features

Convolutional neural networks (CNNs) have shown their promising performance for natural language processing tasks, which extract n-grams as features to represent the input. However, n-gram based CNNs are inherently limited to fixed geometric structure and cannot proactively adapt to the transformations of features. In this paper, we propose two modules to provide CNNs with the flexibility for complex features and the adaptability for transformation, namely, transformable convolution and transformable pooling. Our method fuses dynamic and static deviations to redistribute the sampling locations, which can capture both current and global transformations. Our modules can be easily integrated by other models to generate new transformable networks. We test proposed modules on two state-of-the-art models, and the results demonstrate that our modules can effectively adapt to the feature transformation in text classification.

scholarly journals The 2019 n2c2/OHNLP Track on Clinical Semantic Textual Similarity: Overview

10.2196/23375 ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. e23375 ◽  
Author(s):  
Yanshan Wang ◽  
Sunyang Fu ◽  
Feichen Shen ◽  
Sam Henry ◽  
Ozlem Uzuner ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
State Of The Art ◽  
Shared Task ◽  
Data Set ◽  
Clinical Text ◽  
Clinical Notes ◽  
Clinical Domain ◽  
Semantic Textual Similarity

Background Semantic textual similarity is a common task in the general English domain to assess the degree to which the underlying semantics of 2 text segments are equivalent to each other. Clinical Semantic Textual Similarity (ClinicalSTS) is the semantic textual similarity task in the clinical domain that attempts to measure the degree of semantic equivalence between 2 snippets of clinical text. Due to the frequent use of templates in the Electronic Health Record system, a large amount of redundant text exists in clinical notes, making ClinicalSTS crucial for the secondary use of clinical text in downstream clinical natural language processing applications, such as clinical text summarization, clinical semantics extraction, and clinical information retrieval. Objective Our objective was to release ClinicalSTS data sets and to motivate natural language processing and biomedical informatics communities to tackle semantic text similarity tasks in the clinical domain. Methods We organized the first BioCreative/OHNLP ClinicalSTS shared task in 2018 by making available a real-world ClinicalSTS data set. We continued the shared task in 2019 in collaboration with National NLP Clinical Challenges (n2c2) and the Open Health Natural Language Processing (OHNLP) consortium and organized the 2019 n2c2/OHNLP ClinicalSTS track. We released a larger ClinicalSTS data set comprising 1642 clinical sentence pairs, including 1068 pairs from the 2018 shared task and 1006 new pairs from 2 electronic health record systems, GE and Epic. We released 80% (1642/2054) of the data to participating teams to develop and fine-tune the semantic textual similarity systems and used the remaining 20% (412/2054) as blind testing to evaluate their systems. The workshop was held in conjunction with the American Medical Informatics Association 2019 Annual Symposium. Results Of the 78 international teams that signed on to the n2c2/OHNLP ClinicalSTS shared task, 33 produced a total of 87 valid system submissions. The top 3 systems were generated by IBM Research, the National Center for Biotechnology Information, and the University of Florida, with Pearson correlations of r=.9010, r=.8967, and r=.8864, respectively. Most top-performing systems used state-of-the-art neural language models, such as BERT and XLNet, and state-of-the-art training schemas in deep learning, such as pretraining and fine-tuning schema, and multitask learning. Overall, the participating systems performed better on the Epic sentence pairs than on the GE sentence pairs, despite a much larger portion of the training data being GE sentence pairs. Conclusions The 2019 n2c2/OHNLP ClinicalSTS shared task focused on computing semantic similarity for clinical text sentences generated from clinical notes in the real world. It attracted a large number of international teams. The ClinicalSTS shared task could continue to serve as a venue for researchers in natural language processing and medical informatics communities to develop and improve semantic textual similarity techniques for clinical text.

Sign in / Sign up

Export Citation Format

Share Document