Articles
653 result(s) for 'natural language processing' within BMC Medical Informatics and Decision Making
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Citation: BMC Medical Informatics and Decision Making 2021 21:220
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Special issue of BMC medical informatics and decision making on health natural language processing
Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 3):76 -
Editorial: The second international workshop on health natural language processing (HealthNLP 2019)
Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 5):233 -
Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system
The text descriptions in electronic medical records are a rich source of information. We have developed a Health Information Text Extraction (HITEx) tool and used it to extract key findings for a research stud...
Citation: BMC Medical Informatics and Decision Making 2006 6:30 -
Exploration of biomedical knowledge for recurrent glioblastoma using natural language processing deep learning models
Efficient exploration of knowledge for the treatment of recurrent glioblastoma (GBM) is critical for both clinicians and researchers. However, due to the large number of clinical trials and published articles, se...
Citation: BMC Medical Informatics and Decision Making 2022 22:267 -
Identify diabetic retinopathy-related clinical concepts and their attributes using transformer-based natural language processing methods
In this study, we examined two state-of-the-art transformer-based natural language processing (NLP) models, including BERT and RoBERTa...
Citation: BMC Medical Informatics and Decision Making 2022 22(Suppl 3):255 -
Negation recognition in clinical natural language processing using a combination of the NegEx algorithm and a convolutional neural network
Important clinical information of patients is present in unstructured free-text fields of Electronic Health Records (EHRs). While this information can be extracted using clinical Natural Language Processing (cNLP...
Citation: BMC Medical Informatics and Decision Making 2023 23:216 -
Evaluation of natural language processing from emergency department computerized medical records for intra-hospital syndromic surveillance
The identification of patients who pose an epidemic hazard when they are admitted to a health facility plays a role in preventing the risk of hospital acquired infection. An automated clinical decision support sy...
Citation: BMC Medical Informatics and Decision Making 2011 11:50 -
Intelligent virtual case learning system based on real medical records and natural language processing
Modernizing medical education by using artificial intelligence and other new technologies to improve the clinical thinking ability of medical students is an important research topic in recent years. Prominent med...
Citation: BMC Medical Informatics and Decision Making 2022 22:60 -
RegEMR: a natural language processing system to automatically identify premature ovarian decline from Chinese electronic medical records
We presented RegEMR, an artificial intelligence tool composed of a rule-based natural language processing (NLP) extractor and a knowledge-based...
Citation: BMC Medical Informatics and Decision Making 2023 23:126 -
Natural language processing for identification of hypertrophic cardiomyopathy patients from cardiac magnetic resonance reports
Cardiac magnetic resonance (CMR) imaging is important for diagnosis and risk stratification of hypertrophic cardiomyopathy (HCM) patients. However, collection of information from large numbers of CMR reports by m...
Citation: BMC Medical Informatics and Decision Making 2022 22:272 -
Development of a novel drug information provision system for Kampo medicine using natural language processing technology
Kampo medicine is widely used in Japan; however, most physicians and pharmacists have insufficient knowledge and experience in it. Although a chatbot-style system using machine learning and natural language processing
Citation: BMC Medical Informatics and Decision Making 2023 23:119 -
Analyzing patient experiences using natural language processing: development and validation of the artificial intelligence patient reported experience measure (AI-PREM)
Evaluating patients’ experiences is essential when incorporating the patients’ perspective in improving healthcare. Experiences are mainly collected using closed-ended questions, although the value of open-ended ...
Citation: BMC Medical Informatics and Decision Making 2022 22:183 -
Automated extraction of information of lung cancer staging from unstructured reports of PET-CT interpretation: natural language processing with deep-learning
Extracting metastatic information from previous radiologic-text reports is important, however, laborious annotations have limited the usability of these texts. We developed a deep-learning model for extracting...
Citation: BMC Medical Informatics and Decision Making 2022 22:229 -
Development and web deployment of an automated neuroradiology MRI protocoling tool with natural language processing
A systematic approach to MRI protocol assignment is essential for the efficient delivery of safe patient care. Advances in natural language processing (NLP) allow for the development of...
Citation: BMC Medical Informatics and Decision Making 2021 21:213 -
Natural language processing (NLP) tools in extracting biomedical concepts from research articles: a case study on autism spectrum disorder
Natural language processing (NLP) tools can facilitate the extraction...
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 11):322 -
A UMLS-based spell checker for natural language processing in vaccine safety
The Institute of Medicine has identified patient safety as a key goal for health care in the United States. Detecting vaccine adverse events is an important public health activity that contributes to patient safe...
Citation: BMC Medical Informatics and Decision Making 2007 7:3 -
Can natural language processing help differentiate inflammatory intestinal diseases in China? Models applying random forest and convolutional neural network approaches
Differentiating between ulcerative colitis (UC), Crohn’s disease (CD) and intestinal tuberculosis (ITB) using endoscopy is challenging. We aimed to realize automatic differential diagnosis among these diseases...
Citation: BMC Medical Informatics and Decision Making 2020 20:248 -
Supporting the classification of patients in public hospitals in Chile by designing, deploying and validating a system based on natural language processing
In Chile, a patient needing a specialty consultation or surgery has to first be referred by a general practitioner, then placed on a waiting list. The Explicit Health Guarantees (GES in Spanish) ensures, by la...
Citation: BMC Medical Informatics and Decision Making 2021 21:208 -
Identification of methicillin-resistant Staphylococcus aureus within the Nation’s Veterans Affairs Medical Centers using natural language processing
Herein, we describe a system that securely gathers microbiology data from the Department of Veterans Affairs (VA) network of databases. Using natural language processing methods, we applied an information extract...
Citation: BMC Medical Informatics and Decision Making 2012 12:34 -
Automation of a problem list using natural language processing
For this project, 80 medical problems were selected for their frequency of use in our future clinical field of evaluation (cardiovascular). We have developed an Automated Problem List system composed of two main ...
Citation: BMC Medical Informatics and Decision Making 2005 5:30 -
Extracting health-related causality from twitter messages using natural language processing
Twitter messages (tweets) contain various types of topics in our daily life, which include health-related topics. Analysis of health-related tweets would help us understand health conditions and concerns encounte...
Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 3):79 -
Natural language processing of radiology reports for identification of skeletal site-specific fractures
In this study, we developed a rule-based natural language processing (NLP) algorithm for identification of twenty...
Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 3):73 -
Natural language processing for populating lung cancer clinical research data
Lung cancer is the second most common cancer for men and women; the wide adoption of electronic health records (EHRs) offers a potential to accelerate cohort-related epidemiological studies using informatics appr...
Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 5):239 -
Facilitating accurate health provider directories using natural language processing
Accurate information in provider directories are vital in health care including health information exchange, health benefits exchange, quality reporting, and in the reimbursement and delivery of care. Maintaining...
Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 3):80 -
Using natural language processing methods to classify use status of dietary supplements in clinical notes
Despite widespread use, the safety of dietary supplements is open to doubt due to the fact that they can interact with prescribed medications, leading to dangerous clinical outcomes. Electronic health records ...
Citation: BMC Medical Informatics and Decision Making 2018 18(Suppl 2):51 -
Entity and relation extraction from clinical case reports of COVID-19: a natural language processing approach
This study aims to use natural language processing (NLP) to extract the key information...
Citation: BMC Medical Informatics and Decision Making 2023 23:20 -
A systematic review of natural language processing applied to radiology reports
Natural language processing (NLP) has a significant role in...
Citation: BMC Medical Informatics and Decision Making 2021 21:179 -
Development of a machine learning model to predict mild cognitive impairment using natural language processing in the absence of screening
Patients and their loved ones often report symptoms or complaints of cognitive decline that clinicians note in free clinical text, but no structured screening or diagnostic data are recorded. These symptoms/compl...
Citation: BMC Medical Informatics and Decision Making 2022 22:129 -
Screening pregnant women for suicidal behavior in electronic medical records: diagnostic codes vs. clinical notes processed by natural language processing
We examined the comparative performance of structured, diagnostic codes vs. natural language processing (NLP) of unstructured text for screening...
Citation: BMC Medical Informatics and Decision Making 2018 18:30 -
Developing a portable natural language processing based phenotyping system
This paper presents a portable phenotyping system that is capable of integrating both rule-based and statistical machine learning based approaches.
Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 3):78 -
Early recognition of multiple sclerosis using natural language processing of the electronic health record
Diagnostic accuracy might be improved by algorithms that searched patients’ clinical notes in the electronic health record (EHR) for signs and symptoms of diseases such as multiple sclerosis (MS). The focus th...
Citation: BMC Medical Informatics and Decision Making 2017 17:24 -
Semantic biomedical resource discovery: a Natural Language Processing framework
The aim of the present study is to investigate the use of semantics for biomedical resources annotation with domain specific ontologies and exploit Natural Language Processing methods in empowering the non-Inform...
Citation: BMC Medical Informatics and Decision Making 2015 15:77 -
A bibliometric analysis of natural language processing in medical research
Natural language processing (NLP) has become an increasingly significant ... of NLP methods and applications for medical information processing are available. It is of great significance...
Citation: BMC Medical Informatics and Decision Making 2018 18(Suppl 1):14 -
Development and validation of a pragmatic natural language processing approach to identifying falls in older adults in the emergency department
Falls among older adults are both a common reason for presentation to the emergency department, and a major source of morbidity and mortality. It is critical to identify fall patients quickly and reliably duri...
Citation: BMC Medical Informatics and Decision Making 2019 19:138 -
A validated natural language processing algorithm for brain imaging phenotypes from radiology reports in UK electronic health records
Manual coding of phenotypes in brain radiology reports is time consuming. We developed a natural language processing (NLP) algorithm to enable automatic identification...
Citation: BMC Medical Informatics and Decision Making 2019 19:184 -
The implementation of natural language processing to extract index lesions from breast magnetic resonance imaging reports
There are often multiple lesions in breast magnetic resonance imaging (MRI) reports and radiologists usually focus on describing the index lesion that is most crucial to clinicians in determining the management a...
Citation: BMC Medical Informatics and Decision Making 2019 19:288 -
Medical subdomain classification of clinical notes using a machine learning-based natural language processing approach
The medical subdomain of a clinical note, such as cardiology or neurology, is useful content-derived metadata for developing machine learning downstream applications. To classify the medical subdomain of a note a...
Citation: BMC Medical Informatics and Decision Making 2017 17:155 -
Use of natural language processing to improve predictive models for imaging utilization in children presenting to the emergency department
To examine the association between the medical imaging utilization and information related to patients’ socioeconomic, demographic and clinical factors during the patients’ ED visits; and to develop predictive...
Citation: BMC Medical Informatics and Decision Making 2019 19:287 -
How to automatically turn patient experience free-text responses into actionable insights: a natural language programming (NLP) approach
Patient experience surveys often include free-text responses. Analysis of these responses is time-consuming and often underutilized. This study examined whether Natural Language Processing (NLP) techniques could ...
Citation: BMC Medical Informatics and Decision Making 2020 20:97 -
ClotCatcher: a novel natural language model to accurately adjudicate venous thromboembolism from radiology reports
Accurate identification of venous thromboembolism (VTE) is critical to develop replicable epidemiological studies and rigorous predictions models. Traditionally, VTE studies have relied on international classific...
Citation: BMC Medical Informatics and Decision Making 2023 23:262 -
Temporal bone radiology report classification using open source machine learning and natural langue processing libraries
Radiology reports are a rich resource for biomedical research. Prior to utilization, trained experts must manually review reports to identify discrete outcomes. The Audiological and Genetic Database (AudGenDB)...
Citation: BMC Medical Informatics and Decision Making 2016 16:65 -
Case-based medical informatics
We outline the fundamental differences between Medical Informatics and theoretical sciences and propose that Medical Informatics research should advance individual knowledge processing (case-based reasoning) and ...
Citation: BMC Medical Informatics and Decision Making 2004 4:19 -
ask MEDLINE: a free-text, natural language query tool for MEDLINE/PubMed
Plain language search tools for MEDLINE/PubMed are few. We wanted to develop a search tool that would allow anyone using a free-text, natural language query and without knowing specialized vocabularies that an...
Citation: BMC Medical Informatics and Decision Making 2005 5:5 -
Transformers-sklearn: a toolkit for medical language understanding with transformer-based models
Transformer...is an attention-based architecture proven the state-of-the-art model in natural language processing (NLP). To reduce the difficulty of...transformer-based models in medical language understanding an...
Citation: BMC Medical Informatics and Decision Making 2021 21(Suppl 2):90 -
A comparative study of pre-trained language models for named entity recognition in clinical trial eligibility criteria from multiple corpora
Clinical trial protocols are the foundation for advancing medical sciences, however, the extraction of accurate and meaningful information from the original clinical trials is very challenging due to the complex ...
Citation: BMC Medical Informatics and Decision Making 2022 22(Suppl 3):235 -
Transformer-based deep neural network language models for Alzheimer’s disease risk assessment from targeted speech
We developed transformer-based deep learning models based on natural language processing for early risk assessment of Alzheimer’s...
Citation: BMC Medical Informatics and Decision Making 2021 21:92 -
When BERT meets Bilbo: a learning curve analysis of pretrained language model on disease classification
Natural language processing (NLP) tasks in the health domain ... task at hand. Recently, pretrained large-scale language models such as the Bidirectional Encoder Representations...
Citation: BMC Medical Informatics and Decision Making 2022 21(Suppl 9):377 -
Use of sentiment analysis for capturing hospitalized cancer patients' experience from free-text comments in the Persian language
Today, the Internet provides access to many patients' experiences, which is crucial in assessing the quality of healthcare services. This paper introduces a model for detecting cancer patients' opinions about ...
Citation: BMC Medical Informatics and Decision Making 2023 23:275 -
SNOMED CT in a language isolate: an algorithm for a semiautomatic translation
A translation algorithm that has its basis in Natural Language Processing methods has been designed and partially implemented....
Citation: BMC Medical Informatics and Decision Making 2015 15(Suppl 2):S5
Annual Journal Metrics
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2022 Citation Impact
3.5 - 2-year Impact Factor
3.9 - 5-year Impact Factor
1.384 - SNIP (Source Normalized Impact per Paper)
0.940 - SJR (SCImago Journal Rank)2022 Speed
28 days submission to first editorial decision for all manuscripts (Median)
170 days submission to accept (Median)2022 Usage
2,263,856 downloads
4,290 Altmetric mentions
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