Measuring adherence to a Choosing Wisely recommendation in a regional oncology clinic.

2016 ◽  
Vol 34 (7_suppl) ◽  
pp. 196-196
Author(s):  
Kathryn S. Egan ◽  
Gary H. Lyman ◽  
Karma L. Kreizenbeck ◽  
Catherine R. Fedorenko ◽  
April Alfiler ◽  
...  
Keyword(s):  
Data Processing ◽  
Language Processing ◽  
Cancer Registry ◽  
Choosing Wisely ◽  
National Guidelines ◽  
Billing Data ◽  
Database Queries ◽  
Clinical Text ◽  
Clinical Notes ◽  
Processing Techniques

196 Background: Natural language processing (NLP) has the potential to significantly ease the burden of manual abstraction of unstructured electronic text when measuring adherence to national guidelines. We incorporated NLP into standard data processing techniques such as manual abstraction and database queries in order to more efficiently evaluate a regional oncology clinic’s adherence to ASCO’s Choosing Wisely colony stimulating factor (CSF) recommendation using clinical, billing, and cancer registry data. Methods: Database queries on the clinic’s cancer registry yielded the study population of patients with stage II-IV breast, non-small cell lung (NSCL), and colorectal cancer. We manually abstracted chemotherapy regimens from paper prescription records. CSF orders were collected through queries on the clinic’s facility billing data, when available; otherwise through a custom NLP program and manual abstraction of the electronic medical record. The NLP program was designed to identify clinical note text containing CSF information, which was then manually abstracted. Results: Out of 31,725 clinical notes for the eligible population, the NLP program identified 1,487 clinical notes with CSF-related language, effectively reducing the number of notes requiring abstraction by up to 95%. Between 1/1/2012-12/31/2014, adherence to the ASCO CW CSF recommendation at the regional oncology clinic was 89% for a population of 322 patients. Conclusions: NLP significantly reduced the burden of manual abstraction by singling out relevant clinical text for abstractors. Abstraction is often necessary due to the complexity of data collection tasks or the use of paper records. However, NLP is a valuable addition to the suite of data processing techniques traditionally used to measure adherence to national guidelines.

Systematic review of current natural language processing methods and applications in cardiology

Heart ◽  
2021 ◽  
pp. heartjnl-2021-319769
Author(s):  
Meghan Reading Turchioe ◽  
Alexander Volodarskiy ◽  
Jyotishman Pathak ◽  
Drew N Wright ◽  
James Enlou Tcheng ◽  
...  
Keyword(s):  
Systematic Review ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Clinical Care ◽  
Real World Data ◽  
Clinical Text ◽  
Clinical Notes ◽  
Artery Disease ◽  
Automated Methods

Natural language processing (NLP) is a set of automated methods to organise and evaluate the information contained in unstructured clinical notes, which are a rich source of real-world data from clinical care that may be used to improve outcomes and understanding of disease in cardiology. The purpose of this systematic review is to provide an understanding of NLP, review how it has been used to date within cardiology and illustrate the opportunities that this approach provides for both research and clinical care. We systematically searched six scholarly databases (ACM Digital Library, Arxiv, Embase, IEEE Explore, PubMed and Scopus) for studies published in 2015–2020 describing the development or application of NLP methods for clinical text focused on cardiac disease. Studies not published in English, lacking a description of NLP methods, non-cardiac focused and duplicates were excluded. Two independent reviewers extracted general study information, clinical details and NLP details and appraised quality using a checklist of quality indicators for NLP studies. We identified 37 studies developing and applying NLP in heart failure, imaging, coronary artery disease, electrophysiology, general cardiology and valvular heart disease. Most studies used NLP to identify patients with a specific diagnosis and extract disease severity using rule-based NLP methods. Some used NLP algorithms to predict clinical outcomes. A major limitation is the inability to aggregate findings across studies due to vastly different NLP methods, evaluation and reporting. This review reveals numerous opportunities for future NLP work in cardiology with more diverse patient samples, cardiac diseases, datasets, methods and applications.

GRAPHICAL INDUCTION OF QUALIFIED MEDICAL KNOWLEDGE

2013 ◽  
Vol 07 (04) ◽  
pp. 377-405 ◽  
Author(s):  
TRAVIS GOODWIN ◽  
SANDA M. HARABAGIU
Keyword(s):  
Language Processing ◽  
Clinical Data ◽  
Medical Knowledge ◽  
Knowledge Bases ◽  
Clinical Knowledge ◽  
Biomedical Knowledge ◽  
Clinical Text ◽  
Relational Information ◽  
Processing Techniques ◽  
Medical Concepts

The introduction of electronic medical records (EMRs) enabled the access of unprecedented volumes of clinical data, both in structured and unstructured formats. A significant amount of this clinical data is expressed within the narrative portion of the EMRs, requiring natural language processing techniques to unlock the medical knowledge referred to by physicians. This knowledge, derived from the practice of medical care, complements medical knowledge already encoded in various structured biomedical ontologies. Moreover, the clinical knowledge derived from EMRs also exhibits relational information between medical concepts, derived from the cohesion property of clinical text, which is an attractive attribute that is currently missing from the vast biomedical knowledge bases. In this paper, we describe an automatic method of generating a graph of clinically related medical concepts by considering the belief values associated with those concepts. The belief value is an expression of the clinician's assertion that the concept is qualified as present, absent, suggested, hypothetical, ongoing, etc. Because the method detailed in this paper takes into account the hedging used by physicians when authoring EMRs, the resulting graph encodes qualified medical knowledge wherein each medical concept has an associated assertion (or belief value) and such qualified medical concepts are spanned by relations of different strengths, derived from the clinical contexts in which concepts are used. In this paper, we discuss the construction of a qualified medical knowledge graph (QMKG) and treat it as a BigData problem addressed by using MapReduce for deriving the weighted edges of the graph. To be able to assess the value of the QMKG, we demonstrate its usage for retrieving patient cohorts by enabling query expansion that produces greatly enhanced results against state-of-the-art methods.

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.

scholarly journals 2182

2017 ◽  
Vol 1 (S1) ◽  
pp. 12-12 ◽  
Author(s):  
Rashmee Shah ◽  
Benjamin Steinberg ◽  
Brian Bucher ◽  
Alec Chapman ◽  
Donald Lloyd-Jones ◽  
...  
Keyword(s):  
Language Processing ◽  
Accurate Method ◽  
Median Number ◽  
Bleeding Events ◽  
False Negatives ◽  
Clinical Text ◽  
Clinical Notes ◽  
Large Populations ◽  
Study Population ◽  
Number Of Stems

OBJECTIVES/SPECIFIC AIMS: An accurate method to identify bleeding in large populations does not exist. Our goal was to explore bleeding representation in clinical text in order to develop a natural language processing (NLP) approach to automatically identify bleeding events from clinical notes. METHODS/STUDY POPULATION: We used publicly available notes for ICU patients at high risk of bleeding (n=98,586 notes). Two physicians reviewed randomly selected notes and annotated all direct references to bleeding as “bleeding present” (BP) or “bleeding absent” (BA). Annotations were made at the mention level (if 1 specific sentence/phrase indicated BP or BA) and note level (if overall note indicated BP or BA). A third physician adjudicated discordant annotations. RESULTS/ANTICIPATED RESULTS: In 120 randomly selected notes, bleeding was mentioned 406 times with 76 distinct words. Inter-annotator agreement was 89% by the last batch of 30 notes. In total, 10 terms accounted for 65% of all bleeding mentions. We aggregated these results into 16 common stems (eg, “hemorr” for hemorrhagic and hemorrhage), which accounted for 90% of all 406 mentions. Of all 120 notes, 60% were classified as BP. The median number of stems was 5 (IQR 2, 9) in BP Versus 0 (IQR 0, 1) in BA notes. Zero bleeding mentions in a note was associated with BA (OR 28, 95% CI 6.5, 127). With 40 true negatives and 2 false negatives, the negative predictive value (NPV) of zero bleeding mentions was 95%. DISCUSSION/SIGNIFICANCE OF IMPACT: Few bleeding-related terms are used in clinical practice. Absence of these terms has a high NPV for the absence of bleeding. These results suggest that a high throughput, rules-based NLP tool to identify bleeding is feasible.

scholarly journals Clinical Text Mining of Electronic Health Records to Classify Leprosy Patients Cases

2020 ◽  
Vol 9 (5) ◽  
pp. 2331-2336
Keyword(s):  
Electronic Health Records ◽  
Language Processing ◽  
Neglected Tropical Diseases ◽  
Support Vector ◽  
Health Records ◽  
Clinical Text ◽  
Clinical Notes ◽  
Leprosy Patients ◽  
Electronic Health ◽  
And Performance

Leprosy is one of the major public health problems and listed among the neglected tropical diseases in India. It is also called Hansen's Diseases (HD), which is a long haul contamination by microorganisms Mycobacterium leprae or Mycobacterium lepromatosis.Untreated, leprosy can dynamic and changeless harm to the skin, nerves, appendages, and eyes. This paper intends to depict classification of leprosy cases from the main indication of side effects. Electronic Health Records (EHRs) of Leprosy Patients from verified sources have been generated. The clinical notes included in EHRs have been processed through Natural Language Processing Tools. In order to predict type of leprosy, Rule based classification method has been proposed in this paper. Further our approach is compared with various Machine Learning (ML) algorithms like Support Vector Machine (SVM), Logistic regression (LR) and performance parameters are compared.

scholarly journals MedExt: combining expert knowledge and deep learning for medication extraction from French clinical texts (Preprint)

2020 ◽  
Author(s):  
Jordan Jouffroy ◽  
Sarah F Feldman ◽  
Ivan Lerner ◽  
Bastien Rance ◽  
Anita Burgun ◽  
...  
Keyword(s):  
Language Processing ◽  
Expert Knowledge ◽  
Rule Based ◽  
Clinical Text ◽  
Clinical Notes ◽  
Rule Based System ◽  
Related Information ◽  
Medication Information ◽  
Deep Recurrent Neural Network ◽  
F Measure

BACKGROUND Information related to patient medication is crucial for health care. However, up to 80% of the information resides solely in unstructured text. Manual extraction may be difficult and time-consuming. Many studies have shown the interest of natural language processing for this task but only a few on French corpus. OBJECTIVE We aim at developing a system to extract medication-related information from French clinical text. METHODS We developed a hybrid system combining an expert rule-based system (RBS), contextual word embedding (ELMo) trained on clinical notes and a deep recurrent neural network (BiLSTM-CRF). The task consists in extracting drug mentions and their related information (e.g. dosage, frequency, duration, route, condition). We manually annotated 320 clinical notes extracted from a French clinical data warehouse, to train and evaluate the model. We compared the performances of our approach to standard approaches: rule-based or machine learning only, and classic word embeddings. We evaluated the models using token level recall, precision and F-measure. RESULTS Models including RBS, ELMo and BiLSTM reached the best results: overall F-measure of 89.9%. F-measures per category were 95.3% for the medication name, 64.4% for the drug class mentions, 95.3% for the dosage, 92.2% for the frequency, 78.8% for the duration, and 62.2% for the condition of the intake. CONCLUSIONS Associating expert rules, deep contextualized embedding (ELMo) and deep neural networks improves medication information extraction. Our results reveal a synergy when associating expert knowledge and latent knowledge.

scholarly journals A study of deep learning methods for de-identification of clinical notes in cross-institute settings

2019 ◽  
Vol 19 (S5) ◽  
Author(s):  
Xi Yang ◽  
Tianchen Lyu ◽  
Qian Li ◽  
Chih-Yin Lee ◽  
Jiang Bian ◽  
...  
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Biomedical Literature ◽  
Fine Tuning ◽  
Word Embeddings ◽  
Patient Privacy ◽  
Learning Models ◽  
Linguistic Features ◽  
Clinical Text ◽  
Clinical Notes

Abstract Background De-identification is a critical technology to facilitate the use of unstructured clinical text while protecting patient privacy and confidentiality. The clinical natural language processing (NLP) community has invested great efforts in developing methods and corpora for de-identification of clinical notes. These annotated corpora are valuable resources for developing automated systems to de-identify clinical text at local hospitals. However, existing studies often utilized training and test data collected from the same institution. There are few studies to explore automated de-identification under cross-institute settings. The goal of this study is to examine deep learning-based de-identification methods at a cross-institute setting, identify the bottlenecks, and provide potential solutions. Methods We created a de-identification corpus using a total 500 clinical notes from the University of Florida (UF) Health, developed deep learning-based de-identification models using 2014 i2b2/UTHealth corpus, and evaluated the performance using UF corpus. We compared five different word embeddings trained from the general English text, clinical text, and biomedical literature, explored lexical and linguistic features, and compared two strategies to customize the deep learning models using UF notes and resources. Results Pre-trained word embeddings using a general English corpus achieved better performance than embeddings from de-identified clinical text and biomedical literature. The performance of deep learning models trained using only i2b2 corpus significantly dropped (strict and relax F1 scores dropped from 0.9547 and 0.9646 to 0.8568 and 0.8958) when applied to another corpus annotated at UF Health. Linguistic features could further improve the performance of de-identification in cross-institute settings. After customizing the models using UF notes and resource, the best model achieved the strict and relaxed F1 scores of 0.9288 and 0.9584, respectively. Conclusions It is necessary to customize de-identification models using local clinical text and other resources when applied in cross-institute settings. Fine-tuning is a potential solution to re-use pre-trained parameters and reduce the training time to customize deep learning-based de-identification models trained using clinical corpus from a different institution.

scholarly journals Natural language processing for abstraction of cancer treatment toxicities: accuracy versus human experts

JAMIA Open ◽  
2020 ◽  
Author(s):  
Julian C Hong ◽  
Andrew T Fairchild ◽  
Jarred P Tanksley ◽  
Manisha Palta ◽  
Jessica D Tenenbaum
Keyword(s):  
Radiation Therapy ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Text Analysis ◽  
High Accuracy ◽  
Radiation Dermatitis ◽  
Clinical Text ◽  
Clinical Notes ◽  
Oncology Research

Abstract Objectives Expert abstraction of acute toxicities is critical in oncology research but is labor-intensive and variable. We assessed the accuracy of a natural language processing (NLP) pipeline to extract symptoms from clinical notes compared to physicians. Materials and Methods Two independent reviewers identified present and negated National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE) v5.0 symptoms from 100 randomly selected notes for on-treatment visits during radiation therapy with adjudication by a third reviewer. A NLP pipeline based on Apache clinical Text Analysis Knowledge Extraction System was developed and used to extract CTCAE terms. Accuracy was assessed by precision, recall, and F1. Results The NLP pipeline demonstrated high accuracy for common physician-abstracted symptoms, such as radiation dermatitis (F1 0.88), fatigue (0.85), and nausea (0.88). NLP had poor sensitivity for negated symptoms. Conclusion NLP accurately detects a subset of documented present CTCAE symptoms, though is limited for negated symptoms. It may facilitate strategies to more consistently identify toxicities during cancer therapy.

scholarly journals Psychiatric stressor recognition from clinical notes to reveal association with suicide

2018 ◽  
Vol 25 (4) ◽  
pp. 1846-1862 ◽  
Author(s):  
Yaoyun Zhang ◽  
Olivia R Zhang ◽  
Rui Li ◽  
Aaron Flores ◽  
Salih Selek ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Large Scale ◽  
Suicidal Behaviors ◽  
Statistical Association ◽  
Clinical Text ◽  
Clinical Notes ◽  
Electronic Health ◽  
F Measure

Suicide takes the lives of nearly a million people each year and it is a tremendous economic burden globally. One important type of suicide risk factor is psychiatric stress. Prior studies mainly use survey data to investigate the association between suicide and stressors. Very few studies have investigated stressor data in electronic health records, mostly due to the data being recorded in narrative text. This study takes the initiative to automatically extract and classify psychiatric stressors from clinical text using natural language processing–based methods. Suicidal behaviors were also identified by keywords. Then, a statistical association analysis between suicide ideations/attempts and stressors extracted from a clinical corpus is conducted. Experimental results show that our natural language processing method could recognize stressor entities with an F-measure of 89.01 percent. Mentions of suicidal behaviors were identified with an F-measure of 97.3 percent. The top three significant stressors associated with suicide are health, pressure, and death, which are similar to previous studies. This study demonstrates the feasibility of using natural language processing approaches to unlock information from psychiatric notes in electronic health record, to facilitate large-scale studies about associations between suicide and psychiatric stressors.

scholarly journals Clinical Text De-identification and Other Large Scale Processing Tasks in Resource Constrained Environments

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Richard Jackson ◽  
Richard Dobson ◽  
Robert Stewart
Keyword(s):  
Data Processing ◽  
Language Processing ◽  
Large Scale ◽  
Data Science ◽  
Fault Tolerant ◽  
Data Repositories ◽  
Clinical Text ◽  
Governance Model ◽  
Constrained Environments ◽  
Large Scale Data Processing

ABSTRACT ObjectivesClinical text de-identification is a common requirement of the ‘enclave’ governance model of ethical EHR research. However, there is often little consideration of the engineering task that is required to scale these approaches across the hundreds of millions of clinical documents containing personal identifiers that are resident in the data repositories of a typical NHS Trust. Similarly, natural language processing is an increasingly important field of clinical data science, yet it requires fault tolerant approaches to data processing. This work concerns the development of “turbo-laser” - a distributed document processing architecture based upon the popular ‘battle hardened’ Spring Batch framework - an industry standard for large scale processing tasks. ApproachUsing Spring Batch, we developed a highly scalable unstructured data processing framework, using the concept of remote partitioning. Remote partitioning allows us to offload processing tasks to any and all computers in a network. With this approach, it is possible to harness the entire compute available of an organisation, whether it be an office of 15 desktop PCs that go unused overnight, or a compute cluster of a thousand processors. This method is especially valuable in the NHS, where the provision of sufficient compute to make large scale analytics possible are often hindered by the lack of available hardware, or difficulties in navigating technical governance policies ill equipped for the demands of modern data science. ResultsTurbo-laser was developed in consideration of the processing challenges common in the NHS. Currently, four types of ‘job’ are available - De-identification, using the Cognition algorithm, generic GATE output, text extraction from binary files such as MS Office, PDF and scanned documents, and a document re-compiler to deal with EHR legacy issues. Examples of turbo-laser usage include processing 9 million binary documents on modest hardware, within 48 hours. ConclusionTurbo-laser is an enterprise grade processing tool, in keeping with the software engineering pattern of ‘batch processing’ that has been at the forefront of the informatics movement. An open source project, it is hoped that others may contribute and extend its principles, lowering the barrier of large scale data processing throughout the NHS.

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