scholarly journals Automated de-identification of clinical free-text

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Azad Dehghan ◽  
Tom Liptrot ◽  
Catherine O’hara ◽  
Matthew Barker-Hewitt ◽  
Daniel Tibble ◽  
...  
Keyword(s):  
Active Learning ◽  
Language Processing ◽  
Large Scale ◽  
Linear Chain ◽  
Data Access ◽  
Computational Method ◽  
Free Text ◽  
Clinical Text ◽  
Clinical Notes ◽  
Clinical Records

ABSTRACTObjectivesIncreasing interest to use unstructured electronic patient records for research has attracted attention to automated de-identification methods to conduct large scale removal of Personal Identifiable Information (PII). PII mainly include identifiable information such as person names, dates (e.g., date of birth), reference numbers (e.g., hospital number, NHS number), locations (e.g., hospital names, addresses), contacts (e.g., telephone, e-mail), occupation, age, and other identity information (ethnical, religion, sexual) mentioned in a private context.  De-identification of clinical free-text remains crucial to enable large-scale data access for health research while adhering to legal (Data Protection Act 1998) and ethical obligations. Here we present a computational method developed to automatically remove PII from clinical text. ApproachIn order to automatically identify PII in clinical text, we have developed and validated a Natural Language Processing (NLP) method which combine knowledge- (lexical dictionaries and rules) and data-driven (linear-chain conditional random fields) techniques. In addition, we have designed a novel two-pass recognition approach that uses the output of the initial pass to create patient-level and run-time dictionaries used to identify PII mentions that lack specific contextual clues considered by the initial entity extraction modules. The labelled data used to model and validate our techniques were generated using six human annotators and two distinct types of free-text from The Christie NHS Foundation Trust: (1) clinical correspondence (400 documents) and (2) clinical notes (1,300 documents). ResultsThe de-identification approach was developed and validated using a 60/40 percent split between the development and test datasets. The preliminary results show that our method achieves 97% and 93% token-level F1-measure on clinical correspondence and clinical notes respectively. In addition, the proposed two-pass recognition method was found particularly effective for longitudinal records. Notably, the performances are comparable to human benchmarks (using inter annotator agreements) of 97% and 90% F1 respectively. ConclusionsWe have developed and validated a state-of-the-art method that matches human benchmarks in identification and removal of PII from free-text clinical records. The method has been further validated across multiple institutions and countries (United States and United Kingdom), where we have identified a notable NLP challenge of cross-dataset adaption and have proposed using active learning methods to address this problem. The algorithm, including an active learning component, will be provided as open source to the healthcare community.

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 Cognitive Impairments in Schizophrenia: A Study in a Large Clinical Sample Using Natural Language Processing

2021 ◽  
Vol 3 ◽  
Author(s):  
Aurelie Mascio ◽  
Robert Stewart ◽  
Riley Botelle ◽  
Marcus Williams ◽  
Luwaiza Mirza ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Clinical Outcomes ◽  
Language Processing ◽  
Large Scale ◽  
Rating Scales ◽  
Cognitive Impairments ◽  
Free Text ◽  
Socio Demographic Factors ◽  
Clinical Records

Background: Cognitive impairments are a neglected aspect of schizophrenia despite being a major factor of poor functional outcome. They are usually measured using various rating scales, however, these necessitate trained practitioners and are rarely routinely applied in clinical settings. Recent advances in natural language processing techniques allow us to extract such information from unstructured portions of text at a large scale and in a cost effective manner. We aimed to identify cognitive problems in the clinical records of a large sample of patients with schizophrenia, and assess their association with clinical outcomes.Methods: We developed a natural language processing based application identifying cognitive dysfunctions from the free text of medical records, and assessed its performance against a rating scale widely used in the United Kingdom, the cognitive component of the Health of the Nation Outcome Scales (HoNOS). Furthermore, we analyzed cognitive trajectories over the course of patient treatment, and evaluated their relationship with various socio-demographic factors and clinical outcomes.Results: We found a high prevalence of cognitive impairments in patients with schizophrenia, and a strong correlation with several socio-demographic factors (gender, education, ethnicity, marital status, and employment) as well as adverse clinical outcomes. Results obtained from the free text were broadly in line with those obtained using the HoNOS subscale, and shed light on additional associations, notably related to attention and social impairments for patients with higher education.Conclusions: Our findings demonstrate that cognitive problems are common in patients with schizophrenia, can be reliably extracted from clinical records using natural language processing, and are associated with adverse clinical outcomes. Harvesting the free text from medical records provides a larger coverage in contrast to neurocognitive batteries or rating scales, and access to additional socio-demographic and clinical variables. Text mining tools can therefore facilitate large scale patient screening and early symptoms detection, and ultimately help inform clinical decisions.

scholarly journals Ascertaining Framingham heart failure phenotype from inpatient electronic health record data using natural language processing: a multicentre Atherosclerosis Risk in Communities (ARIC) validation study

BMJ Open ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. e047356
Author(s):  
Carlton R Moore ◽  
Saumya Jain ◽  
Stephanie Haas ◽  
Harish Yadav ◽  
Eric Whitsel ◽  
...  
Keyword(s):  
Language Processing ◽  
Validation Dataset ◽  
Free Text ◽  
Electronic Health Record Data ◽  
Atherosclerosis Risk In Communities ◽  
Clinical Notes ◽  
Atherosclerosis Risk ◽  
Record Data ◽  
Sensitivity Specificity ◽  
Aric Study

ObjectivesUsing free-text clinical notes and reports from hospitalised patients, determine the performance of natural language processing (NLP) ascertainment of Framingham heart failure (HF) criteria and phenotype.Study designA retrospective observational study design of patients hospitalised in 2015 from four hospitals participating in the Atherosclerosis Risk in Communities (ARIC) study was used to determine NLP performance in the ascertainment of Framingham HF criteria and phenotype.SettingFour ARIC study hospitals, each representing an ARIC study region in the USA.ParticipantsA stratified random sample of hospitalisations identified using a broad range of International Classification of Disease, ninth revision, diagnostic codes indicative of an HF event and occurring during 2015 was drawn for this study. A randomly selected set of 394 hospitalisations was used as the derivation dataset and 406 hospitalisations was used as the validation dataset.InterventionUse of NLP on free-text clinical notes and reports to ascertain Framingham HF criteria and phenotype.Primary and secondary outcome measuresNLP performance as measured by sensitivity, specificity, positive-predictive value (PPV) and agreement in ascertainment of Framingham HF criteria and phenotype. Manual medical record review by trained ARIC abstractors was used as the reference standard.ResultsOverall, performance of NLP ascertainment of Framingham HF phenotype in the validation dataset was good, with 78.8%, 81.7%, 84.4% and 80.0% for sensitivity, specificity, PPV and agreement, respectively.ConclusionsBy decreasing the need for manual chart review, our results on the use of NLP to ascertain Framingham HF phenotype from free-text electronic health record data suggest that validated NLP technology holds the potential for significantly improving the feasibility and efficiency of conducting large-scale epidemiologic surveillance of HF prevalence and incidence.

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.

scholarly journals 2166

2017 ◽  
Vol 1 (S1) ◽  
pp. 12-12
Author(s):  
Jianyin Shao ◽  
Ram Gouripeddi ◽  
Julio C. Facelli
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Power Distribution ◽  
Large Scale ◽  
Search Space ◽  
Free Text ◽  
Eligibility Criteria ◽  
Clinical Notes ◽  
Semantic Concepts ◽  
Mimic Iii

OBJECTIVES/SPECIFIC AIMS: This poster presents a detailed characterization of the distribution of semantic concepts used in the text describing eligibility criteria of clinical trials reported to ClincalTrials.gov and patient notes from MIMIC-III. The final goal of this study is to find a minimal set of semantic concepts that can describe clinical trials and patients for efficient computational matching of clinical trial descriptions to potential participants at large scale. METHODS/STUDY POPULATION: We downloaded the free text describing the eligibility criteria of all clinical trials reported to ClinicalTrials.gov as of July 28, 2015, ~195,000 trials and ~2,000,000 clinical notes from MIMIC-III. Using MetaMap 2014 we extracted UMLS concepts (CUIs) from the collected text. We calculated the frequency of presence of the semantic concepts in the texts describing the clinical trials eligibility criteria and patient notes. RESULTS/ANTICIPATED RESULTS: The results show a classical power distribution, Y=210X(−2.043), R2=0.9599, for clinical trial eligibility criteria and Y=513X(−2.684), R2=0.9477 for MIMIC patient notes, where Y represents the number of documents in which a concept appears and X is the cardinal order the concept ordered from more to less frequent. From this distribution, it is possible to realize that from the over, 100,000 concepts in UMLS, there are only ~60,000 and 50,000 concepts that appear in less than 10 clinical trial eligibility descriptions and MIMIC-III patient clinical notes, respectively. This indicates that it would be possible to describe clinical trials and patient notes with a relatively small number of concepts, making the search space for matching patients to clinical trials a relatively small sub-space of the overall UMLS search space. DISCUSSION/SIGNIFICANCE OF IMPACT: Our results showing that the concepts used to describe clinical trial eligibility criteria and patient clinical notes follow a power distribution can lead to tractable computational approaches to automatically match patients to clinical trials at large scale by considerably reducing the search space. While automatic patient matching is not the panacea for improving clinical trial recruitment, better low cost computational preselection processes can allow the limited human resources assigned to patient recruitment to be redirected to the most promising targets for recruitment.

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.

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 Discovering Monogenic Patients with a Confirmed Molecular Diagnosis in Millions of Clinical Notes with MonoMiner

2021 ◽  
Author(s):  
David Wei Wu ◽  
Jon A Bernstein ◽  
Gill Bejerano
Keyword(s):  
Molecular Diagnosis ◽  
Language Processing ◽  
Clinical Care ◽  
Disease Diagnosis ◽  
Monogenic Disease ◽  
Free Text ◽  
Causal Gene ◽  
Hospital System ◽  
Clinical Notes ◽  
Monogenic Diseases

Purpose: Cohort building is a powerful foundation for improving clinical care, performing research, clinical trial recruitment, and many other applications. We set out to build a cohort of all patients with monogenic conditions who have received a definitive causal gene diagnosis in a 3 million patient hospital system. Methods: We define a subset of half (4,461) of OMIM curated diseases for which at least one monogenic causal gene is definitively known. We then introduce MonoMiner, a natural language processing framework to identify molecularly confirmed monogenic patients from free-text clinical notes. Results: We show that ICD-10-CM codes cover only a fraction of known monogenic diseases, and even where available, code-based patient retrieval offers 0.12 precision. Searching by causal gene symbol offers great recall but an even worse 0.09 precision. MonoMiner achieves 7-9 times higher precision (0.82), with 0.88 precision on disease diagnosis alone, tagging 4,259 patients with 560 monogenic diseases and 534 causal genes, at 0.48 recall. Conclusion: MonoMiner enables the discovery of a large, high-precision cohort of monogenic disease patients with an established molecular diagnosis, empowering numerous downstream uses. Because it relies only on clinical notes, MonoMiner is highly portable, and its approach is adaptable to other domains and languages.

Natural language processing for structuring clinical text data on depression using UK-CRIS

2020 ◽  
Vol 23 (1) ◽  
pp. 21-26 ◽  
Author(s):  
Nemanja Vaci ◽  
Qiang Liu ◽  
Andrey Kormilitzin ◽  
Franco De Crescenzo ◽  
Ayse Kurtulmus ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Active Learning ◽  
Electronic Health Records ◽  
Natural Language ◽  
Language Processing ◽  
Real World ◽  
Statistical Models ◽  
Health Records ◽  
Clinical Text ◽  
Electronic Health

BackgroundUtilisation of routinely collected electronic health records from secondary care offers unprecedented possibilities for medical science research but can also present difficulties. One key issue is that medical information is presented as free-form text and, therefore, requires time commitment from clinicians to manually extract salient information. Natural language processing (NLP) methods can be used to automatically extract clinically relevant information.ObjectiveOur aim is to use natural language processing (NLP) to capture real-world data on individuals with depression from the Clinical Record Interactive Search (CRIS) clinical text to foster the use of electronic healthcare data in mental health research.MethodsWe used a combination of methods to extract salient information from electronic health records. First, clinical experts define the information of interest and subsequently build the training and testing corpora for statistical models. Second, we built and fine-tuned the statistical models using active learning procedures.FindingsResults show a high degree of accuracy in the extraction of drug-related information. Contrastingly, a much lower degree of accuracy is demonstrated in relation to auxiliary variables. In combination with state-of-the-art active learning paradigms, the performance of the model increases considerably.ConclusionsThis study illustrates the feasibility of using the natural language processing models and proposes a research pipeline to be used for accurately extracting information from electronic health records.Clinical implicationsReal-world, individual patient data are an invaluable source of information, which can be used to better personalise treatment.

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