scholarly journals Anonymization Framework for Securing Protected Health Information in a Complex Dataset of Medical Narratives

2020 ◽  
Vol 39 (3) ◽  
pp. 612-624
Author(s):  
Saman Hina ◽  
Raheela Asif ◽  
Syed Abbas Ali
Keyword(s):  
Natural Language ◽  
Real Time ◽  
Health Information ◽  
Computational Linguistics ◽  
Language Processing ◽  
Free Text ◽  
Protected Health Information ◽  
Icd 10 ◽  
Medical Narratives ◽  
Medical Domain

It is imperative in a medical domain that protection of information does not allow an individual to be overlooked. In medical domain, research community encourages use of real-time datasets for research purposes. These real-time datasets contain structured and unstructured (natural language free text) information that can be useful to researchers in various disciplines including computational linguistics. On the other hand, these real-time datasets cannot be distributed without anonymization of Protected Health Information (PHI). The information of PHI (such as Name, age, address, etc.) that can identify an individual is unethical. Therefore, we present a rule-based Natural Language Processing (NLP) anonymization system using a challenging corpus containing medical narratives and ICD-10 codes (medical codes). This anonymization module can be used for pre-processing the corpus containing identifiable information. The corpus used in this research contains '2534' PHIs in '1984' medical records in total. 15% of the labelled corpus was used for improvement of guidelines in the identification and classification of PHI groups and 85% was held for the evaluation. Our anonymization system follows two step process: (1) Identification and cataloging PHIs with four PHI categories ('Patients Name', 'Doctors Name', 'Other Name [Names other than patients and doctors]', 'Place Name'), (2) Anonymization of PHIs by replacing identified PHIs with their respective PHI categories. Our method uses basic language processing, dictionaries, rules and heuristics to identify, classify and anonymize PHIs with PHI categories. We use standard metrics for evaluation and our system outperforms against human annotated gold standard with 100% of F-measure by increasing 39% from baseline results, which proves the reliability of data usage for research.

scholarly journals Automatic identification of risk factors for SARS-CoV-2 positivity and severe clinical outcomes of COVID-19 using Data Mining and Natural Language Processing

2021 ◽  
Author(s):  
Verena Schoening ◽  
Evangelia Liakoni ◽  
Juergen Drewe ◽  
Felix Hammann
Keyword(s):  
Risk Factors ◽  
Data Mining ◽  
Natural Language Processing ◽  
Natural Language ◽  
Real Time ◽  
Clinical Outcomes ◽  
Language Processing ◽  
Structured Data ◽  
Diabetic Patients ◽  
Free Text

Objectives: Several risk factors have been identified for severe clinical outcomes of COVID-19 caused by SARS-CoV-2. Some can be found in structured data of patients' Electronic Health Records. Others are included as unstructured free-text, and thus cannot be easily detected automatically. We propose an automated real-time detection of risk factors using a combination of data mining and Natural Language Processing (NLP). Material and methods: Patients were categorized as negative or positive for SARS-CoV-2, and according to disease severity (severe or non-severe COVID-19). Comorbidities were identified in the unstructured free-text using NLP. Further risk factors were taken from the structured data. Results: 6250 patients were analysed (5664 negative and 586 positive; 461 non-severe and 125 severe). Using NLP, comorbidities, i.e. cardiovascular and pulmonary conditions, diabetes, dementia and cancer, were automatically detected (error rate ≤2%). Old age, male sex, higher BMI, arterial hypertension, chronic heart failure, coronary heart disease, COPD, diabetes, insulin only treatment of diabetic patients, reduced kidney and liver function were risk factors for severe COVID-19. Interestingly, the proportion of diabetic patients using metformin but not insulin was significantly higher in the non-severe COVID-19 cohort (p<0.05). Discussion and conclusion: Our findings were in line with previously reported risk factors for severe COVID-19. NLP in combination with other data mining approaches appears to be a suitable tool for the automated real-time detection of risk factors, which can be a time saving support for risk assessment and triage, especially in patients with long medical histories and multiple comorbidities.

scholarly journals Sentiment Analysis Techniques Applied to Raw-Text Data from a Csq-8 Questionnaire about Mindfulness in Times of COVID-19 to Improve Strategy Generation

2021 ◽  
Vol 18 (12) ◽  
pp. 6408
Author(s):  
Mario Jojoa Acosta ◽  
Gema Castillo-Sánchez ◽  
Begonya Garcia-Zapirain ◽  
Isabel de la Torre Díez ◽  
Manuel Franco-Martín
Keyword(s):  
Health Care ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Language Processing ◽  
Health Care Professionals ◽  
Ground Truth ◽  
Relevant Information ◽  
Free Text

The use of artificial intelligence in health care has grown quickly. In this sense, we present our work related to the application of Natural Language Processing techniques, as a tool to analyze the sentiment perception of users who answered two questions from the CSQ-8 questionnaires with raw Spanish free-text. Their responses are related to mindfulness, which is a novel technique used to control stress and anxiety caused by different factors in daily life. As such, we proposed an online course where this method was applied in order to improve the quality of life of health care professionals in COVID 19 pandemic times. We also carried out an evaluation of the satisfaction level of the participants involved, with a view to establishing strategies to improve future experiences. To automatically perform this task, we used Natural Language Processing (NLP) models such as swivel embedding, neural networks, and transfer learning, so as to classify the inputs into the following three categories: negative, neutral, and positive. Due to the limited amount of data available—86 registers for the first and 68 for the second—transfer learning techniques were required. The length of the text had no limit from the user’s standpoint, and our approach attained a maximum accuracy of 93.02% and 90.53%, respectively, based on ground truth labeled by three experts. Finally, we proposed a complementary analysis, using computer graphic text representation based on word frequency, to help researchers identify relevant information about the opinions with an objective approach to sentiment. The main conclusion drawn from this work is that the application of NLP techniques in small amounts of data using transfer learning is able to obtain enough accuracy in sentiment analysis and text classification stages.

scholarly journals Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review

2021 ◽  
Vol 28 (1) ◽  
pp. e100262
Author(s):  
Mustafa Khanbhai ◽  
Patrick Anyadi ◽  
Joshua Symons ◽  
Kelsey Flott ◽  
Ara Darzi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Patient Experience ◽  
Language Processing ◽  
Performance Metrics ◽  
Free Text ◽  
Patient Feedback

ObjectivesUnstructured free-text patient feedback contains rich information, and analysing these data manually would require a lot of personnel resources which are not available in most healthcare organisations.To undertake a systematic review of the literature on the use of natural language processing (NLP) and machine learning (ML) to process and analyse free-text patient experience data.MethodsDatabases were systematically searched to identify articles published between January 2000 and December 2019 examining NLP to analyse free-text patient feedback. Due to the heterogeneous nature of the studies, a narrative synthesis was deemed most appropriate. Data related to the study purpose, corpus, methodology, performance metrics and indicators of quality were recorded.ResultsNineteen articles were included. The majority (80%) of studies applied language analysis techniques on patient feedback from social media sites (unsolicited) followed by structured surveys (solicited). Supervised learning was frequently used (n=9), followed by unsupervised (n=6) and semisupervised (n=3). Comments extracted from social media were analysed using an unsupervised approach, and free-text comments held within structured surveys were analysed using a supervised approach. Reported performance metrics included the precision, recall and F-measure, with support vector machine and Naïve Bayes being the best performing ML classifiers.ConclusionNLP and ML have emerged as an important tool for processing unstructured free text. Both supervised and unsupervised approaches have their role depending on the data source. With the advancement of data analysis tools, these techniques may be useful to healthcare organisations to generate insight from the volumes of unstructured free-text data.

scholarly journals Measuring Adoption of Patient Priorities-Aligned Care Using Natural Language Processing

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 183-183
Author(s):  
Javad Razjouyan ◽  
Jennifer Freytag ◽  
Edward Odom ◽  
Lilian Dindo ◽  
Aanand Naik
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Chart Review ◽  
Group Analysis ◽  
Intervention Group ◽  
Multiple Chronic Conditions ◽  
Free Text ◽  
Term Care

Abstract Patient Priorities Care (PPC) is a model of care that aligns health care recommendations with priorities of older adults with multiple chronic conditions. Social workers (SW), after online training, document PPC in the patient’s electronic health record (EHR). Our goal is to identify free-text notes with PPC language using a natural language processing (NLP) model and to measure PPC adoption and effect on long term services and support (LTSS) use. Free-text notes from the EHR produced by trained SWs passed through a hybrid NLP model that utilized rule-based and statistical machine learning. NLP accuracy was validated against chart review. Patients who received PPC were propensity matched with patients not receiving PPC (control) on age, gender, BMI, Charlson comorbidity index, facility and SW. The change in LTSS utilization 6-month intervals were compared by groups with univariate analysis. Chart review indicated that 491 notes out of 689 had PPC language and the NLP model reached to precision of 0.85, a recall of 0.90, an F1 of 0.87, and an accuracy of 0.91. Within group analysis shows that intervention group used LTSS 1.8 times more in the 6 months after the encounter compared to 6 months prior. Between group analysis shows that intervention group has significant higher number of LTSS utilization (p=0.012). An automated NLP model can be used to reliably measure the adaptation of PPC by SW. PPC seems to encourage use of LTSS that may delay time to long term care placement.

scholarly journals Natural language processing methods for knowledge management—Applying document clustering for fast search and grouping of engineering documents

2021 ◽  
pp. 1063293X2098297
Author(s):  
Ivar Örn Arnarsson ◽  
Otto Frost ◽  
Emil Gustavsson ◽  
Mats Jirstrand ◽  
Johan Malmqvist
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Domain Knowledge ◽  
Clustering Algorithms ◽  
Document Clustering ◽  
Unstructured Data ◽  
Free Text ◽  
Engineering Change ◽  
Engineering Documents

Product development companies collect data in form of Engineering Change Requests for logged design issues, tests, and product iterations. These documents are rich in unstructured data (e.g. free text). Previous research affirms that product developers find that current IT systems lack capabilities to accurately retrieve relevant documents with unstructured data. In this research, we demonstrate a method using Natural Language Processing and document clustering algorithms to find structurally or contextually related documents from databases containing Engineering Change Request documents. The aim is to radically decrease the time needed to effectively search for related engineering documents, organize search results, and create labeled clusters from these documents by utilizing Natural Language Processing algorithms. A domain knowledge expert at the case company evaluated the results and confirmed that the algorithms we applied managed to find relevant document clusters given the queries tested.

An Evaluation of Patient Safety Event Report Categories Using Unsupervised Topic Modeling

2015 ◽  
Vol 54 (04) ◽  
pp. 338-345 ◽  
Author(s):  
A. Fong ◽  
R. Ratwani
Keyword(s):  
Patient Safety ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Topic Modeling ◽  
Free Text ◽  
Event Data ◽  
Event Type ◽  
Modeling Approach ◽  
Safety Event

SummaryObjective: Patient safety event data repositories have the potential to dramatically improve safety if analyzed and leveraged appropriately. These safety event reports often consist of both structured data, such as general event type categories, and unstructured data, such as free text descriptions of the event. Analyzing these data, particularly the rich free text narratives, can be challenging, especially with tens of thousands of reports. To overcome the resource intensive manual review process of the free text descriptions, we demonstrate the effectiveness of using an unsupervised natural language processing approach.Methods: An unsupervised natural language processing technique, called topic modeling, was applied to a large repository of patient safety event data to identify topics, or themes, from the free text descriptions of the data. Entropy measures were used to evaluate and compare these topics to the general event type categories that were originally assigned by the event reporter.Results: Measures of entropy demonstrated that some topics generated from the un-supervised modeling approach aligned with the clinical general event type categories that were originally selected by the individual entering the report. Importantly, several new latent topics emerged that were not originally identified. The new topics provide additional insights into the patient safety event data that would not otherwise easily be detected.Conclusion: The topic modeling approach provides a method to identify topics or themes that may not be immediately apparent and has the potential to allow for automatic reclassification of events that are ambiguously classified by the event reporter.

scholarly journals Utility of Natural Language Processing for Clinical Quality Measures Reporting

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Dino P. Rumoro ◽  
Shital C. Shah ◽  
Gillian S. Gibbs ◽  
Marilyn M. Hallock ◽  
Gordon M. Trenholme ◽  
...  
Keyword(s):  
Health Care ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Medical Center ◽  
Academic Medical Center ◽  
Quality Measures ◽  
Surveillance Program ◽  
Free Text ◽  
Influenza Immunization

ObjectiveTo explain the utility of using an automated syndromic surveillanceprogram with advanced natural language processing (NLP) to improveclinical quality measures reporting for influenza immunization.IntroductionClinical quality measures (CQMs) are tools that help measure andtrack the quality of health care services. Measuring and reportingCQMs helps to ensure that our health care system is deliveringeffective, safe, efficient, patient-centered, equitable, and timely care.The CQM for influenza immunization measures the percentage ofpatients aged 6 months and older seen for a visit between October1 and March 31 who received (or reports previous receipt of) aninfluenza immunization. Centers for Disease Control and Preventionrecommends that everyone 6 months of age and older receive aninfluenza immunization every season, which can reduce influenza-related morbidity and mortality and hospitalizations.MethodsPatients at a large academic medical center who had a visit toan affiliated outpatient clinic during June 1 - 8, 2016 were initiallyidentified using their electronic medical record (EMR). The 2,543patients who were selected did not have documentation of influenzaimmunization in a discrete field of the EMR. All free text notes forthese patients between August 1, 2015 and March 31, 2016 wereretrieved and analyzed using the sophisticated NLP built withinGeographic Utilization of Artificial Intelligence in Real-Timefor Disease Identification and Alert Notification (GUARDIAN)– a syndromic surveillance program – to identify any mention ofinfluenza immunization. The goal was to identify additional cases thatmet the CQM measure for influenza immunization and to distinguishdocumented exceptions. The patients with influenza immunizationmentioned were further categorized by GUARDIAN NLP intoReceived, Recommended, Refused, Allergic, and Unavailable.If more than one category was applicable for a patient, they wereindependently counted in their respective categories. A descriptiveanalysis was conducted, along with manual review of a sample ofcases per each category.ResultsFor the 2,543 patients who did not have influenza immunizationdocumentation in a discrete field of the EMR, a total of 78,642 freetext notes were processed using GUARDIAN. Four hundred fiftythree (17.8%) patients had some mention of influenza immunizationwithin the notes, which could potentially be utilized to meet the CQMinfluenza immunization requirement. Twenty two percent (n=101)of patients mentioned already having received the immunizationwhile 34.7% (n=157) patients refused it during the study time frame.There were 27 patients with the mention of influenza immunization,who could not be differentiated into a specific category. The numberof patients placed into a single category of influenza immunizationwas 351 (77.5%), while 75 (16.6%) were classified into more thanone category. See Table 1.ConclusionsUsing GUARDIAN’s NLP can identify additional patients whomay meet the CQM measure for influenza immunization or whomay be exempt. This tool can be used to improve CQM reportingand improve overall influenza immunization coverage by using it toalert providers. Next steps involve further refinement of influenzaimmunization categories, automating the process of using the NLPto identify and report additional cases, as well as using the NLP forother CQMs.Table 1. Categorization of influenza immunization documentation within freetext notes of 453 patients using NLP

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