Mining Cancer-related Information in Electronic Healthcare Records with Natural Language Processing

Cancer is in the midst of leading causes of death. In 2018, around 1,735,350 new cases of cancer were estimated and 609,640 people will die from cancer in the United States. A wealth of cancer-relevant information is conserved in a variety of types of healthcare records, for example, the electronic health records (EHRs). However, part of the critical information is organized in the free narrative text which hampers machine to interpret the information underlying the text. The development of artificial intelligence provides a variety of solutions to this plight. For example, the technology of natural language processing (NLP) has emerged bridging the gap between free text and structured representation of cancer information. Recently, several researchers have published their work on unearthing cancer-related information in EHRs based on the NLP technology. Apart from the traditional NLP methods, the development of deep learning helps EHRs mining go further.

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

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18126408 ◽

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.

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Natural Language Processing for Automatic Identification of Major Depressive Disorders in Free-Text Electronic Health Records

Biological Psychiatry ◽

10.1016/j.biopsych.2021.02.399 ◽

2021 ◽

Vol 89 (9) ◽

pp. S155

Author(s):

Nicolas Nunez ◽

Joanna M. Biernacka ◽

Manuel Gardea-Resendez ◽

Bhavani Singh Agnikula Kshatriya ◽

Euijung Ryu ◽

...

Keyword(s):

Natural Language Processing ◽

Electronic Health Records ◽

Natural Language ◽

Language Processing ◽

Depressive Disorders ◽

Automatic Identification ◽

Free Text ◽

Major Depressive ◽

Health Records ◽

Major Depressive Disorders

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Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocy173 ◽

2019 ◽

Vol 26 (4) ◽

pp. 364-379 ◽

Cited By ~ 34

Author(s):

Theresa A Koleck ◽

Caitlin Dreisbach ◽

Philip E Bourne ◽

Suzanne Bakken

Keyword(s):

Natural Language Processing ◽

Electronic Health Records ◽

Natural Language ◽

Language Processing ◽

Healthcare Providers ◽

Patient Characteristics ◽

Disease Classification ◽

Free Text ◽

Health Records ◽

Electronic Health

Abstract Objective Natural language processing (NLP) of symptoms from electronic health records (EHRs) could contribute to the advancement of symptom science. We aim to synthesize the literature on the use of NLP to process or analyze symptom information documented in EHR free-text narratives. Materials and Methods Our search of 1964 records from PubMed and EMBASE was narrowed to 27 eligible articles. Data related to the purpose, free-text corpus, patients, symptoms, NLP methodology, evaluation metrics, and quality indicators were extracted for each study. Results Symptom-related information was presented as a primary outcome in 14 studies. EHR narratives represented various inpatient and outpatient clinical specialties, with general, cardiology, and mental health occurring most frequently. Studies encompassed a wide variety of symptoms, including shortness of breath, pain, nausea, dizziness, disturbed sleep, constipation, and depressed mood. NLP approaches included previously developed NLP tools, classification methods, and manually curated rule-based processing. Only one-third (n = 9) of studies reported patient demographic characteristics. Discussion NLP is used to extract information from EHR free-text narratives written by a variety of healthcare providers on an expansive range of symptoms across diverse clinical specialties. The current focus of this field is on the development of methods to extract symptom information and the use of symptom information for disease classification tasks rather than the examination of symptoms themselves. Conclusion Future NLP studies should concentrate on the investigation of symptoms and symptom documentation in EHR free-text narratives. Efforts should be undertaken to examine patient characteristics and make symptom-related NLP algorithms or pipelines and vocabularies openly available.

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Cohort profile: St. Michael's Hospital Tuberculosis Database (SMH-TB), a retrospective cohort of electronic health record data and variables extracted using natural language processing

10.1101/2020.09.11.20192419 ◽

2020 ◽

Author(s):

David Landsman ◽

Ahmed Abdelbasit ◽

Christine Wang ◽

Michael Guerzhoy ◽

Ujash Joshi ◽

...

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Clinical Epidemiology ◽

Relevant Information ◽

Structured Data ◽

Free Text ◽

Electronic Health Record Data ◽

Latent Tb ◽

Electronic Health

Background Tuberculosis (TB) is a major cause of death worldwide. TB research draws heavily on clinical cohorts which can be generated using electronic health records (EHR), but granular information extracted from unstructured EHR data is limited. The St. Michael's Hospital TB database (SMH-TB) was established to address gaps in EHR-derived TB clinical cohorts and provide researchers and clinicians with detailed, granular data related to TB management and treatment. Methods We collected and validated multiple layers of EHR data from the TB outpatient clinic at St. Michael's Hospital, Toronto, Ontario, Canada to generate the SMH-TB database. SMH-TB contains structured data directly from the EHR, and variables generated using natural language processing (NLP) by extracting relevant information from free-text within clinic, radiology, and other notes. NLP performance was assessed using recall, precision and F1 score averaged across variable labels. We present characteristics of the cohort population using binomial proportions and 95% confidence intervals (CI), with and without adjusting for NLP misclassification errors. Results SMH-TB currently contains retrospective patient data spanning 2011 to 2018, for a total of 3298 patients (N=3237 with at least 1 associated dictation). Performance of TB diagnosis and medication NLP rulesets surpasses 93% in recall, precision and F1 metrics, indicating good generalizability. We estimated 20% (95% CI: 18.4-21.2%) were diagnosed with active TB and 46% (95% CI: 43.8-47.2%) were diagnosed with latent TB. After adjusting for potential misclassification, the proportion of patients diagnosed with active and latent TB was 18% (95% CI: 16.8-19.7%) and 40% (95% CI: 37.8-41.6%) respectively Conclusion SMH-TB is a unique database that includes a breadth of structured data derived from structured and unstructured EHR data. The data are available for a variety of research applications, such as clinical epidemiology, quality improvement and mathematical modelling studies.

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Cohort profile: St. Michael’s Hospital Tuberculosis Database (SMH-TB), a retrospective cohort of electronic health record data and variables extracted using natural language processing

PLoS ONE ◽

10.1371/journal.pone.0247872 ◽

2021 ◽

Vol 16 (3) ◽

pp. e0247872

Author(s):

David Landsman ◽

Ahmed Abdelbasit ◽

Christine Wang ◽

Michael Guerzhoy ◽

Ujash Joshi ◽

...

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Clinical Epidemiology ◽

Relevant Information ◽

Structured Data ◽

Free Text ◽

Electronic Health Record Data ◽

Latent Tb ◽

Electronic Health

Background Tuberculosis (TB) is a major cause of death worldwide. TB research draws heavily on clinical cohorts which can be generated using electronic health records (EHR), but granular information extracted from unstructured EHR data is limited. The St. Michael’s Hospital TB database (SMH-TB) was established to address gaps in EHR-derived TB clinical cohorts and provide researchers and clinicians with detailed, granular data related to TB management and treatment. Methods We collected and validated multiple layers of EHR data from the TB outpatient clinic at St. Michael’s Hospital, Toronto, Ontario, Canada to generate the SMH-TB database. SMH-TB contains structured data directly from the EHR, and variables generated using natural language processing (NLP) by extracting relevant information from free-text within clinic, radiology, and other notes. NLP performance was assessed using recall, precision and F1 score averaged across variable labels. We present characteristics of the cohort population using binomial proportions and 95% confidence intervals (CI), with and without adjusting for NLP misclassification errors. Results SMH-TB currently contains retrospective patient data spanning 2011 to 2018, for a total of 3298 patients (N = 3237 with at least 1 associated dictation). Performance of TB diagnosis and medication NLP rulesets surpasses 93% in recall, precision and F1 metrics, indicating good generalizability. We estimated 20% (95% CI: 18.4–21.2%) were diagnosed with active TB and 46% (95% CI: 43.8–47.2%) were diagnosed with latent TB. After adjusting for potential misclassification, the proportion of patients diagnosed with active and latent TB was 18% (95% CI: 16.8–19.7%) and 40% (95% CI: 37.8–41.6%) respectively Conclusion SMH-TB is a unique database that includes a breadth of structured data derived from structured and unstructured EHR data by using NLP rulesets. The data are available for a variety of research applications, such as clinical epidemiology, quality improvement and mathematical modeling studies.

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Natural Language Processing and the Oncologic History: Is There a Match?

Journal of Oncology Practice ◽

10.1200/jop.2011.000240 ◽

2011 ◽

Vol 7 (4) ◽

pp. e15-e19 ◽

Cited By ~ 11

Author(s):

Jeremy L. Warner ◽

Peter Anick ◽

Pengyu Hong ◽

Nianwen Xue

Keyword(s):

Natural Language Processing ◽

Electronic Health Records ◽

Natural Language ◽

Language Processing ◽

Cancer Care ◽

Free Text ◽

Health Records ◽

Care Continuum ◽

Widespread Adoption ◽

Cancer Care Continuum

The widespread adoption of electronic health records within the oncology community is creating rich databases that contain details of the cancer care continuum. Large portions of this information are locked up in free text, but several efforts are underway to address this.

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

BMJ Health & Care Informatics ◽

10.1136/bmjhci-2020-100262 ◽

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.

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

Innovation in Aging ◽

10.1093/geroni/igaa057.592 ◽

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.

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

Concurrent Engineering ◽

10.1177/1063293x20982973 ◽

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.

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