scholarly journals Assessing the Performance of Clinical Natural Language Processing Systems: Development of an Evaluation Methodology

10.2196/20492 ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. e20492
Author(s):  
Lea Canales ◽  
Sebastian Menke ◽  
Stephanie Marchesseau ◽  
Ariel D’Agostino ◽  
Carlos del Rio-Bermudez ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Gold Standard ◽  
Performance Metrics ◽  
Evaluation Methodology ◽  
Free Text ◽  
Use Case ◽  
Five Phases ◽  
Clinical Natural Language Processing

Background Clinical natural language processing (cNLP) systems are of crucial importance due to their increasing capability in extracting clinically important information from free text contained in electronic health records (EHRs). The conversion of a nonstructured representation of a patient’s clinical history into a structured format enables medical doctors to generate clinical knowledge at a level that was not possible before. Finally, the interpretation of the insights gained provided by cNLP systems has a great potential in driving decisions about clinical practice. However, carrying out robust evaluations of those cNLP systems is a complex task that is hindered by a lack of standard guidance on how to systematically approach them. Objective Our objective was to offer natural language processing (NLP) experts a methodology for the evaluation of cNLP systems to assist them in carrying out this task. By following the proposed phases, the robustness and representativeness of the performance metrics of their own cNLP systems can be assured. Methods The proposed evaluation methodology comprised five phases: (1) the definition of the target population, (2) the statistical document collection, (3) the design of the annotation guidelines and annotation project, (4) the external annotations, and (5) the cNLP system performance evaluation. We presented the application of all phases to evaluate the performance of a cNLP system called “EHRead Technology” (developed by Savana, an international medical company), applied in a study on patients with asthma. As part of the evaluation methodology, we introduced the Sample Size Calculator for Evaluations (SLiCE), a software tool that calculates the number of documents needed to achieve a statistically useful and resourceful gold standard. Results The application of the proposed evaluation methodology on a real use-case study of patients with asthma revealed the benefit of the different phases for cNLP system evaluations. By using SLiCE to adjust the number of documents needed, a meaningful and resourceful gold standard was created. In the presented use-case, using as little as 519 EHRs, it was possible to evaluate the performance of the cNLP system and obtain performance metrics for the primary variable within the expected CIs. Conclusions We showed that our evaluation methodology can offer guidance to NLP experts on how to approach the evaluation of their cNLP systems. By following the five phases, NLP experts can assure the robustness of their evaluation and avoid unnecessary investment of human and financial resources. Besides the theoretical guidance, we offer SLiCE as an easy-to-use, open-source Python library.

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 Methodology for a statistically sound evaluation of clinical NLP systems (Preprint)

2020 ◽  
Author(s):  
Lea Canales ◽  
Ariel D’Agostino ◽  
Sebastian Menke
Keyword(s):  
Language Processing ◽  
Gold Standard ◽  
Performance Metrics ◽  
Software Tool ◽  
Chronic Obstructive ◽  
Use Case ◽  
Obstructive Pulmonary Disease ◽  
System P ◽  
Primary Variable ◽  
Clinical Natural Language Processing

BACKGROUND Clinical Natural Language Processing (NLP) systems are of crucial importance, because of their increasing relevance in driving decisions about clinical practice. However, carrying out a sound evaluation of NLP systems is complex and hindered by a lack of guidance on how to approach it. OBJECTIVE This research aims to provide a state-of-the-art methodology for the evaluation of a clinical NLP system, thereby guiding NLP researchers in this process with the final goal to ensure the robustness and representativeness of the performance metrics. METHODS We developed a methodology that guides through the process of developing an evaluation of a clinical NLP system using Savana’s ‘EHRead technology’ applied on a real use-case on chronic obstructive pulmonary disease (COPD). In addition, we further introduce SLiCE, a software tool that assists NLP specialists to create a statistically useful gold standard. RESULTS The gold standard contained 49.6% positive and 50.4% negative examples for COPD. For the COPD study, the confidence interval (CI) of the primary variable COPD, calculated using SLiCE, demonstrated its usefulness with CI widths of 0.074 for Precision, 0.046 for Recall, and 0.061 for F1, respectively. CONCLUSIONS Our proposed methodology aims to assist the process of creating an evaluation of a clinical NLP system. Researchers can follow our suggestions step-by-step and use SLiCE to statistically back up their gold standard. We successfully evaluated Savana’s ‘EHRead technology’ using our proposed methodology on a real use-case. We share here the outcome of our experiences working in developing NLP solutions for the clinical domain, hoping that it might help others to establish sound protocols for the evaluation of their NLP system.

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 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 Cohort Selection for Construction of a Clinical Natural Language Processing Corpus

2021 ◽  
pp. 100024
Author(s):  
Naga Lalitha Valli ALLA ◽  
Aipeng CHEN ◽  
Sean BATONGBACAL ◽  
Chandini NEKKANTTI ◽  
Hong-Jie Dai ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Cohort Selection ◽  
Selection For ◽  
Clinical Natural Language Processing

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.

Sign in / Sign up

Export Citation Format

Share Document