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 Machine learning and natural language processing in mental health: a systematic review (Preprint)

2019 ◽  
Author(s):  
Aziliz Le Glaz ◽  
Yannis Haralambous ◽  
Deok-Hee Kim-Dufor ◽  
Philippe Lenca ◽  
Romain Billot ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Systematic Review ◽  
Social Media ◽  
Natural Language Processing ◽  
Clinical Practice ◽  
Natural Language ◽  
Language Processing ◽  
Care Providers ◽  
Medical Databases

BACKGROUND Machine learning (ML) systems are parts of Artificial Intelligence (AI) that automatically learn models from data in order to make better decisions. Natural Language Processing (NLP), by using corpora and learning approaches, provides good performance in statistical tasks, such as text classification or sentiment mining. OBJECTIVE The primary aim of this systematic review is to summarize and characterize studies that used ML and NLP techniques for mental health, in methodological and technical terms. The secondary aim is to consider the interest of these methods in the mental health clinical practice. METHODS This systematic review follows the PRISMA guidelines and is registered on PROSPERO. The research was conducted on 4 medical databases (Pubmed, Scopus, ScienceDirect and PsycINFO) with the following keywords: machine learning, data mining, psychiatry, mental health, mental disorder. The exclusion criteria are: languages other than English, anonymization process, case studies, conference papers and reviews. No limitations on publication dates were imposed. RESULTS 327 articles were identified, 269 were excluded, and 58 were included in the review. Results were organized through a qualitative perspective. Even though studies had heterogeneous topics and methods, some themes emerged. Population studies could be grouped into three categories: patients included in medical databases, patients who came to the emergency room, and social-media users. The main objectives were symptom extraction, severity of illness classification, comparison of therapy effectiveness, psychopathological clues, and nosography challenging. Data from electronic medical records and that from social media were the two major data sources. With regard to the methods used, preprocessing used the standard methods of NLP and unique identifier extraction dedicated to medical texts. Efficient classifiers were preferred rather than "transparent” functioning classifiers. Python was the most frequently used platform. CONCLUSIONS ML and NLP models have been highly topical issues in medicine in recent years and may be considered a new paradigm in medical research. However, these processes tend to confirm clinical hypotheses rather than developing entirely new knowledge,. and one major category of the population, social-media users, is obviously an imprecise cohort. In addition, some language-specific features can improve the performance of NLP methods, and their extension to other languages should be more closely investigated. However, ML and NLP techniques provide useful information from unexplored data (i.e., patient’s daily habits that are usually inaccessible to care providers). This may be considered to be an additional tool at every step of mental health care: diagnosis, prognosis, treatment efficacy and monitoring. Therefore, ethical issues – like predicting psychiatric troubles or involvement in the physician-patient relationship – remain and should be discussed in a timely manner. ML and NLP methods may offer multiple perspectives in mental health research but should also be considered as tools to support clinical practice. CLINICALTRIAL Number CRD42019107376

Triage and diagnosis of COVID-19 from medical social media (Preprint)

2021 ◽  
Author(s):  
Abul Hasan ◽  
Mark Levene ◽  
David Weston ◽  
Renate Fromson ◽  
Nicolas Koslover ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Learning Models ◽  
Rule Based ◽  
Additional Information ◽  
Processing Pipeline ◽  
Machine Learning Models

BACKGROUND The COVID-19 pandemic has created a pressing need for integrating information from disparate sources, in order to assist decision makers. Social media is important in this respect, however, to make sense of the textual information it provides and be able to automate the processing of large amounts of data, natural language processing methods are needed. Social media posts are often noisy, yet they may provide valuable insights regarding the severity and prevalence of the disease in the population. In particular, machine learning techniques for triage and diagnosis could allow for a better understanding of what social media may offer in this respect. OBJECTIVE This study aims to develop an end-to-end natural language processing pipeline for triage and diagnosis of COVID-19 from patient-authored social media posts, in order to provide researchers and other interested parties with additional information on the symptoms, severity and prevalence of the disease. METHODS The text processing pipeline first extracts COVID-19 symptoms and related concepts such as severity, duration, negations, and body parts from patients’ posts using conditional random fields. An unsupervised rule-based algorithm is then applied to establish relations between concepts in the next step of the pipeline. The extracted concepts and relations are subsequently used to construct two different vector representations of each post. These vectors are applied separately to build support vector machine learning models to triage patients into three categories and diagnose them for COVID-19. RESULTS We report that Macro- and Micro-averaged F_{1\ }scores in the range of 71-96% and 61-87%, respectively, for the triage and diagnosis of COVID-19, when the models are trained on human labelled data. Our experimental results indicate that similar performance can be achieved when the models are trained using predicted labels from concept extraction and rule-based classifiers, thus yielding end-to-end machine learning. Also, we highlight important features uncovered by our diagnostic machine learning models and compare them with the most frequent symptoms revealed in another COVID-19 dataset. In particular, we found that the most important features are not always the most frequent ones. CONCLUSIONS Our preliminary results show that it is possible to automatically triage and diagnose patients for COVID-19 from natural language narratives using a machine learning pipeline, in order to provide additional information on the severity and prevalence of the disease through the eyes of social media.

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 Natural language processing and recurrent network models for identifying genomic mutation-associated cancer treatment change from patient progress notes

JAMIA Open ◽  
2019 ◽  
Vol 2 (1) ◽  
pp. 139-149 ◽  
Author(s):  
Meijian Guan ◽  
Samuel Cho ◽  
Robin Petro ◽  
Wei Zhang ◽  
Boris Pasche ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Cancer Patients ◽  
Language Processing ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Free Text ◽  
Treatment Change ◽  
Progress Notes

Abstract Objectives Natural language processing (NLP) and machine learning approaches were used to build classifiers to identify genomic-related treatment changes in the free-text visit progress notes of cancer patients. Methods We obtained 5889 deidentified progress reports (2439 words on average) for 755 cancer patients who have undergone a clinical next generation sequencing (NGS) testing in Wake Forest Baptist Comprehensive Cancer Center for our data analyses. An NLP system was implemented to process the free-text data and extract NGS-related information. Three types of recurrent neural network (RNN) namely, gated recurrent unit, long short-term memory (LSTM), and bidirectional LSTM (LSTM_Bi) were applied to classify documents to the treatment-change and no-treatment-change groups. Further, we compared the performances of RNNs to 5 machine learning algorithms including Naive Bayes, K-nearest Neighbor, Support Vector Machine for classification, Random forest, and Logistic Regression. Results Our results suggested that, overall, RNNs outperformed traditional machine learning algorithms, and LSTM_Bi showed the best performance among the RNNs in terms of accuracy, precision, recall, and F1 score. In addition, pretrained word embedding can improve the accuracy of LSTM by 3.4% and reduce the training time by more than 60%. Discussion and Conclusion NLP and RNN-based text mining solutions have demonstrated advantages in information retrieval and document classification tasks for unstructured clinical progress notes.

scholarly journals Using social media, machine learning and natural language processing to map multiple recreational beneficiaries

2019 ◽  
Vol 38 ◽  
pp. 100958 ◽  
Author(s):  
Arjan S. Gosal ◽  
Ilse R. Geijzendorffer ◽  
Tomáš Václavík ◽  
Brigitte Poulin ◽  
Guy Ziv
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing

Detection of Cyberbullying on Social Media Using Machine learning

2021 ◽  
Vol 9 (10) ◽  
pp. 1401-1409
Author(s):  
Mitta Roja
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Feature Extraction ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Hate Speech ◽  
Text Data ◽  
Model Based

Abstract: Cyberbullying is a major problem encountered on internet that affects teenagers and also adults. It has lead to mishappenings like suicide and depression. Regulation of content on Social media platorms has become a growing need. The following study uses data from two different forms of cyberbullying, hate speech tweets from Twittter and comments based on personal attacks from Wikipedia forums to build a model based on detection of Cyberbullying in text data using Natural Language Processing and Machine learning. Threemethods for Feature extraction and four classifiers are studied to outline the best approach. For Tweet data the model provides accuracies above 90% and for Wikipedia data it givesaccuracies above 80%. Keywords: Cyberbullying, Hate speech, Personal attacks,Machine learning, Feature extraction, Twitter, Wikipedia

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