Text Analysis System Research Articles

Application of natural language processing techniques to identify off-label drug usage from various online health communities.

ObjectiveOutcomes mentioned on online health communities (OHCs) by patients can serve as a source of evidence for off-label drug usage evaluation, but identifying these outcomes manually is tedious work. We have built a natural language processing model to identify off-label usage of drugs mentioned in these patient posts.Materials and MethodsSingle patient posts from 4 major OHCs were considered for this study. A text classification model was built to classify the posts as either relevant or not relevant based on patient experience. The relevant posts were passed through a spelling correction tool, CSpell, and then medications and indications from these posts were identified using cTAKES (clinical Text Analysis and Knowledge Extraction System), a named entity recognition tool. Drug and indication pairs were identified using a dependency parser. Finally, if the paired indication was not mentioned on the label of the drug approved by U.S. Food and Drug Administration, it was tagged as off-label use of that drug.ResultsUsing this algorithm, we identified 289 off-label indications, achieving a recall of 76%.ConclusionsThe method designed in this study identifies and extracts the semantic relationship between drugs and indications from demotic posts in OHCs. The results demonstrate the feasibility of using natural language processing techniques in identifying off-label drug usage across online health forums for a variety of drugs. Understanding patients’ off-label use of drugs may be able to help manufacturers innovate to better address patients’ needs and assist doctors’ prescribing decisions.

Automated Complexity Assessment of English Informational Texts for EFL Pre-service Teachers and Translators

Automated text complexity assessment tools are of enormous practical value in solving the time-consuming task of analyzing English informational texts for their complexity at the pre-reading stage. The present study depicts the application of the automated text analysis system the TextEvaluator as an effective tool that helps analyze texts on eight dimensions of text complexity as follows: syntactic complexity; academic vocabulary; word unfamiliarity; word concreteness; lexical cohesion; interactive style; level of argumentation; degree of narrativity, with further summarizing them with an overall genre-dependent complexity score. This research examines the complexity dimensions of English informational texts of four genres – legal, linguistic, news, and medical – that are used for teaching reading comprehension to EFL (English as a foreign language) pre-service teachers and translators at universities in Ukraine. The data obtained with the help of the TextEvaluator has shown that English legal texts are the most difficult for reading comprehension in comparison to linguistic, news, and medical texts. In contrast, medical texts are the least challenging out of the four genres compared. The TextEvaluator has provided insight into the complexity of English informational texts across their different genres that would be useful for assembling the corpora of reading passages scaled on specific dimensions of text complexity that predict text difficulty to EFL pre-service teachers and translators.

Text detection and localization in scene images: a broad review

Nowadays, text detection and localization have gained much popularity in the field of text analysis systems as they pave the way for the number of real-time based applications like mobile transliteration technologies, assistive methods for visually impaired persons, etc. Text detection and localization techniques are used to find the position of text area in the image.This paper intends to present a broad review in this field as five-fold: (1) comparison of document images with scene images and applications of natural scene images, (2) significant and up-to-date traditional machine learning and deep learning-based approaches for the text detection and localization for different languages, (3) various publicly available benchmarked datasets, (4) comparative analysis for other benchmarked datasets and, (5) related challenges and future scope on the field. The paper summarises some of the potential ways in this field, which can serve as a useful reference for the researchers for future exploration of the area.

Natural Language Processing Workflow for Customer Request Analysis in a Company

Abstract The paper presents a comparison of existing text analysis systems from the point of view of their applicability to customer request analysis tasks for company needs. The tasks of named entity search and text classification for preliminary analysis of customer requests to the company’s support department are considered. In the course of work, five systems were considered and preliminarily evaluated in terms of the functionality required for solving the above tasks. The final assessment was based on training of the systems using a marked-up corpus of texts provided by the company’s support department.

Does One Size Fit All? Exploring the Contribution of Text features, Text content, and Grade of Use on Comprehension

Readability systems have once more become prominent in policy and practice because of recommendations in the Common Core State Standards. This study revisited two features of current text analysis (readability) systems: their generalizability to all grade levels and to all content areas. A database that encompassed texts across the grade bands and content areas and included aggregate comprehension performance on the texts was used to: (a) describe how the text features (i.e., word frequency, word length, sentence length) varied at different grade levels and within different subject areas and (b) examine if the prediction of comprehension with the text features was moderated by the grade or content area of the text. Results indicated that texts did having differing levels of various word features along both grade and content lines especially in the area of sentence length. In addition, content and grade moderated the relationship between sentence length and comprehension.

Technological fundamentals of logical and linguistic research of legislation

The article substantiates and describes the use of the author's software products – the Text Analysis System and the Cognitive IT Platform «POLYHEDRON» in the prospective logical-linguistic studies of Ukrainian legislation. Authors use the complex logical and linguistic research of the Law of Ukraine "On special procedure for the removal of the President of Ukraine from office (impeachment) "No. 39-IX of September 10, 2019 as an example.

RETRACTED: Analysis of connection behaviour of communication network flow based on semantic understanding

It presents a way to build support for knowledge, analytical reasoning and explore analysis methods in visual text analysis. To do this, use automatically from the use of semantic network model parameters K- unstructured text data obtained for the neighbourhood. Semantic network analysis and network analysis methods to obtain quantitative and qualitative insights. Qualitative analysis and quantitative indicators to explore the semantic structure can support it. have discussed the basic theoretical assumptions about the text modelling to analyse the semantic network and subsequent semantic network. For a systematic overview, demonstrated the essential network elements of the qualitative meaning, which means the network analysis, to understand the meaning of a given network, support analyst doing. Possible exploration strategy. As proof of concept, use a visual survey and analysis of the semantic network. To explain the method proposed a typical analysis of the Wikipedia article, use the visual text analysis system.

Detecting changes in attitudes toward depression on Chinese social media: A text analysis

Background & Aims: Depression is a common and sometimes severe form of mental illness, and public attitudes towards depression can impact the psychological and social functioning of depressed patients. The purpose of the present study was to investigate public attitudes toward depression and three-year trends in these attitudes using big data analysis of social media posts in China.Methods: A search of publically available Sina Weibo posts from January 2014 to July 2017 identified 20,129 hot posts with the keyword term “depression”. We first used a Chinese Linguistic Psychological Text Analysis System (TextMind) to analyze linguistic features of the posts. And, then we used topic models to conduct semantic content analysis to identify specific themes in Weibo users’ attitudes toward depression.Results: Linguistic features analysis showed a significant increase over time in the frequency of terms related to affect, positive emotion, anger, cognition (including the subcategory of insight), and conjunctions. Semantic content analysis identified five common themes: severe effects of depression, stigma, combating stigma, appeals for understanding, and providing support. There was a significant increase over time in references to social (as opposed to professional) support, and a significant decrease over time in references to the severe consequences of depression.Conclusions: Big data analysis of Weibo posts is likely to provide less biased information than other methods about the public's attitudes toward depression. The results suggest that although there is ongoing stigma about depression, there is also an upward trend in mentions of social support for depressed persons. A supervised learning statistical model can be developed in future research to provide an even more precise analysis of specific attitudes.

APPLICATION OF INFORMATION TECHNOLOGIES FOR SEMANTIC TEXT PROCESSING

An expert system for text analysis based on the heuristic knowledge of an expert linguist is proposed. Methods of linguistic analysis of the text through the use of computer technology have been further developed. Data verification was performed on the example of the Germanic language group. The algorithm of the system operation is given. The sequence of actions of the text analysis process is described. Research relates to the subject of computational linguistics and helps to automate text analysis processes. The main purpose of the research is to improve the machine's understanding of the semantic structure of the text by finding current connections between the main members of the sentence, current connections between secondary members of the sentence, the best concept of the current word and the function of the current word in the sentence. Semantic networks are used in the software solution. The Java programming shell, such as NetBeans IDE 8.1, and the CLIPS shell, were used to create the software product. The main logical connections and structure of the program are described in the article. Methods and relations are considered on the example of the Germanic group of languages. All languages of the Germanic group are similar because they have a direct line of words, which makes them even more similar: subject + predicate + subordinate clauses. Thus, to reflect the structure of the Germanic group of languages, it is sufficient to consider one of them. Namely, English, as it is the most common (1.5 billion people), international, has the largest vocabulary among the group (500 thousand words) and, in our opinion, the most complex.

Detecting and Filtering Immune-Related Adverse Events Signal Based on Text Mining and Observational Health Data Sciences and Informatics Common Data Model: Framework Development Study.

BackgroundImmune checkpoint inhibitors are associated with unique immune-related adverse events (irAEs). As most of the immune checkpoint inhibitors are new to the market, it is important to conduct studies using real-world data sources to investigate their safety profiles.ObjectiveThe aim of the study was to develop a framework for signal detection and filtration of novel irAEs for 6 Food and Drug Administration–approved immune checkpoint inhibitors.MethodsIn our framework, we first used the Food and Drug Administration’s Adverse Event Reporting System (FAERS) standardized in an Observational Health Data Sciences and Informatics (OHDSI) common data model (CDM) to collect immune checkpoint inhibitor-related event data and conducted irAE signal detection. OHDSI CDM is a standard-driven data model that focuses on transforming different databases into a common format and standardizing medical terms to a common representation. We then filtered those already known irAEs from drug labels and literature by using a customized text-mining pipeline based on clinical text analysis and knowledge extraction system with Medical Dictionary for Regulatory Activities (MedDRA) as a dictionary. Finally, we classified the irAE detection results into three different categories to discover potentially new irAE signals.ResultsBy our text-mining pipeline, 490 irAE terms were identified from drug labels, and 918 terms were identified from the literature. In addition, of the 94 positive signals detected using CDM-based FAERS, 53 signals (56%) were labeled signals, 10 (11%) were unlabeled published signals, and 31 (33%) were potentially new signals.ConclusionsWe demonstrated that our approach is effective for irAE signal detection and filtration. Moreover, our CDM-based framework could facilitate adverse drug events detection and filtration toward the goal of next-generation pharmacovigilance that seamlessly integrates electronic health record data for improved signal detection.

Single Concatenated Input is Better than Indenpendent Multiple-input for CNNs to Predict Chemical-induced Disease Relation from Literature

Single Concatenated Input is Better than Indenpendent Multiple-input for CNNs to Predict Chemical-induced Disease Relation from Literature

Identification of Mind-Set of Students through Web based Basic Psychological Text and Graphical Analysis System

In this era of 21st century the most talked subject regarding children is mind-set. According to Carol Dweck, mind-set can predict success in life. It’s an internal voice that keeps you down or lifts you up. It is very important to understand student’s mind-set and mental health, so proper care, inputs, facility and necessary arrangement can be taken at very early stage for uplifting career growth. Mind-set of a person may be guessed from the outlook of the person and from the mental status of the person i.e mentality. Mind-set of a person reflects in psyche, attitude, and ethos. Now, the parameters that influenced mind-set of a person are family members with whom they are born and brought up, behaviour of family members, surroundings environment, situation under which they are born and brought up, facilities provided and friends circle etc. There are two types of mind-set basically resides in students one is growth Mind-set and the other is Fixed Mind-set. It has been observed that for last one decade the number of suicide and depression in students at the age group of 15 to 20 years has increased a lot. State of mind-set is not permanent. The parameters affects positive growth of mind-set can be nullified with some practice and change of habits. Right mind-set can be nourished choosing growth over negativity. Though this research paper, attempt was made to identify the different stages of mind-set of students and problems faced by them which hinders students’ progress and makes them depressed through web based graphical interactive system by using Gradient and Pearson Correlation Co-efficient method.

MAP Estimation With Bernoulli Randomness, and Its Application to Text Analysis and Recommender Systems

MAP estimation plays an important role in many probabilistic models. However, in many cases, the MAP problem is non-convex and intractable. In this work, we propose a novel algorithm, called BOPE, which uses Bernoulli randomness for Online Maximum a Posteriori Estimation. We show that BOPE has a fast convergence rate. In particular, BOPE implicitly employs a prior which plays as regularization. Such a prior is different from the one of the MAP problem and will be vanishing as BOPE does more iterations. This property of BOPE is significant and enables to reduce severe overfitting for probabilistic models in ill-posed cases, including short text, sparse data, and noisy data. We validate the practical efficiency of BOPE in two contexts: text analysis and recommender systems. Both contexts show the superior of BOPE over the baselines.

Analysis and prediction in sparse and high dimensional text data: The case of Dow Jones stock market

In this research, we proposed a text analysis system to predict stock market movements using news and social media data. It is a scalable prediction system for sparse and high dimensional feature sets. Using the developed system, we collected 12,560 articles from New York Times covering one year time period, and 2,854,333 tweets from Twitter covering 4 months time period. We analysed the collected data using entity extraction, sentiment analysis and topic modelling techniques. We applied our feature set creation and elastic net regression based training method . The analyses have been used to train different prediction models. Using these trained prediction models, we predicted stock market movements for Dow Jones Index and showed that the proposed method can make promising predictions. In different sets of experiments, highly accurate (up to 70.90% accuracy) predictions are made by the proposed approach. These predicted values also correlated (up to 0.2315 correlation coefficient value) with real Dow Jones Index values. Further, we report performance comparison results for various prediction models that we trained with different set of features to analyse the importance of time interval and feature space size. Our test results show that it is possible to make reasonable stock movement prediction by integrating news and related social media data, analysing them using named entity extraction, sentiment analysis and topic modelling techniques together with prediction models which use features that are created from these analysis results.

Data-driven method to enhance craniofacial and oral phenotype vocabularies

BackgroundA significant amount of clinical information captured as free-text narratives could be better used for several applications, such as clinical decision support, ontology development, evidence-based practice, and research. The Human Phenotype Ontology (HPO) is specifically used for semantic comparisons for diagnostic purposes. All these functions require quality coverage of the domain of interest. The authors used natural language processing to capture craniofacial and oral phenotype signatures from electronic health records and then used these signatures for evaluation of existing oral phenotype ontology coverage. MethodsThe authors applied a text-processing pipeline based on the clinical Text Analysis and Knowledge Extraction System to annotate the clinical notes with Unified Medical Language System codes. The authors extracted the disease or disorder phenotype terms, which were then compared with HPO terms and their synonyms. ResultsThe authors retrieved 2,153 deidentified clinical notes from 558 patients. Finally, 2,416 unique diseases or disorders phenotype terms were extracted, which included 210 craniofacial or oral phenotype terms. Twenty-six of these phenotypes were not found in the HPO. ConclusionsThe authors demonstrated that natural language processing tools could extract relevant phenotype terms from clinical narratives, which could help identify gaps in existing ontologies and enhance craniofacial and dental phenotyping vocabularies. Practical ImplicationsThe expansion of terms in the dental, oral, and craniofacial domains in the HPO is particularly important as the dental community moves toward electronic health records.

Type of Supervised Text Classification System for Unstructured Text Comments using Probability Theory Technique

In this paper, an improved sentimental text analysis system called Probability Based Text Classifier (PBTC) is presented. It aims to train the existing unstructured text command set and to classify the sampled text command belongs into positive or negative polarity based on probability theory and supervised concepts. It consists of three stages pre-processed, training and classification. In the first stage, the proposed (PBTC) system identifies the relevant and irrelevant words in the unstructured text command set based on pre-determined text pattern model. In the second stage it identifies two dissimilar classes over the preprocessed text command set based on predetermined text pattern model and simple probability theory concepts. Next stage, the PBTC identifies the sample test text command without class label belong on which class based on Naive Bayer scheme and trained existing text command set. Experimental result shows that the proposed (PBTC) system is well suitable to train the unstructured text command set and classify the new text command belongs into positive or negative polarity with higher accuracy

Validation of an alcohol misuse classifier in hospitalized patients.

Current modes of identifying alcohol misuse in hospitalized patients rely on self-report questionnaires and diagnostic codes that have limitations, including low sensitivity. Information in the clinical notes of the electronic health record (EHR) may further augment the identification of alcohol misuse. Natural language processing (NLP) with supervised machine learning has been successful at analyzing clinical notes and identifying cases of alcohol misuse in trauma patients. An alcohol misuse NLP classifier, previously developed on trauma patients who completed the Alcohol Use Disorders Identification Test, was validated in a cohort of 1000 hospitalized patients at a large, tertiary health system between January 1, 2007 and September 1, 2017. The clinical notes were processed using the clinical Text Analysis and Knowledge Extraction System. The National Institute on Alcohol Abuse and Alcoholism (NIAAA) guidelines for alcohol misuse were used during annotation of the medical records in our validation dataset. The alcohol misuse classifier had an area under the receiver operating characteristic curve of 0.91 (95% CI 0.90-0.93) in the cohort of hospitalized patients. The sensitivity, specificity, positive predictive value, and negative predictive value were 0.88 (95% CI 0.85-0.90), 0.78 (95% CI 0.74-0.82), 0.85 (95% CI 0.82-0.87), and 0.82 (95% CI 0.78-0.86), respectively. The Hosmer-Lemeshow Test (p=0.13) demonstrates good model fit. Additionally, there was a dose-dependent response in alcohol consumption behaviors across increasing strata of predicted probabilities for alcohol misuse. The alcohol misuse NLP classifier had good discrimination and test characteristics in hospitalized patients. An approach using the clinical notes with NLP and supervised machine learning may better identify alcohol misuse cases than conventional methods solely relying on billing diagnostic codes.

Development and application of a high throughput natural language processing architecture to convert all clinical documents in a clinical data warehouse into standardized medical vocabularies.

Natural language processing (NLP) engines such as the clinical Text Analysis and Knowledge Extraction System are a solution for processing notes for research, but optimizing their performance for a clinical data warehouse remains a challenge. We aim to develop a high throughput NLP architecture using the clinical Text Analysis and Knowledge Extraction System and present a predictive model use case. The CDW was comprised of 1 103 038 patients across 10 years. The architecture was constructed using the Hadoop data repository for source data and 3 large-scale symmetric processing servers for NLP. Each named entity mention in a clinical document was mapped to the Unified Medical Language System concept unique identifier (CUI). The NLP architecture processed 83 867 802 clinical documents in 13.33 days and produced 37 721 886 606 CUIs across 8 standardized medical vocabularies. Performance of the architecture exceeded 500 000 documents per hour across 30 parallel instances of the clinical Text Analysis and Knowledge Extraction System including 10 instances dedicated to documents greater than 20 000 bytes. In a use-case example for predicting 30-day hospital readmission, a CUI-based model had similar discrimination to n-grams with an area under the curve receiver operating characteristic of 0.75 (95% CI, 0.74-0.76). Our health system's high throughput NLP architecture may serve as a benchmark for large-scale clinical research using a CUI-based approach.

A systematic approach for developing a corpus of patient reported adverse drug events: A case study for SSRI and SNRI medications.

"Psychiatric Treatment Adverse Reactions" (PsyTAR) corpus is an annotated corpus that has been developed using patients narrative data for psychiatric medications, particularly SSRIs (Selective Serotonin Reuptake Inhibitor) and SNRIs (Serotonin Norepinephrine Reuptake Inhibitor) medications. This corpus consists of three main components: sentence classification, entity identification, and entity normalization. We split the review posts into sentences and labeled them for presence of adverse drug reactions (ADRs) (2168 sentences), withdrawal symptoms (WDs) (438 sentences), sign/symptoms/illness (SSIs) (789 sentences), drug indications (517), drug effectiveness (EF) (1087 sentences), and drug infectiveness (INF) (337 sentences). In the entity identification phase, we identified and extracted ADRs (4813 mentions), WDs (590 mentions), SSIs (1219 mentions), and DIs (792). In the entity normalization phase, we mapped the identified entities to the corresponding concepts in both UMLS (918 unique concepts) and SNOMED CT (755 unique concepts). Four annotators double coded the sentences and the span of identified entities by strictly following guidelines rules developed for this study. We used the PsyTAR sentence classification component to automatically train a range of supervised machine learning classifiers to identifying text segments with the mentions of ADRs, WDs, DIs, SSIs, EF, and INF. SVMs classifiers had the highest performance with F-Score 0.90. We also measured performance of the cTAKES (clinical Text Analysis and Knowledge Extraction System) in identifying patients' expressions of ADRs and WDs with and without adding PsyTAR dictionary to the core dictionary of cTAKES. Augmenting cTAKES dictionary with PsyTAR improved the F-score cTAKES by 25%. The findings imply that PsyTAR has significant implications for text mining algorithms aimed to identify information about adverse drug events and drug effectiveness from patients' narratives data, by linking the patients' expressions of adverse drug events to medical standard vocabularies. The corpus is publicly available at Zolnoori et al. [30].

Natural language processing and machine learning to identify alcohol misuse from the electronic health record in trauma patients: development and internal validation.

Alcohol misuse is present in over a quarter of trauma patients. Information in the clinical notes of the electronic health record of trauma patients may be used for phenotyping tasks with natural language processing (NLP) and supervised machine learning. The objective of this study is to train and validate an NLP classifier for identifying patients with alcohol misuse. An observational cohort of 1422 adult patients admitted to a trauma center between April 2013 and November 2016. Linguistic processing of clinical notes was performed using the clinical Text Analysis and Knowledge Extraction System. The primary analysis was the binary classification of alcohol misuse. The Alcohol Use Disorders Identification Test served as the reference standard. The data corpus comprised 91 045 electronic health record notes and 16 091 features. In the final machine learning classifier, 16 features were selected from the first 24 hours of notes for identifying alcohol misuse. The classifier's performance in the validation cohort had an area under the receiver-operating characteristic curve of 0.78 (95% confidence interval [CI], 0.72 to 0.85). Sensitivity and specificity were at 56.0% (95% CI, 44.1% to 68.0%) and 88.9% (95% CI, 84.4% to 92.8%). The Hosmer-Lemeshow goodness-of-fit test demonstrates the classifier fits the data well (P = .17). A simpler rule-based keyword approach had a decrease in sensitivity when compared with the NLP classifier from 56.0% to 18.2%. The NLP classifier has adequate predictive validity for identifying alcohol misuse in trauma centers. External validation is needed before its application to augment screening.