scholarly journals Interactive use of online health resources: a comparison of consumer and professional questions

2016 ◽  
Vol 23 (4) ◽  
pp. 802-811 ◽  
Author(s):  
Kirk Roberts ◽  
Dina Demner-Fushman
Keyword(s):  
Language Processing ◽  
Information Access ◽  
Health Resources ◽  
Background Information ◽  
Special Focus ◽  
Health And Wellness ◽  
Medical Problems ◽  
Online Resource ◽  
The Individual ◽  
Processing Techniques

Abstract Objective To understand how consumer questions on online resources differ from questions asked by professionals, and how such consumer questions differ across resources. Materials and Methods Ten online question corpora, 5 consumer and 5 professional, with a combined total of over 40 000 questions, were analyzed using a variety of natural language processing techniques. These techniques analyze questions at the lexical, syntactic, and semantic levels, exposing differences in both form and content. Results Consumer questions tend to be longer than professional questions, more closely resemble open-domain language, and focus far more on medical problems. Consumers ask more sub-questions, provide far more background information, and ask different types of questions than professionals. Furthermore, there is substantial variance of these factors between the different consumer corpora. Discussion The form of consumer questions is highly dependent upon the individual online resource, especially in the amount of background information provided. Professionals, on the other hand, provide very little background information and often ask much shorter questions. The content of consumer questions is also highly dependent upon the resource. While professional questions commonly discuss treatments and tests, consumer questions focus disproportionately on symptoms and diseases. Further, consumers place far more emphasis on certain types of health problems (eg, sexual health). Conclusion Websites for consumers to submit health questions are a popular online resource filling important gaps in consumer health information. By analyzing how consumers write questions on these resources, we can better understand these gaps and create solutions for improving information access. This article is part of the Special Focus on Person-Generated Health and Wellness Data, which published in the May 2016 issue, Volume 23, Issue 3.

Psychotherapeutic interventions for chronic pain: Evidence, rationale, and advantages

2018 ◽  
Vol 54 (2) ◽  
pp. 140-149 ◽  
Author(s):  
Muhammad Hassan Majeed ◽  
Ali Ahsan Ali ◽  
Donna M Sudak
Keyword(s):  
Primary Care ◽  
Chronic Pain ◽  
Behavioral Therapy ◽  
Health Resources ◽  
Opioid Use ◽  
Medical Problems ◽  
Pharmacological Management ◽  
Acceptance And Commitment ◽  
Primary Care Clinicians ◽  
The Individual

Background Long-term use of opioids to treat chronic pain incurs serious risks for the individual—including misuse, abuse, addiction, overdose and death—as well as creating economic, social, and cultural impacts on society as a whole. Chronic pain and substance use disorders are often co-morbid with other medical problems and at the present time, primary care clinicians serve most of this population. Primary care clinicians would benefit from having alternatives to opioids to employ in treating such patients. Method We electronically searched different medical databases for studies evaluating the effect of nonpharmacological treatments for chronic pain. We describe alternative approaches for the treatment of chronic pain and cite studies that provide substantial evidence in favor of the use of these treatments. Results Cognitive behavioral therapy, acceptance and commitment therapy, and mindfulness-based programs have well-documented effectiveness for the treatment of chronic nonmalignant pain. Integration of such behavioral health therapies into primary care settings may optimize health resources and improve treatment outcomes. Conclusion Evidence-based psychotherapy for chronic pain has established efficacy and safety and improves quality of life and physical and emotional functioning. Such interventions may be used as an alternative or adjunct to pharmacological management. Chronic opioid use should be reserved for individuals undergoing active cancer treatment, palliative care, or end-of-life care.

Mining the Web for New Words: Semi-Automatic Neologism Identification with the NeoCrawler

2018 ◽  
Vol 136 (2) ◽  
pp. 239-268 ◽  
Author(s):  
Daphné Kerremans ◽  
Jelena Prokić
Keyword(s):  
Language Processing ◽  
Detection Methods ◽  
Close Monitoring ◽  
Speech Community ◽  
Dictionary Matching ◽  
New Words ◽  
Lexical Innovation ◽  
The Individual ◽  
Processing Techniques ◽  
The Web

AbstractLexical innovation is omnipresent and constantly at work. Studies aiming to understand the process of lexical innovation and the subsequent diffusion of neologisms therefore benefit from systematic methods of neologism identification. Retrieval procedures in the past have largely consisted of manual activities of participant observations and close reading. Recently, attempts have been made at designing automatized identification procedures, assisted by state-of-the-art natural language processing techniques and tools. Beginning with a discussion of the most commonly used neologism detection methods and applications in linguistics, the present paper will describe a semi-automatic approach to identifying new words on the web, the NeoCrawler’s Discoverer, which has been developed as part of a project on the incipient diffusion of lexical innovations. The Discoverer daily processes large batches of online text in English and automatically identifies unknown grapheme sequences as potential neologism candidates by means of a dictionary matching procedure, in which the individual tokens are matched against a very large dictionary. These potential neologisms subsequently are presented to the user for manual evaluation of their neologism status. Finally, candidates are added to the NeoCrawler’s database for continuous close monitoring of their development in the online speech community. We argue that the use of dictionary matching in neologism identification offers an efficient method to semi-automatically extract potential instances of lexical innovation with high precision and high recall when compared to previous approaches.

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

scholarly journals Does higher education properly prepare graduates for the growing artificial intelligence market? Gaps identification using text mining

2021 ◽  
pp. 1-13
Author(s):  
Lamiae Benhayoun ◽  
Daniel Lang
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
Academic Training ◽  
Market Requirements ◽  
Job Advertisements ◽  
The Individual

BACKGROUND: The renewed advent of Artificial Intelligence (AI) is inducing profound changes in the classic categories of technology professions and is creating the need for new specific skills. OBJECTIVE: Identify the gaps in terms of skills between academic training on AI in French engineering and Business Schools, and the requirements of the labour market. METHOD: Extraction of AI training contents from the schools’ websites and scraping of a job advertisements’ website. Then, analysis based on a text mining approach with a Python code for Natural Language Processing. RESULTS: Categorization of occupations related to AI. Characterization of three classes of skills for the AI market: Technical, Soft and Interdisciplinary. Skills’ gaps concern some professional certifications and the mastery of specific tools, research abilities, and awareness of ethical and regulatory dimensions of AI. CONCLUSIONS: A deep analysis using algorithms for Natural Language Processing. Results that provide a better understanding of the AI capability components at the individual and the organizational levels. A study that can help shape educational programs to respond to the AI market requirements.

scholarly journals Twitter Sentiment Analysis towards COVID-19 Vaccines in the Philippines Using Naïve Bayes

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 204
Author(s):  
Charlyn Villavicencio ◽  
Julio Jerison Macrohon ◽  
X. Alphonse Inbaraj ◽  
Jyh-Horng Jeng ◽  
Jer-Guang Hsieh
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Data Science ◽  
Naive Bayes ◽  
The Philippines ◽  
Naïve Bayes ◽  
Social Networking Site ◽  
Bayes Model ◽  
The Government ◽  
Processing Techniques

A year into the COVID-19 pandemic and one of the longest recorded lockdowns in the world, the Philippines received its first delivery of COVID-19 vaccines on 1 March 2021 through WHO’s COVAX initiative. A month into inoculation of all frontline health professionals and other priority groups, the authors of this study gathered data on the sentiment of Filipinos regarding the Philippine government’s efforts using the social networking site Twitter. Natural language processing techniques were applied to understand the general sentiment, which can help the government in analyzing their response. The sentiments were annotated and trained using the Naïve Bayes model to classify English and Filipino language tweets into positive, neutral, and negative polarities through the RapidMiner data science software. The results yielded an 81.77% accuracy, which outweighs the accuracy of recent sentiment analysis studies using Twitter data from the Philippines.

scholarly journals Shall I Work with Them? A Knowledge Graph-Based Approach for Predicting Future Research Collaborations

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 664
Author(s):  
Nikos Kanakaris ◽  
Nikolaos Giarelis ◽  
Ilias Siachos ◽  
Nikos Karacapilidis
Keyword(s):  
Language Processing ◽  
Scientific Knowledge ◽  
Link Prediction ◽  
Performance Metrics ◽  
Future Research ◽  
Knowledge Graph ◽  
Prediction Problem ◽  
Textual Information ◽  
Research Collaborations ◽  
Processing Techniques

We consider the prediction of future research collaborations as a link prediction problem applied on a scientific knowledge graph. To the best of our knowledge, this is the first work on the prediction of future research collaborations that combines structural and textual information of a scientific knowledge graph through a purposeful integration of graph algorithms and natural language processing techniques. Our work: (i) investigates whether the integration of unstructured textual data into a single knowledge graph affects the performance of a link prediction model, (ii) studies the effect of previously proposed graph kernels based approaches on the performance of an ML model, as far as the link prediction problem is concerned, and (iii) proposes a three-phase pipeline that enables the exploitation of structural and textual information, as well as of pre-trained word embeddings. We benchmark the proposed approach against classical link prediction algorithms using accuracy, recall, and precision as our performance metrics. Finally, we empirically test our approach through various feature combinations with respect to the link prediction problem. Our experimentations with the new COVID-19 Open Research Dataset demonstrate a significant improvement of the abovementioned performance metrics in the prediction of future research collaborations.

scholarly journals A Natural Language Processing Approach to Measuring Treatment Adherence and Consistency Using Semantic Similarity

AERA Open ◽  
2021 ◽  
Vol 7 ◽  
pp. 233285842110286
Author(s):  
Kylie L. Anglin ◽  
Vivian C. Wong ◽  
Arielle Boguslav
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Semantic Similarity ◽  
Language Processing ◽  
Intervention Implementation ◽  
Proof Of Concept ◽  
Coaching Intervention ◽  
Processing Techniques ◽  
Teacher Coaching ◽  
The Impact

Though there is widespread recognition of the importance of implementation research, evaluators often face intense logistical, budgetary, and methodological challenges in their efforts to assess intervention implementation in the field. This article proposes a set of natural language processing techniques called semantic similarity as an innovative and scalable method of measuring implementation constructs. Semantic similarity methods are an automated approach to quantifying the similarity between texts. By applying semantic similarity to transcripts of intervention sessions, researchers can use the method to determine whether an intervention was delivered with adherence to a structured protocol, and the extent to which an intervention was replicated with consistency across sessions, sites, and studies. This article provides an overview of semantic similarity methods, describes their application within the context of educational evaluations, and provides a proof of concept using an experimental study of the impact of a standardized teacher coaching intervention.

Exploring Library Core Competencies: A Text Mining Study of American Library Association’s Job Advertisements From 2006 Through 2017

2021 ◽  
pp. 089443932110272
Author(s):  
Qinghong Yang ◽  
Zehong Shi ◽  
Yan Quan Liu
Keyword(s):  
Language Processing ◽  
Work Experience ◽  
Core Competencies ◽  
Core Competency ◽  
Analytical Investigation ◽  
Market Demand ◽  
American Library ◽  
Job Titles ◽  
Job Advertisements ◽  
Processing Techniques

Are core competency requirements for relevant positions in the library shifting? Applying natural language processing techniques to understand the current market demand for core competencies, this study explores job advertisements issued by the American Library Association (ALA) from 2006 to 2017. Research reveals that the job demand continues to rise at a rate of 13% (2006–2017) and that the requirements for work experience are substantially extended, diversity of job titles becomes prevalent, and rich service experience and continuous lifelong learning skills are becoming more and more predominant for librarians. This analytical investigation informs the emerging demands in the American job market debriefing the prioritization and reprioritization of the current core competency requirements for ALA librarians.

Compansion: From research prototype to practical integration

1998 ◽  
Vol 4 (1) ◽  
pp. 73-95 ◽  
Author(s):  
KATHLEEN F. MCCOY ◽  
CHRISTOPHER A. PENNINGTON ◽  
ARLENE LUBEROFF BADMAN
Keyword(s):  
Language Processing ◽  
Augmentative And Alternative Communication ◽  
Communication Aids ◽  
Alternative Communication ◽  
Communicative Ability ◽  
Linguistic Ability ◽  
Research Prototype ◽  
Intelligent Communication ◽  
Processing Techniques ◽  
Parser Generator

Augmentative and Alternative Communication (AAC) is the field of study concerned with providing devices and techniques to augment the communicative ability of a person whose disability makes it difficult to speak or otherwise communicate in an understandable fashion. For several years, we have been applying natural language processing techniques to the field of AAC to develop intelligent communication aids that attempt to provide linguistically correct output while increasing communication rate. Previous effort has resulted in a research prototype called Compansion that expands telegraphic input. In this paper we describe that research prototype and introduce the Intelligent Parser Generator (IPG). IPG is intended to be a practical embodiment of the research prototype aimed at a group of users who have cognitive impairments that affect their linguistic ability. We describe both the theoretical underpinnings of Compansion and the practical considerations in developing a usable system for this population of users.

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