scholarly journals Exploratory Analysis of Fundamental Spiritual Support Factors to a Positive Attitude in Patients with COVID-19 Using Natural-Language Processing Algorithms

2021 ◽  
Vol 11 (20) ◽  
pp. 9524
Author(s):  
Jorge Sánchez-Garcés ◽  
Javier Linkolk López-Gonzales ◽  
Miguel Palacio-Farfán ◽  
Víctor Coronel-Sacón ◽  
Yonny Ferney-Teheran ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Positive Attitude ◽  
Healing Process ◽  
Exploratory Analysis ◽  
Second Phase ◽  
Extreme Stress ◽  
Processing Algorithms ◽  
Key Phrases

The SARS-CoV-2 virus that causes COVID-19 affects the respiratory tract and is highly infectious. Those patients who knew that the disease could cause death or that their healing process is quite painful because of the symptoms and conditions developed extreme stress, anxiety, and depression, which aggravated the effects of the disease. Therefore, it is vital to conduct research to analyze these effects and generate self-help and support mechanisms during the disease process. This paper presents exploratory analysis related to stress, coping attitudes, emotional responses, and sources of support that were vital in patients affected by COVID-19; the focus of this study is the consideration of the spiritual factor, which may influence religious resilience that allows for a positive attitude and tenacity. To carry out this research, interviews were conducted with patients who had suffered from COVID-19 disease, and the collected information was processed using text-mining techniques using a two-phase methodology. The first phase is based on the Colaizzi method. Interview responses were coded through the search for patterns in the key phrases, and these codes were grouped, forming semantic relationships. In the second phase, natural-language processing algorithms (WordCloud, WordEmbedding, sentiment analysis of opinions) were used, summarizing the interviews in relevant factors of the patient’s experience during the disease. Spiritual resilience stood out the most of all key phrases of the code group tables. Likewise, words such as security, confidence, tranquility, and peace indicated that the patients took a positive attitude towards the symptoms and complications of the disease. Therefore, it is important to be the resilience to face a crisis process, and one of the factors that generated such resilience in COVID-19 patients was religious faith, which was expressed in the interviews using the factors of security, trust, promises of healing, tranquility, and the impossibility of discouragement. All this contributed to the positive attitude of the interviewees during the process of recovery from the disease.

scholarly journals Predicting Verification Methods from Natural Language Requirements

2020 ◽  
Author(s):  
Michael Prendergast
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Semantic Analysis ◽  
Nearest Neighbor ◽  
Large Set ◽  
Technical Requirements ◽  
Verification Methods ◽  
Natural Language Requirements ◽  
Processing Algorithms

Abstract – A Verification Cross-Reference Matrix (VCRM) is a table that depicts the verification methods for requirements in a specification. Usually requirement labels are rows, available test methods are columns, and an “X” in a cell indicates usage of a verification method for that requirement. Verification methods include Demonstration, Inspection, Analysis and Test, and sometimes Certification, Similarity and/or Analogy. VCRMs enable acquirers and stakeholders to quickly understand how a product’s requirements will be tested.Maintaining consistency of very large VCRMs can be challenging, and inconsistent verification methods can result in a large set of uncoordinated “spaghetti tests”. Natural language processing algorithms that can identify similarities between requirements offer promise in addressing this challenge.This paper applies and compares compares four natural language processing algorithms to the problem of automatically populating VCRMs from natural language requirements: Naïve Bayesian inference, (b) Nearest Neighbor by weighted Dice similarity, (c) Nearest Neighbor with Latent Semantic Analysis similarity, and (d) an ensemble method combining the first three approaches. The VCRMs used for this study are for slot machine technical requirements derived from gaming regulations from the countries of Australia and New Zealand, the province of Nova Scotia (Canada), the state of Michigan (United States) and recommendations from the International Association of Gaming Regulators (IAGR).

scholarly journals Natural Language Processing Algorithms for Normalizing Expressions of Synonymous Symptoms in Traditional Chinese Medicine

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Lu Zhou ◽  
Shuangqiao Liu ◽  
Caiyan Li ◽  
Yuemeng Sun ◽  
Yizhuo Zhang ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Text Classification ◽  
Classification Model ◽  
Generation Model ◽  
Sigmoid Function ◽  
Sequence Generation ◽  
Model Based ◽  
Processing Algorithms

Background. The modernization of traditional Chinese medicine (TCM) demands systematic data mining using medical records. However, this process is hindered by the fact that many TCM symptoms have the same meaning but different literal expressions (i.e., TCM synonymous symptoms). This problem can be solved by using natural language processing algorithms to construct a high-quality TCM symptom normalization model for normalizing TCM synonymous symptoms to unified literal expressions. Methods. Four types of TCM symptom normalization models, based on natural language processing, were constructed to find a high-quality one: (1) a text sequence generation model based on a bidirectional long short-term memory (Bi-LSTM) neural network with an encoder-decoder structure; (2) a text classification model based on a Bi-LSTM neural network and sigmoid function; (3) a text sequence generation model based on bidirectional encoder representation from transformers (BERT) with sequence-to-sequence training method of unified language model (BERT-UniLM); (4) a text classification model based on BERT and sigmoid function (BERT-Classification). The performance of the models was compared using four metrics: accuracy, recall, precision, and F1-score. Results. The BERT-Classification model outperformed the models based on Bi-LSTM and BERT-UniLM with respect to the four metrics. Conclusions. The BERT-Classification model has superior performance in normalizing expressions of TCM synonymous symptoms.

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