Computer aided sentiment analysis of anorexia nervosa patients’ vocabulary

Spińczyk, Dominik; Nabrdalik, Karolina; Rojewska, Katarzyna

doi:10.1186/s12938-018-0451-2

Cited by 19 publications

(15 citation statements)

References 4 publications

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“…Sentiment analysis of written accounts helps to uncover additional information within a data set ( [36][37]), and our descriptive analysis suggests institutional differences among the mental health narratives (Figs 1-3). These differences were most apparent when examining sentiment scores of students who attended social science programs within elite/Ivy League institutions (Fig 3).…”

Section: Plos Onementioning

confidence: 88%

“…Other work has found differences between men and women when it comes to the emotional content of work-place email [35]. Sentiment analysis has also been used to investigate the mental health status reflected through written language (e.g., [36]), and has been used with geographic information system data to identify communities in need of mental health services in the wake of natural disasters [37].…”

Section: Sentiment Analysismentioning

confidence: 99%

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Sexual harassment in academe is underreported, especially by students in the life and physical sciences

Aguilar

Baek

2020

PLoS ONE

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What factors predict the underreporting of sexual harassment in academe? We used logistic regression and sentiment analysis to examine 2,343 reports of sexual harassment involving members of university communities. Results indicate students were 1.6 times likely to not report their experiences when compared to faculty. Respondents in the life and physical sciences were 1.7 times more likely to not report their experiences when compared to respondents in other disciplines. Men represented 90% of the reported perpetrators of sexual harassment. Analysis of respondents' written accounts show variation of overall sentiment based on discipline, student type, and the type of institution attended, particularly with regard to mental health. Our results suggest that institutional and departmental barriers driven by power asymmetries play a large role in the underreporting sexual harassment among students-especially those in STEM disciplines.

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Section: Plos Onementioning

confidence: 88%

Section: Sentiment Analysismentioning

confidence: 99%

Sexual harassment in academe is underreported, especially by students in the life and physical sciences

Aguilar

Baek

2020

PLoS ONE

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“…It consists of the following steps: 4. The analysis of particular areas of difficulties (from the previous authors' paper [22]).…”

Section: Methodsmentioning

confidence: 99%

“…The previous research showed the relationship between the vocabulary used in the patient's statement and the morbidity. The proposed method intends to provide additional support for the psychologist by developing 6 detailed categories corresponding to the patient's areas of difficulties [22]:…”

Section: The Assessment Of Particular Areas Of Difficultiesmentioning

confidence: 99%

Computer-aided therapeutic diagnosis for anorexia

Spińczyk

Bas

Dzieciątko³

et al. 2020

BioMed Eng OnLine

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Background: Anorexia nervosa is a clinical disorder syndrome of the wide spectrum without a fully recognized etiology. The necessary issue in the clinical diagnostic process is to detect the causes of this disease (e.g., my body image, food, family, peers), which the therapist gradually comes to by verifying assumptions using proper methods and tools for diagnostic process. When a person is diagnosed with anorexia, a clinician (a doctor, a therapist or a psychologist) proposes a therapeutic diagnosis and considers the kind of treatment that should be applied. This process is also continued during therapeutic diagnosis. In both cases, it is recommended to apply computeraided tools designed for testing and confirming the assumptions made by a psychologist. The paper aims to present the computer-aided therapeutic diagnosis method for anorexia. The proposed method consists of 4 stages: free statements of a patient about his/her body image, the general sentiment analysis of statement based on Recurrent Neural Network, assessment of the intensity of five basic emotions: happiness, anger, sadness, fear and disgust (using the Nencki Affective Word List and conversion of words to their basic form), and the assessment of particular areas of difficulties-the sentiment analysis based on the dictionary approach was applied. Results: The sentiment analysis of a document achieved 72% and 51% of effectiveness, respectively, for RNN and dictionary-based methods. The intensity of sadness (emotion) occurring within the dictionary method is differentiated between control and research group at the level of 10%. Conclusion: The quick access to the sentiment analysis of a statement on the image of patient's body, emotions experienced by the patient and particular areas of difficulties of people prone to the anorexia nervosa disorders, may help to establish the diagnosis in a very short time and start an immediate therapy. The proposed automatic method helps to avoid patient's aversions towards the therapy, which may include avoiding patient-therapist communication, talking about less essential topics, coming late for the sessions. These circumstances can guarantee promising prognosis for recovering.

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“…3 Several other examples of using NLP for detection and prediction of disease and adverse events are reported in the literature. [34][35][36][37][38]…”

Section: Examplementioning

confidence: 99%

Machine Learning in Relation to Emergency Medicine Clinical and Operational Scenarios: An Overview

Lee¹,

Mohr²,

Street³

et al. 2019

WestJEM

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Health informatics is a vital technology that holds great promise in the healthcare setting. We describe two prominent health informatics tools relevant to emergency care, as well as the historical background and the current state of informatics. We also identify recent research findings and practice changes. The recent advances in machine learning and natural language processing (NLP) are a prominent development in health informatics overall and relevant in emergency medicine (EM). A basic comprehension of machine-learning algorithms is the key to understand the recent usage of artificial intelligence in healthcare. We are using NLP more in clinical use for documentation. NLP has started to be used in research to identify clinically important diseases and conditions. Health informatics has the potential to benefit both healthcare providers and patients. We cover two powerful tools from health informatics for EM clinicians and researchers by describing the previous successes and challenges and conclude with their implications to emergency care. [West J Emerg Med. 2019;20(2)219-227.] * † ‡ § "Natural Language Processing" [Majr], which results in nearly 2400 publications related to machine learning and natural language processing (NLP) in 2017-up from 123 in 2010. This evolving technology also influences emergency care and research by aiding emergency medicine (EM) providers in several ways. First, it helps them to identify a high-risk condition by capturing data from available records or to prevent misdiagnoses by providing decision support. 3 Second, it improves workflow efficiency by providing integrated decision aids within the electronic health record (EHR). 4 Third, providers can maintain high-quality documentation in EHR in a hightempo environment. 5 We provide synopses of two clinically important health informatics applications: machine learning and NLP. These topics are diverse, and each of them is complex. Even in researching these areas, different researchers specialize in different sub-areas of machine learning or NLP. For example,

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Computer aided sentiment analysis of anorexia nervosa patients’ vocabulary

Cited by 19 publications

References 4 publications

Sexual harassment in academe is underreported, especially by students in the life and physical sciences

Sexual harassment in academe is underreported, especially by students in the life and physical sciences

Computer-aided therapeutic diagnosis for anorexia

Machine Learning in Relation to Emergency Medicine Clinical and Operational Scenarios: An Overview

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