Improving sentiment analysis on clinical narratives by exploiting UMLS semantic types

Sanglerdsinlapachai, Nuttapong; Plangprasopchok, Anon; Ho, Tu Bao; Nantajeewarawat, Ekawit

doi:10.1016/j.artmed.2021.102033

Cited by 14 publications

(3 citation statements)

References 24 publications

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“…Peng et al use the normalization method to collect the data signal input to the model and realize the information simulation processing of the data signal through the PSO-BP neural network algorithm [ 9 ]. Sanglerdsinlapachai et al summarize the current music sentiment analysis methods, classify them according to their recognition principles, and conduct a differentiated analysis of the advantages and disadvantages of different types of music sentiment analysis process [ 10 ]. According to different types of music data information, Carvalho and Plastino have performed ultra-high discrimination recognition of existing music sentiment analysis methods and realized high-accuracy analysis in terms of their composition and expression methods [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Music Emotion Analysis Based on PSO‐BP Neural Network and Big Data Analysis

Chen

2021

Computational Intelligence and Neuroscience

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The current music teaching can effectively improve students’ music emotional expression indirectly. How to use the PSO-BP neural network to realize the quantitative research of music emotional expression is the current development trend. Based on this, this paper studies the influence factors of music emotion expression based on PSO-BP neural network and big data analysis. Firstly, a music emotion expression analysis model based on PSO-BP neural network algorithm is proposed. The autocorrelation function is used to simulate the emotion expression information in music. Through the maximum value of the autocorrelation function curve in the detection process, the vocal music signal is restored, and then the emotion expressed is analyzed. Secondly, the influence factors of PSO-BP neural network algorithm in music emotion expression are analyzed. The improved PSO-BP neural network algorithm and multidimensional data model are used for comprehensive analysis to accurately analyze the emotion in music expression, and the fuzzy evaluation method and analytic hierarchy process are used for quality evaluation. Finally, the validity of the music emotion analysis model is verified by many experiments.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Music Emotion Analysis Based on PSO‐BP Neural Network and Big Data Analysis

Chen

2021

Computational Intelligence and Neuroscience

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“…Sentic computing is one of the most crucial techniques used for this purpose [15,50] and has been exploiting in many patient-centered applications [51][52][53][54][55][56], which exploits semantics and their sentics based on the relation dependency, and linguistics properties to annotate automatically concept-level sentiment of an input phrase. Where semantics are concepts that are most semantically-related to the input concept, and sentics are emotion categorization values expressed in terms of varied affective dimensions such as pleasantness, attention, sensitivity, and aptitude.…”

Section: Biomedical Sentiment Annotation and Sentic Computingmentioning

confidence: 99%

Affective Concept-Based Encoding of Patient Narratives via Sentic Computing and Neural Networks

Grissette

Nfaoui

2021

Cogn Comput

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The automatic generation of features without human intervention is the most critical task for biomedical sentiment analysis. Regarding the high dynamicity of shared patient narrative data, the lack of formal medical language sentiment dictionaries prevents retrieval of the appropriate sentiment, which is unapproachable and can be prone to annotator bias. We propose a novel affective biomedical concept-based encoding via sentic computing and neural networks. The main contributions include four aspects. First, a biomedical embedding, in which a medical entity is defined, normalized, and synthesized from a text, is built using online patient narratives after being combined with label propagation from a widely used comprehensive biomedical vocabulary. Second, considering the dependence on biomedical definitions, drug reaction sample selection based on general matching is suggested. These feature settings are then used to build and recognize affective semantics and sentics based on an extreme learning machine. Finally, a semisupervised LSTM-BiLSTM model for biomedical sentiment analysis is constructed. There was a massive influx of patient self-reports related to the COVID-19 pandemic. A study was conducted in this direction, and we tested the validity, medical language familiarity, and transferability of our approach by analyzing millions of COVID-19 tweets. Comparisons to affective lexicons also indicate that integrating extreme learning machine cognitive capabilities has advantages over biomedical sentiment analysis. By considering sentics vectors on top of the formed embeddings, our semisupervised LSTM-BiLSTM achieved an accuracy of 87.5%. The evaluations of unsupervised learning approximated the results of the previous model when dealing with a serious loss of biomedical data. In this paper, we demonstrate the effectiveness of integrating deep-learning-based cognitive capabilities for both enhancing distributed biomedical definitions and inferring sentiment compositions from many patient self-reports on social networks. The relevant encoding of affective information conveyed regarding medication subjects clearly reveals defined roles and expectations that can have a positive impact on public health.

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“…Electronic nursing clinical notes (ENCN) are documents in which nurses describe health status, nursing care, medication, and other observations about patients [21]. In these texts, they also describe their observations and opinions in an attempt to better understand the patients' condition and opinions [22] and among them, the feelings perceived during their interaction [21]. The appropriate use of ENCN can help improve both physical and mental health care of hospitalized patients [23].…”

Section: Introductionmentioning

confidence: 99%

Exploring Sentiment and Care Management of Hospitalized Patients During the First Wave of the COVID-19 Pandemic Using Electronic Nursing Health Records: Descriptive Study

Cuenca-Zaldívar¹,

Torrente²,

Martín-Losada³

et al. 2022

JMIR Med Inform

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Background The COVID-19 pandemic has changed the usual working of many hospitalization units (or wards). Few studies have used electronic nursing clinical notes (ENCN) and their unstructured text to identify alterations in patients' feelings and therapeutic procedures of interest. Objective This study aimed to analyze positive or negative sentiments through inspection of the free text of the ENCN, compare sentiments of ENCN with or without hospitalized patients with COVID-19, carry out temporal analysis of the sentiments of the patients during the start of the first wave of the COVID-19 pandemic, and identify the topics in ENCN. Methods This is a descriptive study with analysis of the text content of ENCN. All ENCNs between January and June 2020 at Guadarrama Hospital (Madrid, Spain) extracted from the CGM Selene Electronic Health Records System were included. Two groups of ENCNs were analyzed: one from hospitalized patients in post–intensive care units for COVID-19 and a second group from hospitalized patients without COVID-19. A sentiment analysis was performed on the lemmatized text, using the National Research Council of Canada, Affin, and Bing dictionaries. A polarity analysis of the sentences was performed using the Bing dictionary, SO Dictionaries V1.11, and Spa dictionary as amplifiers and decrementators. Machine learning techniques were applied to evaluate the presence of significant differences in the ENCN in groups of patients with and those without COVID-19. Finally, a structural analysis of thematic models was performed to study the abstract topics that occur in the ENCN, using Latent Dirichlet Allocation topic modeling. Results A total of 37,564 electronic health records were analyzed. Sentiment analysis in ENCN showed that patients with subacute COVID-19 have a higher proportion of positive sentiments than those without COVID-19. Also, there are significant differences in polarity between both groups (Z=5.532, P<.001) with a polarity of 0.108 (SD 0.299) in patients with COVID-19 versus that of 0.09 (SD 0.301) in those without COVID-19. Machine learning modeling reported that despite all models presenting high values, it is the neural network that presents the best indicators (>0.8) and with significant P values between both groups. Through Structural Topic Modeling analysis, the final model containing 10 topics was selected. High correlations were noted among topics 2, 5, and 8 (pressure ulcer and pharmacotherapy treatment), topics 1, 4, 7, and 9 (incidences related to fever and well-being state, and baseline oxygen saturation) and topics 3 and 10 (blood glucose level and pain). Conclusions The ENCN may help in the development and implementation of more effective programs, which allows patients with COVID-19 to adopt to their prepandemic lifestyle faster. Topic modeling could help identify specific clinical problems in patients and better target the care they receive.

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Improving sentiment analysis on clinical narratives by exploiting UMLS semantic types

Cited by 14 publications

References 24 publications

[Retracted] Music Emotion Analysis Based on PSO‐BP Neural Network and Big Data Analysis

[Retracted] Music Emotion Analysis Based on PSO‐BP Neural Network and Big Data Analysis

Affective Concept-Based Encoding of Patient Narratives via Sentic Computing and Neural Networks

Exploring Sentiment and Care Management of Hospitalized Patients During the First Wave of the COVID-19 Pandemic Using Electronic Nursing Health Records: Descriptive Study

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