Topic Modeling for Analyzing Patients’ Perceptions and Concerns of Hearing Loss on Social Q&amp;A Sites: Incorporating Patients’ Perspective

Bahng, Junghwa; Lee, Chang Heon

doi:10.3390/ijerph17176209

Cited by 17 publications

(17 citation statements)

References 64 publications

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“…20 However, the use of topic modelling techniques in Ear, Nose and Throat-related research is sparse, and there is limited manual evaluation of the accuracy of automatically-generated topics. 21,22…”

Section: Introductionmentioning

confidence: 99%

Exploring patient experiences and concerns in the online Cochlear Implant community: a natural language processing approach

Williams

Luo

et al. 2022

Preprint

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Importance: There is a paucity of research examining patient experiences of cochlear implants, with existing studies limited by small sample sizes and closed question-answer style formats. Objective: To use natural language processing methods to explore patient experiences and concerns in the online cochlear implant (CI) community when in conversation with each other. Design: Cross-sectional study of the online Reddit r/CochlearImplants forum. No date restrictions were imposed; data were retrieved on 11 November 2021 and analysed between November 2021 and March 2022. The following details were extracted for each post: 1) original post content, 2) post comments, and 3) metadata for the original post and user. Setting: Online Reddit forum r/CochlearImplants. Participants: Consecutive sample of all users posting on the r/CochlearImplants forum from 1 March 2015 to 11 November 2021. Main Outcomes and Measures: Natural language processing using the BERTopic automated topic modelling technique was employed to cluster posts into semantically similar topics. Topic categorisation was manually validated by two independent reviewers and Cohen's Kappa calculated to determine inter-rater reliability between machine vs human and human vs human categorisation. Results: We retrieved 987 posts from 588 unique Reddit users on the r/CochlearImplants forum. Posts were initially categorised by BERTopic into 16 different Topics, which were increased to 22 Topics following manual inspection. The most popular topics related to CI connectivity (n = 112),adults considering getting a CI (n=107), surgery-related posts (n = 89) and day-to-day living with a CI (n = 85). Topics with the most comments included Choosing cochlear implant brand (mean = 12.9 comments) and Adults considering getting a CI (mean = 12.2 comments). Cohen's kappa among all posts was 0.62 (machine vs human) and 0.72 (human vs human), and among categorised posts was 0.85 (machine vs human) and 0.84 (human vs human). Conclusions and Relevance: This cross-sectional study of social media discussions amongst the online cochlear implant community identified common attitudes, experiences and concerns of patients living with, or seeking, a cochlear implant. Our validation of natural language processing methods to categorise topics shows that automated analysis of similar Otolaryngology-related content is a viable and accurate alternative to manual qualitative approaches.

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Section: Introductionmentioning

confidence: 99%

Exploring patient experiences and concerns in the online Cochlear Implant community: a natural language processing approach

Williams

Luo

et al. 2022

Preprint

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“…Medical and dental clinicians can utilize ML algorithms to manage and analyze the immense data present in patients' health records to extract meaningful information that can be used to improve population health and wellbeing (12). Diagnostic medicine incorporates advanced AI technologies to predict or diagnose disease and assist in the clinical decision-making process (9)(10)(11), and several studies have shown that AI-based comprehensive care system has great potential to enhance patient care, spur innovative research, and facilitate information sharing (13)(14)(15).…”

Section: Introductionmentioning

confidence: 99%

An Application of Machine Learning Techniques to Analyze Patient Information to Improve Oral Health Outcomes

Ameli¹,

Gibson²,

Khanna³

et al. 2022

Front. Dent. Med

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ObjectiveVarious health-related fields have applied Machine learning (ML) techniques such as text mining, topic modeling (TM), and artificial neural networks (ANN) to automate tasks otherwise completed by humans to enhance patient care. However, research in dentistry on the integration of these techniques into the clinic arena has yet to exist. Thus, the purpose of this study was to: introduce a method of automating the reviewing patient chart information using ML, provide a step-by-step description of how it was conducted, and demonstrate this method's potential to identify predictive relationships between patient chart information and important oral health-related contributors.MethodsA secondary data analysis was conducted to demonstrate the approach on a set of anonymized patient charts collected from a dental clinic. Two ML applications for patient chart review were demonstrated: (1) text mining and Latent Dirichlet Allocation (LDA) were used to preprocess, model, and cluster data in a narrative format and extract common topics for further analysis, (2) Ordinal logistic regression (OLR) and ANN were used to determine predictive relationships between the extracted patient chart data topics and oral health-related contributors. All analysis was conducted in R and SPSS (IBM, SPSS, statistics 22).ResultsData from 785 patient charts were analyzed. Preprocessing of raw data (data cleaning and categorizing) identified 66 variables, of which 45 were included for analysis. Using LDA, 10 radiographic findings topics and 8 treatment planning topics were extracted from the data. OLR showed that caries risk, occlusal risk, biomechanical risk, gingival recession, periodontitis, gingivitis, assisted mouth opening, and muscle tenderness were highly predictable using the extracted radiographic and treatment planning topics and chart information. Using the statistically significant predictors obtained from OLR, ANN analysis showed that the model can correctly predict >72% of all variables except for bruxism and tooth crowding (63.1 and 68.9%, respectively).ConclusionOur study presents a novel approach to address the need for data-enabled innovations in the field of dentistry and creates new areas of research in dental analytics. Utilizing ML methods and its application in dental practice has the potential to improve clinicians' and patients' understanding of the major factors that contribute to oral health diseases/conditions.

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“…To date, not many studies have performed auditory training for improving spatial hearing abilities. A comprehensive understanding of hearing loss is crucial for designing an intervention program (Bahng & Lee, 2020). Firszt et al (2015) developed the localizationspecific auditory training for unilateral severe to profound hearing loss and reported that eight out of 11 participants' spatial hearing abilities improved significantly.…”

Section: Introductionmentioning

confidence: 99%

Auditory Training for Spatial Hearing Loss: Two Case Reports of the Older Adults Who Wear Hearing Aids

Shin

Bahng

2021

Audiol Speech Res

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The elderly with hearing loss have often experienced spatial hearing deficits when only wearing hearing aids. This case study aimed to investigate whether auditory training for spatial hearing loss could improve the spatial hearing ability of the elderly who wear hearing aids. Two older adults (males, aged 75 years and 78 years) who were wearing bilateral hearing aids participated. They had symmetric sensorineural hearing loss. A total of 16 auditory-training sessions (40 minutes, twice per week) for auditory training for spatial hearing ability provided. For outcome measurements, we assessed speech reception thresholds (SRTs) and spatial release from masking (SRM), using two types of noise, steady-state noise and speech-like noise at pre- and post-auditory training. Both subjects increased SRTs after the auditory training, but the SRM of only one participant improved. Also, there were no masking effects for SRM in terms of noise types. Both subjects reported that their trained hearing skills helped in communication with their families and friends. These results suggested that audiologists can consider auditory training for focusing on the spatial hearing loss for the elderly hearing aids users.

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Topic Modeling for Analyzing Patients’ Perceptions and Concerns of Hearing Loss on Social Q&A Sites: Incorporating Patients’ Perspective

Cited by 17 publications

References 64 publications

Exploring patient experiences and concerns in the online Cochlear Implant community: a natural language processing approach

Exploring patient experiences and concerns in the online Cochlear Implant community: a natural language processing approach

An Application of Machine Learning Techniques to Analyze Patient Information to Improve Oral Health Outcomes

Auditory Training for Spatial Hearing Loss: Two Case Reports of the Older Adults Who Wear Hearing Aids

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