Analysis of mental and physical disorders associated with COVID-19 in online health forums: a natural language processing study

Patel, Rashmi; Smeraldi, Fabrizio; Abdollahyan, Maryam; Irving, Jessica; Bessant, Conrad

doi:10.1136/bmjopen-2021-056601

Cited by 10 publications

(8 citation statements)

References 25 publications

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“…During the present COVID-19 era, large-scale text mining of real-world data, such as interview data, social media data, and online health forum data, reveals the possibility of the design of a central surveillance system to manage emotional and mental health issues concerning COVID-19 (and possibly other future pandemic-like) infection. This system can be harnessed to inform and guide public healthcare policy and provide useful insights into developing novel therapeutics related to mental health symptoms [25].…”

Section: Discussionmentioning

confidence: 99%

“…However, they are syntagmatically combined with words of negation and represent patients' positive emotions concerning their confidence in recovery. emergence of emotional disorders and mental illness [23][24][25][26]. Emotional narratives of patients with confirmed COVID-19 disease have proven to be an important marker to gain a deeper understanding of emotion-related mental health issues due to COVID-19 [22].…”

Section: ++Happy(pe)++ I Never Felt Irritable In Mood; I Felt Peacefu...mentioning

confidence: 99%

“…To date, the emotional discourse analysis of patients' lived experiences of COVID-19 extensively focuses on the qualitative approach [22], whereas the quantitative approach is currently underrepresented. Recent studies have used natural language processing (NLP) techniques to extract real-world text data concerning COVID-19 from online resources such as Twitter, Facebook, and Reddit, in order to provide automated population-level health surveillance [23][24][25][26]. For instance, Crocamo et al [23] conducted sentiment analysis of 3,308,476 English tweets concerning COVID-19 discussions between January 19 and March 3, 2020, by computing a polarity compound score and using a transformer-based model.…”

Section: Introductionmentioning

confidence: 99%

“…Health anxiety was found to be a salient theme across Reddit through machine learning analyses. Patel et al [25] used data mining methods to compute 739,434 COVID-19 related posts by 53,134 users from online health forums such as Health Boards and Inspire & Health Unlocked from 1 January 2020 to 31 May 2020. Their results showed that the global lockdown policy had led to an increase of discussion related to COVID-19-related disorders, and 25% of the COVID-19-related titles mentioned an associated physical or mental health comorbidity.…”

Section: Introductionmentioning

confidence: 99%

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Emotional discourse analysis of COVID-19 patients and their mental health: A text mining study

et al. 2022

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COVID-19 has caused negative emotional responses in patients, with significant mental health consequences for the infected population. The need for an in-depth analysis of the emotional state of COVID-19 patients is imperative. This study employed semi-structured interviews and the text mining method to investigate features in lived experience narratives of COVID-19 patients and healthy controls with respect to five basic emotions. The aim was to identify differences in emotional status between the two matched groups of participants. The results indicate generally higher complexity and more expressive emotional language in healthy controls than in COVID-19 patients. Specifically, narratives of fear, happiness, and sadness by COVID-19 patients were significantly shorter as compared to healthy controls. Regarding lexical features, COVID-19 patients used more emotional words, in particular words of fear, disgust, and happiness, as opposed to those used by healthy controls. Emotional disorder symptoms of COVID-19 patients at the lexical level tended to focus on the emotions of fear and disgust. They narrated more in relation to self or family while healthy controls mainly talked about others. Our automatic emotional discourse analysis potentially distinguishes clinical status of COVID-19 patients versus healthy controls, and can thus be used to predict mental health disorder symptoms in COVID-19 patients.

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Section: Discussionmentioning

confidence: 99%

Section: ++Happy(pe)++ I Never Felt Irritable In Mood; I Felt Peacefu...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Emotional discourse analysis of COVID-19 patients and their mental health: A text mining study

et al. 2022

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“…Due to the rapid development of the medical and healthcare industry over the past few years and the continuous advancement of Internet technology, Internet healthcare has become an increasingly popular trend in recent years. Especially following the COVID-19 outbreak, there has been a dramatic increase in demand for noncontact consulting [ 1 ], which provides a rare opportunity for the development of Internet-based medical consultation services. An increasing number of online medical consultation (OMC) service platforms, such as Haodf.com and WeMed.com, are experiencing rapid growth throughout China.…”

Section: Introductionmentioning

confidence: 99%

Doctors’ Preferences in the Selection of Patients in Online Medical Consultations: An Empirical Study with Doctor–Patient Consultation Data

Wang

2022

Healthcare

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Online medical consultation (OMC) allows doctors and patients to communicate with each other in an online synchronous or asynchronous setting. Unlike face-to-face consultations in which doctors are only passively chosen by patients with appointments, doctors engaging in voluntary online consultation have the option of choosing patients they hope to treat when faced with a large number of online questions from patients. It is necessary to characterize doctors’ preferences for patient selection in OMC, which can contribute to their more active participation in OMC services. We proposed to exploit a bipartite graph to describe the doctor–patient interaction and use an exponential random graph model (ERGM) to analyze the doctors’ preferences for patient selection. A total of 1404 doctor–patient consultation data retrieved from an online medical platform in China were used for empirical analysis. It was found that first, mildly ill patients will be prioritized by doctors, but the doctors with more professional experience may be more likely to prefer more severely ill patients. Second, doctors appear to be more willing to provide consultation services to patients from urban areas, but the doctors with more professional experience or from higher-quality hospitals give higher priority to patients from rural and medically underserved areas. Finally, doctors generally prefer asynchronous communication methods such as picture/text consultation, while the doctors with more professional experience may be more willing to communicate with patients via synchronous communication methods, such as voice consultation or video consultation.

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A survey on clinical natural language processing in the United Kingdom from 2007 to 2022

Wang

et al. 2022

npj Digit. Med.

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Much of the knowledge and information needed for enabling high-quality clinical research is stored in free-text format. Natural language processing (NLP) has been used to extract information from these sources at scale for several decades. This paper aims to present a comprehensive review of clinical NLP for the past 15 years in the UK to identify the community, depict its evolution, analyse methodologies and applications, and identify the main barriers. We collect a dataset of clinical NLP projects (n = 94; £ = 41.97 m) funded by UK funders or the European Union’s funding programmes. Additionally, we extract details on 9 funders, 137 organisations, 139 persons and 431 research papers. Networks are created from timestamped data interlinking all entities, and network analysis is subsequently applied to generate insights. 431 publications are identified as part of a literature review, of which 107 are eligible for final analysis. Results show, not surprisingly, clinical NLP in the UK has increased substantially in the last 15 years: the total budget in the period of 2019–2022 was 80 times that of 2007–2010. However, the effort is required to deepen areas such as disease (sub-)phenotyping and broaden application domains. There is also a need to improve links between academia and industry and enable deployments in real-world settings for the realisation of clinical NLP’s great potential in care delivery. The major barriers include research and development access to hospital data, lack of capable computational resources in the right places, the scarcity of labelled data and barriers to sharing of pretrained models.

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Analysis of mental and physical disorders associated with COVID-19 in online health forums: a natural language processing study

Cited by 10 publications

References 25 publications

Emotional discourse analysis of COVID-19 patients and their mental health: A text mining study

Emotional discourse analysis of COVID-19 patients and their mental health: A text mining study

Doctors’ Preferences in the Selection of Patients in Online Medical Consultations: An Empirical Study with Doctor–Patient Consultation Data

A survey on clinical natural language processing in the United Kingdom from 2007 to 2022

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