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Spontaneously generated online patient experience of Modafinil : a qualitative and NLP analysis
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Walsh, Julia, Cave, Jonathan A. K. and Griffiths, Frances (2021) Spontaneously generated online patient experience of Modafinil : a qualitative and NLP analysis. Frontiers in Digital Health, 3 . 598431. doi:10.3389/fdgth.2021.598431 ISSN 2673-253X.
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WRAP-spontaneously-generated-online-patient-experience-Modafinil-qualitative-NLP-analysis-Walsh-2021.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (588Kb) | Preview |
Official URL: http://dx.doi.org/10.3389/fdgth.2021.598431
Abstract
Objective: To compare the findings from a qualitative and a natural language processing (NLP) based analysis of online patient experience posts on patient experience of the effectiveness and impact of the drug Modafinil. Methods: Posts (n = 260) from 5 online social media platforms where posts were publicly available formed the dataset/corpus. Three platforms asked posters to give a numerical rating of Modafinil. Thematic analysis: data was coded and themes generated. Data were categorized into PreModafinil, Acquisition, Dosage, and PostModafinil and compared to identify each poster's own view of whether taking Modafinil was linked to an identifiable outcome. We classified this as positive, mixed, negative, or neutral and compared this with numerical ratings. NLP: Corpus text was speech tagged and keywords and key terms extracted. We identified the following entities: drug names, condition names, symptoms, actions, and side-effects. We searched for simple relationships, collocations, and co-occurrences of entities. To identify causal text, we split the corpus into PreModafinil and PostModafinil and used n-gram analysis. To evaluate sentiment, we calculated the polarity of each post between −1 (negative) and +1 (positive). NLP results were mapped to qualitative results. Results: Posters had used Modafinil for 33 different primary conditions. Eight themes were identified: the reason for taking (condition or symptom), impact of symptoms, acquisition, dosage, side effects, other interventions tried or compared to, effectiveness of Modafinil, and quality of life outcomes. Posters reported perceived effectiveness as follows: 68 positive, 12 mixed, 18 negative. Our classification was consistent with poster ratings. Of the most frequent 100 keywords/keyterms identified by term extraction 88/100 keywords and 84/100 keyterms mapped directly to the eight themes. Seven keyterms indicated negation and temporal states. Sentiment was as follows 72 positive sentiment 4 neutral 24 negative. Matching of sentiment between the qualitative and NLP methods was accurate in 64.2 of posts. If we allow for one category difference matching was accurate in 85 of posts. Conclusions: User generated patient experience is a rich resource for evaluating real world effectiveness, understanding patient perspectives, and identifying research gaps. Both methods successfully identified the entities and topics contained in the posts. In contrast to current evidence, posters with a wide range of other conditions found Modafinil effective. Perceived causality and effectiveness were identified by both methods demonstrating the potential to augment existing knowledge.
| Item Type: | Journal Article | ||||||||
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| Subjects: | H Social Sciences > HM Sociology Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software R Medicine > R Medicine (General) R Medicine > RA Public aspects of medicine Z Bibliography. Library Science. Information Resources > ZA Information resources |
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| Divisions: | Faculty of Social Sciences > Economics Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Health Sciences Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Health Sciences > Social Science & Systems in Health (SSSH) Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School |
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| Library of Congress Subject Headings (LCSH): | Patient satisfaction -- Data processing, Social media -- Data processing, Natural language processing (Computer science) , Evidence-based medicine -- Data processing, User-generated content | ||||||||
| Journal or Publication Title: | Frontiers in Digital Health | ||||||||
| Publisher: | Frontiers | ||||||||
| ISSN: | 2673-253X | ||||||||
| Official Date: | 17 February 2021 | ||||||||
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| Volume: | 3 | ||||||||
| Article Number: | 598431 | ||||||||
| DOI: | 10.3389/fdgth.2021.598431 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||||
| Copyright Holders: | Julia Walsh | ||||||||
| Date of first compliant deposit: | 22 February 2021 | ||||||||
| Date of first compliant Open Access: | 23 February 2021 | ||||||||
| RIOXX Funder/Project Grant: |
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