Detection of Cases of Noncompliance to Drug Treatment in Patient Forum Posts: Topic Model Approach

Abdellaoui, Redhouane; Foulquié, Pierre; Texier, Nathalie; Faviez, Carole; Burgun, Anita; Schück, Stéphane

doi:10.2196/jmir.9222

Cited by 34 publications

(20 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Automatic and manual extraction methods were utilized, depending on the source of the posts. For generic French medical web forums, posts were automatically extracted using the Detec’t WebCrawler [ 22 ]. Collection of the posts was performed according to the HTML structure of each forum.…”

Section: Methodsmentioning

confidence: 99%

Exploring the Health-Related Quality of Life of Patients Treated With Immune Checkpoint Inhibitors: Social Media Study

Cotté¹,

Voillot²,

Bennett³

et al. 2020

J Med Internet Res

Self Cite

View full text Add to dashboard Cite

Background Immune checkpoint inhibitors (ICIs) are increasingly used to treat several types of tumors. Impact of this emerging therapy on patients’ health-related quality of life (HRQoL) is usually collected in clinical trials through standard questionnaires. However, this might not fully reflect HRQoL of patients under real-world conditions. In parallel, users’ narratives from social media represent a potential new source of research concerning HRQoL. Objective The aim of this study is to assess and compare coverage of ICI-treated patients’ HRQoL domains and subdomains in standard questionnaires from clinical trials and in real-world setting from social media posts. Methods A retrospective study was carried out by collecting social media posts in French language written by internet users mentioning their experiences with ICIs between January 2011 and August 2018. Automatic and manual extractions were implemented to create a corpus where domains and subdomains of HRQoL were classified. These annotations were compared with domains covered by 2 standard HRQoL questionnaires, the EORTC QLQ-C30 and the FACT-G. Results We identified 150 users who described their own experience with ICI (89/150, 59.3%) or that of their relative (61/150, 40.7%), with 137 users (91.3%) reporting at least one HRQoL domain in their social media posts. A total of 8 domains and 42 subdomains of HRQoL were identified: Global health (1 subdomain; 115 patients), Symptoms (13; 76), Emotional state (10; 49), Role (7; 22), Physical activity (4; 13), Professional situation (3; 9), Cognitive state (2; 2), and Social state (2; 2). The QLQ-C30 showed a wider global coverage of social media HRQoL subdomains than the FACT-G, 45% (19/42) and 29% (12/42), respectively. For both QLQ-C30 and FACT-G questionnaires, coverage rates were particularly suboptimal for Symptoms (68/123, 55.3% and 72/123, 58.5%, respectively), Emotional state (7/49, 14% and 24/49, 49%, respectively), and Role (17/22, 77% and 15/22, 68%, respectively). Conclusions Many patients with cancer are using social media to share their experiences with immunotherapy. Collecting and analyzing their spontaneous narratives are helpful to capture and understand their HRQoL in real-world setting. New measures of HRQoL are needed to provide more in-depth evaluation of Symptoms, Emotional state, and Role among patients with cancer treated with immunotherapy.

show abstract

Section: Methodsmentioning

confidence: 99%

Exploring the Health-Related Quality of Life of Patients Treated With Immune Checkpoint Inhibitors: Social Media Study

Cotté¹,

Voillot²,

Bennett³

et al. 2020

J Med Internet Res

Self Cite

View full text Add to dashboard Cite

show abstract

“…For example, Abdellaoui et al , 9 outlined a methodology to detect medication non-compliance behaviours amongst people on antidepressant and antipsychotic medications by modelling the way dosage variation and treatment interruption behaviours were discussed online. Jones et al , 10 demonstrated it was possible to uncover the hidden, less obvious aspects of breast cancer management and recovery in online discussion forums, including aspects that are not easily ascertainable in patient clinics (e.g.…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence in Health: New Opportunities, Challenges, and Practical Implications

Lau

Staccini

2019

Yearb Med Inform

View full text Add to dashboard Cite

Objectives: To summarise the state of the art during the year 2018 in consumer health informatics and education, with a special emphasis on the special topic of the International Medical Informatics Association (IMIA) Yearbook for 2019: “Artificial intelligence in health: new opportunities, challenges, and practical implications”. Methods: We conducted a systematic search of articles published in PubMed using a predefined set of queries that identified 99 potential articles for review. These articles were screened according to topic relevance and 14 were selected for consideration as best paper candidates. The 14 papers were then presented to a panel of international experts for full paper review and scoring. Three papers that received the highest score were discussed in a consensus meeting and were agreed upon as best papers on artificial intelligence in health for patients and consumers in the year 2018. Results: Only a small number of 2018 papers reported Artificial Intelligence (AI) research for patients and consumers. No studies were found on AI applications designed specifically for patients or consumers, nor were there studies that elicited patient and consumer input on AI. Currently, the most common use of AI for patients and consumers lies in secondary analysis of social media data (e.g., online discussion forums). In particular, the three best papers shared a common methodology of using data-driven algorithms (such as text mining, topic modelling, Latent Dirichlet allocation modelling), combined with insight-led approaches (e.g., visualisation, qualitative analysis and manual review), to uncover patient and consumer experiences of health and illness in online communities. Conclusions: While discussion remains active on how AI could 'revolutionise' healthcare delivery, there is a lack of direction and evidence on how AI could actually benefit patients and consumers. Perhaps instead of primarily focusing on data and algorithms, researchers should engage with patients and consumers early in the AI research agenda to ensure we are indeed asking the right questions, and that important use cases and critical contexts are identified together with patients and consumers. Without a clear understanding on why patients and consumers need AI in the first place, or how AI could support individuals with their healthcare needs, it is difficult to imagine the kinds of AI applications that would have meaningful and sustainable impact on individual daily lives.

show abstract

“…The hybrid approach is a combination of the two methods. 19 The topic modelling approach has been previously adopted by medical researchers to identify patterns of comorbid medical conditions, 20 detect medication noncompliance using posts in patient forums, 21 describe public health information from the social media, 22 cluster analysis of large biomedical datasets 23 and predict inpatient clinical order patterns. 24 The method used in this study assigns words to topics (themes) based on the probability of membership of the topic but the clinical interpretation of the topic is still needed.…”

Section: Discussionmentioning

confidence: 99%

Discovering themes in medical records of patients with psychogenic non-epileptic seizures

et al. 2020

View full text Add to dashboard Cite

IntroductionEpileptic and psychogenic non-epileptic seizures (PNES) are common diagnostic problems encountered in hospital practice. This study explores the use of unsupervised machine learning in discovering themes in medical records of patients presenting with PNES. We hypothesised that themes generated by machine learning are comparable with the classification by human experts.MethodsThis is a retrospective analysis of the medical records in the emergency department of patients (age >18 years) with PNES who underwent inpatient video-electroencephalography monitoring from May 2009 to June 2014 and received a final diagnosis of PNES. Prior to machine learning of written text, we applied a standardised approach in natural language processing to create a document-term matrix (removal of numbers, stop-words and punctuations, transforming fonts to lower case). The words were separated into tokens and treated as if existing within a bag-of-words. A probability of each word existing within a topic (theme) was modelled on multivariate Dirichlet distribution (R Foundation, V.3.5.0). Next, we asked four experts to independently provide a clinical interpretation of the generated topics. When the majority of (≥3) experts agreed, it was regarded as highly congruent. Interactive data are available on the web at (https://gntem2.github.io/PNES/%23topic=1&lambda=0.6&term=).ResultsThere were 39 patients (74.4% women, median age 35 years with range 20–82). A total of 121 documents were converted to text files for text mining. There were 15 generated topics with 12/15 topics rated as highly congruent. The main themes were about descriptors of seizures and medication use.ConclusionsThe findings from machine learning on PNES-related documentation provides evidence for the feasibility of applying machine-learning methodology to analyse large volumes of medical records. The topics generated by machine learning were congruent with interpretations by clinicians indicating this method can be used for screening of medical conditions among large volumes of medical records.

show abstract

Detection of Cases of Noncompliance to Drug Treatment in Patient Forum Posts: Topic Model Approach

Cited by 34 publications

References 31 publications

Exploring the Health-Related Quality of Life of Patients Treated With Immune Checkpoint Inhibitors: Social Media Study

Exploring the Health-Related Quality of Life of Patients Treated With Immune Checkpoint Inhibitors: Social Media Study

Artificial Intelligence in Health: New Opportunities, Challenges, and Practical Implications

Discovering themes in medical records of patients with psychogenic non-epileptic seizures

Contact Info

Product

Resources

About