Online discourse on fibromyalgia: text-mining to identify clinical distinction and patient concerns

Understanding the patient perspective of epilepsy treatment through text mining of online patient support groups

OBJECTIVE

Epilepsy is among the most common chronic neurologic diseases. There is a need for more data on patient perspectives of treatment to guide patient-centered care initiatives. Patients with epilepsy share their experiences on social media anonymously, but little is known about those discussions. Our aim was to learn what patients with epilepsy discuss regarding their condition and identify treatment-related themes from online patient support groups.

METHODS

A total of 355,838 posts were collected from three online support groups for patients with epilepsy through a crawling script, and an analytical pipeline was built to identify patient conversation content through leveraging of multiple text mining methods. Results were also displayed by network visualization methods.

RESULTS

Patients with epilepsy sought information about medical treatments, shared their treatment experiences, and sought help from other posters. Key themes related to treatments included the search for optimal personalized treatment strategies as well as identifying and coping with adverse effects.

SIGNIFICANCE

This study showed the feasibility of learning about concerns of patients with epilepsy, especially treatment issues, through text mining methods. However, some manual selection and filtering were necessary to ensure quality results for the treatment analysis. Providers should be aware of online discussions and use analyses of such discussions to help guide effective patient engagement during care.

A systematic review of natural language processing and text mining of symptoms from electronic patient-authored text data

OBJECTIVE

In this systematic review, we aim to synthesize the literature on the use of natural language processing (NLP) and text mining as they apply to symptom extraction and processing in electronic patient-authored text (ePAT).

MATERIALS AND METHODS

A comprehensive literature search of 1964 articles from PubMed and EMBASE was narrowed to 21 eligible articles. Data related to purpose, text source, number of users and/or posts, evaluation metrics, and quality indicators were recorded.

RESULTS

Pain (n = 18) and fatigue and sleep disturbance (n = 18) were the most frequently evaluated symptom clinical content categories. Studies accessed ePAT from sources such as Twitter and online community forums or patient portals focused on diseases, including diabetes, cancer, and depression. Fifteen studies used NLP as a primary methodology. Studies reported evaluation metrics including the precision, recall, and F-measure for symptom-specific research questions.

DISCUSSION

NLP and text mining have been used to extract and analyze patient-authored symptom data in a wide variety of online communities. Though there are computational challenges with accessing ePAT, the depth of information provided directly from patients offers new horizons for precision medicine, characterization of sub-clinical symptoms, and the creation of personal health libraries as outlined by the National Library of Medicine.

CONCLUSION

Future research should consider the needs of patients expressed through ePAT and its relevance to symptom science. Understanding the role that ePAT plays in health communication and real-time assessment of symptoms, through the use of NLP and text mining, is critical to a patient-centered health system.

Assessment of patient concerns: a review

BACKGROUND

Patient-centred communication is the mainstay of the medical consultation. It includes the assessment of patient concerns and is important in building the doctor-patient therapeutic relationship. Our aim was to perform a literature review to identify relevance of patient concern assessment, to identify what concerns are encountered in various clinical settings and to explore the different methodologies used to obtain them.

METHODS

A narrative review was performed.

RESULTS

Addressing patient concerns is associated with increased patient satisfaction. Unvoiced concerns are associated with unresolved health issues and poor doctor-patient relationships. Different specialities have focused on different aspects of concern assessment. Patient concern assessment studies are mostly qualitative in nature. Not all qualitative methods give similar results. Interviews are more reliable sources of information than questionnaires as no one questionnaire captures all patient concerns. The location where interviews take place is also relevant.

CONCLUSIONS

Assessing patient concerns is associated with positive outcomes. Patient concerns are handled differently by different clinical specialties. Various methodologies have been used for concern assessment. Interviews and questionnaires can often yield different results.

Analysis of Patient Narratives in Disease Blogs on the Internet: An Exploratory Study of Social Pharmacovigilance

BACKGROUND

Although several reports have suggested that patient-generated data from Internet sources could be used to improve drug safety and pharmacovigilance, few studies have identified such data sources in Japan. We introduce a unique Japanese data source: tōbyōki, which translates literally as "an account of a struggle with disease."

OBJECTIVE

The objective of this study was to evaluate the basic characteristics of the TOBYO database, a collection of tōbyōki blogs on the Internet, and discuss potential applications for pharmacovigilance.

METHODS

We analyzed the overall gender and age distribution of the patient-generated TOBYO database and compared this with other external databases generated by health care professionals. For detailed analysis, we prepared separate datasets for blogs written by patients with depression and blogs written by patients with rheumatoid arthritis (RA), because these conditions were expected to entail subjective patient symptoms such as discomfort, insomnia, and pain. Frequently appearing medical terms were counted, and their variations were compared with those in an external adverse drug reaction (ADR) reporting database. Frequently appearing words regarding patients with depression and patients with RA were visualized using word clouds and word cooccurrence networks.

RESULTS

As of June 4, 2016, the TOBYO database comprised 54,010 blogs representing 1405 disorders. Overall, more entries were written by female bloggers (68.8%) than by male bloggers (30.8%). The most frequently observed disorders were breast cancer (4983 blogs), depression (3556), infertility (2430), RA (1118), and panic disorder (1090). Comparison of medical terms observed in tōbyōki blogs with those in an external ADR reporting database showed that subjective and symptomatic events and general terms tended to be frequently observed in tōbyōki blogs (eg, anxiety, headache, and pain), whereas events using more technical medical terms (eg, syndrome and abnormal laboratory test result) tended to be observed frequently in the ADR database. We also confirmed the feasibility of using visualization techniques to obtain insights from unstructured text-based tōbyōki blog data. Word clouds described the characteristics of each disorder, such as "sleeping" and "anxiety" in depression and "pain" and "painful" in RA.

CONCLUSIONS

Pharmacovigilance should maintain a strong focus on patients' actual experiences, concerns, and outcomes, and this approach can be expected to uncover hidden adverse event signals earlier and to help us understand adverse events in a patient-centered way. Patient-generated tōbyōki blogs in the TOBYO database showed unique characteristics that were different from the data in existing sources generated by health care professionals. Analysis of tōbyōki blogs would add value to the assessment of disorders with a high prevalence in women, psychiatric disorders in which subjective symptoms have important clinical meaning, refractory disorders, and other chronic disorders.