Dataset Information

Induced lexico-syntactic patterns improve information extraction from online medical forums.

ABSTRACT:

Objective

To reliably extract two entity types, symptoms and conditions (SCs), and drugs and treatments (DTs), from patient-authored text (PAT) by learning lexico-syntactic patterns from data annotated with seed dictionaries.

Background and significance

Despite the increasing quantity of PAT (eg, online discussion threads), tools for identifying medical entities in PAT are limited. When applied to PAT, existing tools either fail to identify specific entity types or perform poorly. Identification of SC and DT terms in PAT would enable exploration of efficacy and side effects for not only pharmaceutical drugs, but also for home remedies and components of daily care.

Materials and methods

We use SC and DT term dictionaries compiled from online sources to label several discussion forums from MedHelp (http://www.medhelp.org). We then iteratively induce lexico-syntactic patterns corresponding strongly to each entity type to extract new SC and DT terms.

Results

Our system is able to extract symptom descriptions and treatments absent from our original dictionaries, such as 'LADA', 'stabbing pain', and 'cinnamon pills'. Our system extracts DT terms with 58-70% F1 score and SC terms with 66-76% F1 score on two forums from MedHelp. We show improvements over MetaMap, OBA, a conditional random field-based classifier, and a previous pattern learning approach.

Conclusions

Our entity extractor based on lexico-syntactic patterns is a successful and preferable technique for identifying specific entity types in PAT. To the best of our knowledge, this is the first paper to extract SC and DT entities from PAT. We exhibit learning of informal terms often used in PAT but missing from typical dictionaries.

SUBMITTER: Gupta S

PROVIDER: S-EPMC4147618 | biostudies-literature | 2014 Sep-Oct

REPOSITORIES: biostudies-literature

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Publications

Induced lexico-syntactic patterns improve information extraction from online medical forums.

Gupta Sonal S MacLean Diana L DL Heer Jeffrey J Manning Christopher D CD

Journal of the American Medical Informatics Association : JAMIA 20140626 5

<h4>Objective</h4>To reliably extract two entity types, symptoms and conditions (SCs), and drugs and treatments (DTs), from patient-authored text (PAT) by learning lexico-syntactic patterns from data annotated with seed dictionaries.<h4>Background and significance</h4>Despite the increasing quantity of PAT (eg, online discussion threads), tools for identifying medical entities in PAT are limited. When applied to PAT, existing tools either fail to identify specific entity types or perform poorly. ...[more]

PMID: 24970840

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Project description:BackgroundPatient education materials given to breast cancer survivors may not be a good fit for their information needs. Needs may change over time, be forgotten, or be misreported, for a variety of reasons. An automated content analysis of survivors' postings to online health forums can identify expressed information needs over a span of time and be repeated regularly at low cost. Identifying these unmet needs can guide improvements to existing education materials and the creation of new resources.ObjectiveThe primary goals of this project are to assess the unmet information needs of breast cancer survivors from their own perspectives and to identify gaps between information needs and current education materials.MethodsThis approach employs computational methods for content modeling and supervised text classification to data from online health forums to identify explicit and implicit requests for health-related information. Potential gaps between needs and education materials are identified using techniques from information retrieval.ResultsWe provide a new taxonomy for the classification of sentences in online health forum data. 260 postings from two online health forums were selected, yielding 4179 sentences for coding. After annotation of data and training alternative one-versus-others classifiers, a random forest-based approach achieved F1 scores from 66% (Other, dataset2) to 90% (Medical, dataset1) on the primary information types. 136 expressions of need were used to generate queries to indexed education materials. Upon examination of the best two pages retrieved for each query, 12% (17/136) of queries were found to have relevant content by all coders, and 33% (45/136) were judged to have relevant content by at least one.ConclusionsText from online health forums can be analyzed effectively using automated methods. Our analysis confirms that breast cancer survivors have many information needs that are not covered by the written documents they typically receive, as our results suggest that at most a third of breast cancer survivors' questions would be addressed by the materials currently provided to them.

Project description:BackgroundMental health (MH) peer online forums offer robust support where internet access is common, but healthcare is not, e.g., in countries with under-resourced MH support, rural areas, and during pandemics. Despite their widespread use, little is known about who posts in such forums, and in what mood states. The discussion platform Reddit is ideally suited to study this as it hosts forums (subreddits) for MH and non-MH topics. In bipolar disorder (BD), where extreme mood states are core defining features, mood influences are particularly relevant.ObjectivesThis exploratory study investigated posting patterns of Reddit users with a self-reported BD diagnosis and the associations between posting and emotions, specifically: 1) What proportion of the identified users posts in MH versus non-MH subreddits? 2) What differences exist in the emotions that they express in MH or non-MH subreddit posts? 3) How does mood differ between those users who post in MH subreddits compared to those who only post in non-MH subreddits?MethodsReddit users were automatically identified via self-reported BD diagnosis statements and all their 2005-2019 posts were downloaded. First, the percentages of users who posted only in MH (non-MH) subreddits were calculated. Second, affective vocabulary use was compared in MH versus non-MH subreddits by measuring the frequency of words associated with positive emotions, anxiety, sadness, anger, and first-person singular pronouns via the LIWC text analysis tool. Third, a logistic regression distinguished users who did versus did not post in MH subreddits, using the same LIWC variables (measured from users' non-MH subreddit posts) as predictors, controlling for age, gender, active days, and mean posts/day.Results1) Two thirds of the identified 19,685 users with a self-reported BD diagnosis posted in both MH and non-MH subreddits. 2) Users who posted in both MH and non-MH subreddits exhibited less positive emotion but more anxiety and sadness and used more first-person singular pronouns in their MH subreddit posts. 3) Feminine gender, higher positive emotion, anxiety, and sadness were significantly associated with posting in MH subreddits.ConclusionsMany Reddit users who disclose a BD diagnosis use a single account to discuss MH and other concerns. Future work should determine whether users exhibit more anxiety and sadness in their MH subreddit posts because they more readily post in MH subreddits when experiencing lower mood or because they feel more able to express negative emotions in these spaces. MH forums may reflect the views of people who experience more extreme mood (outside of MH subreddits) compared to people who do not post in MH subreddits. These findings can be useful for MH professionals to discuss online forums with their clients. For example, they may caution them that forums may underrepresent people living well with BD.

Dataset Information

Induced lexico-syntactic patterns improve information extraction from online medical forums.

Objective

Background and significance

Materials and methods

Results

Conclusions

Publications

Induced lexico-syntactic patterns improve information extraction from online medical forums.

Similar Datasets

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