Adrian Benton

Identifying potential adverse effects using the web: A new approach to medical hypothesis generation

Medical message boards are online resources where users with a particular condition exchange information, some of which they might not otherwise share with medical providers. Many of these boards contain a large number of posts and contain patient opinions and experiences that would be potentially useful to clinicians and researchers. We present an approach that is able to collect a corpus of medical message board posts, de-identify the corpus, and extract information on potential adverse drug effects discussed by users. Using a corpus of posts to breast cancer message boards, we identified drug event pairs using co-occurrence statistics. We then compared the identified drug event pairs with adverse effects listed on the package labels of tamoxifen, anastrozole, exemestane, and letrozole. Of the pairs identified by our system, 75-80% were documented on the drug labels. Some of the undocumented pairs may represent previously unidentified adverse drug effects.

Online discussion of drug side effects and discontinuation among breast cancer survivors

While patients often use the internet as a medium to search for and exchange health‐related information, little is known about the extent to which patients use social media to discuss side effects related to medications. We aim to understand the frequency and content of side effects and associated adherence behaviors discussed by breast cancer patients related to using aromatase inhibitors (AIs), with particular emphasis on AI‐related arthralgia.

A system for de-identifying medical message board text

There are millions of public posts to medical message boards by users seeking support and information on a wide range of medical conditions. It has been shown that these posts can be used to gain a greater understanding of patients’ experiences and concerns. As investigators continue to explore large corpora of medical discussion board data for research purposes, protecting the privacy of the members of these online communities becomes an important challenge that needs to be met. Extant entity recognition methods used for more structured text are not sufficient because message posts present additional challenges: the posts contain many typographical errors, larger variety of possible names, terms and abbreviations specific to Internet posts or a particular message board, and mentions of the authors’ personal lives. The main contribution of this paper is a system to de-identify the authors of message board posts automatically, taking into account the aforementioned challenges. We demonstrate our system on two different message board corpora, one on breast cancer and another on arthritis. We show that our approach significantly outperforms other publicly available named entity recognition and de-identification systems, which have been tuned for more structured text like operative reports, pathology reports, discharge summaries, or newswire.

medpie: an information extraction package for medical message board posts

SUMMARY We have developed medpie, a software package for preparing medical message board corpora and extracting patient mentions and statistics for drugs, herbs and adverse effects experienced from them. The package is divided into web-crawling, HTML-cleaning, de-identification and information extraction modules. It also includes a sample controlled vocabulary of drugs, herbs and adverse effect terms. AVAILABILITY http://www.cis.upenn.edu/~ungar/medpie.zip. DEPENDENCIES Python 2.6 or 2.7.

Prepared for The Journal of Pharmacoepidemiology and Drug Safety

While patients often use the internet as a medium to search for and exchange health‐related information, little is known about the extent to which patients use social media to discuss side effects related to medications. We aim to understand the frequency and content of side effects and associated adherence behaviors discussed by breast cancer patients related to using aromatase inhibitors (AIs), with particular emphasis on AI‐related arthralgia.

Online discussion of drug side effects and discontinuation among breast cancer survivors: ONLINE DISCUSSION OF DRUG SIDE EFFECTS

While patients often use the internet as a medium to search for and exchange health‐related information, little is known about the extent to which patients use social media to discuss side effects related to medications. We aim to understand the frequency and content of side effects and associated adherence behaviors discussed by breast cancer patients related to using aromatase inhibitors (AIs), with particular emphasis on AI‐related arthralgia.