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.

Background and objectiveAs people increasingly engage in online health-seeking behavior and contribute to health-oriented websites, the volume of medical text authored by patients and other medical novices grows rapidly. However, we lack an effective method for automatically identifying medical terms in patient-authored text (PAT). We demonstrate that crowdsourcing PAT medical term identification tasks to non-experts is a viable method for creating large, accurately-labeled PAT datasets; moreover, such datasets can be used to train classifiers that outperform existing medical term identification tools.Materials and methodsTo evaluate the viability of using non-expert crowds to label PAT, we compare expert (registered nurses) and non-expert (Amazon Mechanical Turk workers; Turkers) responses to a PAT medical term identification task. Next, we build a crowd-labeled dataset comprising 10 000 sentences from MedHelp. We train two models on this dataset and evaluate their performance, as well as that of MetaMap, Open Biomedical Annotator (OBA), and NaCTeM's TerMINE, against two gold standard datasets: one from MedHelp and the other from CureTogether.ResultsWhen aggregated according to a corroborative voting policy, Turker responses predict expert responses with an F1 score of 84%. A conditional random field (CRF) trained on 10 000 crowd-labeled MedHelp sentences achieves an F1 score of 78% against the CureTogether gold standard, widely outperforming OBA (47%), TerMINE (43%), and MetaMap (39%). A failure analysis of the CRF suggests that misclassified terms are likely to be either generic or rare.ConclusionsOur results show that combining statistical models sensitive to sentence-level context with crowd-labeled data is a scalable and effective technique for automatically identifying medical terms in PAT.

Stress has a wide range of negative impacts on people, ranging from declines in real-time task performance to development of chronic health conditions. Despite the increasing availability of sensors and methods for detecting stress, little work has focused on automated stress interventions and their effect. We present MoodWings: a wearable butterfly that mirrors a user's real-time stress state through actuated wing motion. We designed MoodWings to function both as an early-stress-warning system as well as a physical interface through which users could manipulate their affective state. Accordingly, we hypothesized that MoodWings would help users both calm down and perform better during stressful tasks. We tested our hypotheses on a common stressful task: driving. While users drove significantly more safely with MoodWings, they experienced higher stress levels (physiologically and selfperceived). Despite this, users were enthusiastic about MoodWings, expressing several alternative contexts in which they would find it useful. We discuss these results and future design implications for building externalized manifestations of real-time affective state.

Prescription drug abuse is a pressing public health issue, and people who misuse prescription drugs are turning to online forums for help. Are such forums effective? We analyze the process of opioid withdrawal, recovery and relapse on Forum77, MedHelp.org's online health forum for substance abuse recovery. Applying Prochashka's Transtheoretical Model for behavior change, we develop a taxonomy describing phases of addiction expressed by Forum77 members. We examine activity and linguistic features across the phases USING, WITHDRAWING and RECOVERING. We train statistical classifiers to identify addiction phase, relapse and whether a user was RECOVERING at the time of her last post. Applying our classifiers to 2,848 users, we find that while almost 50% relapse, the prognosis for ending in RECOVERING is favorable. Supplementing our results with users' own accounts of their experiences, we discuss Forum77's efficacy and shortcomings, and implications for future technologies.