Temporal Causality Analysis of Sentiment Change in a Cancer Survivor Network.

Online health communities constitute a useful source of information and social support for patients. American Cancer Society's Cancer Survivor Network (CSN), a 173,000-member community, is the largest online network for cancer patients, survivors, and caregivers. A discussion thread in CSN is often initiated by a cancer survivor seeking support from other members of CSN.

What Do People Like to "Share" About Obesity? A Content Analysis of Frequent Retweets About Obesity on Twitter.

Twitter has been recognized as a useful channel for the sharing and dissemination of health information, owing in part to its "retweet" function. This study reports findings from a content analysis of frequently retweeted obesity-related tweets to identify the prevalent beliefs and attitudes about obesity on Twitter, as well as key message features that prompt retweeting behavior conducive to maximizing the reach of health messages on Twitter. The findings show that tweets that are emotionally evocative, humorous, and concern individual-level causes for obesity were more frequently retweeted than their counterparts.

"Where Have All the Good Men Gone?" Gendered Interactions in Online Dating.

This article explores gendered patterns of online dating and their implications for heterosexual union formation. The authors hypothesized that traditional gender norms combine with preferences for more socially desirable partners to benefit men and disadvantage women in the earliest stages of dating. They tested this with 6 months of online dating data from a mid-sized southwestern city (N = 8,259 men and 6,274 women).

Finding influential users of online health communities: a new metric based on sentiment influence.

Objective: Online health communities (OHCs) have become a major source of support for people with health problems. This research tries to improve our understanding of social influence and to identify influential users in OHCs. The outcome can facilitate OHC management, improve community sustainability, and eventually benefit OHC users.

Understanding topics and sentiment in an online cancer survivor community.

Online cancer communities help members support one another, provide new perspectives about living with cancer, normalize experiences, and reduce isolation. The American Cancer Society's 166000-member Cancer Survivors Network (CSN) is the largest online peer support community for cancer patients, survivors, and caregivers. Sentiment analysis and topic modeling were applied to CSN breast and colorectal cancer discussion posts from 2005 to 2010 to examine how sentiment change of thread initiators, a measure of social support, varies by discussion topic.

SRCAST-Diagnosis: understanding how different members of a patient-care team interact with clinical decision support system.

A clinical diagnosis is a decision-making process that consists of not only the final diagnostic decision but also a series of information seeking decisions. Members of a patient-care team such as nurses, residents, and attending physicians play different roles but work collaboratively during this process. To better support the different roles and their collaborations during this process, we need to understand how different users interact with decision support systems.

Modeling cognitive loads for evolving shared mental models in human-agent collaboration.

Recent research on human-centered teamwork highly demands the design of cognitive agents that can model and exploit human partners' cognitive load to enhance team performance. In this paper, we focus on teams composed of human-agent pairs and develop a system called Shared Mental Models for all--SMMall. SMMall implements a hidden Markov model (HMM)-based cognitive load model for an agent to predict its human partner's instantaneous cognitive load status.

A distributed, collaborative intelligent agent system approach for proactive postmarketing drug safety surveillance.

Discovering unknown adverse drug reactions (ADRs) in postmarketing surveillance as early as possible is of great importance. The current approach to postmarketing surveillance primarily relies on spontaneous reporting. It is a passive surveillance system and limited by gross underreporting (<10% reporting rate), latency, and inconsistent reporting.

Hypothesis-driven story building framework: enhancing iterative process support in clinical diagnostic decision support systems.

Clinical diagnosis is an iterative process because of partial and ambiguous information, changing conditions, and resource constraints. Although clinical diagnostic decision support systems have been successfully used to support clinical care, they face certain limitations in supporting clinical diagnosis as an iterative process. An approach is required to enhance the iterative process support in clinical diagnostic decision support systems.

Challenges to effective crisis management: using information and communication technologies to coordinate emergency medical services and emergency department teams.

Objective: The purpose of this study is to identify the major challenges to coordination between emergency department (ED) teams and emergency medical services (EMS) teams.

Design: We conducted a series of focus groups involving both ED and EMS team members using a crisis scenario as the basis of the focus group discussion. We also collected organizational workflow data.

A team agent approach to postmarketing surveillance of adverse drug reactions.

Current postmarketing surveillance methods largely rely on spontaneous reports which suffer from serious underreporting, latency, and inconsistent reporting. Thus they are not ideal for rapidly identifying rare adverse drug reactions (ADRs). We propose an active, multi-agent computer software system, where each agent is empowered with teamwork capabilities such as anticipating information needs, identifying relevant ADR information, and continuously monitoring and proactively sharing such information in a collaborative fashion with other agents.