Accuracy and self correction of information received from an internet breast cancer list: content analysis.

OBJECTIVES: To determine the prevalence of false or misleading statements in messages posted by internet cancer support groups and whether these statements were identified as false or misleading and corrected by other participants in subsequent postings. DESIGN: Analysis of content of postings. SETTING: Internet cancer support group Breast Cancer Mailing List. MAIN OUTCOME MEASURES: Number of false or misleading statements posted from 1 January to 23 April 2005 and whether these were identified and corrected by participants in subsequent postings. RESULTS: 10 of 4600 postings (0.22%) were found to be false or misleading. Of these, seven were identified as false or misleading by other participants and corrected within an average of four hours and 33 minutes (maximum, nine hours and nine minutes). CONCLUSIONS: Most posted information on breast cancer was accurate. Most false or misleading statements were rapidly corrected by participants in subsequent postings

Predicting multiple sclerosis disease severity with multimodal deep neural networks

Multiple Sclerosis (MS) is a chronic disease developed in human brain and spinal cord, which can cause permanent damage or deterioration of the nerves. The severity of MS disease is monitored by the Expanded Disability Status Scale (EDSS), composed of several functional sub-scores. Early and accurate classification of MS disease severity is critical for slowing down or preventing disease progression via applying early therapeutic intervention strategies. Recent advances in deep learning and the wide use of Electronic Health Records (EHR) creates opportunities to apply data-driven and predictive modeling tools for this goal. Previous studies focusing on using single-modal machine learning and deep learning algorithms were limited in terms of prediction accuracy due to the data insufficiency or model simplicity. In this paper, we proposed an idea of using patients' multimodal longitudinal and longitudinal EHR data to predict multiple sclerosis disease severity at the hospital visit. This work has two important contributions. First, we describe a pilot effort to leverage structured EHR data, neuroimaging data and clinical notes to build a multi-modal deep learning framework to predict patient's MS disease severity. The proposed pipeline demonstrates up to 25% increase in terms of the area under the Area Under the Receiver Operating Characteristic curve (AUROC) compared to models using single-modal data. Second, the study also provides insights regarding the amount useful signal embedded in each data modality with respect to MS disease prediction, which may improve data collection processes

Quality of weight loss advice on internet forums.

BACKGROUND: Adults use the Internet for weight loss information, sometimes by participating in discussion forums. Our purpose was to analyze the quality of advice exchanged on these forums. METHODS: This was a retrospective analysis of messages posted to 18 Internet weight loss forums during 1 month in 2006. Advice was evaluated for congruence with clinical guidelines; potential for causing harm; and subsequent correction when it was contradictory to guidelines (erroneous) or potentially harmful. Message- and forum-specific characteristics were evaluated as predictors of advice quality and self-correction. RESULTS: Of 3368 initial messages, 266 (7.9%) were requests for advice. Of 654 provisions of advice, 56 (8.6%) were erroneous and 19 of these 56 (34%) were subsequently corrected. Forty-three (6.6%) provisions of advice were harmful, and 12 of these 43 (28%) were subsequently corrected. Messages from low-activity forums (fewer messages) were more likely than those from high-activity forums to be erroneous (10.6% vs 2.4%, P \u3c .001) or harmful (8.4% vs 1.2%, P \u3c .001). In high-activity forums, 2 of 4 (50%) erroneous provisions of advice and 2 of 2 (100%) potentially harmful provisions of advice were corrected by subsequent postings. Compared with general weight loss advice, medication-related advice was more likely to be erroneous (P = .02) or harmful (P = .01). CONCLUSIONS: Most advice posted on highly active Internet weight loss forums is not erroneous or harmful. However, clinical and research strategies are needed to address the quality of medication-related advice

Screening for obstructive sleep apnea on the internet: randomized trial.

BACKGROUND: Obstructive sleep apnea is underdiagnosed. We conducted a pilot randomized controlled trial of an online intervention to promote obstructive sleep apnea screening among members of an Internet weight-loss community. METHODS: Members of an Internet weight-loss community who have never been diagnosed with obstructive sleep apnea or discussed the condition with their healthcare provider were randomized to intervention (online risk assessment+feedback) or control. The primary outcome was discussing obstructive sleep apnea with a healthcare provider at 12 weeks. RESULTS: Of 4700 members who were sent e-mail study announcements, 168 (97% were female, age 39.5 years [standard deviation 11.7], body mass index 30.3 [standard deviation 7.8]) were randomized to intervention (n=84) or control (n=84). Of 82 intervention subjects who completed the risk assessment, 50 (61%) were low risk and 32 (39%) were high risk for obstructive sleep apnea. Intervention subjects were more likely than control subjects to discuss obstructive sleep apnea with their healthcare provider within 12 weeks (11% [9/84] vs 2% [2/84]; P=.02; relative risk=4.50; 95% confidence interval, 1.002-20.21). The number needed to treat was 12. High-risk intervention subjects were more likely than control subjects to discuss obstructive sleep apnea with their healthcare provider (19% [6/32] vs 2% [2/84]; P=.004; relative risk=7.88; 95% confidence interval, 1.68-37.02). One high-risk intervention subject started treatment for obstructive sleep apnea. CONCLUSION: An online screening intervention is feasible and likely effective in encouraging members of an Internet weight-loss community to discuss obstructive sleep apnea with their healthcare provider

BigMouth: a multi-institutional dental data repository

Few oral health databases are available for research and the advancement of evidence-based dentistry. In this work we developed a centralized data repository derived from electronic health records (EHRs) at four dental schools participating in the Consortium of Oral Health Research and Informatics. A multi-stakeholder committee developed a data governance framework that encouraged data sharing while allowing control of contributed data. We adopted the i2b2 data warehousing platform and mapped data from each institution to a common reference terminology. We realized that dental EHRs urgently need to adopt common terminologies. While all used the same treatment code set, only three of the four sites used a common diagnostic terminology, and there were wide discrepancies in how medical and dental histories were documented. BigMouth was successfully launched in August 2012 with data on 1.1 million patients, and made available to users at the contributing institutions

Improving Chat in an Online Graduate Class

Introduction: Foundations of Health Information Sciences I is the first class many students take to introduce them to the field of health informatics. It is completely online, and uses optional weekly text-only chats to provide real time interaction between faculty and students. Chat sessions were very disorganized and difficult to follow, both real time and on the transcript. Research suggests that the disorganization contributes to cognitive load. [See PDF for complete abstract

Ontology driven integration platform for clinical and translational research

Semantic Web technologies offer a promising framework for integration of disparate biomedical data. In this paper we present the semantic information integration platform under development at the Center for Clinical and Translational Sciences (CCTS) at the University of Texas Health Science Center at Houston (UTHSC-H) as part of our Clinical and Translational Science Award (CTSA) program. We utilize the Semantic Web technologies not only for integrating, repurposing and classification of multi-source clinical data, but also to construct a distributed environment for information sharing, and collaboration online. Service Oriented Architecture (SOA) is used to modularize and distribute reusable services in a dynamic and distributed environment. Components of the semantic solution and its overall architecture are described

SCOR: A secure international informatics infrastructure to investigate COVID-19

Global pandemics call for large and diverse healthcare data to study various risk factors, treatment options, and disease progression patterns. Despite the enormous efforts of many large data consortium initiatives, scientific community still lacks a secure and privacy-preserving infrastructure to support auditable data sharing and facilitate automated and legally compliant federated analysis on an international scale. Existing health informatics systems do not incorporate the latest progress in modern security and federated machine learning algorithms, which are poised to offer solutions. An international group of passionate researchers came together with a joint mission to solve the problem with our finest models and tools. The SCOR Consortium has developed a ready-to-deploy secure infrastructure using world-class privacy and security technologies to reconcile the privacy/utility conflicts. We hope our effort will make a change and accelerate research in future pandemics with broad and diverse samples on an international scale