Characterizing Health-Related Community Question Answering

Our ongoing project is aimed at improving information access to narrow-domain collections of questions and answers. This poster demonstrates how out-of-the-box tools and domain dictionaries can be applied to community question answering (CQA) content in health domain. This approach can be used to improve user interfaces and search over CQA data, as well as to evaluate content quality. The study is a first-time use of a sizable dataset from the Russian CQA site [email protected]

Estimating Potential Infection Transmission Routes in Hospital Wards Using Wearable Proximity Sensors

Contacts between patients, patients and health care workers (HCWs) and among HCWs represent one of the important routes of transmission of hospital-acquired infections (HAI). A detailed description and quantification of contacts in hospitals provides key information for HAIs epidemiology and for the design and validation of control measures. We used wearable sensors to detect close-range interactions ("contacts") between individuals in the geriatric unit of a university hospital. Contact events were measured with a spatial resolution of about 1.5 meters and a temporal resolution of 20 seconds. The study included 46 HCWs and 29 patients and lasted for 4 days and 4 nights. 14037 contacts were recorded. The number and duration of contacts varied between mornings, afternoons and nights, and contact matrices describing the mixing patterns between HCW and patients were built for each time period. Contact patterns were qualitatively similar from one day to the next. 38% of the contacts occurred between pairs of HCWs and 6 HCWs accounted for 42% of all the contacts including at least one patient, suggesting a population of individuals who could potentially act as super-spreaders. Wearable sensors represent a novel tool for the measurement of contact patterns in hospitals. The collected data provides information on important aspects that impact the spreading patterns of infectious diseases, such as the strong heterogeneity of contact numbers and durations across individuals, the variability in the number of contacts during a day, and the fraction of repeated contacts across days. This variability is associated with a marked statistical stability of contact and mixing patterns across days. Our results highlight the need for such measurement efforts in order to correctly inform mathematical models of HAIs and use them to inform the design and evaluation of prevention strategies

Antecedents of Citizen Self-Disclosure on Social Media Health Platforms: Towards an Improved Understanding (1)

Social media platform usage and online community participation has increased to a near ubiquitous level, (Pew Research Centre, 2016). However, to date, much attention has focused on the factors that influence individual’s trust and adoption of social media networks and online communities in general. In contrast, research on the factors that influence trust and self-disclosure on social media health platforms and associated online health communities remains remarkably limited. This is particularly surprising as adoption and usage of these health platforms remains comparatively constrained, thereby limiting potential social and health benefits to consumers, whilst also being an issue of concern to those who develop and design these platforms. This paper examines the extant literature on the factors that influence usage and participation in social media platforms and online communities and which are therefore likely to be relevant to examinations of self-disclosure in an online health context. In doing so, it contributes to technology adoption research in the area of user trust and self-disclosure on social media health platforms and online health communities

TOWARDS AN UNDERSTANDING OF THE FACTORS THAT INFLUENCE CITIZEN TRUST IN SOCIAL MEDIA HEALTH PLATFORMS (12)

Internet penetration rates continue to grow, in the United States for example, it stands at 87% of the population (WorldBank, 2016). In addition, the variety of purposes for which citizens use the Internet is increasing. This is particularly evident in the area of health, where a growing number of Internet users utilise the Internet as a source of health information. The growth in citizens seeking health information online has coincided with the emergence of social media health platforms and applications. While such initiatives have potential to empower health consumers through increased diffusion of targeted health information, the success of these platforms is dependent on their acceptance and adoption. Moreover, there is a lack of understanding as to what factors can generate trust in such platforms. This is despite the fact that trust is an essential component of traditional healthcare delivery and results in increased engagement and participation in health forums

Automatic Identification of Biomedical Concepts in Spanish Language Unstructured Clinical Texts

[Poster]. IHI'10 ACM International Health Informatics Symposium Arlington, VA, USA - November 11-12, 2010The processing of health information from medical records and, especially, clinical notes is a complex task due to the nature of the texts themeselves (i.e., hand-written and containing semi-structured or unstructured data) and the diversity of the terminology used. While certain technologies exist to process these types of texts and data in the English language, only a few such initiatives exist for similar texts and data in the Spanish language. This paper presents a new proposal for the semantic annotation of Spanish-language clinical notes, implementing an automated tool similar to the UMLS MetaMap Transfer (MMTx) for the identification of biomedical concepts in the Spanish-language SNOMED CT ontology. Moreover, an assessment of the tool using 100 Spanish-language clinical notes is presented. Using the clinical notes manually annotated by specialists of a Spanish hospital as the gold standard, it is concluded that precision scores are sufficiently good for the several types of matching achieved by the automated tool proposed. The research presented in this contribution offers a launching point for the establishment of semantic relationships between concepts and the application of mining techniques to Spanish-language clinical notes.This study has been partially supported by the MAVIR Consortium (S2009/TIC-1542) and by the TIN2007-67407-C03-01 project BRAVOPublicad

GSA: A Framework for Rapid Prototyping of Smart Alarm Systems

We describe the Generic Smart Alarm, an architectural framework for the development of decision support modules for a variety of clinical applications. The need to quickly process patient vital signs and detect patient health events arises in many clinical scenarios, from clinical decision support to tele-health systems to home-care applications. The events detected during monitoring can be used as caregiver alarms, as triggers for further downstream processing or logging, or as discrete inputs to decision support systems or physiological closed-loop applications. We believe that all of these scenarios are similar, and share a common framework of design. In attempting to solve a particular instance of the problem, that of device alarm fatigue due to numerous false alarms, we devised a modular system based around this framework. This modular design allows us to easily customize the framework to address the specific needs of the various applications, and at the same time enables us to perform checking of consistency of the system. In the paper we discuss potential specific clinical applications of a generic smart alarm framework, present the proposed architecture of such a framework, and motivate the benefits of a generic framework for the development of new smart alarm or clinical decision support systems

Identificación de términos a partir de enumeraciones sintagmáticas nominales: una aplicación al dominio médico

Partiendo de la hipótesis de que las enumeraciones sintagmáticas nominales (ESN) que se encuentran en los textos médicos se componen de términos específicos del dominio, presentamos un método de reconocimiento de dichas enumeraciones con el objetivo de contribuir a la extracción automática. La metodología se conforma de tres etapas: (i) reconocimiento de enumeraciones sintagmáticas nominales, aquí se utiliza exclusivamente información lingüística, a partir de la cual se elaboran reglas de análisis sintáctico; (ii) extracción automática de los candidatos a términos que se correspondían con unigramas y bigramas, y (iii) evaluación de los candidatos extraídos con el asesoramiento de expertos del área médica. Los experimentos fueron realizados en el corpus IULA, conformado por textos médicos en español. Los resultados obtenidos fueron alentadores, ya que se logró un 67% y 68% de precisión en las enumeraciones detectadas para unigramas y bigramas respectivamente.Sociedad Argentina de Informática e Investigación Operativ

Large-scale evaluation of automated clinical note de-identification and its impact on information extraction

Objective: (1) To evaluate a state-of-the-art natural language processing (NLP)-based approach to automatically de-identify a large set of diverse clinical notes. (2) To measure the impact of de-identification on the performance of information extraction algorithms on the de-identified documents. Material and methods A cross-sectional study that included 3503 stratified, randomly selected clinical notes (over 22 note types) from five million documents produced at one of the largest US pediatric hospitals. Sensitivity, precision, F value of two automated de-identification systems for removing all 18 HIPAA-defined protected health information elements were computed. Performance was assessed against a manually generated ‘gold standard’. Statistical significance was tested. The automated de-identification performance was also compared with that of two humans on a 10% subsample of the gold standard. The effect of de-identification on the performance of subsequent medication extraction was measured. Results: The gold standard included 30 815 protected health information elements and more than one million tokens. The most accurate NLP method had 91.92% sensitivity (R) and 95.08% precision (P) overall. The performance of the system was indistinguishable from that of human annotators (annotators' performance was 92.15%(R)/93.95%(P) and 94.55%(R)/88.45%(P) overall while the best system obtained 92.91%(R)/95.73%(P) on same text). The impact of automated de-identification was minimal on the utility of the narrative notes for subsequent information extraction as measured by the sensitivity and precision of medication name extraction. Discussion and conclusion NLP-based de-identification shows excellent performance that rivals the performance of human annotators. Furthermore, unlike manual de-identification, the automated approach scales up to millions of documents quickly and inexpensively