Catching a Viral Video

The sharing and re-sharing of videos on social sites, blogs e-mail, and other means has given rise to the phenomenon of viral videos - videos that become popular through internet sharing. In this paper we seek to better understand viral videos on YouTube by analyzing sharing and its relationship to video popularity using millions of YouTube videos. The socialness of a video is quantified by classifying the referrer sources for video views as social (e.g. an emailed link, Facebook referral) or non-social (e.g. a link from related videos). We find that viewership patterns of highly social videos are very different from less social videos. For example, the highly social videos rise to, and fall from, their peak popularity more quickly than less social videos. We also find that not all highly social videos become popular, and not all popular videos are highly social. By using our insights on viral videos we are able develop a method for ranking blogs and websites on their ability to spread viral videos

Expert-Augmented Machine Learning

Machine Learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption by the level of trust that models afford users. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of man and machine. Here we present Expert-Augmented Machine Learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We use a large dataset of intensive care patient data to predict mortality and show that we can extract expert knowledge using an online platform, help reveal hidden confounders, improve generalizability on a different population and learn using less data. EAML presents a novel framework for high performance and dependable machine learning in critical applications

Expert-augmented machine learning.

Machine learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption is limited by the level of trust afforded by given models. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of humans and machines. Here, we present expert-augmented machine learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We used a large dataset of intensive-care patient data to derive 126 decision rules that predict hospital mortality. Using an online platform, we asked 15 clinicians to assess the relative risk of the subpopulation defined by each rule compared to the total sample. We compared the clinician-assessed risk to the empirical risk and found that, while clinicians agreed with the data in most cases, there were notable exceptions where they overestimated or underestimated the true risk. Studying the rules with greatest disagreement, we identified problems with the training data, including one miscoded variable and one hidden confounder. Filtering the rules based on the extent of disagreement between clinician-assessed risk and empirical risk, we improved performance on out-of-sample data and were able to train with less data. EAML provides a platform for automated creation of problem-specific priors, which help build robust and dependable machine-learning models in critical applications

Evaluation of a Fotonovela to Increase Depression Knowledge and Reduce Stigma Among Hispanic Adults

Fotonovelas—small booklets that portray a dramatic story using photographs and captions— represent a powerful health education tool for low-literacy and ethnic minority audiences. This study evaluated the effectiveness of a depression fotonovela in increasing depression knowledge, decreasing stigma, increasing self-efficacy to recognize depression, and increasing intentions to seek treatment, relative to a text pamphlet. Hispanic adults attending a community adult school (N = 157, 47.5 % female, mean age = 35.8 years, 84 % immigrants, 63 % with less than high school education) were randomly assigned to read the fotonovela or a low-literacy text pamphlet about depression. They completed surveys before reading the material, immediately after reading the material, and 1 month later. The fotonovela and text pamphlet both produced significant improvements in depression knowledge and self-efficacy to identify depression, but the fotonovela produced significantly larger reductions in antidepressant stigma and mental health care stigma. The fotonovela also was more likely to be passed on to family or friends after the study, potentially increasing its reach throughout the community. Results indicate that fotonovelas can be useful for improving health literacy among underserved populations, which could reduce health disparities

Identificaçao da Via Lenta na Reentrada Nodal Atrioventricular Usando o Intervalo Atrioventricular Mais Curto

Em 10 pacientes consecutivos, realizou-se o mapeamento da parede septal do átrio direito durante taquicardia supraventricular por reentrada nodal AV, para comprovar a hipótese de que o intervalo AV mais curto identificava a área de conduçao da via lenta. O septo atrial foi dividido em quatro zonas distintas. Em sete dos pacientes o intervalo AV anterógrado mais curto foi encontrado na zona 3; em dois, na zona 4; no último, na zona 2. A modificaçao por radiofreqüência da via lenta foi obtida com sucesso, em todos os pacientes, na área de conduçao AV mais curta. O intervalo AV durante ritmo sinusal permaneceu inalterado antes e após a ablaçao. Após um seguimento de 21 ±4 meses, nenhum deles teve recorrência dos sintomas

Identificaçao da Via Lenta na Reentrada Nodal Atrioventricular Usando o Intervalo Atrioventricular Mais Curto

Em 10 pacientes consecutivos, realizou-se o mapeamento da parede septal do átrio direito durante taquicardia supraventricular por reentrada nodal AV, para comprovar a hipótese de que o intervalo AV mais curto identificava a área de conduçao da via lenta. O septo atrial foi dividido em quatro zonas distintas. Em sete dos pacientes o intervalo AV anterógrado mais curto foi encontrado na zona 3; em dois, na zona 4; no último, na zona 2. A modificaçao por radiofreqüência da via lenta foi obtida com sucesso, em todos os pacientes, na área de conduçao AV mais curta. O intervalo AV durante ritmo sinusal permaneceu inalterado antes e após a ablaçao. Após um seguimento de 21 ±4 meses, nenhum deles teve recorrência dos sintomas

Analyzing and predicting the spatial penetration of Airbnb in U.S. cities

In the hospitality industry, the room and apartment sharing platform of Airbnb has been accused of unfair competition. Detractors have pointed out the chronic lack of proper legislation. Unfortunately, there is little quantitative evidence about Airbnb's spatial penetration upon which to base such a legislation. In this study, we analyze Airbnb's spatial distribution in eight U.S. urban areas, in relation to both geographic, socio-demographic, and economic information. We find that, despite being very different in terms of population composition, size, and wealth, all eight cities exhibit the same pattern: that is, areas of high Airbnb presence are those occupied by the \newpart{``talented and creative''} classes, and those that are close to city centers. This result is consistent so much so that the accuracy of predicting Airbnb's spatial penetration is as high as 0.725

Sick-listed employees with severe medically unexplained physical symptoms: burden or routine for the occupational health physician? A cross sectional study

Background: The two primary objectives of this study were to the assess consultation load of occupational health physicians (OHPs), and their difficulties and needs with regard to their sickness certification tasks in sick-listed employees with severe medical unexplained physical symptoms (MUPS). Third objective was to determine which disease-, patient-, doctor- and practice-related factors are associated with the difficulties and needs of the OHPs. Methods: In this cross-sectional study, 43 participating OHPs from 5 group practices assessed 489 sick-listed employees with and without severe MUPS. The OHPs filled in a questionnaire about difficulties concerning sickness certification tasks, consultation time, their needs with regard to consultation with or referral to a psychiatrist or psychologist, and communication with GPs. The OHPs also completed a questionnaire about their personal characteristics. Results: OHPs only experienced task difficulties in employees with severe MUPS in relation to their communication with the treating physician. This only occured in cases in which the OHP attributed the physical symptoms to somatoform causes. If they attributed the physical symptoms to mental causes, the OHPs reported a need to consultate a psychiatrist about the diagnosis and treatment. Conclusions: OHPs experience few difficulties with their sickness certification tasks and consultation load concerning employees with severe MUPS. However, they encounter problems if the diagnostic uncertainties of the treating physician interfere with the return to work process. OHPs have a need for psychiatric expertise whenever they are uncertain about the psychiatric causes of a delayed return to work process. We recommend further training programs for OHPs. They should also have more opportunity for consultation and referral to a psychiatrist, and their communication with treating physicians should be improved