Fatty liver classification via risk controlled neural networks trained on grouped ultrasound image data.

Ultrasound imaging is a widely used technique for fatty liver diagnosis as it is practically affordable and can be quickly deployed by using suitable devices. When it is applied to a patient, multiple images of the targeted tissues are produced. We propose a machine learning model for fatty liver diagnosis from multiple ultrasound images. The machine learning model extracts features of the ultrasound images by using a pre-trained image encoder. It further produces a summary embedding on these features by using a graph neural network. The summary embedding is used as input for a classifier on fatty liver diagnosis. We train the machine learning model on a ultrasound image dataset collected by Taiwan Biobank. We also carry out risk control on the machine learning model using conformal prediction. Under the risk control procedure, the classifier can improve the results with high probabilistic guarantees.

Quality of Life and Multilevel Contact Network Structures Among Healthy Adults in Taiwan: Online Participatory Cohort Study.

BACKGROUND: People's quality of life diverges on their demographics, socioeconomic status, and social connections. OBJECTIVE: By taking both demographic and socioeconomic features into account, we investigated how quality of life varied on social networks using data from both longitudinal surveys and contact diaries in a year-long (2015-2016) study. METHODS: Our 4-wave, repeated measures of quality of life followed the brief version of the World Health Organization Quality of Life scale (WHOQOL-BREF). In our regression analysis, we integrated these survey measures with key time-varying and multilevel network indices based on contact diaries. RESULTS: People's quality of life may decrease if their daily contacts contain high proportions of weak ties. In addition, people tend to perceive a better quality of life when their daily contacts are face-to-face or initiated by others or when they contact someone who is in a good mood or someone with whom they can discuss important life issues. CONCLUSIONS: Our findings imply that both functional and structural aspects of the social network play important but different roles in shaping people's quality of life.

Detecting concurrent mood in daily contact networks: an online participatory cohort study with a diary approach.

OBJECTIVES: This paper examines how people express personal mood concurrently with those connected with them by one or two degrees of separation. DESIGN: Participatory cohort study. SETTING: Online contact diary. PARTICIPANTS: 133 participants kept online diaries for 7 months in 2014, which included 127 455 contacts with 12 070 persons. MAIN OUTCOME MEASURES: Diary keepers rated a contacted person's mood during each specific contact, as well as the strength of ties between any pairs of such contacted persons. Such rich information about ties and contacts enable us to construct a complete contact network for each diary keeper, along with the network members' mood and tie strength. We calculate one's overall mood by that person's average mood score during the study period and take the shortest path between any given pair of contacted persons as the degree of separation. We further assume that two connecting persons in a contact network have made contact with each other during the study period, which allows us to examine whether and how personal moods occur concurrently within these contact networks. RESULTS: Using mixed-effects models while controlling for covariates at individual, tie and contact levels, we show that personal mood score positively and significantly correlates with the average mood among those directly tied to the person. The same effect remains positive and significant for those connected to the person by two degrees, although the effect size is reduced by about one-half. The mood of anyone separated by more than two degrees is statistically irrelevant. CONCLUSIONS: Applying network perspectives and rich data at both tie and contact levels to inquiries about subjective well-being, the current study sheds new light on how an improved diary approach can help explain the sophisticated ways in which individuals express their personal moods concurrently during social interactions in everyday life, contact by contact.

ClickDiary: Online Tracking of Health Behaviors and Mood.

BACKGROUND: Traditional studies of health behaviors are typically conducted using one-shot, cross-sectional surveys. Thus, participants' recall bias may undermine the reliability and validity of the data. To capture mood changes and health behaviors in everyday life, we designed an online survey platform, ClickDiary, which helped collect more complete information for comprehensive data analyses. OBJECTIVE: We aim to understand whether daily mood changes are related to one's personal characteristics, demographic factors, and daily health behaviors. METHODS: The ClickDiary program uses a Web-based platform to collect data on participants' health behaviors and their social-contact networks. The name ClickDiary comes from the platform's interface, which is designed to allow the users to respond to most of the survey questions simply by clicking on the options provided. Participants were recruited from the general population and came from various backgrounds. To keep the participants motivated and interested, the ClickDiary program included a random drawing for rewards. We used descriptive statistics and the multilevel proportional-odds mixed model for our analysis. RESULTS: We selected 130 participants who had completed at least 30 days of ClickDiary entries from May 1 to October 31, 2014 as our sample for the study. According to the results of the multilevel proportional-odds mixed model, a person tended to be in a better mood on a given day if he or she ate more fruits and vegetables, took in more sugary drinks, ate more fried foods, showed no cold symptoms, slept better, exercised longer, and traveled farther away from home. In addition, participants were generally in a better mood during the weekend than on weekdays. CONCLUSIONS: Sleeping well, eating more fruits and vegetables, and exercising longer each day all appear to put one in a better mood. With the online ClickDiary survey, which reduces the recall biases that are common in traditional one-shot surveys, we were able to collect and analyze the daily variations of each subject's health behaviors and mood status.