Understanding How People with Binge Eating Disorder and Bulimia Interact with Digital Food Content

A large body of research has focused on understanding how online content and disordered eating behaviors are associated. However, there is a lack of comprehensive studies investigating digital food content's influence on individuals with eating disorders. We conducted two rounds of studies (N=23 and 22, respectively) with individuals with binge eating disorder (BED) or bulimia nervosa (BN) to understand their motivations and practices of consuming digital food content. Our study reveals that individuals with BED and BN anticipate positive effects from food media to overcome their condition, but in practice, it often exacerbates their disorder. We also discovered that many individuals have experienced a cycle of quitting and returning to digital food content consumption. Based on these findings, we articulate design implications for digital food content and multimedia platforms to support vulnerable individuals in everyday online platform interactions.Comment: 28 pages, 6 figure

No association between serum cholesterol and death by suicide in patients with schizophrenia, bipolar affective disorder, or major depressive disorder

BACKGROUND: Previous research on serum total cholesterol and suicidality has yielded conflicting results. Several studies have reported a link between low serum total cholesterol and suicidality, whereas others have failed to replicate these findings, particularly in patients with major affective disorders. These discordant findings may reflect the fact that studies often do not distinguish between patients with bipolar and unipolar depression; moreover, definitions and classification schemes for suicide attempts in the literature vary widely. METHODS: Subjects were patients with one of the three major psychiatric disorders commonly associated with suicide: schizophrenia, bipolar affective disorder, and major depressive disorder (MDD). We compared serum lipid levels in patients who died by suicide (82 schizophrenia, 23 bipolar affective disorder, and 67 MDD) and non-suicide controls (200 schizophrenia, 49 bipolar affective disorder, and 175 MDD). RESULTS: Serum lipid profiles did not differ between patients who died by suicide and control patients in any diagnostic group. CONCLUSIONS: Our results do not support the use of biological indicators such as serum total cholesterol to predict suicide risk among patients with a major psychiatric disorder

An Interacting Multiple Model Approach for Target Intent Estimation at Urban Intersection for Application to Automated Driving Vehicle

Research shows that urban intersections are a hotspot for traffic accidents which cause major human injuries. Predicting turning, passing, and stop maneuvers against surrounding vehicles is considered to be fundamental for advanced driver assistance systems (ADAS), or automated driving systems in urban intersections. In order to estimate the target intent in such situations, an interacting multiple model (IMM)-based intersection-target-intent estimation algorithm is proposed. A driver model is developed to represent the driver’s maneuvering on the intersection using an IMM-based target intent classification algorithm. The performance of the intersection-target-intent estimation algorithm is examined through simulation studies. It is demonstrated that the intention of a target vehicle is successfully predicted based on observations at an individual intersection by proposed algorithms. Document type: Articl

Addressing the Gap in Data Communication from Home Health Care to Primary Care during Care Transitions: Completeness of an Interoperability Data Standard

In a future where home health care is no longer an information silo, patient information will be communicated along transitions in care to improve care. Evidence-based practice in the United States supports home health care patients to see their primary care team within the first two weeks of hospital discharge to reduce rehospitalization risk. We sought to identify a parsimonious set of home health care data to be communicated to primary care for the post-hospitalization visit. Anticipating electronic dataset communication, we investigated the completeness of the international reference terminology, Logical Observation Identifiers Names and Codes (LOINC), for coverage of the data to be communicated. We conducted deductive qualitative analysis in three steps: (1) identify home health care data available for the visit by mapping home health care to the information needed for the visit; (2) reduce the resulting home health care data set to a parsimonious set clinicians wanted for the post-hospitalization visit by eliciting primary care clinician input; and (3) map the parsimonious dataset to LOINC and assess LOINC completeness. Our study reduced the number of standardized home health care assessment questions by 40% to a parsimonious set of 33 concepts that primary care team physicians wanted for the post-hospitalization visit. Findings indicate all home health care concepts in the parsimonious dataset mapped to the information needed for the post-hospitalization visit, and 84% of the home health care concepts mapped to a LOINC term. The results indicate data flow of parsimonious home health care dataset to primary care for the post-hospitalization visit is possible using existing LOINC codes, and would require adding some codes to LOINC for communication of a complete parsimonious data set

ASAP: Accurate semantic segmentation for real time performance

Feature fusion modules from encoder and self-attention module have been adopted in semantic segmentation. However, the computation of these modules is costly and has operational limitations in real-time environments. In addition, segmentation performance is limited in autonomous driving environments with a lot of contextual information perpendicular to the road surface, such as people, buildings, and general objects. In this paper, we propose an efficient feature fusion method, Feature Fusion with Different Norms (FFDN) that utilizes rich global context of multi-level scale and vertical pooling module before self-attention that preserves most contextual information while reducing the complexity of global context encoding in the vertical direction. By doing this, we could handle the properties of representation in global space and reduce additional computational cost. In addition, we analyze low performance in challenging cases including small and vertically featured objects. We achieve the mean Interaction of-union(mIoU) of 73.1 and the Frame Per Second(FPS) of 191, which are comparable results with state-of-the-arts on Cityscapes test datasets.Comment: 5 pages, 4 figure