RESUMEN
Traditionally, the use of genomic information for personalized medical decisions relies on prior discovery and validation of genotype-phenotype associations. This approach constrains care for patients presenting with undescribed problems. The National Institutes of Health (NIH) Undiagnosed Diseases Program (UDP) hypothesized that defining disease as maladaptation to an ecological niche allows delineation of a logical framework to diagnose and evaluate such patients. Herein, we present the philosophical bases, methodologies, and processes implemented by the NIH UDP. The NIH UDP incorporated use of the Human Phenotype Ontology, developed a genomic alignment strategy cognizant of parental genotypes, pursued agnostic biochemical analyses, implemented functional validation, and established virtual villages of global experts. This systematic approach provided a foundation for the diagnostic or non-diagnostic answers provided to patients and serves as a paradigm for scalable translational research.
RESUMEN
Nutrient-based meal recommendations have the potential to help individuals prevent or manage conditions such as diabetes and obesity. However, learning people's food preferences and making recommendations that simultaneously appeal to their palate and satisfy nutritional expectations are challenging. Existing approaches either only learn high-level preferences or require a prolonged learning period. We propose Yum-me, a personalized nutrient-based meal recommender system designed to meet individuals' nutritional expectations, dietary restrictions, and fine-grained food preferences. Yum-me enables a simple and accurate food preference profiling procedure via a visual quiz-based user interface and projects the learned profile into the domain of nutritionally appropriate food options to find ones that will appeal to the user. We present the design and implementation of Yum-me and further describe and evaluate two innovative contributions. The first contriution is an open source state-of-the-art food image analysis model, named FoodDist. We demonstrate FoodDist's superior performance through careful benchmarking and discuss its applicability across a wide array of dietary applications. The second contribution is a novel online learning framework that learns food preference from itemwise and pairwise image comparisons. We evaluate the framework in a field study of 227 anonymous users and demonstrate that it outperforms other baselines by a significant margin. We further conducted an end-to-end validation of the feasibility and effectiveness of Yum-me through a 60-person user study, in which Yum-me improves the recommendation acceptance rate by 42.63%.
RESUMEN
Active and passive mobile sensing has garnered much attention in recent years. In this paper, we focus on chronic pain measurement and management as a case application to exemplify the state of the art. We present a consolidated discussion on the leveraging of various sensing modalities along with modular server-side and on-device architectures required for this task. Modalities included are: activity monitoring from accelerometry and location sensing, audio analysis of speech, image processing for facial expressions as well as modern methods for effective patient self-reporting. We review examples that deliver actionable information to clinicians and patients while addressing privacy, usability, and computational constraints. We also discuss open challenges in the higher level inferencing of patient state and effective feedback with potential directions to address them. The methods and challenges presented here are also generalizable and relevant to a broad range of other applications in mobile sensing.
RESUMEN
OBJECTIVE: To create a relevant and clinically informative visualization of passively collected patient mobility data from smartphones of rheumatoid arthritis (RA) patients for rheumatologists. METHODS: (1) Pilot analysis of smartphone mobility data in RA; (2) Assessment of rheumatologists' needs for patient data through semi-structured interviews; and (3) Evaluation of the visual format of the RA data using scenario-based usability methods. RESULTS: We created a color-scale mobility index superimposed on a calendar to summarize the passive mobility measures from the smartphone that the rheumatologists confirmed would be clinically relevant. CONCLUSION: This assessment of clinician data needs and preferences demonstrates the potential value of passively collected smartphone data to resolve an important data question in RA. Efforts such as these are necessary to ensure that any smartphone data that patients share with their doctors will not exacerbate clinician information overload, but actually facilitate clinical decisions.