Why does ethics matter in participatory health?

Social media and participatory health has emerged as a promising tool for health, including developing diagnostic tools and therapeutic interventions. In the realm of online health care delivery, artificial intelligence based counseling apps now enable patients to consult with a chatbot instead of an actual therapist. However, several ethical issues and implications became relevant with this shift to digital interventions and healthcare delivery. This panel will describe ethical issues related to recent developments in participatory health and social media including the digital exposome, importance of involving patients in the design of AI-based applications and ethics of social media research in healthcare

On the road to personalised and precision geomedicine: medical geology and a renewed call for interdisciplinarity

Our health depends on where we currently live, as well as on where we have lived in the past and for how long in each place. An individual’s place history is particularly relevant in conditions with long latency between exposures and clinical manifestations, as is the case in many types of cancer and chronic conditions. A patient’s geographic history should routinely be considered by physicians when diagnosing and treating individual patients. It can provide useful contextual environmental information (and the corresponding health risks) about the patient, and should thus form an essential part of every electronic patient/health record. Medical geology investigations, in their attempt to document the complex relationships between the environment and human health, typically involve a multitude of disciplines and expertise. Arguably, the spatial component is the one factor that ties in all these disciplines together in medical geology studies. In a general sense, epidemiology, statistical genetics, geoscience, geomedical engineering and public and environmental health informatics tend to study data in terms of populations, whereas medicine (including personalised and precision geomedicine, and lifestyle medicine), genetics, genomics, toxicology and biomedical/health informatics more likely work on individuals or some individual mechanism describing disease. This article introduces with examples the core concepts of medical geology and geomedicine. The ultimate goals of prediction, prevention and personalised treatment in the case of geology-dependent disease can only be realised through an intensive multiple-disciplinary approach, where the various relevant disciplines collaborate together and complement each other in additive (multidisciplinary), interactive (interdisciplinary) and holistic (transdisciplinary and cross-disciplinary) manners

El Exposoma Humano y la Epidemiología: Hacia la Salud de Precisión

Presentación sobre: Historia y definición del concepto Exposoma. Exposoma y Epidemiología. Retos en el procesamiento de datos. Áreas de investigación. Hacia la Salud de PrecisiónN

Emerging concepts and challenges in implementing the exposome paradigm in allergic diseases and asthma.

Exposome research can improve the understanding of the mechanistic connections between exposures and health to help mitigate adverse health outcomes across the life span. The exposomic approach provides a risk profile instead of single predictors and thus is particularly applicable to allergic diseases and asthma. Under the PRACTALL collaboration between the European Academy of Allergy and Clinical Immunology (EAACI) and the American Academy of Allergy, Asthma, and Immunology (AAAAI), we evaluated the current concepts and the unmet needs on the role of the exposome in allergic diseases and asthma

Information retrieval using machine learning for database curation

Tese de mestrado, Bioinformática e Biologia Computacional, Universidade de Lisboa, Faculdade de Ciências, 2019Em 2016, a Agência Internacional de Pesquisa sobre o Cancro da Organização Mundial de Saúde lançou a primeira base de dados de biomarcadores de exposição, chamada Exposome-Explorer. Para construir a base de dados, mais de 8500 citações foram manualmente analisadas, mas apenas 480 foram consideradas relevantes e usadas para extrair informação para integrar a base de dados. Curar manualmente uma base de dados é uma tarefa demorada e que requer especialistas capazes de recolher e analisar dados que se encontram espalhados por milhões de artigos. Esta tese propõe o uso de técnicas de Recuperação de Informação com uma abordagem de aprendizagem supervisionada para classificar automaticamente artigos como relevantes ou irrelevantes para auxiliar o processo de criação e atualização da Exposome-Explorer. Esta abordagem restringe a literatura a um conjunto de publicações relevantes sobre biomarcadores de exposição de uma maneira eficiente, reduzindo o tempo e esforço necessários para identificar documentos relevantes. Além disso, as queries originais usadas pelos curadores para pesquisar sobre literatura de biomarcadores de exposição foram melhoradas para incluir alguns artigos relevantes que anteriormente não estavam a ser encontrados. Os dados manualmente recolhidos d a Exposome-Explorer, foram usados para treinar e testar os modelos de aprendizagem automática (classificadores). Vários parâmetros e seis algoritmos diferentes foram avaliados para averiguar quais previam melhor a relevância de um artigo com base no título, resumo ou metadados. O melhor classificador foi construído com o algoritmo SVM e treinado com os resumos dos artigos, obtendo um recall de 85.8%.Este classificador reduz o número de citações sobre biomarcadores dietéticos a serem manualmente analisadas pelos curadores em quase 88%,classificando apenas incorrectamente 14.2% dos artigos relevantes.Esta metodologia também pode ser aplicada a outros dados de biomarcadores ou ser adaptada para auxiliar o processo de criação manual de outras bases de dados químicas ou de doenças

Cross-Modal Health State Estimation

Individuals create and consume more diverse data about themselves today than any time in history. Sources of this data include wearable devices, images, social media, geospatial information and more. A tremendous opportunity rests within cross-modal data analysis that leverages existing domain knowledge methods to understand and guide human health. Especially in chronic diseases, current medical practice uses a combination of sparse hospital based biological metrics (blood tests, expensive imaging, etc.) to understand the evolving health status of an individual. Future health systems must integrate data created at the individual level to better understand health status perpetually, especially in a cybernetic framework. In this work we fuse multiple user created and open source data streams along with established biomedical domain knowledge to give two types of quantitative state estimates of cardiovascular health. First, we use wearable devices to calculate cardiorespiratory fitness (CRF), a known quantitative leading predictor of heart disease which is not routinely collected in clinical settings. Second, we estimate inherent genetic traits, living environmental risks, circadian rhythm, and biological metrics from a diverse dataset. Our experimental results on 24 subjects demonstrate how multi-modal data can provide personalized health insight. Understanding the dynamic nature of health status will pave the way for better health based recommendation engines, better clinical decision making and positive lifestyle changes.Comment: Accepted to ACM Multimedia 2018 Conference - Brave New Ideas, Seoul, Korea, ACM ISBN 978-1-4503-5665-7/18/1

Health State Estimation

Life's most valuable asset is health. Continuously understanding the state of our health and modeling how it evolves is essential if we wish to improve it. Given the opportunity that people live with more data about their life today than any other time in history, the challenge rests in interweaving this data with the growing body of knowledge to compute and model the health state of an individual continually. This dissertation presents an approach to build a personal model and dynamically estimate the health state of an individual by fusing multi-modal data and domain knowledge. The system is stitched together from four essential abstraction elements: 1. the events in our life, 2. the layers of our biological systems (from molecular to an organism), 3. the functional utilities that arise from biological underpinnings, and 4. how we interact with these utilities in the reality of daily life. Connecting these four elements via graph network blocks forms the backbone by which we instantiate a digital twin of an individual. Edges and nodes in this graph structure are then regularly updated with learning techniques as data is continuously digested. Experiments demonstrate the use of dense and heterogeneous real-world data from a variety of personal and environmental sensors to monitor individual cardiovascular health state. State estimation and individual modeling is the fundamental basis to depart from disease-oriented approaches to a total health continuum paradigm. Precision in predicting health requires understanding state trajectory. By encasing this estimation within a navigational approach, a systematic guidance framework can plan actions to transition a current state towards a desired one. This work concludes by presenting this framework of combining the health state and personal graph model to perpetually plan and assist us in living life towards our goals.Comment: Ph.D. Dissertation @ University of California, Irvin

GEM: Scalable and flexible gene-environment interaction analysis in millions of samples

MOTIVATION: Gene-environment interaction (GEI) studies are a general framework that can be used to identify genetic variants that modify the effects of environmental, physiological, lifestyle or treatment effects on complex traits. Moreover, accounting for GEIs can enhance our understanding of the genetic architecture of complex diseases and traits. However, commonly used statistical software programs for GEI studies are either not applicable to testing certain types of GEI hypotheses or have not been optimized for use in large samples. RESULTS: Here, we develop a new software program, GEM (Gene-Environment interaction analysis in Millions of samples), which supports the inclusion of multiple GEI terms, adjustment for GEI covariates and robust inference, while allowing multi-threading to reduce computation time. GEM can conduct GEI tests as well as joint tests of genetic main and interaction effects for both continuous and binary phenotypes. Through simulations, we demonstrate that GEM scales to millions of samples while addressing limitations of existing software programs. We additionally conduct a gene-sex interaction analysis on waist-hip ratio in 352 768 unrelated individuals from the UK Biobank, identifying 24 novel loci in the joint test that have not previously been reported in combined or sex-specific analyses. Our results demonstrate that GEM can facilitate the next generation of large-scale GEI studies and help advance our understanding of the genetic architecture of complex diseases and traits. AVAILABILITY AND IMPLEMENTATION: GEM is freely available as an open source project at https://github.com/large-scale-gxe-methods/GEM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online