Supporting Personalized Health Care With Social Media Analytics: An Application to Hypothyroidism

Social media analytics can considerably contribute to understanding health conditions beyond clinical practice, by capturing patients’ discussions and feelings about their quality of life in relation to disease treatments. In this article, we propose a methodology to support a detailed analysis of the therapeutic experience in patients affected by a specific disease, as it emerges from health forums. As a use case to test the proposed methodology, we analyze the experience of patients affected by hypothyroidism and their reactions to standard therapies. Our approach is based on a data extraction and filtering pipeline, a novel topic detection model named Generative Text Compression with Agglomerative Clustering Summarization ( GTCACS ), and an in-depth data analytic process. We advance the state of the art on automated detection of adverse drug reactions ( ADRs ) since, rather than simply detecting and classifying positive or negative reactions to a therapy, we are capable of providing a fine characterization of patients along different dimensions, such as co-morbidities, symptoms, and emotional states.

Identifying temporal and spatial patterns of variation from multimodal data using MEFISTO

Factor analysis is a widely used method for dimensionality reduction in genome biology, with applications from personalized health to single-cell biology. Existing factor analysis models assume independence of the observed samples, an assumption that fails in spatio-temporal profiling studies. Here we present MEFISTO, a flexible and versatile toolbox for modeling high-dimensional data when spatial or temporal dependencies between the samples are known. MEFISTO maintains the established benefits of factor analysis for multimodal data, but enables the performance of spatio-temporally informed dimensionality reduction, interpolation, and separation of smooth from non-smooth patterns of variation. Moreover, MEFISTO can integrate multiple related datasets by simultaneously identifying and aligning the underlying patterns of variation in a data-driven manner. To illustrate MEFISTO, we apply the model to different datasets with spatial or temporal resolution, including an evolutionary atlas of organ development, a longitudinal microbiome study, a single-cell multi-omics atlas of mouse gastrulation and spatially resolved transcriptomics.

Experimental Phenomenology as an Approach to the Study of Contemplative Practices

During history humans have developed a large variety of contemplative practices, in many different areas of life, and as part of many different traditions and contexts. Although some contemplative practices are very old, the research field of Contemplation Studies is young, and there are no agreed-upon definitions of central concepts such as contemplative practices and contemplative experiences. The present paper focuses on contemplative practices, defined as practices that are engaged in for the sake of the contemplative experiences they afford (e.g., the contemplation of nature, or the contemplation of various aspects of being-in-the world). The purpose of the present paper is to discuss the potential of experimental phenomenology to contribute to the development of the research field of Contemplation Studies. Experimental phenomenology is defined as the investigation of phenomenological practices and their effects on experience. Phenomenological practices involve intentional variations of experiencing by means of changes in the direction of attention and the choice of attitude, typically as guided by verbal instructions or self-instructions. It is suggested that contemplative practices represent a subcategory of phenomenological practices. Two different varieties of experimental phenomenology are described and illustrated in the present paper: (1) an informal variety which involves the development of new phenomenological practices by creative variation of procedures and observation of effects; and (2) a more rigorously scientific variety, which involves the systematic variation of phenomenological practices in accordance with experimental designs to study their experiential effects. It is suggested that the development of contemplative practices during the ages is the result of an informal experimenting of the first kind; this variety of experimental phenomenology can also be used to develop personalized health interventions in a clinical setting. As to the more rigorously scientific experimental phenomenology, it is possible that it may lead not only to an improved understanding of general principles underlying contemplative practices, but also to a more systematic development of new contemplative practices. The experimental-phenomenological approach to contemplative practices is illustrated by various examples involving mindfulness, gratitude, receiving and giving.

Supporting Vulnerable Populations During the Pandemic: Stakeholders’ Experiences and Perceptions of Social Prescribing in Scotland During Covid-19

Social prescribing schemes refer people toward personalized health/wellbeing interventions in local communities. Since schemes hold different representations of social prescribing, responses to the pandemic crisis will vary. Intersectionality states that social divisions build on one another, sustaining unequal health outcomes. We conducted and inductively analysed interviews with twenty-three professional and volunteer stakeholders across three social prescribing schemes in urban and rural Scotland at the start and end of year one of the pandemic. Concerns included identifying and digitally supporting disadvantaged and vulnerable individuals and reduced capacity statutory and third-sector services, obliging link workers to assume new practical and psychological responsibilities. Social prescribing services in Scotland, we argue, represent a collage of practices superimposed on a struggling healthcare system. Those in need of such services are unlikely to break through disadvantage whilst situated within a social texture wherein inequalities of education, health and environmental arrangements broadly intersect with one another.

Permeability of Gemcitabine and PBPK Modeling to Assess Oral Administration

Gemcitabine is a nucleoside analog effective against several solid tumors. Standard treatment consists of an intravenous infusion over 30 min. This is an invasive, uncomfortable and often painful method, involving recurring visits to the hospital and costs associated with medical staff and equipment. Gemcitabine’s activity is significantly limited by numerous factors, including metabolic inactivation, rapid systemic clearance of gemcitabine and transporter deficiency-associated resistance. As such, there have been research efforts to improve gemcitabine-based therapy efficacy, as well as strategies to enhance its oral bioavailability. In this work, gemcitabine in vitro and clinical data were analyzed and in silico tools were used to study the pharmacokinetics of gemcitabine after oral administration following different regimens. Several physiologically based pharmacokinetic (PBPK) models were developed using simulation software GastroPlus™, predicting the PK parameters and plasma concentration–time profiles. The integrative biomedical data analyses presented here are promising, with some regimens of oral administration reaching higher AUC in comparison to the traditional IV infusion, supporting this route of administration as a viable alternative to IV infusions. This study further contributes to personalized health care based on potential new formulations for oral administration of gemcitabine, as well nanotechnology-based drug delivery systems.

DCC Terminology Service—An Automated CI/CD Pipeline for Converting Clinical and Biomedical Terminologies in Graph Format for the Swiss Personalized Health Network

One goal of the Swiss Personalized Health Network (SPHN) is to provide an infrastructure for FAIR (Findable, Accessible, Interoperable and Reusable) health-related data for research purposes. Semantic web technology and biomedical terminologies are key to achieving semantic interoperability. To enable the integrative use of different terminologies, a terminology service is a important component of the SPHN Infrastructure for FAIR data. It provides both the current and historical versions of the terminologies in an SPHN-compliant graph format. To minimize the usually high maintenance effort of a terminology service, we developed an automated CI/CD pipeline for converting clinical and biomedical terminologies in an SPHN-compatible way. Hospitals, research infrastructure providers, as well as any other data providers, can download a terminology bundle (currently composed of SNOMED CT, LOINC, UCUM, ATC, ICD-10-GM, and CHOP) and deploy it in their local terminology service. The distributed service architecture allows each party to fulfill their local IT and security requirements, while still having an up-to-date interoperable stack of SPHN-compliant terminologies. In the future, more terminologies and mappings will be added to the terminology service according to the needs of the SPHN community.

The SPHN Ecosystem Towards FAIR Data

Health-related data originating from diverse sources are commonly stored in manifold databases and formats, making it difficult to find, access and gather data for research purposes. In addition, so-called secondary use scenarios for health data are usually hindered by local data codes, missing dictionaries and the lack of metadata and context descriptions. Following the FAIR principles (Findable, Accessible, Interoperable and Reusable), we developed a decentralized infrastructure to overcome these hurdles and enable collaborative research by making the meaning of health-related data understandable to both, humans and machines. This infrastructure is currently being implemented in the realm of the Swiss Personalized Health Network (SPHN), a research infrastructure initiative for enabling the use and exchange of health-related data for research in Switzerland. The SPHN ecosystem for FAIR data consists of the SPHN Dataset (semantic definitions), the SPHN RDF Schema (linkage and transport of the semantics in a machine-readable format), a project RDF template, extensive guidelines and conventions on how to generate SPHN RDF schema, a Terminology Service (converter of clinical terminologies in RDF), and a Quality Assurance Framework (automated data validation with SHACLs and SPARQLs). The SPHN ecosystem has been built in a way that it can easily be adapted and extended by any SPHN project to fit individual needs. By providing such a national ecosystem, SPHN supports researchers in generating, processing and sharing FAIR data.