Combining semantic web technologies with evolving fuzzy classifier eClass for EHR-based phenotyping : a feasibility study

In parallel to nation-wide efforts for setting up shared electronic health records (EHRs) across healthcare settings, several large-scale national and international projects are developing, validating, and deploying electronic EHR oriented phenotype algorithms that aim at large-scale use of EHRs data for genomic studies. A current bottleneck in using EHRs data for obtaining computable phenotypes is to transform the raw EHR data into clinically relevant features. The research study presented here proposes a novel combination of Semantic Web technologies with the on-line evolving fuzzy classifier eClass to obtain and validate EHR-driven computable phenotypes derived from 1956 clinical statements from EHRs. The evaluation performed with clinicians demonstrates the feasibility and practical acceptability of the approach proposed

Women’s Perceptions of Journeying Toward an Unknown Future With Breast Cancer: The “Lives at Risk Study”

Breast cancer risk classifications are useful for prognosis, yet little is known of their effect on patients. This study clarified women's understandings of risk as they "journeyed" through the health care system. Breast cancer patients and women undergoing genetic investigation were recruited ( N = 25) from a large UK Health Board, 2014-2015, completing a "Book of Experience," and Bio-photographic elicitation interviews. Stakeholder and Participant Feedback Forums were undertaken with key stakeholders, including patients, oncologists, funders, and policy developers, to inform team understanding. Thematic and visual frameworks from multidisciplinary analysis workshops uncovered two themes: "Subjective Understandings of Risk" and "Journeying Toward an Unknown Future." Breast cancer patients and women undergoing investigation experienced risk intuitively. Statistical formulations were often perplexing, diverting attention away from concrete life-and-death facts. Following risk classification, care must be co-defined to reduce patients' foreboding about an unknown future, taking into consideration personal risk management strategies and aspirations for a cancer-free future

Novel Processing and Transmission Techniques Leveraging Edge Computing for Smart Health Systems

L'abstract è presente nell'allegato / the abstract is in the attachmen