Ubiquitous computing environments that are involved in healthcare applications are typically characterized by dynamically changing contexts. The contextual information must be efficiently processed in order to support medical decision making. The ubiquitous computing healthcare ecosystem must be capable of extracting medically valuable characteristics, making precise decisions, and taking medically appropriate actions. In this framework, deep learning networks can be used for data fusion of large and complex sets of information in order to make the appropriate medical diagnoses. The quality of decisions depends on the selection of appropriate network weights, which define a transformation of the given input into a diagnosis. Denotational mathematics provide a promising framework for modeling deep learning networks and adjusting their behavior by adapting their weights for the given input. Furthermore, the fidelity of the network's output can be controlled by applying a regulator to the weights values. The authors show that Denotational Mathematics can serve as a rigorous framework for modeling and controlling deep learning networks, thereby enhancing the quality of medical decision making.
This paper presents the design and implementation of terminological and specialized textual resources that were produced in the framework of the Greek research project "IATROLEXI". The aim of the project was to create the critical infrastructure for the Greek language, i.e. linguistic resources and tools for use in high level Natural Language Processing (NLP) applications in the domain of biomedicine. The project was built upon existing resources developed by the project partners and further enhanced within its framework, i.e. a Greek morphological lexicon of about 100,000 words, and language processing tools such as a lemmatiser and a morphosyntactic tagger. Christos Tsalidis, Additionally, it developed new assets, such as a specialized corpus of biomedical texts and an ontology of medical terminology.
According to the ubiquitous computing paradigm, dispersed computers within the home environment can support the residents’ health by being aware of all the developing and evolving situations. The context-awareness of the supporting computers stems from the data acquisition of the occurring events at home. In some cases, different sensors provide input of identical type, thereby raising conflict-related issues. Thus, for each type of input data, fusion methods must be applied on the raw data to obtain a dominant input value. Also, for diagnostic inference purpose, data fusion methods must be applied on the values of the available classes of multiple contextual data structures. Dempster-Shafer theory offers the algorithmic tools to efficiently fuse the data of each input type or class. The employment of threading technology accelerates the computational process and carrying out benchmarks on publicly available data set, is shown to be more efficient. Thus, threading technology proved promising for home UbiHealth applications by lowering the number of required cooperating computers.
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