Ph.D. Theses
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Browsing Ph.D. Theses by Subject "Biomedical engineering."
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Item Ontology-based entity tagging and normalization in the biomedical domain(Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2019., 2019.) Karadeniz Erol, Zeynep İlknur.; Özgür, Arzucan.One of the challenges for scientists in the biomedical domain is the huge amount and the rapid growth of information buried in the text of electronic resources. Developing text mining methods to automatically extract biomedical entities from the text of these electronic resources and identifying the relations between the extracted entities is crucial for facilitating research in many areas in the biomedical domain. Two main problems, which have to be solved to accomplish this goal, are the extraction and normalization of entities, and the identi cation of the relations between them from a given text. In this thesis, we proposed two approaches with two di erent perspectives for the extraction and normalization of biomedical named entities. The rst approach makes use of shallow linguistic knowledge to extract entities and normalize them through an ontology. On the other hand, the second approach makes use of word embeddings, which convey semantic information, for the normalization of the entities in a given text. The word-embedding based approach obtained the state-of-the-art results on the BioNLP Shared Task 2016 Bacteria Biotope data set. Both of the proposed methods are unsupervised and can be adapted to di erent domains. We also developed two applications, one of which is a pipeline, which is composed of modules based on the approaches that we proposed in this thesis, for the extraction of bacteria biotope information from scienti c abstracts. The other application is developed for extracting Brucella-host interaction relevant data from the biomedical literature, whose results reveal the importance of using a wider context than a sentence for biomedical relation extraction.Item Stress detection and management in daily life using wearable sensors(Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2020., 2020.) Can, Yekta Said.; Ersoy, Cem.Stress has become an integral part of our modern society. Researchers investi gated ways to cope with it to alleviate its negative effects on human health, society and economy. At this point, widespread usage of smartphones, smartwatches and smart wrist-bands raised the question of whether we can detect and alleviate stress with them. Although research has traditionally been conducted in laboratory settings, a set of new studies have recently begun to be conducted in ecological environments with unobtrusive wearable devices. In this thesis, we developed a stress detection system for daily life. Unobtrusive wearable devices were used for physiological data collection. For that purpose, we used heart rate variability (HRV) and electrodermal activity (EDA) signals. Modality specific artifact detection and removal algorithms, feature extraction and advanced machine learning methods were proposed. We tested our system in a lab oratory environment, restricted, semi-restricted and unrestricted real-life environments by collecting data in each environment. We proposed different techniques to improve the state of the art in real life environments. We worked on prominent environment specific research questions. We further examined different stress alleviation methods including those which can be applied indoors. We also discussed promising techniques, alleviation methods and research challenges for daily life stress management.