| 1. |
- Bundschuh, Jochen, et al.
(author)
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Medical geology in the framework of the sustainable development goals
- 2017
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In: Science of the Total Environment. - : Elsevier BV. - 0048-9697 .- 1879-1026. ; 581, s. 87-104
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Journal article (peer-reviewed)abstract
- Exposure to geogenic contaminants (GCs) such as metal(loid)s, radioactive metals and isotopes as well as transuraniums occurring naturally in geogenic sources (rocks, minerals) can negatively impact on environmental and human health. The GCs are released into the environment by natural biogeochemical processes within the near-surface environments and/or by anthropogenic activities such as mining and hydrocarbon exploitation as well as exploitation of geothermal resources. They can contaminate soil, water, air and biota and subsequently enter the food chain with often serious health impacts which are mostly underestimated and poorly recognized. Global population explosion and economic growth and the associated increase in demand for water, energy, food, and mineral resources result in accelerated release of GCs globally. The emerging science of "medical geology" assesses the complex relationships between geo-environmental factors and their impacts on humans and environments and is related to the majority of the 17 Sustainable Development Goals in the 2030 Agenda of the United Nations for Sustainable Development. In this paper, we identify multiple lines of evidence for the role of GCs in the incidence of diseases with as yet unknown etiology (causation). Integrated medical geology promises a more holistic understanding of the occurrence, mobility, bioavailability, bio-accessibility, exposure and transfer mechanisms of GCs to the food-chain and humans, and the related ecotoxicological impacts and health effects. Scientific evidence based on this approach will support adaptive solutions for prevention, preparedness and response regarding human and environmental health impacts originating from exposure to GCs.
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| 2. |
- Jiang, Zhihan, et al.
(author)
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Leveraging Machine Learning for Disease Diagnoses based on Wearable Devices : A Survey
- 2023
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In: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 10:24, s. 21959-21981
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Journal article (peer-reviewed)abstract
- Many countries around the world are facing a shortage of healthcare resources, especially during the post-epidemic era, leading to a dramatic increase in the need for self-detection and self-management of diseases. The popularity of smart wearable devices, such as smartwatches, and the development of machine learning bring new opportunities for the early detection and management of various prevalent diseases, such as cardiovascular diseases, Parkinson’s disease, and diabetes. In this survey, we comprehensively review the articles related to specific diseases or health issues based on small wearable devices and machine learning. More specifically, we first present an overview of the articles selected and classify them according to their targeted diseases. Then, we summarize their objectives, wearable device and sensor data, machine learning techniques, and wearing locations. Based on the literature review, we discuss the challenges and propose future directions from the perspectives of privacy concerns, security concerns, transmission latency and reliability, energy consumption, multi-modality, multi-sensor, multi-devices, evaluation metrics, explainability, generalization and personalization, social influence, and human factors, aiming to inspire researchers in this field.
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