The application of artificial intelligence (AI) to the behavioral health domain has led to a growing interest in the use of machine learning (ML) techniques to identify patterns in people's personal data with the goal of detecting-and even predicting-conditions such as depression, bipolar disorder, and schizophrenia. This paper investigates the data science practices and design narratives that underlie AI-mediated behavioral health through the situational analysis of three natural language processing (NLP) training datasets. Examining datasets as a sociotechnical system inextricably connected to particular social worlds, discourses, and infrastructural arrangements, we identify several misalignments between the technical project of dataset construction and benchmarking (a current focus of AI research in the behavioral health domain) and the social complexity of behavioral health. Our study contributes to a growing critical CSCW literature of AI systems by articulating the sensitizing concept ofdisordering datasets that aims to productively trouble dominant logics of AI/ML applications in behavioral health, and also support researchers and designers in reflecting on their roles and responsibilities working within this emerging and sensitive design space.
The health needs of those living in resource-limited settings are a vastly overlooked and understudied area in the intersection of machine learning (ML) and health care. While the use of ML in health care is more recently popularized over the last few years from the advancement of deep learning, low-and-middle income countries (LMICs) have already been undergoing a digital transformation of their own in health care over the last decade, leapfrogging milestones due to the adoption of mobile health (mHealth). With the introduction of new technologies, it is common to start afresh with a top-down approach, and implement these technologies in isolation, leading to lack of use and a waste of resources. In this paper, we outline the necessary considerations both from the perspective of current gaps in research, as well as from the lived experiences of health care professionals in resource-limited settings. We also outline briefly several key components of successful implementation and deployment of technologies within health systems in LMICs, including technical and cultural considerations in the development process relevant to the building of machine learning solutions. We then draw on these experiences to address where key opportunities for impact exist in resourcelimited settings, and where AI/ML can provide the most benefit.
Multiple sclerosis and its animal model, experimental autoimmune encephalitis (EAE), are primarily characterized by inflammatory degeneration. It has also been shown that deposition of fibrinogen from the blood in spinal cord tissue increased in MS and EAE. Fibrinogen is a substrate molecule found in the blood for a receptor called CD11 expressed on the surface of both brain and blood immune cells. Using a technique to light up specific tissue (called immunofluorescence), we demonstrate that there is a positive correlation between levels of fibrinogen deposition in spinal cord and the progression of clinical symptoms. Contrary to previous research, our past work indicated that brain immune cells play a limited role in diseaseprogression, while immune cells in the blood are critical. Thus an alternative interpretation of these results may be that blocking fibrinogen deposition within the spinal cord during EAE reduces the infiltration of blood immune cells into the spinal cord. Investigating this experimentally, however, is complicated by the inability to distinguish blood and brain resident immune cells. To circumvent this, we use parabiosisirradiation-separation models to investigate the replacement of circulating immune cells, which leads to the dynamic assessment of blood immune cell infiltration in spinal cord. We have used fibrinogen deposits during EAE, thereby successfully identifying a pathological component, and a immunofluorescence tovisualize fluorescent blood cells within spinal cord and their relationship to therapeutic target for multiple sclerosis.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.