A machine-learning scraping tool for data fusion in the analysis of sentiments about pandemics for supporting business decisions with human-centric AI explanations.
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ABSTRACT:
SUBMITTER: Kumar SA
PROVIDER: S-EPMC8459777 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The implementation of the COVID-19 national lockdown announced suddenly in March 2020 in India provided a unique opportunity to capture real-time changes in business sentiments during episodes of unexpected and sudden disruptions. Using a logit-probability model to analyse data of this natural experiment showed that firms' 6-months ahead sentiments for its financial condition worsened drastically during lockdown compared to firms surveyed immediately prior to the announcement. Further, smaller firms showed a relatively higher impact. We also find that firms perceive this as a relatively higher demand shock in terms of falling domestic sales post-lockdown whereas supply shocks are perceived to be on the downside. Lastly the mitigation strategy of firms involved reducing employment for unskilled workers and wages for skilled workers. This unique study gives insights not only about firms and their strategies but regarding appropriate policy choices during lockdown. The lessons are applicable for governments which imposed local lockdowns during the second wave and potential disruption for the expected third wave.Supplementary informationThe online version contains supplementary material available at 10.1007/s41775-021-00121-w.
| S-EPMC8628138 | biostudies-literature
Project description:Recently, the eXplainable AI (XAI) research community has focused on developing methods making Machine Learning (ML) predictors more interpretable and explainable. Unfortunately, researchers are struggling to converge towards an unambiguous definition of notions such as interpretation, or, explanation—which are often (and mistakenly) used interchangeably. Furthermore, despite the sound metaphors that Multi-Agent System (MAS) could easily provide to address such a challenge, and agent-oriented perspective on the topic is still missing. Thus, this paper proposes an abstract and formal framework for XAI-based MAS, reconciling notions, and results from the literature.
| S-EPMC7338184 | biostudies-literature
Project description:Tree-based machine learning models such as random forests, decision trees, and gradient boosted trees are popular non-linear predictive models, yet comparatively little attention has been paid to explaining their predictions. Here, we improve the interpretability of tree-based models through three main contributions: 1) The first polynomial time algorithm to compute optimal explanations based on game theory. 2) A new type of explanation that directly measures local feature interaction effects. 3) A new set of tools for understanding global model structure based on combining many local explanations of each prediction. We apply these tools to three medical machine learning problems and show how combining many high-quality local explanations allows us to represent global structure while retaining local faithfulness to the original model. These tools enable us to i) identify high magnitude but low frequency non-linear mortality risk factors in the US population, ii) highlight distinct population sub-groups with shared risk characteristics, iii) identify non-linear interaction effects among risk factors for chronic kidney disease, and iv) monitor a machine learning model deployed in a hospital by identifying which features are degrading the model's performance over time. Given the popularity of tree-based machine learning models, these improvements to their interpretability have implications across a broad set of domains.
| S-EPMC7326367 | biostudies-literature
Project description:BackgroundCoronavirus Disease 2019 (Covid-19) continues to challenge the limits of our knowledge and our healthcare system. Here we sought to define the host immune response, a.k.a, the "cytokine storm" that has been implicated in fatal COVID-19 using an AI-based approach.MethodOver 45,000 transcriptomic datasets of viral pandemics were analyzed to extract a 166-gene signature using ACE2 as a 'seed' gene; ACE2 was rationalized because it encodes the receptor that facilitates the entry of SARS-CoV-2 (the virus that causes COVID-19) into host cells. An AI-based approach was used to explore the utility of the signature in navigating the uncharted territory of Covid-19, setting therapeutic goals, and finding therapeutic solutions.FindingsThe 166-gene signature was surprisingly conserved across all viral pandemics, including COVID-19, and a subset of 20-genes classified disease severity, inspiring the nomenclatures ViP and severe-ViP signatures, respectively. The ViP signatures pinpointed a paradoxical phenomenon wherein lung epithelial and myeloid cells mount an IL15 cytokine storm, and epithelial and NK cell senescence and apoptosis determine severity/fatality. Precise therapeutic goals could be formulated; these goals were met in high-dose SARS-CoV-2-challenged hamsters using either neutralizing antibodies that abrogate SARS-CoV-2•ACE2 engagement or a directly acting antiviral agent, EIDD-2801. IL15/IL15RA were elevated in the lungs of patients with fatal disease, and plasma levels of the cytokine prognosticated disease severity.InterpretationThe ViP signatures provide a quantitative and qualitative framework for titrating the immune response in viral pandemics and may serve as a powerful unbiased tool to rapidly assess disease severity and vet candidate drugs.FundingThis work was supported by the National Institutes for Health (NIH) [grants CA151673 and GM138385 (to DS) and AI141630 (to P.G), DK107585-05S1 (SD) and AI155696 (to P.G, D.S and S.D), U19-AI142742 (to S.C, cchiCooperative Centers for Human Immunology)]; Research Grants Program Office (RGPO) from the University of California Office of the President (UCOP) (R00RG2628 & R00RG2642 to P.G, D.S and S.D); the UC San Diego Sanford Stem Cell Clinical Center (to P.G, D.S and S.D); LJI Institutional Funds (to S.C); the VA San Diego Healthcare System Institutional funds (to L.C.A). GDK was supported through The American Association of Immunologists Intersect Fellowship Program for Computational Scientists and Immunologists.One sentence summaryThe host immune response in COVID-19.
| S-EPMC8193764 | biostudies-literature
Project description:Online videos have become a prevalent means for people to acquire information. Videos, however, are often polarized, misleading, or contain topics on which people have different, contradictory views. In this work, we introduce natural language explanations to stimulate more deliberate reasoning about videos and raise users' awareness of potentially deceiving or biased information. With these explanations, we aim to support users in actively deciding and reflecting on the usefulness of the videos. We generate the explanations through an end-to-end pipeline that extracts reflection triggers so users receive additional information to the video based on its source, covered topics, communicated emotions, and sentiment. In a between-subjects user study, we examine the effect of showing the explanations for videos on three controversial topics. Besides, we assess the users' alignment with the video's message and how strong their belief is about the topic. Our results indicate that respondents' alignment with the video's message is critical to evaluate the video's usefulness. Overall, the explanations were found to be useful and of high quality. While the explanations do not influence the perceived usefulness of the videos compared to only seeing the video, people with an extreme negative alignment with a video's message perceived it as less useful (with or without explanations) and felt more confident in their assessment. We relate our findings to cognitive dissonance since users seem to be less receptive to explanations when the video's message strongly challenges their beliefs. Given these findings, we provide a set of design implications for explanations grounded in theories on reducing cognitive dissonance in light of raising awareness about online deception.
| S-EPMC8507585 | biostudies-literature
Project description:In an era where Artificial Intelligence (AI) integration into business processes is crucial for maintaining competitiveness, there is a growing need for structured guidance on designing AI solutions that align with human needs. To this end, we present "technical assistance concerning human-centered AI development" (tachAId), an interactive advisory tool which comprehensively guides AI developers and decision makers in navigating the machine learning lifecycle with a focus on human-centered design. tachAId motivates and presents concrete technical advice to ensure human-centeredness across the phases of AI development. The tool's effectiveness is evaluated through a catalog of criteria for human-centered AI in the form of relevant challenges and goals, derived from existing methodologies and guidelines. Lastly, tachAId and one other comparable advisory tool were examined to determine their adherence to these criteria in order to provide an overview of the human-centered aspects covered by these tools and to allow interested parties to quickly assess whether the tools meet their needs.
| S-EPMC10963619 | biostudies-literature
Project description:BackgroundDuring disasters, supply chain vulnerabilities, such as power, transportation, and communication, may affect the delivery of medications and medical supplies and hamper the ability to deliver critical care services. Disasters also have the potential to disrupt information technology (IT) in health-care systems, resulting in interruptions in patient care, particularly critical care, and other health-care business functions. The suggestions in this article are important for all of those involved in a large-scale pandemic or disaster with multiple critically ill or injured patients, including front-line clinicians, hospital administrators, and public health or government officials.MethodsThe Business and Continuity of Operations Panel followed the American College of Chest Physicians (CHEST) Guidelines Oversight Committee's methodology in developing key questions regarding medication and supply shortages and the impact disasters may have on healthcare IT. Task force members met in person to develop the 13 key questions believed to be most relevant for Business and Continuity of Operations. A systematic literature review was then performed for relevant articles and documents, reports, and gray literature reported since 2007. No studies of sufficient quality were identified upon which to make evidence-based recommendations. Therefore, the panel developed expert opinion-based suggestions using a modified Delphi process.ResultsEighteen suggestions addressing mitigation strategies for supply chain vulnerabilities including medications and IT were generated. Suggestions offered to hospitals and health system leadership regarding medication and supply shortages include: (1) purchase key medications and supplies from more than one supplier, (2) substituted medications or supplies should ideally be similar to those already used by an institution's providers, (3) inventories should be tracked electronically to monitor medication/supply levels, (4) consider higher inventories of medications and supplies known or projected to be in short supply, (5) institute alternate use protocols when a (potential) shortage is identified, and 6) support government and nongovernmental organizations in efforts to address supply chain vulnerability. Health-care IT can be damaged in a disaster, and hospitals and health system leadership should have plans for urgently reestablishing local area networks. Planning should include using portable technology, plans for providing power, maintenance of a patient database that can accompany each patient, and protection of patient privacy. Additionally, long-term planning should include prioritizing servers and memory disk drives and possibly increasing inventory of critical IT supplies in preparedness planning.ConclusionsThe provision of care to the critically ill or injured during a pandemic or disaster is dependent on key processes, such as the supply chain, and infrastructure, such as IT systems. Hospitals and health systems will help minimize the impact of medication and supply shortages with a focused strategy using the steps suggested. IT preparedness for maintaining local area networks, functioning clinical information systems, and adequate server and memory storage capacity will greatly enhance preparedness for hospital and health system clinical and business operations.
| S-EPMC4504250 | biostudies-literature
Project description:Although the COVID-19 pandemic resulted in about a 24% decline in India's GDP during the April-June 2020 quarter, the nation's agricultural sector, somewhat surprisingly, seems to have done remarkably well. This paper examines whether the public transfer program Pradhan Mantri Garib Kalyan Yojana (PMGKY), announced immediately after the lockdown, benefited farmers in dealing with the COVID shock. Overall, 95% of the smallholders received support from at least one of PMGKY's four components. Direct cash transfers had significantly more impact than in-kind transfer schemes. The result shows that farmers receiving cash transfers under PM-KISAN, one component of PMGKY, were more likely to invest in buying seeds. In contrast, farmers receiving cash transfers under PM-UY, another piece of PMGKY, were more likely to invest in fertilizer and pesticides. Finally, smallholders who received benefits from all four components of PMGKY were more likely to invest in purchasing seeds, fertilizer, and pesticides. Findings suggest the fungibility of public cash transfers from the recent PMGKY scheme is significant in alleviating credit constraints and increasing future investments in modern inputs.
| S-EPMC8013271 | biostudies-literature
Project description:In the present article, we explore prospects for using artificial intelligence (AI) to distribute cognition via cognitive offloading (i.e., to delegate thinking tasks to AI-technologies). Modern technologies for cognitive support are rapidly developing and increasingly popular. Today, many individuals heavily rely on their smartphones or other technical gadgets to support their daily life but also their learning and work. For instance, smartphones are used to track and analyze changes in the environment, and to store and continually update relevant information. Thus, individuals can offload (i.e., externalize) information to their smartphones and refresh their knowledge by accessing it. This implies that using modern technologies such as AI empowers users via offloading and enables them to function as always-updated knowledge professionals, so that they can deploy their insights strategically instead of relying on outdated and memorized facts. This AI-supported offloading of cognitive processes also saves individuals' internal cognitive resources by distributing the task demands into their environment. In this article, we provide (1) an overview of empirical findings on cognitive offloading and (2) an outlook on how individuals' offloading behavior might change in an AI-enhanced future. More specifically, we first discuss determinants of offloading such as the design of technical tools and links to metacognition. Furthermore, we discuss benefits and risks of cognitive offloading. While offloading improves immediate task performance, it might also be a threat for users' cognitive abilities. Following this, we provide a perspective on whether individuals will make heavier use of AI-technologies for offloading in the future and how this might affect their cognition. On one hand, individuals might heavily rely on easily accessible AI-technologies which in return might diminish their internal cognition/learning. On the other hand, individuals might aim at enhancing their cognition so that they can keep up with AI-technologies and will not be replaced by them. Finally, we present own data and findings from the literature on the assumption that individuals' personality is a predictor of trust in AI. Trust in modern AI-technologies might be a strong determinant for wider appropriation and dependence on these technologies to distribute cognition and should thus be considered in an AI-enhanced future.
| S-EPMC9329671 | biostudies-literature
Project description:The strain on healthcare resources brought forth by the recent COVID-19 pandemic has highlighted the need for efficient resource planning and allocation through the prediction of future consumption. Machine learning can predict resource utilization such as the need for hospitalization based on past medical data stored in electronic medical records (EMR). We conducted this study on 3194 patients (46% male with mean age 56.7 (±16.8), 56% African American, 7% Hispanic) flagged as COVID-19 positive cases in 12 centers under Emory Healthcare network from February 2020 to September 2020, to assess whether a COVID-19 positive patient's need for hospitalization can be predicted at the time of RT-PCR test using the EMR data prior to the test. Five main modalities of EMR, i.e., demographics, medication, past medical procedures, comorbidities, and laboratory results, were used as features for predictive modeling, both individually and fused together using late, middle, and early fusion. Models were evaluated in terms of precision, recall, F1-score (within 95% confidence interval). The early fusion model is the most effective predictor with 84% overall F1-score [CI 82.1-86.1]. The predictive performance of the model drops by 6 % when using recent clinical data while omitting the long-term medical history. Feature importance analysis indicates that history of cardiovascular disease, emergency room visits in the past year prior to testing, and demographic factors are predictive of the disease trajectory. We conclude that fusion modeling using medical history and current treatment data can forecast the need for hospitalization for patients infected with COVID-19 at the time of the RT-PCR test.
| S-EPMC8175333 | biostudies-literature