“…Our challenges in developing meaningful metrics include limited data science competencies in public health, validation of analytic methodologies in the complex and evolving pandemic environment, and the lack of integration with the public health infrastructure. …”
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“…In the machine learning and data science pipelines, feature extraction is considered the most crucial component according to researchers, where generating a discriminative feature matrix is the utmost challenging task to achieve high classification accuracy. …”
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“…Here, we present the results of the first Genetic Engineering Attribution Challenge, a public data-science competition to advance GEA techniques. Top-scoring teams dramatically outperformed previous models at identifying the true lab-of-origin of engineered plasmid sequences, including an increase in top-1 and top-10 accuracy of 10 percentage points. …”
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“…Owing to recent advances in data science, drug-repositioning technologies have become one of the most promising strategies available for developing effective treatment options. …”
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“…METHODS: Data from the Health and Welfare Data Science Center were used to conduct this nationwide population-based study. …”
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“…The evolution of AI and data science has aided in mechanizing several aspects of medical care requiring critical thinking: diagnosis, risk stratification, and management, thus mitigating the burden of physicians and reducing the likelihood of human error. …”
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“…Referring to the Research Project “Enhancing Healthcare and Well-Being Through the Potential of Big Data: An Integration of Survey, Administrative, and Open Data to Assess Health Risk in the City of Milan with Data Science” the Authors present preliminary results regarding a survey distributed to a sample of citizens across all neighborhoods of Milano city. …”
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“…We will use a Fine-Gray Cox proportional hazards model for one risk modeling approach and DataRobot, a data science and analytics platform, to run and compare several widely used supervised machine learning algorithms, including Random Forest, Support Vector Machine, Extreme Gradient Boosting (XGBoost), Decision Tree, Naïve Bayes, and K-Nearest Neighbors. …”
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“…In the era of health technology and data science, it is crucial to consider how these tools could improve healthcare delivery. …”
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“…The KDDM approach is a promising way of combining simulation models in agriculture with the fast developments in data science while mechanisms of many genetic and physiological processes are still under investigation, especially at the nexus of increasing food production, mitigating climate change and achieving sustainability.…”
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“…High resolution clinical databases from electronic health records are increasingly being used in the field of health data science. Compared to traditional administrative databases and disease registries, these newer highly granular clinical datasets offer several advantages, including availability of detailed clinical information for machine learning and the ability to adjust for potential confounders in statistical models. …”
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“…For this research we reviewed 250 research methods syllabi from across the disciplines, as well as 80 syllabi from data science programmes to understand how or if data ethics was taught. …”
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“…Finally, we offer a perspective on the use of neutron scattering with machine learning to enable high-throughput parametrization of accurate and experimentally validated CP FFs enabled not only by the ongoing advancements in computational chemistry, data science, and high-performance computing but also using oligomers as proxies for longer polymer chains during FF development.…”
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“…In this symposium, we invited six scientists who are continuing to expand the frontiers in the fields of health data science. Herein, the organizers present a summary of the symposium.…”
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“…In 2014, he completed the Data Science Specialization course authorized by Johns Hopkins University offered through Coursera. …”
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“…After presenting and discussing methodological challenges, we call for collective efforts to build coalitions between social sciences, conflict and violence studies, political science, data science, social psychologists and epidemiology to advance theory, measurement, and analysis of health effects of local political climates.…”
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“…Such flows are a common technique used for data generation and density estimation in machine learning and data science. The density estimate obtained with a NF requires a change of variables formula that involves the computation of the Jacobian determinant of the NF transformation. …”
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“…However, it remains unclear how these algorithms compare to each other on the same data set and if these modern data science approaches improve the analytical validity of sleep outcomes based on wrist-worn acceleration data. …”
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“…CONCLUSIONS: Library‐based partnerships to support training and consultation offer an important vehicle for clinical data science capacity building in learning health systems. …”
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“…Social media, as an exploratory data science tool, can be leveraged to identify the presence and popularity of online abortion identity labels and how they are contextualized online. …”
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