16,236 research outputs found
Data-driven Simulation and Optimization for Covid-19 Exit Strategies
The rapid spread of the Coronavirus SARS-2 is a major challenge that led
almost all governments worldwide to take drastic measures to respond to the
tragedy. Chief among those measures is the massive lockdown of entire countries
and cities, which beyond its global economic impact has created some deep
social and psychological tensions within populations. While the adopted
mitigation measures (including the lockdown) have generally proven useful,
policymakers are now facing a critical question: how and when to lift the
mitigation measures? A carefully-planned exit strategy is indeed necessary to
recover from the pandemic without risking a new outbreak. Classically, exit
strategies rely on mathematical modeling to predict the effect of public health
interventions. Such models are unfortunately known to be sensitive to some key
parameters, which are usually set based on rules-of-thumb.In this paper, we
propose to augment epidemiological forecasting with actual data-driven models
that will learn to fine-tune predictions for different contexts (e.g., per
country). We have therefore built a pandemic simulation and forecasting toolkit
that combines a deep learning estimation of the epidemiological parameters of
the disease in order to predict the cases and deaths, and a genetic algorithm
component searching for optimal trade-offs/policies between constraints and
objectives set by decision-makers. Replaying pandemic evolution in various
countries, we experimentally show that our approach yields predictions with
much lower error rates than pure epidemiological models in 75% of the cases and
achieves a 95% R2 score when the learning is transferred and tested on unseen
countries. When used for forecasting, this approach provides actionable
insights into the impact of individual measures and strategies
Processing of Electronic Health Records using Deep Learning: A review
Availability of large amount of clinical data is opening up new research
avenues in a number of fields. An exciting field in this respect is healthcare,
where secondary use of healthcare data is beginning to revolutionize
healthcare. Except for availability of Big Data, both medical data from
healthcare institutions (such as EMR data) and data generated from health and
wellbeing devices (such as personal trackers), a significant contribution to
this trend is also being made by recent advances on machine learning,
specifically deep learning algorithms
Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine
Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.11Ysciescopu
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