A Deep Residual Network Integrating Spatial-temporal Properties to Predict Influenza Trends at an Intra-urban Scale

Xi, Guikai; Yin, Ling; Li, Ye; Mei, Shujiang

doi:10.1145/3281548.3281558

Cited by 18 publications

(3 citation statements)

References 17 publications

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“…It should also be noted that the dimensions of the x and y must be equal. The residual learning strategy is successfully applied to problems such as classification 20 , the spatiotemporal estimation of citywide crowd flows 21 and influenza trends 22 .…”

Section: Methodsmentioning

confidence: 99%

High-resolution temporal gravity field data products: Monthly mass grids and spherical harmonics from 1994 to 2021

Uz,

Akyılmaz,

Shum

et al. 2024

Sci Data

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Since April 2002, Gravity Recovery and Climate Experiment (GRACE) and GRACE-FO (FollowOn) satellite gravimetry missions have provided precious data for monitoring mass variations within the hydrosphere, cryosphere, and oceans with unprecedented accuracy and resolution. However, the long-term products of mass variations prior to GRACE-era may allow for a better understanding of spatio-temporal changes in climate-induced geophysical phenomena, e.g., terrestrial water cycle, ice sheet and glacier mass balance, sea level change and ocean bottom pressure (OBP). Here, climate-driven mass anomalies are simulated globally at 1.0° × 1.0° spatial and monthly temporal resolutions from January 1994 to January 2021 using an in-house developed hybrid Deep Learning architecture considering GRACE/-FO mascon and SLR-inferred gravimetry, ECMWF Reanalysis-5 data, and normalized time tag information as training datasets. Internally, we consider mathematical metrics such as RMSE, NSE and comparisons to previous studies, and externally, we compare our simulations to GRACE-independent datasets such as El-Nino and La-Nina indexes, Global Mean Sea Level, Earth Orientation Parameters-derived low-degree spherical harmonic coefficients, and in-situ OBP measurements for validation.

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Section: Methodsmentioning

confidence: 99%

High-resolution temporal gravity field data products: Monthly mass grids and spherical harmonics from 1994 to 2021

Uz,

Akyılmaz,

Shum

et al. 2024

Sci Data

View full text Add to dashboard Cite

show abstract

“…The deep residual model improves prediction performance a week or two earlier than the other four basic models. The planned deep residual network can integrate influenza's spatial context and predict the impact on the city's finer spatial scale, which can provide vital support for more accurate public health interventions [74]. Since a slide scanner can improve the ability to scan the entire slide image regularly, there has been a concentration on the progress of computer image analysis algorithms that can automatically detect the degree of disease in digital pathological images.…”

Section: Respiratory Systemmentioning

confidence: 99%

Healthcare Techniques Through Deep Learning: Issues, Challenges and Opportunities

et al. 2021

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In artificial intelligence, deep learning (DL) is a process that replicates the working mechanism of the human brain in data processing, and it also creates patterns for decision making. Deep learning or neural networks have been deployed in several fields, such as computer vision, natural language processing, and speech recognition. It has been used in many healthcare applications for the diagnosis and treatment of many chronic diseases. These algorithms have the power to avoid outbreaks of illness, recognize and diagnose illnesses, minimize running expenses for hospital management and patients. This paper discusses the deep learning methods used in different healthcare fields, i.e., identifying depression, heart diseases, physiological signals, lymph node metastases from breast cancer, etc. These diseases are categorized into the central nervous system, cardiovascular system, and respiratory system. For each category, after summarizing the studies, comparison tables are laid down using some important factors. Different applications, tools, methods, and data sets used for DL models are leveraged. Finally, research opportunities and challenges being faced for deep learning models are discussed.

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“…Other papers make important contributions to tracking ILI in specific regions, incorporating outside data sources, or developing improved model architectures. In the most spatially granular study to date, Xi et al (36) predicted intraurban ILI trends in Shenzen, China, with several neural network variants, making a case for the network's ability to capture synchronicities and spatial disease spread. Burdakov et al (37) and Yang et al (38) introduced the use of climate data in deep learning-based ILI prediction.…”

Section: Introductionmentioning

confidence: 99%

Toward the use of neural networks for influenza prediction at multiple spatial resolutions

et al. 2021

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Mitigating the effects of disease outbreaks with timely and effective interventions requires accurate real-time surveillance and forecasting of disease activity, but traditional health care–based surveillance systems are limited by inherent reporting delays. Machine learning methods have the potential to fill this temporal “data gap,” but work to date in this area has focused on relatively simple methods and coarse geographic resolutions (state level and above). We evaluate the predictive performance of a gated recurrent unit neural network approach in comparison with baseline machine learning methods for estimating influenza activity in the United States at the state and city levels and experiment with the inclusion of real-time Internet search data. We find that the neural network approach improves upon baseline models for long time horizons of prediction but is not improved by real-time internet search data. We conduct a thorough analysis of feature importances in all considered models for interpretability purposes.

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A Deep Residual Network Integrating Spatial-temporal Properties to Predict Influenza Trends at an Intra-urban Scale

Cited by 18 publications

References 17 publications

High-resolution temporal gravity field data products: Monthly mass grids and spherical harmonics from 1994 to 2021

High-resolution temporal gravity field data products: Monthly mass grids and spherical harmonics from 1994 to 2021

Healthcare Techniques Through Deep Learning: Issues, Challenges and Opportunities

Toward the use of neural networks for influenza prediction at multiple spatial resolutions

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