C-LIME: A Consistency-Oriented LIME for Time-Series Health-Risk Predictions

2021 ◽  
pp. 58-69
Author(s):  
Taku Ito ◽  
Keiichi Ochiai ◽  
Yusuke Fukazawa
Keyword(s):  
Time Series ◽  
Health Risk

scholarly journals Development of health risk-based metrics for defining a heatwave: a time series study in Brisbane, Australia

2014 ◽  
Vol 14 (1) ◽  
Author(s):  
Shilu Tong ◽  
Xiao Yu Wang ◽  
Gerry FitzGerald ◽  
David McRae ◽  
Gerard Neville ◽  
...  
Keyword(s):  
Time Series ◽  
Health Risk ◽  
Time Series Study ◽  
Series Study

scholarly journals Effectiveness of Cascading Time Series Models Based on Meteorological Factors in Improving Health Risk Prediction

2021 ◽  
Author(s):  
Shuopeng Jia ◽  
Weibin She ◽  
Zhipeng Pi ◽  
Buying Niu ◽  
Jinhua Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Health Risk ◽  
Risk Prediction ◽  
Prediction Models ◽  
Time Window ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Time Series Prediction ◽  
Epidemic Disease ◽  
Tract Infections

Abstract Meteorological factors, which are cyclical and regular, have an essential impact on human health. Accurate health risk prediction based on meteorological factors is essential for optimal resource allocation in healthcare units. However, due to the non-stationary and nonlinear of the original hospitalization sequence, traditional methods are less robust to predict it. This study aims to investigate hospital admission prediction models using time series pre-processing algorithms and deep learning approach based on Meteorological factors. Using the hospital electronic medical record data of Panyu Central Hospital and meteorological data of Panyu district from 2003-2019, 46,089 eligible patients with lower respiratory tract infections (LRTIs) and four meteorological factors were identified to build and evaluate the prediction models. A novel hybrid model, Cascade GAM-CEEMDAN-LSTM Model (CGCLM) was established in combination with Generalized Additive Model (GAM), Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Long-Short Term Memory Networks (LSTM) for predicting daily admissions of patients with LRTIs. The experimental results show that the CGCLM multistep method proposed in this paper outperforms the single LSTM model in the prediction of health risk time series at different time window sizes. Moreover, our results also indicate that the CGCLM has the best prediction performance when the time window is set to 61 days (RMSE=1.12, MAE=0.87, R2=0.93). Adequate extraction of exposure-response relationships between meteorological factors and diseases and suitable handling of sequence pre-processing have an important role in time series prediction. This hybrid climate-based model to LRTIs disease prediction can also be extended to the time series prediction of other epidemic disease.

The LDE-Type Flare Occurrence (1969-1993)

1994 ◽  
Vol 144 ◽  
pp. 279-282
Author(s):  
A. Antalová
Keyword(s):  
Time Series ◽  
Fourier Analysis ◽  
Sunspot Cycle ◽  
List Type ◽  
Type Number ◽  
Solar Cycles ◽  
Type Iv ◽  
Long Duration ◽  
Cycle Maximum ◽  
Flare Index

AbstractThe occurrence of LDE-type flares in the last three cycles has been investigated. The Fourier analysis spectrum was calculated for the time series of the LDE-type flare occurrence during the 20-th, the 21-st and the rising part of the 22-nd cycle. LDE-type flares (Long Duration Events in SXR) are associated with the interplanetary protons (SEP and STIP as well), energized coronal archs and radio type IV emission. Generally, in all the cycles considered, LDE-type flares mainly originated during a 6-year interval of the respective cycle (2 years before and 4 years after the sunspot cycle maximum). The following significant periodicities were found:• in the 20-th cycle: 1.4, 2.1, 2.9, 4.0, 10.7 and 54.2 of month,• in the 21-st cycle: 1.2, 1.6, 2.8, 4.9, 7.8 and 44.5 of month,• in the 22-nd cycle, till March 1992: 1.4, 1.8, 2.4, 7.2, 8.7, 11.8 and 29.1 of month,• in all interval (1969-1992):a)the longer periodicities: 232.1, 121.1 (the dominant at 10.1 of year), 80.7, 61.9 and 25.6 of month,b)the shorter periodicities: 4.7, 5.0, 6.8, 7.9, 9.1, 15.8 and 20.4 of month.Fourier analysis of the LDE-type flare index (FI) yields significant peaks at 2.3 - 2.9 months and 4.2 - 4.9 months. These short periodicities correspond remarkably in the all three last solar cycles. The larger periodicities are different in respective cycles.

Severe Drooling and Treatment With Botulinum Toxin

2012 ◽  
Vol 21 (1) ◽  
pp. 15-21
Author(s):  
Merete Bakke ◽  
Allan Bardow ◽  
Eigild Møller
Keyword(s):  
Botulinum Toxin ◽  
Side Effects ◽  
Health Risk ◽  
Flow Rate ◽  
Clinical Evidence ◽  
Acetylcholine Release ◽  
Psychosocial Problems ◽  
Rate Reduction ◽  
Local Inhibition ◽  
Surgical Treatments

Severe drooling is associated with discomfort and psychosocial problems and may constitute a health risk. A variety of different surgical and non-surgical treatments have been used to diminish drooling, some of them with little or uncertain effect and others more effective but irreversible or with side effects. Based on clinical evidence, injection with botulinum toxin (BTX) into the parotid and submandibular glands is a useful treatment option, because it is local, reversible, and with few side effects, although it has to be repeated. The mechanism of BTX is a local inhibition of acetylcholine release, which diminishes receptor-coupled secretion and results in a flow rate reduction of 25–50% for 2–7 months.

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