scholarly journals A Machine-Learning Algorithm for Estimating and Ranking the Impact of Environmental Risk Factors in Exploratory Epidemiological Studies

2020 ◽  
pp. 137-156
Author(s):  
Jessica G. Young ◽  
Alan E. Hubbard ◽  
Brenda Eskenazi ◽  
Nicholas P. Jewell
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Environmental Risk ◽  
Learning Algorithm ◽  
Epidemiological Studies ◽  
Environmental Risk Factors ◽  
Machine Learning Algorithm ◽  
The Impact

scholarly journals Analyzing the Impact of Climate Factors on GNSS-Derived Displacements by Combining the Extended Helmert Transformation and XGboost Machine Learning Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Hanlin Liu ◽  
Linqiang Yang ◽  
Linchao Li
Keyword(s):  
Machine Learning ◽  
Puerto Rico ◽  
Reference Frame ◽  
Learning Algorithm ◽  
Virgin Islands ◽  
Machine Learning Algorithm ◽  
Climate Factors ◽  
Helmert Transformation ◽  
The Impact

A variety of climate factors influence the precision of the long-term Global Navigation Satellite System (GNSS) monitoring data. To precisely analyze the effect of different climate factors on long-term GNSS monitoring records, this study combines the extended seven-parameter Helmert transformation and a machine learning algorithm named Extreme Gradient boosting (XGboost) to establish a hybrid model. We established a local-scale reference frame called stable Puerto Rico and Virgin Islands reference frame of 2019 (PRVI19) using ten continuously operating long-term GNSS sites located in the rigid portion of the Puerto Rico and Virgin Islands (PRVI) microplate. The stability of PRVI19 is approximately 0.4 mm/year and 0.5 mm/year in the horizontal and vertical directions, respectively. The stable reference frame PRVI19 can avoid the risk of bias due to long-term plate motions when studying localized ground deformation. Furthermore, we applied the XGBoost algorithm to the postprocessed long-term GNSS records and daily climate data to train the model. We quantitatively evaluated the importance of various daily climate factors on the GNSS time series. The results show that wind is the most influential factor with a unit-less index of 0.013. Notably, we used the model with climate and GNSS records to predict the GNSS-derived displacements. The results show that the predicted displacements have a slightly lower root mean square error compared to the fitted results using spline method (prediction: 0.22 versus fitted: 0.31). It indicates that the proposed model considering the climate records has the appropriate predict results for long-term GNSS monitoring.

Genetic and environmental influences on last-year major depression in adulthood: a highly heritable stable liability but strong environmental effects on 1-year prevalence

2017 ◽  
Vol 47 (10) ◽  
pp. 1816-1824 ◽  
Author(s):  
K. S. Kendler ◽  
C. O. Gardner
Keyword(s):  
Risk Factors ◽  
Major Depression ◽  
Environmental Effects ◽  
Environmental Risk ◽  
Structural Equation ◽  
Environmental Influences ◽  
Diagnostic Error ◽  
Environmental Risk Factors ◽  
Multiple Time ◽  
The Impact

BackgroundThis study seeks to clarify the contribution of temporally stable and occasion-specific genetic and environmental influences on risk for major depression (MD).MethodOur sample was 2153 members of female–female twin pairs from the Virginia Twin Registry. We examined four personal interview waves conducted over an 8-year period with MD in the last year defined by DSM-IV criteria. We fitted a structural equation model to the data using classic Mx. The model included genetic and environmental risk factors for a latent, stable vulnerability to MD and for episodes in each of the four waves.ResultsThe best-fit model was simple and included genetic and unique environmental influences on the latent liability to MD and unique wave-specific environmental effects. The path from latent liability to MD in the last year was constant over time, moderate in magnitude (+0.65) and weaker than the impact of occasion-specific environmental effects (+0.76). Heritability of the latent stable liability to MD was much higher (78%) than that estimated for last-year MD (32%). Of the total unique environmental influences on MD, 13% reflected enduring consequences of earlier environmental insults, 17% diagnostic error and 70% wave-specific short-lived environmental stressors.ConclusionsBoth genetic influences on MD and MD heritability are stable over middle adulthood. However, the largest influence on last-year MD is short-lived environmental effects. As predicted by genetic theory, the heritability of MD is increased substantially by measurement at multiple time points largely through the reduction of the effects of measurement error and short-term environmental risk factors.

Impact of Encoding of High Cardinality Categorical Data to Solve Prediction Problems

2020 ◽  
Vol 17 (9) ◽  
pp. 4197-4201
Author(s):  
Heena Gupta ◽  
V. Asha
Keyword(s):  
Machine Learning ◽  
Categorical Data ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Prediction Problem ◽  
Encoding Scheme ◽  
The Impact ◽  
Prediction Problems

The prediction problem in any domain is very important to assess the prices and preferences among people. This issue varies for different kinds of data. Data may be nominal or ordinal, it may involve more categories or less. For any category to be considered by a machine learning algorithm, it needs to be encoded before any other operation can be further performed. There are various encoding schemes available like label encoding, count encoding and one hot encoding. This paper aims to understand the impact of various encoding schemes and the accuracy among the prediction problems of high cardinality categorical data. The paper also proposes an encoding scheme based on curated strings. The domain chosen for this purpose is predicting doctors’ fees in various cities having different profiles and qualification.

scholarly journals Initial cohort characteristics and protocol for SIGMA: An accelerated longitudinal study of environmental factors, inter- and intrapersonal processes, and mental health in adolescence

2021 ◽  
Author(s):  
Olivia J Kirtley ◽  
Robin Achterhof ◽  
Noëmi Hagemann ◽  
Karlijn Susanna Francisca Maria Hermans ◽  
Anu Pauliina Hiekkaranta ◽  
...  
Keyword(s):  
Mental Health ◽  
Risk Factors ◽  
Longitudinal Study ◽  
Health Outcomes ◽  
Environmental Risk ◽  
Daily Life ◽  
Experience Sampling ◽  
Environmental Risk Factors ◽  
Mental Health Outcomes ◽  
The Impact

Background: Over half of all mental health conditions have their onset in adolescence. Large-scale epidemiological studies have identified relevant environmental risk factors for mental health problems. Yet, few have focused on potential mediating inter- and intrapersonal processes in daily life, hampering intervention development. Objectives: To investigate 1) the impact of environmental risk factors on changes in inter- and intrapersonal processes; 2) the impact of altered inter- and intrapersonal processes on the development of (sub)clinical mental health symptoms in adolescents and; 3) the extent to which changes in inter- and intrapersonal processes mediate the association between environmental risk factors and the mental health outcomes in adolescents.Methods: ‘SIGMA’ is an accelerated longitudinal study of adolescents aged 12 to 18 from across Flanders, Belgium. Using self-report questionnaires, experience sampling, an experimental task, and wearables, we are investigating the relationship between environmental risk factors (e.g. trauma, parenting), inter- and intrapersonal processes (e.g. real-life social interaction and interpersonal functioning) and mental health outcomes (e.g. psychopathology, self-harm) over time. Results: N= 1913 adolescents (63% female) aged 11 – 20, from 22 schools, participated. The range of educational trajectories within the sample was broadly representative of the Flemish general adolescent population.Conclusions: Our findings will enable us to answer fundamental questions about inter- and intrapersonal processes involved in the development and maintenance of poor mental health in adolescence. This includes insights regarding the role of daily-life social and cognitive-affective processes, gained by using experience sampling. The accelerated longitudinal design enables rapid insights into developmental and cohort effects.

scholarly journals Environmental risk factors of pregnancy outcomes: a summary of recent meta-analyses of epidemiological studies

2013 ◽  
Vol 12 (1) ◽  
Author(s):  
Mark J Nieuwenhuijsen ◽  
Payam Dadvand ◽  
James Grellier ◽  
David Martinez ◽  
Martine Vrijheid
Keyword(s):  
Risk Factors ◽  
Environmental Risk ◽  
Pregnancy Outcomes ◽  
Epidemiological Studies ◽  
Environmental Risk Factors ◽  
Meta Analyses
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