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Automatic identification of variables in epidemiological datasets using logic regression

Matthias W. Lorenz, Negin Ashtiani Abdi, Frank Scheckenbach, Anja Pflug, Alpaslan Bülbül, Alberico L. Catapano, Stefan Agewall, Marat Ezhov, Michiel L. Bots, Stefan Kiechl, Andreas Orth
2017 BMC Medical Informatics and Decision Making  
With logic regression, an optimal Boolean combination of these rules was searched for every target variable, using a random subset of a large database of epidemiological and clinical cohort data (construction  ...  Conclusions: We demonstrated that the application of logic regression in a complex data management task in large epidemiological IPD meta-analyses is feasible.  ...  When using simulated annealing for logic regression in the context of identifying source variable names, the states of the system are logical expressions, like for example (R 1 v R 2 ) R 3 that assign  ... 
doi:10.1186/s12911-017-0429-1 pmid:28407816 pmcid:PMC5390441 fatcat:vrbivhnujrc6lbx63elf3da44q

Automated data-adaptive analytics for electronic healthcare data to study causal treatment effects

Sebastian Schneeweiss
2018 Clinical Epidemiology  
This work was funded in part by the Patient-centered Outcomes Research Institute. SS was also funded by the National Institutes of Health and the US Food and Drug Administration.  ...  This work is the fruit of years of close collaboration in the Methods Incubator Group in the Division of Pharmacoepidemiology, for which the author is most thankful.  ...  These predictions will be similar to those from the regression with the optimum number of important variables, in terms of minimizing a cross-validated loss of function for predicting treatment assignment  ... 
doi:10.2147/clep.s166545 pmid:30013400 pmcid:PMC6039060 fatcat:ryp6oybswbbwfllnvivlfcur44

Identification of Risk Factors Associated with Obesity and Overweight—A Machine Learning Overview

Ayan Chatterjee, Martin W. Gerdes, Santiago G. Martinez
2020 Sensors  
(d) which classification and regression models are performing better with a corresponding limited volume of the dataset following performance metrics?  ...  (c) why have we used the existing "Kaggle" and "UCI" datasets for our preliminary study?  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s20092734 pmid:32403349 fatcat:enxjacosmzcxhd3rxbht5qldtq

Sex- and age-specific genetic analysis of chronic back pain

Maxim B Freidin, Yakov A Tsepilov, Ian B Stanaway, Weihua Meng, Caroline Hayward, Blair H Smith, Samar Khoury, Marc Parisien, Andrey Bortsov, Luda Diatchenko, Sigrid Børte, Bendik S Winsvold (+6 others)
2020 Pain  
This provides an insight into the possible causes of sex- and age-specificity in epidemiology and pathophysiology of cBP and chronic pain at other anatomical sites.  ...  There was a stronger genetic correlation of cBP with self-reported diagnosis of intervertebral disc degeneration in males than in females (0.889 vs 0.638; p = 3.7E-06).  ...  Display of various epidemiological modelling results in a medically understandable format The function 'tableGlm' is not for general use.  ... 
doi:10.1097/j.pain.0000000000002100 pmid:33021770 fatcat:fbqzrayxnjhkxfklu3kl5ascwq

Artificial Intelligence and its Application in Animal Disease Diagnosis

Neelesh Sharma
2022 Journal of Animal Research  
Commonly used algorithms are linear regression, random forest, decision tree, K-nearest and support vector machines.  ...  Regarding use of AI technique in veterinary sciences, this paper reviewed some of the documented data of its application in disease prediction and diagnosis; The National Animal Disease Referral Expert  ...  If single independent variable is utilised for prediction then it is termed as single linear regression whereas use of multiple variables form multiple linear regression.  ... 
doi:10.30954/2277-940x.01.2022.1 fatcat:bp5g5jpvkfhpvarmrxwkpe3aua

Artificial intelligence in healthcare—the road to precision medicine

Tran Quoc Bao Tran, Clea du Toit, Sandosh Padmanabhan
2021 Journal of Hospital Management and Health Policy  
In this review, we provide a broad overview of the prospects and potential for AI in precision medicine and discuss some of the challenges and evolving solutions that are revolutionising healthcare.  ...  Distillation of high-dimensional data across clinical, biological, patient-generated and environmental domains using ML and translating garnered insights into clinical practice requires not only extant  ...  patients Linear regression (60) Differentiating severe septic patients with acute respiratory distress syndrome from those without Logistic regression (61) Early identification of patients with acute  ... 
doi:10.21037/jhmhp-20-132 fatcat:4rszxxfto5hkzc5ixo5dixxck4

EPIPOI: A user-friendly analytical tool for the extraction and visualization of temporal parameters from epidemiological time series

Wladimir J Alonso, Benjamin JJ McCormick
2012 BMC Public Health  
A prototype has already been used to assist researchers in a variety of contexts from didactic use in public health workshops to the main analytical tool in published research.  ...  Its friendly interface guides users intuitively through useful comparative analyses including the comparison of spatial patterns in temporal parameters.  ...  The authors are grateful to the Department of Vital Statistics from the Brazilian Ministry of Health for providing the mortality data used as an example dataset.  ... 
doi:10.1186/1471-2458-12-982 pmid:23153033 pmcid:PMC3527308 fatcat:fdtgy3ckqvhpbdff7o76zddazi

The identification of complex interactions in epidemiology and toxicology: a simulation study of boosted regression trees

Erik Lampa, Lars Lind, P Monica Lind, Anna Bornefalk-Hermansson
2014 Environmental Health  
Conclusions: We conclude that boosted regression trees can be used to uncover complex interaction effects in epidemiological studies.  ...  In this paper, we present an approach to search for interaction effects among several variables using boosted regression trees.  ...  Logic regression [49] share some similarities with CARTs in that they both generate rules, or logical conditions, and was developed to examine interactions in genetic association studies.  ... 
doi:10.1186/1476-069x-13-57 pmid:24993424 pmcid:PMC4120739 fatcat:5h652jili5a7hl7myupu7qaymi

A Review for Detecting Gene-Gene Interactions Using Machine Learning Methods in Genetic Epidemiology

Ching Lee Koo, Mei Jing Liew, Mohd Saberi Mohamad, Abdul Hakim Mohamed Salleh
2013 BioMed Research International  
Recently, the greatest statistical computational challenge in genetic epidemiology is to identify and characterize the genes that interact with other genes and environment factors that bring the effect  ...  These gene-gene interactions are also denoted as epitasis in which this phenomenon cannot be solved by traditional statistical method due to the high dimensionality of the data and the occurrence of multiple  ...  In addition, GPNN had been used to optimize the variables, weights, and connectivity of the network.  ... 
doi:10.1155/2013/432375 pmid:24228248 pmcid:PMC3818807 fatcat:5exezft7q5a47iibtf2cowljsm

Implementation of Machine Learning Models for the Prevention of Kidney Diseases (CKD) or Their Derivatives

Khalid Twarish Alhamazani, Jalawi Alshudukhi, Saud Aljaloud, Solomon Abebaw, Deepika Koundal
2021 Computational Intelligence and Neuroscience  
Artificial intelligence (AI) (logistic regression, decision forest, neural network, and jungle of decisions).  ...  The decision forest outperformed the other machine learning models with a score of 92%, indicating that the approach used in this study provides a good baseline for solutions in the production.  ...  With the identification of the scale used to classify patients with CKD, the variables to be used to evaluate a patient according to their degree of severity were determined. e initial list of 58 variables  ... 
doi:10.1155/2021/3941978 pmid:35003242 pmcid:PMC8739929 fatcat:hsaepfjahrel3ff6h4bojft6by

Integrating a Trimble Recon X 400 MHz Intel PXA255 Xscale CPU® Mobile Field Data Collection System Using Differentially Corrected Global Positioning System Technology and a Real-Time Bidirectional Actionable Platform within an ArcGIS Cyberenvironment for Implementing Mosquito Control

Benjamin G. Jacob, Robert J. Novak
2014 Advances in Remote Sensing  
In this research, we determined the feasibility of using a Personal Digital Assistant (PDA) as a mobile field data collection system by monitoring mapping and regressing digitized sub-meter resolution  ...  Two assumptions must be satisfied if PDA technology, GIS, GPS, and imaging spectrometry are to be useful in remote biophysical analysis of seasonal, vector, mosquito, aquatic, larval habitats.  ...  an output source of identification may be added for use in for joining an output georeferencable explanatory dataset to the original dataset.  ... 
doi:10.4236/ars.2014.33012 fatcat:sg5srenclnatlilciuy3pv3nx4

CONAN: copy number variation analysis software for genome-wide association studies

Lukas Forer, Sebastian Schönherr, Hansi Weissensteiner, Florian Haider, Thomas Kluckner, Christian Gieger, Heinz-Erich Wichmann, Günther Specht, Florian Kronenberg, Anita Kloss-Brandstätter
2010 BMC Bioinformatics  
Moreover, CONAN assists the evaluation process by visualizing detected associations via Manhattan plots in order to enable a rapid identification of genome-wide significant CNV regions.  ...  in-house solutions, thus strongly limiting the performance of GWAS based on CNVs.  ...  In addition, we are grateful for the advice of Claudia Lamina and Stefan Coassin (Division of Genetic Epidemiology, Innsbruck Medical University).  ... 
doi:10.1186/1471-2105-11-318 pmid:20546565 pmcid:PMC2894823 fatcat:wvx6av2erzh7lmpyrtmq64uzzu

Predicting breast cancer survivability: a comparison of three data mining methods

Dursun Delen, Glenn Walker, Amit Kadam
2005 Artificial Intelligence in Medicine  
Using sensitivity analysis on neural network models provided us with the prioritized importance of the prognostic factors used in the study. #  ...  prediction models using a large dataset (more than 200,000 cases).  ...  Logistic regression Logistic regression is a generalization of linear regression [35] . It is used primarily for predicting binary or multi-class dependent variables.  ... 
doi:10.1016/j.artmed.2004.07.002 pmid:15894176 fatcat:zxjhdj4wufdjjf2773giblm4fq

Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine

2020 Database: The Journal of Biological Databases and Curation  
the way for a new data-centric era of discovery in healthcare.  ...  Implementation of artificial intelligence in healthcare is a compelling vision that has the potential in leading to the significant improvements for achieving the goals of providing real-time, better personalized  ...  Linear regression is an ML approach to model relationships between dependent and independent variables using linear predictor functions to identify errors of prediction in a scatter plot and characterize  ... 
doi:10.1093/database/baaa010 pmid:32185396 pmcid:PMC7078068 fatcat:ypsuz5dewvcgtpjx4vjkhi545q

Real-time prediction of visibility related crashes

Mohamed A. Abdel-Aty, Hany M. Hassan, Mohamed Ahmed, Ali S. Al-Ghamdi
2012 Transportation Research Part C: Emerging Technologies  
Two issues that have not explicitly been addressed in prior studies are; (1) the possibility of predicting VR crashes using traffic data collected from the Automatic Vehicle Identification (AVI) sensors  ...  The approach adopted here involves developing Bayesian matched casecontrol logistic regression models using the historical crashes, LDs and AVI data.  ...  All opinions and results are those of the authors.  ... 
doi:10.1016/j.trc.2012.04.001 fatcat:xyw53copzncmxl6j3bqi33j5pi
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