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Simultaneous prediction of multiple outcomes using revised stacking algorithms [article]

Li Xing, Mary Lesperance, Xuekui Zhang
2019 arXiv   pre-print
Results: We propose two variations of a stacking algorithm which borrow information among multiple prediction tasks to improve multivariate prediction performance.  ...  Our goal is to build a model using data in this database, which simultaneously predicts the resistance of multiple drugs using mutation information from sequences of viruses for any new patient.  ...  Acknowledgements Authors thank Professor Dominik Heider (University of Marburg) for his helpful discussion about pre-processing data from HIV data base.  ... 
arXiv:1901.10153v1 fatcat:42blh2runzcg3m4tkhix6obcx4

Algorithm in the diagnosis of febrile illness using pathogen-specific rapid diagnostic tests

Sunil Pokharel, Lisa J White, Ricardo Aguas, Olivier Celhay, Karell G Pellé, Sabine Dittrich
2019 Clinical Infectious Diseases  
model to predict the probability of correct identification of each disease when diagnostic testing occurs either simultaneously or sequentially in various algorithms.  ...  The implementation of adaptive algorithms can predict better diagnosis and add value to the available RDTs.  ...  However, the diagnostic outcome of an algorithm is very sensitive to the accuracies of component diagnostic tests.  ... 
doi:10.1093/cid/ciz665 pmid:31313805 pmcid:PMC7245147 fatcat:w7rpvjfirvcz3blvz2g6r7dxla

Improved predictive models for acute kidney injury with IDEA: Intraoperative Data Embedded Analytics

Lasith Adhikari, Tezcan Ozrazgat-Baslanti, Matthew Ruppert, R. W. M. A. Madushani, Srajan Paliwal, Haleh Hashemighouchani, Feng Zheng, Ming Tao, Juliano M. Lopes, Xiaolin Li, Parisa Rashidi, Azra Bihorac (+1 others)
2019 PLoS ONE  
The majority of existing perioperative AKI risk prediction models are limited in their generalizability and do not fully utilize intraoperative physiological time-series data.  ...  Thus, there is a need for intelligent, accurate, and robust systems to leverage new information as it becomes available to predict the risk of developing postoperative AKI.  ...  Firstly, only the first surgery was used for patients that underwent multiple surgeries for building our proposed predictive models.  ... 
doi:10.1371/journal.pone.0214904 pmid:30947282 pmcid:PMC6448850 fatcat:sxkxto6wr5bjjedmc2nilpzaqq

Classification Algorithm Accuracy Improvement for Student Graduation Prediction Using Ensemble Model

Ace C. Lagman, the FEU Institute of Technology, P. Paredes St. Sampaloc, Manila, Philippines, Lourwel P. Alfonso, Marie Luvett I. Goh, Jay-ar P. Lalata, Juan Paulo H. Magcuyao, Heintjie N. Vicente
2020 International Journal of Information and Education Technology  
According to National Center for Education Statistics, almost half of the first-time freshmen full time students who began seeking a bachelor's degree do not graduate. The imbalance between  ...  CONFLICT OF INTEREST The authors declare no conflict of interest. AUTHOR CONTRIBUTIONS All authors contributed equally to this work.  ...  To determine the accuracy level of the classification table of the algorithms the formula was used = + + + + (1) where true positive (TP) refers to as number of actual outcomes of graduation yes accurately  ... 
doi:10.18178/ijiet.2020.10.10.1449 fatcat:dcptmdrpwncevj47kbf5zhxjbu

Predicting Outcome of Endovascular Treatment for Acute Ischemic Stroke: Potential Value of Machine Learning Algorithms

Hendrikus J A van Os, Lucas A Ramos, Adam Hilbert, Matthijs van Leeuwen, Marianne A A van Walderveen, Nyika D Kruyt, Diederik W J Dippel, Ewout W Steyerberg, Irene C van der Schaaf, Hester F Lingsma, Wouter J Schonewille, Charles B L M Majoie (+6 others)
2018 Frontiers in Neurology  
For all models at time of admission radiological outcome was more difficult to predict than clinical outcome.  ...  To further improve personalized stroke care, it is essential to accurately predict outcome after EVT.  ...  ACKNOWLEDGMENTS See supplementary file for full details of the acknowledgements (MR CLEAN Registry acknowledgements).  ... 
doi:10.3389/fneur.2018.00784 pmid:30319525 pmcid:PMC6167479 fatcat:6stkxarxcjds3ls3od6cn4x4me

Prediction Model for Occupational Incidents in Chemical and Gas Industries

2019 International journal of recent technology and engineering  
Lambda functions are used to implement the scoring algorithm and prediction algorithm to write out the results back to AWS S3 buckets.  ...  The primary technology stack used in this architecture is Apache Kafka, Apache Spark Streaming, KSQL, Data frames, and AWS Lambda functions.  ...  Multiple sources can send the data simultaneously to meet the requirements of real-time data analytics.  ... 
doi:10.35940/ijrte.d8212.118419 fatcat:kdkojuyaj5axvmucny6hlwxjeq

Stock Market Prediction and Investment using Deep Reinforcement Learning- a Continuous Training Pipeline

2020 International Journal of Engineering and Advanced Technology  
To evaluate the performance of the proposed method, comparison of our portfolio results was done with various other reinforcement learning algorithms by keeping the same configuration.  ...  The experimental result shows how natural language processing and statistical prediction can help us to choose the trending stock based on news headlines and historical data so that model invests money  ...  DDPG is an algorithm that simultaneously learns Q-function by using off-policy data, the Bellman equation and a policy which is learned based on this Qfunction.  ... 
doi:10.35940/ijeat.b2034.1210220 fatcat:eksgsnx4hrb5flsqpbxo6dabc4

From Structure Prediction to Genomic Screens for Novel Non-Coding RNAs

Jan Gorodkin, Ivo L. Hofacker, Michael Levitt
2011 PLoS Computational Biology  
The field has moved from folding prediction of single sequences to computational screens for ncRNAs in genomic sequence using the RNA structure as the main characteristic feature.  ...  A key feature of RNA function is its structure. Computational methods were developed early for folding and prediction of RNA structure with the aim of assisting in functional analysis.  ...  Thanks to Christian Anthon for providing us figures illustrating the pipeline searches.  ... 
doi:10.1371/journal.pcbi.1002100 pmid:21829340 pmcid:PMC3150283 fatcat:qs3d5ne7pnbx3blxaob6b4j4qq

Improved Classification of Blockchain Transactions Using Feature Engineering and Ensemble Learning

Chandrashekar Jatoth, Rishabh Jain, Ugo Fiore, Subrahmanyam Chatharasupalli
2021 Future Internet  
In this paper, the proposed approach is to use ensemble learning with or without feature selection using correlation-based feature selection.  ...  Although the blockchain technology is gaining a widespread adoption across multiple sectors, its most popular application is in cryptocurrency.  ...  Ensemble methods, in general, are used to increase a model's overall performance accuracy by combining several separate models, also known as base learners, to predict the outcomes, in spite of using a  ... 
doi:10.3390/fi14010016 fatcat:2q7eea2unncxdbwehori2k4hn4

Gender Differences in Participation and Reward on Stack Overflow [article]

Anna May, Johannes Wachs, Aniko Hannak
2018 arXiv   pre-print
In this paper we audit the differences in behavior and outcomes between men and women on Stack Overflow, the most popular of these Q&A sites.  ...  Online question and answer platforms serve a dual purpose in this field: they form a body of knowledge useful as a reference and learning tool, and they provide opportunities for individuals to demonstrate  ...  We also acknowledge the comments of anonymous referees.  ... 
arXiv:1811.11539v1 fatcat:2jdlm637rzfhngivbjwiek6ala

A stacking-based model for predicting 30-day all-cause hospital readmissions of patients with acute myocardial infarction

Zhen Zhang, Hang Qiu, Weihao Li, Yucheng Chen
2020 BMC Medical Informatics and Decision Making  
The predictions of the base-layer were used to train the meta-layer in order to make the final forecast.  ...  Methods In this study, we propose a stacking-based model to predict the risk of 30-day unplanned all-cause hospital readmissions for AMI patients based on clinical data.  ...  [20] presented a joint ensemble-learning model, using stacking algorithm to integrate the base ML model and boosting algorithm to predict readmission risk.  ... 
doi:10.1186/s12911-020-01358-w pmid:33317534 fatcat:ixfw4t6e4fgmbfgbzlwldrxmx4


Austin S. Hembd, Nicholas T. Haddock, Sumeet S. Teotia
2018 Plastic and Reconstructive Surgery, Global Open  
The addition of the stacked LTP flap to the perforator flap collection allows the reconstructive surgeon to tailor breast reconstruction to the patient while focusing on body habitus and minimizing donor  ...  of excess lateral hip adiposity.  ...  The purpose of this study is to elucidate the global effects of TAP blocks on reconstructive and institutional outcomes after microvascular breast reconstruction.  ... 
doi:10.1097/01.gox.0000547055.45277.18 fatcat:x2fqweunw5ft7bswurzjracwsi

Gender differences in participation and reward on Stack Overflow

Anna May, Johannes Wachs, Anikó Hannák
2019 Empirical Software Engineering  
In this paper we audit the differences in behavior and outcomes between men and women on Stack Overflow, the most popular of these Q&A sites.  ...  Online question and answer platforms serve a dual purpose in this field: they form a body of knowledge useful as a reference and learning tool, and they provide opportunities for individuals to demonstrate  ...  Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution  ... 
doi:10.1007/s10664-019-09685-x fatcat:wa2hy4xfsfb4becdhms2hsaioq

Artificial Intelligence and Digital Microscopy Applications in Diagnostic Hematopathology

Hanadi El Achi, Joseph D Khoury
2020 Cancers  
Digital Pathology is the process of converting histology glass slides to digital images using sophisticated computerized technology to facilitate acquisition, evaluation, storage, and portability of histologic  ...  The application of DL/AI to digital pathology data holds promise, even if the scope of use cases and regulatory framework for deploying such applications in the clinical environment remains in the early  ...  Outcome of patients after treatment based on the molecular subtyping algorithms N/A SVM 855 cases 94% Biccler et al. [65] DLBCL LN Prediction of prognosis N/A Stacking approach of ML  ... 
doi:10.3390/cancers12040797 pmid:32224980 pmcid:PMC7226574 fatcat:4tw7ws7ysncypgabwjvipgmdmi

Automated Detection of Vaping-Related Tweets on Twitter During the 2019 EVALI Outbreak Using Machine Learning Classification

Yang Ren, Dezhi Wu, Avineet Singh, Erin Kasson, Ming Huang, Patricia Cavazos-Rehg
2022 Frontiers in Big Data  
There are increasingly strict regulations surrounding the purchase and use of combustible tobacco products (i.e., cigarettes); simultaneously, the use of other tobacco products, including e-cigarettes  ...  After comparing the performance of each model, we found that the stacking ensemble learning achieved the highest performance with an F1-score of 0.97.  ...  Using Naïve Bayes algorithm in detection of hate tweets. Int. J. Sci. Res.  ... 
doi:10.3389/fdata.2022.770585 pmid:35224484 pmcid:PMC8866955 fatcat:6myp5adggzdonlbvbyq3h2gmtq
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