Real-Time Prediction of the Water Accumulation Process of Urban Stormy Accumulation Points Based on Deep Learning

Arguello Casteleiro M, Demetriou G, Read W, Fernandez Prieto MJ, Maroto N, Maseda Fernandez D, Nenadic G, Klein J, Keane J, Stevens R. Deep learning meets ontologies: experiments to anchor the cardiovascular disease ontology in the biomedical literature. J Biomed Semantics 2018;9(1):13 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5896136/ Le KK, Whiteside MD, Hopkins JE, Gannon VPJ, Laing CR. Spfy: an integrated graph database for real-time prediction of bacterial phenotypes and downstream comparative analyses. Database (Oxford) 2018;2018:1-10 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6146121/ Osumi-Sutherland DJ, Ponta E, Courtot M, Parkinson H, Badi L. Using OWL reasoning to support the generation of novel gene sets for enrichment analysis. J Biomed Semantics 2018;9(1):10 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5813370/ Yu S, Ma Y, Gronsbell J, Cai T, Ananthakrishnan AN, Gainer VS, Churchill SE, Szolovits P, Murphy SN, Kohane IS, Liao KP, Cai T. Enabling phenotypic big data with PheNorm. J Am Med Inform Assoc 2018;25(1):54-60 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6251688/

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The input vector space optimization for LSTM deep learning model in real-time prediction of ship motions

Ocean Engineering ◽

10.1016/j.oceaneng.2020.107681 ◽

2020 ◽

Vol 213 ◽

pp. 107681

Author(s):

Yucheng Liu ◽

Wenyang Duan ◽

Limin Huang ◽

Shiliang Duan ◽

Xuewen Ma

Keyword(s):

Deep Learning ◽

Vector Space ◽

Real Time ◽

Learning Model ◽

Input Vector ◽

Ship Motions ◽

Time Prediction ◽

Deep Learning Model

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Real-Time Prediction of AKI Among Middle-Aged and Older in ICU: A Retrospective and Machine Learning Study

10.21203/rs.3.rs-57905/v1 ◽

2020 ◽

Author(s):

Hui Wang ◽

Fuxing Deng ◽

Buyao Zhang ◽

Shuangping Zhao

Keyword(s):

Machine Learning ◽

Intensive Care ◽

Deep Learning ◽

Real Time ◽

Older Patients ◽

Support Vector ◽

Research Database ◽

Middle Aged ◽

Data Set ◽

Time Prediction

Abstract BackgroundAcute Kidney Injury (AKI), a major public health problem，is responsible for two-thirds of intensive care unit patients’ cost, and aging is an independent risk factor for AKI and its associated mortality and morbidity. The early recognition of AKI helps ICU caregivers to guide fluid treatment and titrate the dosing of the nephrotoxic drug. Therefore, it is desirable to build models to predict their position. The study is to build models based machine learning to predict AKI stage after 24 hours and 48 hours among middle-aged and older patients respectively in ICU. Methods and FindingsWe used two real-world databases to build and test models. The Medical Information Mart for Intensive Care (MIMIC-III v1.4) database for training, funded by National Institutes of Health (NIIH) built by the Computational Physiology Laboratory of MIT, Beth Israel Dikon Medical Center, and Philips Medical. The eICU Collaborative Research Database (eICU-CRD v 2.0) for the test is open-access, de-identified data sets of patients admitted to ICUs. 26316 patients in the overall cohort were generally older (median age ranging from 57 to 79) and 54% were male. Here we present three models, using the support vector machine (SVM), Long short-term memory (LSTM), and convolutional LSTM ConvLSTM respectively. the ConvLSTM model had the best performance in the test data set, and it has good ability and surpasses any previous model to predict whether older patients have AKI or not. The area under the receiver operating characteristic curve (AUC) of 24-hour prediction AKI is 99.79%, 48-hours AKI 99.43% during the hospital. we demonstrate that deep learning can handle lots of variables which may be predictors and that the algorithm achieved robust and excellent performance.ConclusionsTo our knowledge, this study is the first to use large-scale data collected from electronic health record（EHR）to prove the contribution of big data and deep learning methods to the real-time prediction of AKI prognosis in middle-aged and elderly patients. The model performance is better than any previous models. This work provides novel evidence to change clinical practice and precise personalized interventions.

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