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Comparison of methods for early-readmission prediction in a high-dimensional heterogeneous covariates and time-to-event outcome framework [article]

Simon Bussy, Raphaël Veil, Vincent Looten, Anita Burgun, Stéphane Gaïffas, Agathe Guilloux, Brigitte Ranque, Anne-Sophie Jannot
<span title="2018-07-25">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Choosing the most performing method in terms of outcome prediction or variables selection is a recurring problem in prognosis studies, leading to many publications on methods comparison.  ...  Methods: Using a high-dimensional case study on a sickle-cell disease (SCD) cohort, we compare 8 statistical methods.  ...  Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2):301-320, 2005.Supplementary Material for the paper: Comparison of methods for early-readmission prediction in a high-dimensional  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1807.09821v1">arXiv:1807.09821v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4nm5g65hqnapflklxecjfdz4mq">fatcat:4nm5g65hqnapflklxecjfdz4mq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200907070746/https://arxiv.org/pdf/1807.09821v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ea/f1/eaf1b6d596ea152c1e1c3ac8388c4a09e989bf02.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1807.09821v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Comparison of methods for early-readmission prediction in a high-dimensional heterogeneous covariates and time-to-event outcome framework

Simon Bussy, Raphaël Veil, Vincent Looten, Anita Burgun, Stéphane Gaïffas, Agathe Guilloux, Brigitte Ranque, Anne-Sophie Jannot
<span title="2019-03-06">2019</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/llc2bilew5glxlrb6bkj3lko2a" style="color: black;">BMC Medical Research Methodology</a> </i> &nbsp;
Choosing the most performing method in terms of outcome prediction or variables selection is a recurring problem in prognosis studies, leading to many publications on methods comparison.  ...  In this paper, we propose a comparison methodology to weight up those different settings both in terms of prediction and variables selection, while incorporating advanced machine learning strategies.  ...  Availability of data and materials We do not have permission to distribute the data. 1  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12874-019-0673-4">doi:10.1186/s12874-019-0673-4</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/30841867">pmid:30841867</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6404305/">pmcid:PMC6404305</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mzapyl4j5fhwfisufjvg3l7xdy">fatcat:mzapyl4j5fhwfisufjvg3l7xdy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190426215034/https://bmcmedresmethodol.biomedcentral.com/track/pdf/10.1186/s12874-019-0673-4" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/2a/0a/2a0a4b05656cfa2fcce0983ab159e804ae875bc9.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12874-019-0673-4"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> springer.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6404305" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

An Integrated Framework for Reducing Hospital Readmissions using Risk Trajectories Characterization and Discharge Timing Optimization

Adel Alaeddini, Dr Jonathan E. Helm, Dr Pengyi Shi, Syed Hasib Akhter Faruqui
<span title="2019-03-05">2019</span> <i title="Informa UK Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/chcanbcgo5g7zjov2j2dvu4xzu" style="color: black;">IISE Transactions on Healthcare Systems Engineering</a> </i> &nbsp;
In this paper we develop an integrated framework of risk prediction and discharge optimization that supports both types of interventions: discharge timing and post-discharge monitoring.  ...  Our method combines a kernel approach for capturing the non-linear relationship between length of stay and risk of an adverse event, with a Principle Component Analysis method that makes the resulting  ...  Eng and Hanlon (2012) describe a Cox mixture model to cluster heterogeneity in time-to-event data and apply it to a cancer genomic study. Decision Support.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1080/24725579.2019.1584133">doi:10.1080/24725579.2019.1584133</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31673670">pmid:31673670</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6822616/">pmcid:PMC6822616</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qqou4wusuzdw7b4xytjtn63g2i">fatcat:qqou4wusuzdw7b4xytjtn63g2i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200504161129/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC6822616&amp;blobtype=pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/90/1d/901d1130394829af024e6777ab4bbb069446cf33.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1080/24725579.2019.1584133"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> tandfonline.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6822616" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Survivor average causal effects for continuous time: a principal stratification approach to causal inference with semicompeting risks [article]

Leah Comment, Fabrizia Mealli, Sebastien Haneuse, Corwin Zigler
<span title="2019-02-15">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In semicompeting risks problems, nonterminal time-to-event outcomes such as time to hospital readmission are subject to truncation by death.  ...  on survival (a post-treatment outcome) that is embedded in the definition of a hazard.  ...  LC was also supported by a Rose Traveling Fellowship. We thank Alessandra Mattei for helpful discussions about discrepancy measures.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1902.09304v1">arXiv:1902.09304v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/aljqaj3v4nhzpm3qqimwlxc2ey">fatcat:aljqaj3v4nhzpm3qqimwlxc2ey</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200911064014/https://arxiv.org/pdf/1902.09304v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/89/83/898392324563169d97e9fc1dd33db35e08694c56.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1902.09304v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Editorial: Predictive Intelligence in Biomedical and Health Informatics

E. Adeli, S. H. Rekik, S. H. Park, D. Shen
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q2z26obphvchndqieqd65vltle" style="color: black;">IEEE journal of biomedical and health informatics</a> </i> &nbsp;
Using predictive analytics based on electronic health record (EHR) for hospital readmission is faced with multiple challenges such as high dimensionality and event sparsity of medical codes and the class  ...  Specifically, they presented MATCH-Net: a Missingness-Aware Temporal Convolutional Hitting-time Network, designed to capture temporal dependencies and heterogeneous interactions in covariate trajectories  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/jbhi.2019.2962852">doi:10.1109/jbhi.2019.2962852</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mp2kymg7yjb7ziukia7kjgjula">fatcat:mp2kymg7yjb7ziukia7kjgjula</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201108140235/https://ieeexplore.ieee.org/ielx7/6221020/8984617/08984668.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/92/f4/92f4c067f001b3e821dc4d287ac64d64d71f7636.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/jbhi.2019.2962852"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Outcome Prediction for Heart Failure Telemonitoring Via Generalized Linear Models with Functional Covariates

STEFANO BARALDO, FRANCESCA IEVA, ANNA MARIA PAGANONI, VALERIA VITELLI
<span title="2012-12-18">2012</span> <i title="Wiley"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/occ3rnqmirbpvpgzpjqr6rzacq" style="color: black;">Scandinavian Journal of Statistics</a> </i> &nbsp;
A model for recurrent events is used for modelling the occurrence of hospital readmissions in time, thus deriving a suitable way to compute individual cumulative hazard functions.  ...  Estimated cumulative hazard trajectories are then treated as functional data, and they are used as covariates along with clinical survey data within the framework of generalized linear models with functional  ...  , and used to construct a generalized linear model with functional covariates for predicting telemonitoring outcome.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1111/j.1467-9469.2012.00818.x">doi:10.1111/j.1467-9469.2012.00818.x</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ogtuyelpdnhlvf53sd5jwuc2am">fatcat:ogtuyelpdnhlvf53sd5jwuc2am</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170923033519/https://air.unimi.it/retrieve/handle/2434/233393/309700/SJS_2013.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c3/fb/c3fb3e528e95afcc02b0b64b3aa5fa0e403c6ac2.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1111/j.1467-9469.2012.00818.x"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

MedGraph: Structural and Temporal Representation Learning of Electronic Medical Records [article]

Bhagya Hettige, Yuan-Fang Li, Weiqing Wang, Suong Le, Wray Buntine
<span title="2020-07-23">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
To address these limitations, we present MedGraph, a supervised EMR embedding method that captures two types of information: (1) the visit-code associations in an attributed bipartite graph, and (2) the  ...  Furthermore, current work considers visits of the same patient as discrete-time events and ignores time gaps between them.  ...  Acknowledgements This work has been supported by the Monash Institute of Medical Engineering (MIME), Australia.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1912.03703v3">arXiv:1912.03703v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ymoqtgafpjelvpwhci34nirtyi">fatcat:ymoqtgafpjelvpwhci34nirtyi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200729054323/https://arxiv.org/pdf/1912.03703v3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1912.03703v3" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Gaussian Processes for Personalized Interpretable Volatility Metrics in the Step-down Ward

Glen Wright Colopy, Stephen Roberts, David A. Clifton
<span title="2019-01-22">2019</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q2z26obphvchndqieqd65vltle" style="color: black;">IEEE journal of biomedical and health informatics</a> </i> &nbsp;
Patients in a hospital step-down unit require a level of care that is between that of the intensive care unit (ICU) and that of the general ward.  ...  While many patients remain physiologically stabilized, others will suffer clinical emergencies and be readmitted to the ICU, with a subsequent high risk of mortality.  ...  The comparison of a current time-series to a dictionary of reference patients, as in Figure 1 (d) can provide early warning gains over currently available methods.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/jbhi.2019.2890823">doi:10.1109/jbhi.2019.2890823</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/30676986">pmid:30676986</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/inji3gu5zna3fd32t5vndf6hla">fatcat:inji3gu5zna3fd32t5vndf6hla</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201108123233/https://ieeexplore.ieee.org/ielx7/6221020/8705605/08621007.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e8/d2/e8d2db8d724c69935424ee2d88dc196bee3c76c1.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/jbhi.2019.2890823"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Tutorial in Joint Modeling and Prediction: A Statistical Software for Correlated Longitudinal Outcomes, Recurrent Events and a Terminal Event

Agnieszka Król, Audrey Mauguen, Yassin Mazroui, Alexandre Laurent, Stefan Michiels, Virginie Rondeau
<span title="">2017</span> <i title="Foundation for Open Access Statistic"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/yr3cv2yayvhvdcoj6lb4qw36kq" style="color: black;">Journal of Statistical Software</a> </i> &nbsp;
In particular, it fits models for recurrent events and a terminal event (frailtyPenal), models for two survival outcomes for clustered data (frailtyPenal), models for two types of recurrent events and  ...  Extensions in the field of joint modeling of correlated data and dynamic predictions improve the development of prognosis research.  ...  Acknowledgments The authors thank the Fédération Francophone de Cancérologie Digestive and Gustave Roussy for sharing the data of the FFCD 2000-05 trial supported by an unrestricted Grant from Sanofi.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18637/jss.v081.i03">doi:10.18637/jss.v081.i03</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pzixszlibnf27bwz5t3mboihcq">fatcat:pzixszlibnf27bwz5t3mboihcq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200214140545/https://www.jstatsoft.org/article/view/v081i03/v81i03.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d9/c4/d9c4dfafd9a4ffe72aff0f3ede788a9d5ac19e04.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18637/jss.v081.i03"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Trajectories, bifurcations and pseudotime in large clinical datasets: applications to myocardial infarction and diabetes data [article]

Sergey E. Golovenkin, Jonathan Bac, Alexander Chervov, Evgeny M. Mirkes, Yuliya V. Orlova, Emmanuel Barillot, Alexander N. Gorban, Andrei Zinovyev
<span title="2020-10-05">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Overall, our pseudo-time quantification-based approach gives a possibility to apply the methods developed for dynamical disease phenotyping and illness trajectory analysis (diachronic data analysis) to  ...  We test the methodology in two large publicly available datasets: myocardial infarction complications and readmission of diabetic patients data.  ...  . % of early readmissions for not measured HbA c against . % for normal levels of HbA c and . % for high levels of HbA c).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2007.03788v2">arXiv:2007.03788v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kv3lurvoyjcoxck2zrrnesz6gu">fatcat:kv3lurvoyjcoxck2zrrnesz6gu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201010222242/https://arxiv.org/pdf/2007.03788v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2007.03788v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Mining Electronic Health Records (EHRs)

Pranjul Yadav, Michael Steinbach, Vipin Kumar, Gyorgy Simon
<span title="2018-01-03">2018</span> <i title="Association for Computing Machinery (ACM)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/eiea26iqqjcatatlgxdpzt637y" style="color: black;">ACM Computing Surveys</a> </i> &nbsp;
In this manuscript, we provide a structured and comprehensive overview of data mining techniques for modeling EHR data.  ...  With this foundation, we then provide a systematic and methodological organization of existing data mining techniques used to model EHRs and discuss ideas for future research.  ...  Survival analysis, a branch of statistics is generally defined as a set of methods for analyzing data where the outcome variable is the time until the occurrence of an event of interest.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3127881">doi:10.1145/3127881</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xil7qev3xbf3pmfv5vtak4f2jq">fatcat:xil7qev3xbf3pmfv5vtak4f2jq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20161208003353/https://www.cs.umn.edu/sites/cs.umn.edu/files/tech_reports/15-016.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e5/2c/e52cd5ede497d150db7c36ec33749f176fa797e1.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3127881"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

A Knowledge Distillation Ensemble Framework for Predicting Short and Long-term Hospitalisation Outcomes from Electronic Health Records Data

Zina Ibrahim, Daniel Bean, Thomas Searle, Linglong Qian, Honghan Wu, Anthony Shek, Zeljko Kraljevic, James Galloway, Sam Norton, James Teo, Richard J Dobson
<span title="2021-06-15">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q2z26obphvchndqieqd65vltle" style="color: black;">IEEE journal of biomedical and health informatics</a> </i> &nbsp;
We present a highly-scalable and robust machine learning framework to automatically predict adversity represented by mortality and ICU admission from time-series vital signs and laboratory results obtained  ...  Existing outcome prediction models suffer from a low recall of infrequent positive outcomes.  ...  to learn a vector-form separation between the majority (nonadverse outcome) time-series and time-series corresponding to adverse events.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/jbhi.2021.3089287">doi:10.1109/jbhi.2021.3089287</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34129509">pmid:34129509</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kdkhhs3dejgt5l6m32gsxf7rii">fatcat:kdkhhs3dejgt5l6m32gsxf7rii</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210718045727/https://ieeexplore.ieee.org/ielx7/6221020/6363502/09456095.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/92/fb/92fb6b0c9f37fecb251b787bcccd8d3b4da4d84b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/jbhi.2021.3089287"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Machine Learning for Survival Analysis: A Survey [article]

Ping Wang, Yan Li, Chandan K. Reddy
<span title="2017-08-15">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Accurately predicting the time of occurrence of an event of interest is a critical problem in longitudinal data analysis.  ...  One of the main challenges in this context is the presence of instances whose event outcomes become unobservable after a certain time point or when some instances do not experience any event during the  ...  The penalized regression method can provide better prediction results in the presence of either multi-collinearity of the covariates or high-dimensionality.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1708.04649v1">arXiv:1708.04649v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2rpjmrftfzfjhac3ihjbh7ge2u">fatcat:2rpjmrftfzfjhac3ihjbh7ge2u</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200825001113/https://arxiv.org/pdf/1708.04649v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/1c/d2/1cd21e0af5489947a65c72b1030015b6ce1462d6.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1708.04649v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Mining Electronic Health Records: A Survey [article]

Pranjul Yadav, Michael Steinbach, Vipin Kumar, Gyorgy Simon
<span title="2017-03-23">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this manuscript, we provide a structured and comprehensive overview of data mining techniques for modeling EHR data.  ...  With this foundation, we then provide a systematic and methodological organization of existing data mining techniques used to model EHRs and discuss ideas for future research.  ...  The lack of an outcome combined with the high dimensionality and the associated heterogeneity of EHR data often lead to increased complexity, which translates to exponentially large number of patterns.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1702.03222v2">arXiv:1702.03222v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/aizt3bnmibcc7kv67h6qf7ts7q">fatcat:aizt3bnmibcc7kv67h6qf7ts7q</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200910093021/https://arxiv.org/pdf/1702.03222v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e9/46/e946a808e8293557378e5a105d5e843d7f0dd3a6.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1702.03222v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Health Care Transitions and the Aging Population

Dianne Morrow Ross, Bernardo Ramirez, Timothy Rotarius, Aaron Liberman
<span title="">2011</span> <i title="Ovid Technologies (Wolters Kluwer Health)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/rpfjdk2pezclbilbdqvt5r3qdu" style="color: black;">The Health Care Manager</a> </i> &nbsp;
A poorly executed transition can result in complications for the patient, duplication of tests and services, discharge delays, increased lengths of stay, early readmissions to the acute care setting, frustration  ...  Path analysis was method of choice for data analysis and AMOS 7.0 was utilized for the measurement model.  ...  contributes to the development of a multi-dimensional framework in a single construct.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1097/hcm.0b013e318216ed89">doi:10.1097/hcm.0b013e318216ed89</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/21537131">pmid:21537131</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/x3goiajc6rcqzeq4minvslopq4">fatcat:x3goiajc6rcqzeq4minvslopq4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200710043840/https://stars.library.ucf.edu/cgi/viewcontent.cgi?article=4779&amp;context=etd" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a9/55/a95514c7794f07153b4a38e28823ea339b1dfcef.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1097/hcm.0b013e318216ed89"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>
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