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A multi-modal machine learning approach towards predicting patient readmission [article]

Somya D Mohanty, Deborah Lekan, Thomas P McCoy, Marjorie Jenkins, Prashanti Manda
<span title="2020-11-20">2020</span> <i title="Cold Spring Harbor Laboratory"> bioRxiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The availability of large volumes of electronic health care records make it possible to develop and deploy automated machine learning models that can predict unplanned readmissions and pinpoint the most  ...  Here, we develop and compare four machine learning models (Random Forest, XGBoost, CatBoost, and Logistic Regression) for predicting 30-day unplanned readmission for patients deemed frail (Age ≥ 50).  ...  Specifically, we conducted a comparison of four parametric and non-parametric machine learning models to identify the best model for predicting readmission in older patients.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/2020.11.20.391904">doi:10.1101/2020.11.20.391904</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nogil26yn5aq5m5r2ds4ujotci">fatcat:nogil26yn5aq5m5r2ds4ujotci</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210427070425/https://www.biorxiv.org/content/biorxiv/early/2020/11/20/2020.11.20.391904.full.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/e4/92e4b4fa5c20fcf8b1951de2404d193445c8a480.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/2020.11.20.391904"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> biorxiv.org </button> </a>

Analysis and prediction of unplanned intensive care unit readmission using recurrent neural networks with long short-term memory

Yu-Wei Lin, Yuqian Zhou, Faraz Faghri, Michael J. Shaw, Roy H. Campbell, Robert Moskovitch
<span title="2019-07-08">2019</span> <i title="Public Library of Science (PLoS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s3gm7274mfe6fcs7e3jterqlri" style="color: black;">PLoS ONE</a> </i> &nbsp;
Unplanned readmission of a hospitalized patient is an indicator of patients' exposure to risk and an avoidable waste of medical resources.  ...  The emergence of machine learning solutions to detect hidden patterns in complex, multi-dimensional datasets provides unparalleled opportunities for developing an efficient discharge decision-making support  ...  First, the scope of some predictive models is limited to a specific disease or treatment rather than a general solution.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0218942">doi:10.1371/journal.pone.0218942</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31283759">pmid:31283759</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6613707/">pmcid:PMC6613707</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3vgahpphinda3ir2jelr5xgs4y">fatcat:3vgahpphinda3ir2jelr5xgs4y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191020144740/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC6613707&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/b8/0f/b80f0d5bb0fde8be65db99d5f4e6505b38502d08.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0218942"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> plos.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6613707" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Using Random Forests with Asymmetric Costs to Predict Hospital Readmissions [article]

Justin Bleich, Brian Cole, Adam Kapelner, Charles A. Baillie, Rohit Gupta, Asaf Hanish, Erwin Calgua, Craig A. Umscheid, Richard Berk
<span title="2021-03-24">2021</span> <i title="Cold Spring Harbor Laboratory"> medRxiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Results: We developed a machine learning-based model using random forests with a 5:1 relative cost ratio for 30-day all-cause readmissions that achieves a sensitivity of 65% and specificity of 71% on validation  ...  We describe the use of a data-driven approach that relies on machine learning algorithms to predict readmission at the time of discharge.  ...  Fuchs, and Mark E. Mikkelsen for their input in developing the model examined in this study, their assistance in data acquisition, and for advice regarding data analysis.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/2021.03.15.21253416">doi:10.1101/2021.03.15.21253416</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/huvoju6cbrajfn4akbm3itzvau">fatcat:huvoju6cbrajfn4akbm3itzvau</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210715145209/https://www.medrxiv.org/content/medrxiv/early/2021/03/24/2021.03.15.21253416.full.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/88/b9/88b90a3f60584b95ee3f5277ff3a78dda081875a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/2021.03.15.21253416"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> medrxiv.org </button> </a>

Machine Learning Algorithms for COPD patients' readmission prediction -A Data Analytic Approach

Israa Mohamed, Mostafa M. Fouda, Khalid M. Hosny
<span title="">2022</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
In this study, we aim at predicting the re-admission of COPD (Chronic Obstructive Pulmonary Disease) patients through the deployment of machine learning algorithms.  ...  Area Under Curve (AUC) and ACCuracy (ACC) were considered as the main criteria for evaluating models' prediction power in each time frame.  ...  Acknowledgments The authors would like to thank the medical and management team in GH for their support in data collection and preparation.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2022.3148600">doi:10.1109/access.2022.3148600</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/iusstuvsabe7xf62zhvfr62b7i">fatcat:iusstuvsabe7xf62zhvfr62b7i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220212104120/https://ieeexplore.ieee.org/ielx7/6287639/6514899/09701336.pdf?tp=&amp;arnumber=9701336&amp;isnumber=6514899&amp;ref=" 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/16/9e/169e96aadcd85d2c96c036f552553ddc45571264.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2022.3148600"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

HealtheDataLab – a cloud computing solution for data science and advanced analytics in healthcare with application to predicting multi-center pediatric readmissions

Louis Ehwerhemuepha, Gary Gasperino, Nathaniel Bischoff, Sharief Taraman, Anthony Chang, William Feaster
<span title="2020-06-19">2020</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/bnylrk2y7bfnrn7u2f2vjkx7ta" style="color: black;">BMC Medical Informatics and Decision Making</a> </i> &nbsp;
We validated the platform by replicating and improving on a previous single pediatric institution study/model on readmission and developing a multi-center model of all-cause readmission for pediatric-age  ...  We showed the distribution in model performance across hospitals and identified a set of novel variables under previous resource utilization and generic medications that may be used to improve existing  ...  planned and unplanned readmission model (Model M2), and a multi-center MLP model of readmission (Model M3:MLP) using Spark Machine Learning Library version 2.3.0) at their default value.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12911-020-01153-7">doi:10.1186/s12911-020-01153-7</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/32560653">pmid:32560653</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ccoz2ujxnnebtlsw4sr3owqtl4">fatcat:ccoz2ujxnnebtlsw4sr3owqtl4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210715004615/https://escholarship.org/content/qt08j002s7/qt08j002s7.pdf?t=qpn0c9" 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/95/fc/95fc89ad9f72db4b1042dc8c508e43a9e0eff340.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12911-020-01153-7"> <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>

Blending Knowledge in Deep Recurrent Networks for Adverse Event Prediction at Hospital Discharge

Prithwish Chakraborty, James Codella, Piyush Madan, Ying Li, Hu Huang, Yoonyoung Park, Chao Yan, Ziqi Zhang, Cheng Gao, Steve Nyemba, Xu Min, Sanjib Basak (+13 others)
<span title="2021-05-17">2021</span> <i title="American Medical Informatics Association"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/g6q27b56yzeupjryv6lby2r2pa" style="color: black;">AMIA Annual Symposium Proceedings</a> </i> &nbsp;
Deep learning architectures have an extremely high-capacity for modeling complex data in a wide variety of domains.  ...  We conduct extensive experiments on a large claims dataset and show that the blended method outperforms the standard machine learning approaches.  ...  We report the comparison of model performance on the 10% hold-out test fold for readmission prediction in Table 3a .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34457127">pmid:34457127</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8378633/">pmcid:PMC8378633</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xjus66ptlfetjke6fkkavjnwhm">fatcat:xjus66ptlfetjke6fkkavjnwhm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210914034639/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC8378633&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/b1/3d/b13d3ea15df10482695faa260a84d923d286445c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8378633" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Evaluating Patient Readmission Risk: A Predictive Analytics Approach [article]

Avishek Choudhury, Dr. Christopher M Greene
<span title="2018-12-11">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Our study considers patient readmission risk as the objective for optimization and develops a useful risk prediction model to address unplanned readmissions.  ...  With the emergence of the Hospital Readmission Reduction Program of the Center for Medicare and Medicaid Services on October 1, 2012, forecasting unplanned patient readmission risk became crucial to the  ...  Turgeman and May (2016) developed a predictive model for hospital readmissions using a boosted C5.0 tree and Support Vector Machine as base and secondary classifiers respectively.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1812.11028v1">arXiv:1812.11028v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2mcqlrx4fndltpz45girschtdi">fatcat:2mcqlrx4fndltpz45girschtdi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200929151133/https://arxiv.org/vc/arxiv/papers/1812/1812.11028v1.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/fe/12/fe12084a0ee44b91d1ecaf469bac18f6d7de255f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1812.11028v1" 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>

Development of Prediction Models for Unplanned Hospital Readmission within 30 Days Based on Common Data Model: A Feasibility Study

Sooyoung Yoo, Jinwook Choi, Borim Ryu, Seok Kim
<span title="2021-09-28">2021</span> <i title="Georg Thieme Verlag KG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7rg5h5xpe5fghfe7wwstoiuubm" style="color: black;">Methods of Information in Medicine</a> </i> &nbsp;
Objectives The purpose of this study was to develop and validate prediction models for all-cause unplanned hospital readmissions within 30 days of discharge, based on a common data model (CDM), which can  ...  Conclusions This study showed that readmission prediction models developed using machine-learning techniques and CDM can be a useful tool to compare two hospitals in terms of patient-data features.  ...  Conflict of Interest None declared.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1055/s-0041-1735166">doi:10.1055/s-0041-1735166</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34583416">pmid:34583416</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8714301/">pmcid:PMC8714301</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/67gjvwyqpzd3pcdbvmck7vd6iy">fatcat:67gjvwyqpzd3pcdbvmck7vd6iy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210929051728/https://www.thieme-connect.de/products/ejournals/pdf/10.1055/s-0041-1735166.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/b3/04/b304ff8df3eb00d7acf18a5cd7e80a45b7fc1635.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1055/s-0041-1735166"> <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> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8714301" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Identifying Children at Readmission Risk: At-Admission Versus Traditional At-Discharge Readmission Prediction Model

Hasan Symum, José Zayas-Castro
<span title="2021-10-07">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7happfjl2zh5fbym2owb7p7kni" style="color: black;">Healthcare</a> </i> &nbsp;
In this study, we proposed a novel pediatric readmission prediction model at the time of hospital admission which can improve the high-risk patient selection process.  ...  However, pediatric readmission studies are thus far limited to the development of the prediction model after hospital discharges.  ...  Out of the total of 87,865 hospital visits, We developed and investigated several established machine learning algorithms for each model cohort to identify children at high risk for 30-day unplanned readmission  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/healthcare9101334">doi:10.3390/healthcare9101334</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34683014">pmid:34683014</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2lglirwmajep5as62bmgi32aru">fatcat:2lglirwmajep5as62bmgi32aru</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211012081933/https://mdpi-res.com/d_attachment/healthcare/healthcare-09-01334/article_deploy/healthcare-09-01334-v2.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/e7/bd/e7bd275eaac3a6624c05b124051059520622838f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/healthcare9101334"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

Machine Learning Prediction of Postoperative Emergency Department Hospital Readmission

Velibor V. Mišic´, Eilon Gabel, Ira Hofer, Kumar Rajaram, Aman Mahajan
<span title="2020-01-30">2020</span> <i title="Ovid Technologies (Wolters Kluwer Health)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/cjmzhtkkdjgjphz5zuzahenz2a" style="color: black;">Anesthesiology</a> </i> &nbsp;
A machine learning approach to predicting postoperative readmission can produce hospital-specific models for accurately predicting 30-day readmissions via the emergency department.  ...  Unplanned hospital readmissions are a focus of quality improvement, national benchmarking, and payment incentives in the United StatesThe accuracy of commonly used peer-reviewed readmission prediction  ...  Our primary hypothesis is that machine learning methods are capable of producing hospital-specific readmission prediction models with excellent discrimination.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1097/aln.0000000000003140">doi:10.1097/aln.0000000000003140</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/32011336">pmid:32011336</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jgl3j6dcgfg2nlyuqykyzfwqii">fatcat:jgl3j6dcgfg2nlyuqykyzfwqii</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210831213830/https://watermark.silverchair.com/20200500_0-00013.pdf?token=AQECAHi208BE49Ooan9kkhW_Ercy7Dm3ZL_9Cf3qfKAc485ysgAAAuUwggLhBgkqhkiG9w0BBwagggLSMIICzgIBADCCAscGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQM0w5tG4x_dPRaRytZAgEQgIICmBOKXBlU_CsyayPu-LZlH8n0H9KRCCr3Zj9pj1vMUP4WvATaAF_sSkMrE-vtemwxOSCxPJGR9Zip6HGW3Qyl_664N_o2UB4Pa7vBgBzfD1l6HjaxOsx3NtXepBbw88fQ_LsUOduTfOZ9gcHasiepUv5e62h_ZJQ95WD5CNPym1T0Jq-x5ocZCahyps4WbJ8Csb_QB18Kt0SCqJfsXqz6TB5pVnB_5RafyoczZ2szll3Vxo-35wM_ZslB2kS3_eol227s8jkRru35HXun-VgOuoqHOa-G-UcqAILP-Q0WMIrOrERQ406k4zcyO3uPbC-uVOjnHl8pX-fviDllJUeTQ-qUL668ixYqMLcasiP4IzaW6NgxKqCb_qo1BMfYHbWs1UnWNp06CSAKpJ-JCMSdk6HqHX6L8vZebPgXEp2w-HXGNHAPg3LXnD6s9uo80-_lvTw9QGDq2rd3Nwj6_QkrEKojefaPOEoiWt0sBcNDy6E987iwIlMwTr8LakRi7GblNkxRQ2GzTpswTvLz_7vOhe9rSM1oloAzxiDaeeTbv8rSymZY6sUaXCYJnsGFLTUTSc_OthCT7EDV8HiZ9k3rnemPJUPpIhRrGdRp2vHZiaUpZ2SyHLgiv_efBeEixeYBkmv_fKWEFTiPQggcKIEN5n7Wml_7FJ4Pdnvg-2WFd5v_P4drfJNDPfq6URbi9Qb4I77xoxTfIg1NKmEuTsJicSkMNJlfbwKvBPqf9pKGJ0kDbx5vTjezO_lSN5Z9c7hyAGeLH1yY1iIyCXQPN2-dCj6bnSlRLSF99mOHNRzTp2ZJKUSkAWkhdAwdEjFqN98BtyZbORCc7CGQ79xvTdx-K1Ga8Y2kDqsMc7PGbweYsfuXjBtkLtInH-c" 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/65/5a/655af04eddf1ba9a378a2695efa9a8115ef94b1b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1097/aln.0000000000003140"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Evaluating Patient Readmission Risk: A Predictive Analytics Approach

Avishek Choudhury, Dr. Christopher M. Greene
<span title="2018-04-01">2018</span> <i title="Science Publications"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/56jvtobdgzeibi4xy2nmkyuoya" style="color: black;">American Journal of Engineering and Applied Sciences</a> </i> &nbsp;
Our study considers patient readmission risk as the objective for optimization and develops a useful risk prediction model to address unplanned readmissions.  ...  With the emergence of the Hospital Readmission Reduction Program of the Center for Medicare and Medicaid Services on October 1, 2012, forecasting unplanned patient readmission risk became crucial to the  ...  Turgeman and May ( 2016 ) developed a predictive model for hospital readmissions using a boosted C5.0 tree and Support Vector Machine as base and secondary classifiers respectively.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3844/ajeassp.2018.1320.1331">doi:10.3844/ajeassp.2018.1320.1331</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/m4cmf4q3ofbohhpllq7iu7dl4e">fatcat:m4cmf4q3ofbohhpllq7iu7dl4e</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190427221745/https://thescipub.com/pdf/10.3844/ajeassp.2018.1320.1331" 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/0f/77/0f77003c72d868b77504cc96ccf88da2f18fb614.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3844/ajeassp.2018.1320.1331"> <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>

Predicting Diabetic Readmission Rates: Moving Beyond Hba1c

Damian Mingle
<span title="2017-08-23">2017</span> <i title="Juniper Publishers"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/fqve6hnrwnew5nuh2olo4rku6e" style="color: black;">Current Trends in Biomedical Engineering &amp; Biosciences</a> </i> &nbsp;
It also investigates the hypothesis that using machine learning on a wide feature, making use of model diversity, and blending prediction will improve the accuracy of readmission risk predictions compared  ...  Being able to risk identify patients facing a high likelihood of unplanned hospital readmission in the next 30-days could allow for further investigation and possibly prevent the readmission.  ...  Acknowledgment The authors thank Cerner Corp. and the VCU cent for Clinical and Translation Research for the data used in the study.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.19080/ctbeb.2017.07.555715">doi:10.19080/ctbeb.2017.07.555715</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cka56dtvzjdnxpnhizf7la6qbm">fatcat:cka56dtvzjdnxpnhizf7la6qbm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200505151324/https://juniperpublishers.com/ctbeb/pdf/CTBEB.MS.ID.555715.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/8a/c4/8ac486230ebc2f11e8a61b6b292010234e1121f5.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.19080/ctbeb.2017.07.555715"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

External validation of EPIC's Risk of Unplanned Readmission model, the LACE+ index and SQLape as predictors of unplanned hospital readmissions: A monocentric, retrospective, diagnostic cohort study in Switzerland

Aljoscha Benjamin Hwang, Guido Schuepfer, Mario Pietrini, Stefan Boes, Michele Provenzano
<span title="2021-11-12">2021</span> <i title="Public Library of Science (PLoS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s3gm7274mfe6fcs7e3jterqlri" style="color: black;">PLoS ONE</a> </i> &nbsp;
It is only relatively recently that strategies were developed with the specific aim of reducing unplanned readmissions using prediction models to identify patients at risk.  ...  EPIC's Risk of Unplanned Readmission model promises superior performance. However, it has only been validated for the US setting.  ...  Acknowledgments The authors would like to thank Johannes Rogger, clinical pharmacist and EPIC Willow Analyst, for the development of Swiss specific therapeutic subgroups of the Anatomical Therapeutic Chemical  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0258338">doi:10.1371/journal.pone.0258338</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34767558">pmid:34767558</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8589185/">pmcid:PMC8589185</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xee4y3qslvac3gtjuplyaqrwdi">fatcat:xee4y3qslvac3gtjuplyaqrwdi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220115023555/https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0258338&amp;type=printable" 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/75/33/7533ca2c6f8cacc5dc1f6539ba870042f3807b10.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0258338"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> plos.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8589185" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Prediction of Patient Readmission via Machine Learning Algorithms

<span title="2020-03-30">2020</span> <i title="Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/3sfifsouvjgadp4gfj54u3z2ku" style="color: black;">International journal of recent technology and engineering</a> </i> &nbsp;
Despite there is a plainly visible shortage on this topic, this paper seeks to spot most of the studies related to predict the probability of hospital readmission by the usage of machine learning techniques  ...  Specifically, we explore the different techniques used in a medical area under the machine learning research field.  ...  In future work, we intend to execute all these models of machine learning in the prediction of hospital readmission and assess which is the best technique for this prediction.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.35940/ijrte.f7770.038620">doi:10.35940/ijrte.f7770.038620</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6a6bwuywirceviiebn65zgox7a">fatcat:6a6bwuywirceviiebn65zgox7a</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200504105203/https://www.ijrte.org/wp-content/uploads/papers/v8i6/F7770038620.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/01/92/01920a02601f110f34c967cabb6955daf0957e6e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.35940/ijrte.f7770.038620"> <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>

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
<span title="2020-12-14">2020</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/bnylrk2y7bfnrn7u2f2vjkx7ta" style="color: black;">BMC Medical Informatics and Decision Making</a> </i> &nbsp;
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.  ...  Conclusion It is evident that our model could effectively predict the risk of 30-day all cause hospital readmissions for AMI patients and provide decision support for the administration.  ...  Conclusions This study proposes a stacking-based model to predict the risk of 30-day unplanned all-cause hospital readmissions of patients with AMI based on clinical data.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12911-020-01358-w">doi:10.1186/s12911-020-01358-w</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33317534">pmid:33317534</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ixfw4t6e4fgmbfgbzlwldrxmx4">fatcat:ixfw4t6e4fgmbfgbzlwldrxmx4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201218073147/https://bmcmedinformdecismak.biomedcentral.com/track/pdf/10.1186/s12911-020-01358-w.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/47/0e/470e40c9e6489922dae3195d91771b81d7dd42ac.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12911-020-01358-w"> <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>
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