Determination of Disease from Discharge Summaries
Author
Abstract
Suggested Citation
DOI: 10.1007/s12626-021-00076-7
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Karatzoglou, Alexandros & Smola, Alexandros & Hornik, Kurt & Zeileis, Achim, 2004. "kernlab - An S4 Package for Kernel Methods in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 11(i09).
- Kim, Ji-Hyun, 2009. "Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3735-3745, September.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Shusaku Tsumoto & Tomohiro Kimura & Shoji Hirano, 2022. "Expectation–Maximization (EM) Clustering as a Preprocessing Method for Clinical Pathway Mining," The Review of Socionetwork Strategies, Springer, vol. 16(1), pages 25-52, April.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Tsukioka, Yasutomo & Yanagi, Junya & Takada, Teruko, 2018. "Investor sentiment extracted from internet stock message boards and IPO puzzles," International Review of Economics & Finance, Elsevier, vol. 56(C), pages 205-217.
- Daniel J. Luckett & Eric B. Laber & Samer S. El‐Kamary & Cheng Fan & Ravi Jhaveri & Charles M. Perou & Fatma M. Shebl & Michael R. Kosorok, 2021. "Receiver operating characteristic curves and confidence bands for support vector machines," Biometrics, The International Biometric Society, vol. 77(4), pages 1422-1430, December.
- Grabisch, Michel & Kojadinovic, Ivan & Meyer, Patrick, 2008.
"A review of methods for capacity identification in Choquet integral based multi-attribute utility theory: Applications of the Kappalab R package,"
European Journal of Operational Research, Elsevier, vol. 186(2), pages 766-785, April.
- Michel Grabisch & Ivan Kojadinovic & Patrick Meyer, 2008. "A review of methods for capacity identification in Choquet integral based multi-attribute utility theory: Applications of the Kappalab R package," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00187175, HAL.
- Michel Grabisch & Ivan Kojadinovic & Patrick Meyer, 2008. "A review of methods for capacity identification in Choquet integral based multi-attribute utility theory: Applications of the Kappalab R package," Post-Print halshs-00187175, HAL.
- Bellotti, Anthony & Brigo, Damiano & Gambetti, Paolo & Vrins, Frédéric, 2021.
"Forecasting recovery rates on non-performing loans with machine learning,"
International Journal of Forecasting, Elsevier, vol. 37(1), pages 428-444.
- Bellotti, Anthony & Brigo, Damiano & Gambetti, Paolo & Vrins, Frédéric, 2020. "Forecasting recovery rates on non-performing loans with machine learning," LIDAM Reprints LFIN 2020002, Université catholique de Louvain, Louvain Finance (LFIN).
- Bellotti, Anthony & Brigo, Damiano & Gambetti, Paolo & Vrins, Frédéric, 2020. "Forecasting recovery rates on non-performing loans with machine learning," LIDAM Discussion Papers LFIN 2020002, Université catholique de Louvain, Louvain Finance (LFIN).
- Riza, Lala Septem & Bergmeir, Christoph & Herrera, Francisco & Benítez, José M., 2015. "frbs: Fuzzy Rule-Based Systems for Classification and Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 65(i06).
- Karin Wolffhechel & Amanda C Hahn & Hanne Jarmer & Claire I Fisher & Benedict C Jones & Lisa M DeBruine, 2015. "Testing the Utility of a Data-Driven Approach for Assessing BMI from Face Images," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-10, October.
- Mark G E White & Neil E Bezodis & Jonathon Neville & Huw Summers & Paul Rees, 2022. "Determining jumping performance from a single body-worn accelerometer using machine learning," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-25, February.
- Richard A. Johansen & Molly K. Reif & Christina L. Saltus & Kaytee L. Pokrzywinski, 2024. "A Broadscale Assessment of Sentinel-2 Imagery and the Google Earth Engine for the Nationwide Mapping of Chlorophyll a," Sustainability, MDPI, vol. 16(5), pages 1-17, March.
- Airola, Antti & Pahikkala, Tapio & Waegeman, Willem & De Baets, Bernard & Salakoski, Tapio, 2011. "An experimental comparison of cross-validation techniques for estimating the area under the ROC curve," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1828-1844, April.
- Andrea S Martinez-Vernon & James A Covington & Ramesh P Arasaradnam & Siavash Esfahani & Nicola O’Connell & Ioannis Kyrou & Richard S Savage, 2018. "An improved machine learning pipeline for urinary volatiles disease detection: Diagnosing diabetes," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-20, September.
- Khamma, Thulasi Ram & Zhang, Yuming & Guerrier, Stéphane & Boubekri, Mohamed, 2020. "Generalized additive models: An efficient method for short-term energy prediction in office buildings," Energy, Elsevier, vol. 213(C).
- Madhumita Sahoo & Aman Kasot & Anirban Dhar & Amlanjyoti Kar, 2018. "On Predictability of Groundwater Level in Shallow Wells Using Satellite Observations," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(4), pages 1225-1244, March.
- P. J. Zarco-Tejada & T. Poblete & C. Camino & V. Gonzalez-Dugo & R. Calderon & A. Hornero & R. Hernandez-Clemente & M. Román-Écija & M. P. Velasco-Amo & B. B. Landa & P. S. A. Beck & M. Saponari & D. , 2021. "Divergent abiotic spectral pathways unravel pathogen stress signals across species," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
- Grubinger, Thomas & Zeileis, Achim & Pfeiffer, Karl-Peter, 2014.
"evtree: Evolutionary Learning of Globally Optimal Classification and Regression Trees in R,"
Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i01).
- Thomas Grubinger & Achim Zeileis & Karl-Peter Pfeiffer, 2011. "evtree: Evolutionary Learning of Globally Optimal Classification and Regression Trees in R," Working Papers 2011-20, Faculty of Economics and Statistics, Universität Innsbruck.
- Matthias Schmid & Thomas Hielscher & Thomas Augustin & Olaf Gefeller, 2011. "A Robust Alternative to the Schemper–Henderson Estimator of Prediction Error," Biometrics, The International Biometric Society, vol. 67(2), pages 524-535, June.
- Uwe Ligges & Sebastian Krey, 2011. "Feature clustering for instrument classification," Computational Statistics, Springer, vol. 26(2), pages 279-291, June.
- Arnout Van Messem & Andreas Christmann, 2010. "A review on consistency and robustness properties of support vector machines for heavy-tailed distributions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 4(2), pages 199-220, September.
- Luts, Jan & Ormerod, John T., 2014. "Mean field variational Bayesian inference for support vector machine classification," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 163-176.
- Jacobi Liana & Kwok Chun Fung & Ramírez-Hassan Andrés & Nghiem Nhung, 2024. "Posterior Manifolds over Prior Parameter Regions: Beyond Pointwise Sensitivity Assessments for Posterior Statistics from MCMC Inference," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 28(2), pages 403-434, April.
- David Rios Insua & Roi Naveiro & Victor Gallego, 2020. "Perspectives on Adversarial Classification," Mathematics, MDPI, vol. 8(11), pages 1-21, November.
More about this item
Keywords
Discharge summary; Hospital information system; Classification learning; Random forest;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:trosos:v:15:y:2021:i:1:d:10.1007_s12626-021-00076-7. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.