Classifying highly imbalanced ICU data View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2012-11-07

AUTHORS

Yazan F. Roumani, Jerrold H. May, David P. Strum, Luis G. Vargas

ABSTRACT

Highly imbalanced data sets are those where the class of interest is rare. In this paper, we compare the performance of several common data mining methods, logistic regression, discriminant analysis, Classification and Regression Tree (CART) models, C5, and Support Vector Machines (SVM) in predicting the discharge status (alive or deceased, with “deceased” being the class of interest) of patients from an Intensive Care Unit (ICU). Using a variety of misclassification cost ratio (MCR) values and using specificity, recall, precision, the F-measure, and confusion entropy (CEN) as criteria for evaluating each method’s performance, C5 and SVM performed better than the other methods. At a MCR of 100, C5 had the highest recall and SVM the highest specificity and lowest CEN. We also used Hand’s measure to compare the five methods. According to Hand’s measure, logistic regression performed the best. This article makes several contributions. We show how the use of MCR for analyzing imbalanced medical data significantly improves the method’s classification performance. We also found that the F-measure and precision did not improve as the MCR was increased. More... »

PAGES

119-128

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10729-012-9216-9

DOI

http://dx.doi.org/10.1007/s10729-012-9216-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1026101500

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/23132123


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1117", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Public Health and Health Services", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Data Collection", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Data Mining", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Decision Trees", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Discriminant Analysis", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Female", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Hospital Mortality", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Intensive Care Units", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Logistic Models", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Male", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Middle Aged", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Models, Statistical", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Patient Discharge", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Support Vector Machine", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "United States", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Joseph M. Katz Graduate School of Business, University of Pittsburgh, 15260, Pittsburgh, PA, USA", 
          "id": "http://www.grid.ac/institutes/grid.21925.3d", 
          "name": [
            "Joseph M. Katz Graduate School of Business, University of Pittsburgh, 15260, Pittsburgh, PA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Roumani", 
        "givenName": "Yazan F.", 
        "id": "sg:person.01070431547.49", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01070431547.49"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Joseph M. Katz Graduate School of Business, University of Pittsburgh, 15260, Pittsburgh, PA, USA", 
          "id": "http://www.grid.ac/institutes/grid.21925.3d", 
          "name": [
            "Joseph M. Katz Graduate School of Business, University of Pittsburgh, 15260, Pittsburgh, PA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "May", 
        "givenName": "Jerrold H.", 
        "id": "sg:person.013113501215.34", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013113501215.34"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Anesthesiology and Critical Care, University of Pennsylvania, 19104, Philadelphia, PA, USA", 
          "id": "http://www.grid.ac/institutes/grid.25879.31", 
          "name": [
            "Department of Anesthesiology and Critical Care, University of Pennsylvania, 19104, Philadelphia, PA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Strum", 
        "givenName": "David P.", 
        "id": "sg:person.0774605063.24", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0774605063.24"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Joseph M. Katz Graduate School of Business, University of Pittsburgh, 15260, Pittsburgh, PA, USA", 
          "id": "http://www.grid.ac/institutes/grid.21925.3d", 
          "name": [
            "Joseph M. Katz Graduate School of Business, University of Pittsburgh, 15260, Pittsburgh, PA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Vargas", 
        "givenName": "Luis G.", 
        "id": "sg:person.013723757207.56", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013723757207.56"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/978-0-387-21606-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022356842", 
          "https://doi.org/10.1007/978-0-387-21606-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-540-77046-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044564531", 
          "https://doi.org/10.1007/978-3-540-77046-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10994-009-5119-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047736115", 
          "https://doi.org/10.1007/s10994-009-5119-5"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2012-11-07", 
    "datePublishedReg": "2012-11-07", 
    "description": "Highly imbalanced data sets are those where the class of interest is rare. In this paper, we compare the performance of several common data mining methods, logistic regression, discriminant analysis, Classification and Regression Tree (CART) models, C5, and Support Vector Machines (SVM) in predicting the discharge status (alive or deceased, with \u201cdeceased\u201d being the class of interest) of patients from an Intensive Care Unit (ICU). Using a variety of misclassification cost ratio (MCR) values and using specificity, recall, precision, the F-measure, and confusion entropy (CEN) as criteria for evaluating each method\u2019s performance, C5 and SVM performed better than the other methods. At a MCR of 100, C5 had the highest recall and SVM the highest specificity and lowest CEN. We also used Hand\u2019s measure to compare the five methods. According to Hand\u2019s measure, logistic regression performed the best. This article makes several contributions. We show how the use of MCR for analyzing imbalanced medical data significantly improves the method\u2019s classification performance. We also found that the F-measure and precision did not improve as the MCR was increased.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s10729-012-9216-9", 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1119244", 
        "issn": [
          "1386-9620", 
          "1572-9389"
        ], 
        "name": "Health Care Management Science", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "2", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "16"
      }
    ], 
    "keywords": [
      "support vector machine", 
      "F-measure", 
      "classification performance", 
      "common data mining methods", 
      "data mining methods", 
      "method's classification performance", 
      "class of interest", 
      "mining methods", 
      "imbalanced medical data", 
      "medical data", 
      "vector machine", 
      "high recall", 
      "ICU data", 
      "data sets", 
      "tree model", 
      "method performance", 
      "Confusion Entropy", 
      "regression tree model", 
      "performance", 
      "recall", 
      "machine", 
      "discriminant analysis", 
      "precision", 
      "classification", 
      "set", 
      "method", 
      "data", 
      "model", 
      "entropy", 
      "class", 
      "logistic regression", 
      "measures", 
      "interest", 
      "regression", 
      "variety", 
      "use", 
      "units", 
      "article", 
      "intensive care unit", 
      "contribution", 
      "criteria", 
      "analysis", 
      "ratio values", 
      "hand measures", 
      "care unit", 
      "values", 
      "discharge status", 
      "MCR", 
      "high specificity", 
      "specificity", 
      "patients", 
      "status", 
      "C5", 
      "use of MCR", 
      "paper"
    ], 
    "name": "Classifying highly imbalanced ICU data", 
    "pagination": "119-128", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1026101500"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s10729-012-9216-9"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "23132123"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s10729-012-9216-9", 
      "https://app.dimensions.ai/details/publication/pub.1026101500"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-12-01T06:30", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20221201/entities/gbq_results/article/article_566.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s10729-012-9216-9"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s10729-012-9216-9'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10729-012-9216-9'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10729-012-9216-9'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10729-012-9216-9'


 

This table displays all metadata directly associated to this object as RDF triples.

212 TRIPLES      21 PREDICATES      97 URIs      86 LITERALS      22 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s10729-012-9216-9 schema:about N16ce2dc25d194d5685d31391fe29c09d
2 N298a7d65efc3489398cf7ce0a07eb3b2
3 N31636cb025c34f9b8afd132a7b249f2b
4 N35a2317adcf241478c7e36f87d11dc6e
5 N35e56dd746ce4cf8887235eae6c3d8ee
6 N3943810c72914c8694e69529fa81cca5
7 N48b52864a55d42b4ba3b77d0d602d3af
8 N4b1aa284041844cd8b9912782044a3cc
9 N521f9aeb1c504d81b0c8e0d671ea198a
10 N63fc53352e2547ae8faf074e76af3f42
11 N6f895d460cba449e806f2ffa2180ab67
12 Na0ed378282c0467081f9473f44f29d13
13 Na7ea130a499245f9a06fabf95a16b9ef
14 Nb00a4d3853bb47e2a08bdcb93b89d6c2
15 Ne581019acc004ee6aacf736afa8338a4
16 anzsrc-for:11
17 anzsrc-for:1117
18 schema:author Nd867bee6e3a14f7ea3bf736e43091910
19 schema:citation sg:pub.10.1007/978-0-387-21606-5
20 sg:pub.10.1007/978-3-540-77046-6
21 sg:pub.10.1007/s10994-009-5119-5
22 schema:datePublished 2012-11-07
23 schema:datePublishedReg 2012-11-07
24 schema:description Highly imbalanced data sets are those where the class of interest is rare. In this paper, we compare the performance of several common data mining methods, logistic regression, discriminant analysis, Classification and Regression Tree (CART) models, C5, and Support Vector Machines (SVM) in predicting the discharge status (alive or deceased, with “deceased” being the class of interest) of patients from an Intensive Care Unit (ICU). Using a variety of misclassification cost ratio (MCR) values and using specificity, recall, precision, the F-measure, and confusion entropy (CEN) as criteria for evaluating each method’s performance, C5 and SVM performed better than the other methods. At a MCR of 100, C5 had the highest recall and SVM the highest specificity and lowest CEN. We also used Hand’s measure to compare the five methods. According to Hand’s measure, logistic regression performed the best. This article makes several contributions. We show how the use of MCR for analyzing imbalanced medical data significantly improves the method’s classification performance. We also found that the F-measure and precision did not improve as the MCR was increased.
25 schema:genre article
26 schema:isAccessibleForFree false
27 schema:isPartOf N4f1dbad2c1954cefb3f6396027c155aa
28 N93fd7bc589cc43659bf88871e1395985
29 sg:journal.1119244
30 schema:keywords C5
31 Confusion Entropy
32 F-measure
33 ICU data
34 MCR
35 analysis
36 article
37 care unit
38 class
39 class of interest
40 classification
41 classification performance
42 common data mining methods
43 contribution
44 criteria
45 data
46 data mining methods
47 data sets
48 discharge status
49 discriminant analysis
50 entropy
51 hand measures
52 high recall
53 high specificity
54 imbalanced medical data
55 intensive care unit
56 interest
57 logistic regression
58 machine
59 measures
60 medical data
61 method
62 method performance
63 method's classification performance
64 mining methods
65 model
66 paper
67 patients
68 performance
69 precision
70 ratio values
71 recall
72 regression
73 regression tree model
74 set
75 specificity
76 status
77 support vector machine
78 tree model
79 units
80 use
81 use of MCR
82 values
83 variety
84 vector machine
85 schema:name Classifying highly imbalanced ICU data
86 schema:pagination 119-128
87 schema:productId N1432d0081623409597820f73732b0f22
88 N91115a9fa9b0480e8441251c04df9184
89 Nfe4ebc1bf23743baace1fb41027c78d2
90 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026101500
91 https://doi.org/10.1007/s10729-012-9216-9
92 schema:sdDatePublished 2022-12-01T06:30
93 schema:sdLicense https://scigraph.springernature.com/explorer/license/
94 schema:sdPublisher N8a4762fa6eb6498db8f92f71ba88824b
95 schema:url https://doi.org/10.1007/s10729-012-9216-9
96 sgo:license sg:explorer/license/
97 sgo:sdDataset articles
98 rdf:type schema:ScholarlyArticle
99 N1432d0081623409597820f73732b0f22 schema:name pubmed_id
100 schema:value 23132123
101 rdf:type schema:PropertyValue
102 N16ce2dc25d194d5685d31391fe29c09d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
103 schema:name United States
104 rdf:type schema:DefinedTerm
105 N23fcff7a8c2e49b18e598f29e207d5cd rdf:first sg:person.0774605063.24
106 rdf:rest N5cd5cbced47c41ec89fa83ef447b8d3e
107 N298a7d65efc3489398cf7ce0a07eb3b2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
108 schema:name Data Collection
109 rdf:type schema:DefinedTerm
110 N31636cb025c34f9b8afd132a7b249f2b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
111 schema:name Male
112 rdf:type schema:DefinedTerm
113 N35a2317adcf241478c7e36f87d11dc6e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
114 schema:name Models, Statistical
115 rdf:type schema:DefinedTerm
116 N35e56dd746ce4cf8887235eae6c3d8ee schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
117 schema:name Support Vector Machine
118 rdf:type schema:DefinedTerm
119 N3943810c72914c8694e69529fa81cca5 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
120 schema:name Hospital Mortality
121 rdf:type schema:DefinedTerm
122 N48b52864a55d42b4ba3b77d0d602d3af schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
123 schema:name Logistic Models
124 rdf:type schema:DefinedTerm
125 N4b1aa284041844cd8b9912782044a3cc schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
126 schema:name Discriminant Analysis
127 rdf:type schema:DefinedTerm
128 N4f1dbad2c1954cefb3f6396027c155aa schema:volumeNumber 16
129 rdf:type schema:PublicationVolume
130 N521f9aeb1c504d81b0c8e0d671ea198a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
131 schema:name Decision Trees
132 rdf:type schema:DefinedTerm
133 N5cd5cbced47c41ec89fa83ef447b8d3e rdf:first sg:person.013723757207.56
134 rdf:rest rdf:nil
135 N63fc53352e2547ae8faf074e76af3f42 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
136 schema:name Middle Aged
137 rdf:type schema:DefinedTerm
138 N6f895d460cba449e806f2ffa2180ab67 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
139 schema:name Data Mining
140 rdf:type schema:DefinedTerm
141 N82a9175aea8d4bf29f95b257c7a11ab5 rdf:first sg:person.013113501215.34
142 rdf:rest N23fcff7a8c2e49b18e598f29e207d5cd
143 N8a4762fa6eb6498db8f92f71ba88824b schema:name Springer Nature - SN SciGraph project
144 rdf:type schema:Organization
145 N91115a9fa9b0480e8441251c04df9184 schema:name dimensions_id
146 schema:value pub.1026101500
147 rdf:type schema:PropertyValue
148 N93fd7bc589cc43659bf88871e1395985 schema:issueNumber 2
149 rdf:type schema:PublicationIssue
150 Na0ed378282c0467081f9473f44f29d13 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
151 schema:name Humans
152 rdf:type schema:DefinedTerm
153 Na7ea130a499245f9a06fabf95a16b9ef schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
154 schema:name Intensive Care Units
155 rdf:type schema:DefinedTerm
156 Nb00a4d3853bb47e2a08bdcb93b89d6c2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
157 schema:name Patient Discharge
158 rdf:type schema:DefinedTerm
159 Nd867bee6e3a14f7ea3bf736e43091910 rdf:first sg:person.01070431547.49
160 rdf:rest N82a9175aea8d4bf29f95b257c7a11ab5
161 Ne581019acc004ee6aacf736afa8338a4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
162 schema:name Female
163 rdf:type schema:DefinedTerm
164 Nfe4ebc1bf23743baace1fb41027c78d2 schema:name doi
165 schema:value 10.1007/s10729-012-9216-9
166 rdf:type schema:PropertyValue
167 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
168 schema:name Medical and Health Sciences
169 rdf:type schema:DefinedTerm
170 anzsrc-for:1117 schema:inDefinedTermSet anzsrc-for:
171 schema:name Public Health and Health Services
172 rdf:type schema:DefinedTerm
173 sg:journal.1119244 schema:issn 1386-9620
174 1572-9389
175 schema:name Health Care Management Science
176 schema:publisher Springer Nature
177 rdf:type schema:Periodical
178 sg:person.01070431547.49 schema:affiliation grid-institutes:grid.21925.3d
179 schema:familyName Roumani
180 schema:givenName Yazan F.
181 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01070431547.49
182 rdf:type schema:Person
183 sg:person.013113501215.34 schema:affiliation grid-institutes:grid.21925.3d
184 schema:familyName May
185 schema:givenName Jerrold H.
186 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013113501215.34
187 rdf:type schema:Person
188 sg:person.013723757207.56 schema:affiliation grid-institutes:grid.21925.3d
189 schema:familyName Vargas
190 schema:givenName Luis G.
191 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013723757207.56
192 rdf:type schema:Person
193 sg:person.0774605063.24 schema:affiliation grid-institutes:grid.25879.31
194 schema:familyName Strum
195 schema:givenName David P.
196 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0774605063.24
197 rdf:type schema:Person
198 sg:pub.10.1007/978-0-387-21606-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022356842
199 https://doi.org/10.1007/978-0-387-21606-5
200 rdf:type schema:CreativeWork
201 sg:pub.10.1007/978-3-540-77046-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044564531
202 https://doi.org/10.1007/978-3-540-77046-6
203 rdf:type schema:CreativeWork
204 sg:pub.10.1007/s10994-009-5119-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047736115
205 https://doi.org/10.1007/s10994-009-5119-5
206 rdf:type schema:CreativeWork
207 grid-institutes:grid.21925.3d schema:alternateName Joseph M. Katz Graduate School of Business, University of Pittsburgh, 15260, Pittsburgh, PA, USA
208 schema:name Joseph M. Katz Graduate School of Business, University of Pittsburgh, 15260, Pittsburgh, PA, USA
209 rdf:type schema:Organization
210 grid-institutes:grid.25879.31 schema:alternateName Department of Anesthesiology and Critical Care, University of Pennsylvania, 19104, Philadelphia, PA, USA
211 schema:name Department of Anesthesiology and Critical Care, University of Pennsylvania, 19104, Philadelphia, PA, USA
212 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...