Using Intelligence Techniques to Predict Postoperative Morbidity of Endovascular Aneurysm Repair View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2011

AUTHORS

Nan-Chen Hsieh , Jui-Fa Chen , Kuo-Chen Lee , Hsin-Che Tsai

ABSTRACT

Endovascular aneurysm repair (EVAR) is an advanced minimally invasive surgical technology that is helpful for reducing patients’ recovery time, postoperative mortality and morbidity. This study proposes an ensemble model to predict postoperative morbidity after EVAR. The ensemble model was developed using a training set of consecutive patients who underwent EVAR between 2000 and 2009. All data required for prediction modeling, including patient demographics, preoperative, co-morbidities, and complication as outcome variables, was collected prospectively and entered into a clinical database. A discretization approach was used to categorize numerical values into informative feature space. The research outcomes consisted of an ensemble model to predict postoperative morbidity, the occurrence of postoperative complications prospectively recorded, and the causal-effect decision rules. The probabilities of complication calculated by the model were compared to the actual occurrence of complications and a receiver operating characteristic (ROC) curve was used to evaluate the accuracy of postoperative morbidity prediction. In this series, the ensemble of Bayesian network (BN), artificial neural network (ANN) and support vector machine (SVM) models offered satisfactory performance in predicting postoperative morbidity after EVAR. More... »

PAGES

197-206

Book

TITLE

Intelligent Information and Database Systems

ISBN

978-3-642-20038-0
978-3-642-20039-7

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-20039-7_20

DOI

http://dx.doi.org/10.1007/978-3-642-20039-7_20

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1007769709


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/2008/1103", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Clinical Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "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"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "National Taipei University of Nursing and Health Science", 
          "id": "https://www.grid.ac/institutes/grid.412146.4", 
          "name": [
            "Department of Information Management, National Taipei University of Nursing and Health Sciences, Taiwan, Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hsieh", 
        "givenName": "Nan-Chen", 
        "id": "sg:person.012734774203.51", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012734774203.51"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tamkang University", 
          "id": "https://www.grid.ac/institutes/grid.264580.d", 
          "name": [
            "Department of Computer Science and Information Engineering, Tamkang University, Taiwan, Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chen", 
        "givenName": "Jui-Fa", 
        "id": "sg:person.014111764111.18", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014111764111.18"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Cheng Hsin General Hospital", 
          "id": "https://www.grid.ac/institutes/grid.413846.c", 
          "name": [
            "Division of Cardiovascular Surgery, Department of Surgery Heart Center, Cheng-Hsin General Hospital, Taiwan, Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lee", 
        "givenName": "Kuo-Chen", 
        "id": "sg:person.01044577305.50", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01044577305.50"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tamkang University", 
          "id": "https://www.grid.ac/institutes/grid.264580.d", 
          "name": [
            "Department of Computer Science and Information Engineering, Tamkang University, Taiwan, Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tsai", 
        "givenName": "Hsin-Che", 
        "id": "sg:person.010474006456.80", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010474006456.80"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/j.jvs.2006.10.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006102660"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jbi.2007.06.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018735708"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.artmed.2007.04.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026754255"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neunet.2005.10.007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027340947"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jbi.2007.07.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031386906"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.eswa.2006.09.017", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037287183"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ejvs.2008.03.007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042033992"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ejvs.2007.12.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043666564"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0967-2109(02)00081-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046387353"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0967-2109(02)00081-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046387353"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ijmedinf.2006.11.006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047293305"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0195-668x(02)00799-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1054625284"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tkde.2003.1245283", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061661217"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2011", 
    "datePublishedReg": "2011-01-01", 
    "description": "Endovascular aneurysm repair (EVAR) is an advanced minimally invasive surgical technology that is helpful for reducing patients\u2019 recovery time, postoperative mortality and morbidity. This study proposes an ensemble model to predict postoperative morbidity after EVAR. The ensemble model was developed using a training set of consecutive patients who underwent EVAR between 2000 and 2009. All data required for prediction modeling, including patient demographics, preoperative, co-morbidities, and complication as outcome variables, was collected prospectively and entered into a clinical database. A discretization approach was used to categorize numerical values into informative feature space. The research outcomes consisted of an ensemble model to predict postoperative morbidity, the occurrence of postoperative complications prospectively recorded, and the causal-effect decision rules. The probabilities of complication calculated by the model were compared to the actual occurrence of complications and a receiver operating characteristic (ROC) curve was used to evaluate the accuracy of postoperative morbidity prediction. In this series, the ensemble of Bayesian network (BN), artificial neural network (ANN) and support vector machine (SVM) models offered satisfactory performance in predicting postoperative morbidity after EVAR.", 
    "editor": [
      {
        "familyName": "Nguyen", 
        "givenName": "Ngoc Thanh", 
        "type": "Person"
      }, 
      {
        "familyName": "Kim", 
        "givenName": "Chong-Gun", 
        "type": "Person"
      }, 
      {
        "familyName": "Janiak", 
        "givenName": "Adam", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-642-20039-7_20", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-642-20038-0", 
        "978-3-642-20039-7"
      ], 
      "name": "Intelligent Information and Database Systems", 
      "type": "Book"
    }, 
    "name": "Using Intelligence Techniques to Predict Postoperative Morbidity of Endovascular Aneurysm Repair", 
    "pagination": "197-206", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1007769709"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-642-20039-7_20"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "2502735d835186bc0e358afb4d12425da32166fbbf748a3085599c38e814fd73"
        ]
      }
    ], 
    "publisher": {
      "location": "Berlin, Heidelberg", 
      "name": "Springer Berlin Heidelberg", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-642-20039-7_20", 
      "https://app.dimensions.ai/details/publication/pub.1007769709"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-16T08:41", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000366_0000000366/records_112035_00000000.jsonl", 
    "type": "Chapter", 
    "url": "https://link.springer.com/10.1007%2F978-3-642-20039-7_20"
  }
]
 

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.10.1007/978-3-642-20039-7_20'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-20039-7_20'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-20039-7_20'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-20039-7_20'


 

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

138 TRIPLES      23 PREDICATES      39 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-642-20039-7_20 schema:about anzsrc-for:11
2 anzsrc-for:1103
3 schema:author N6a1e1a99b7db42c88b7afd904f6bf4c9
4 schema:citation https://doi.org/10.1016/j.artmed.2007.04.005
5 https://doi.org/10.1016/j.ejvs.2007.12.003
6 https://doi.org/10.1016/j.ejvs.2008.03.007
7 https://doi.org/10.1016/j.eswa.2006.09.017
8 https://doi.org/10.1016/j.ijmedinf.2006.11.006
9 https://doi.org/10.1016/j.jbi.2007.06.001
10 https://doi.org/10.1016/j.jbi.2007.07.003
11 https://doi.org/10.1016/j.jvs.2006.10.005
12 https://doi.org/10.1016/j.neunet.2005.10.007
13 https://doi.org/10.1016/s0195-668x(02)00799-6
14 https://doi.org/10.1016/s0967-2109(02)00081-9
15 https://doi.org/10.1109/tkde.2003.1245283
16 schema:datePublished 2011
17 schema:datePublishedReg 2011-01-01
18 schema:description Endovascular aneurysm repair (EVAR) is an advanced minimally invasive surgical technology that is helpful for reducing patients’ recovery time, postoperative mortality and morbidity. This study proposes an ensemble model to predict postoperative morbidity after EVAR. The ensemble model was developed using a training set of consecutive patients who underwent EVAR between 2000 and 2009. All data required for prediction modeling, including patient demographics, preoperative, co-morbidities, and complication as outcome variables, was collected prospectively and entered into a clinical database. A discretization approach was used to categorize numerical values into informative feature space. The research outcomes consisted of an ensemble model to predict postoperative morbidity, the occurrence of postoperative complications prospectively recorded, and the causal-effect decision rules. The probabilities of complication calculated by the model were compared to the actual occurrence of complications and a receiver operating characteristic (ROC) curve was used to evaluate the accuracy of postoperative morbidity prediction. In this series, the ensemble of Bayesian network (BN), artificial neural network (ANN) and support vector machine (SVM) models offered satisfactory performance in predicting postoperative morbidity after EVAR.
19 schema:editor Nfee46c23b68a45a08c7ab2411b566264
20 schema:genre chapter
21 schema:inLanguage en
22 schema:isAccessibleForFree false
23 schema:isPartOf N283ac8d8d5b94d5fbd4ed25d48f2419b
24 schema:name Using Intelligence Techniques to Predict Postoperative Morbidity of Endovascular Aneurysm Repair
25 schema:pagination 197-206
26 schema:productId N7c745cf2731a4095a162c890f8712cad
27 Nd7dbb8ea6ebf460d9824253712d8c145
28 Nfca526e4b9dd4314807eda5d47a5c713
29 schema:publisher N4b84518f365d427a866b87ea33fc131a
30 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007769709
31 https://doi.org/10.1007/978-3-642-20039-7_20
32 schema:sdDatePublished 2019-04-16T08:41
33 schema:sdLicense https://scigraph.springernature.com/explorer/license/
34 schema:sdPublisher N5918bf5b7e6b4c6f894e97b096cb058f
35 schema:url https://link.springer.com/10.1007%2F978-3-642-20039-7_20
36 sgo:license sg:explorer/license/
37 sgo:sdDataset chapters
38 rdf:type schema:Chapter
39 N283ac8d8d5b94d5fbd4ed25d48f2419b schema:isbn 978-3-642-20038-0
40 978-3-642-20039-7
41 schema:name Intelligent Information and Database Systems
42 rdf:type schema:Book
43 N306e76c9444c4f199075012eabac67dd schema:familyName Janiak
44 schema:givenName Adam
45 rdf:type schema:Person
46 N4b84518f365d427a866b87ea33fc131a schema:location Berlin, Heidelberg
47 schema:name Springer Berlin Heidelberg
48 rdf:type schema:Organisation
49 N4df10bec91404668a45f72919dfa01a6 rdf:first N306e76c9444c4f199075012eabac67dd
50 rdf:rest rdf:nil
51 N5918bf5b7e6b4c6f894e97b096cb058f schema:name Springer Nature - SN SciGraph project
52 rdf:type schema:Organization
53 N619226091272403d83a2220a4ce35c8a schema:familyName Kim
54 schema:givenName Chong-Gun
55 rdf:type schema:Person
56 N6a1e1a99b7db42c88b7afd904f6bf4c9 rdf:first sg:person.012734774203.51
57 rdf:rest Nbd716fcc524e4a458bb3db4fbde00bdd
58 N709988d59bcf4842828abb236377a821 rdf:first sg:person.010474006456.80
59 rdf:rest rdf:nil
60 N7c745cf2731a4095a162c890f8712cad schema:name doi
61 schema:value 10.1007/978-3-642-20039-7_20
62 rdf:type schema:PropertyValue
63 N843b5e49702a4326b876b03e81682032 rdf:first N619226091272403d83a2220a4ce35c8a
64 rdf:rest N4df10bec91404668a45f72919dfa01a6
65 Na1d4ac078d78488a9b49aaff0047d9cf rdf:first sg:person.01044577305.50
66 rdf:rest N709988d59bcf4842828abb236377a821
67 Nbd716fcc524e4a458bb3db4fbde00bdd rdf:first sg:person.014111764111.18
68 rdf:rest Na1d4ac078d78488a9b49aaff0047d9cf
69 Nd7dbb8ea6ebf460d9824253712d8c145 schema:name readcube_id
70 schema:value 2502735d835186bc0e358afb4d12425da32166fbbf748a3085599c38e814fd73
71 rdf:type schema:PropertyValue
72 Nd8c91237ab764170a871cfd4c2fcf14d schema:familyName Nguyen
73 schema:givenName Ngoc Thanh
74 rdf:type schema:Person
75 Nfca526e4b9dd4314807eda5d47a5c713 schema:name dimensions_id
76 schema:value pub.1007769709
77 rdf:type schema:PropertyValue
78 Nfee46c23b68a45a08c7ab2411b566264 rdf:first Nd8c91237ab764170a871cfd4c2fcf14d
79 rdf:rest N843b5e49702a4326b876b03e81682032
80 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
81 schema:name Medical and Health Sciences
82 rdf:type schema:DefinedTerm
83 anzsrc-for:1103 schema:inDefinedTermSet anzsrc-for:
84 schema:name Clinical Sciences
85 rdf:type schema:DefinedTerm
86 sg:person.01044577305.50 schema:affiliation https://www.grid.ac/institutes/grid.413846.c
87 schema:familyName Lee
88 schema:givenName Kuo-Chen
89 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01044577305.50
90 rdf:type schema:Person
91 sg:person.010474006456.80 schema:affiliation https://www.grid.ac/institutes/grid.264580.d
92 schema:familyName Tsai
93 schema:givenName Hsin-Che
94 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010474006456.80
95 rdf:type schema:Person
96 sg:person.012734774203.51 schema:affiliation https://www.grid.ac/institutes/grid.412146.4
97 schema:familyName Hsieh
98 schema:givenName Nan-Chen
99 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012734774203.51
100 rdf:type schema:Person
101 sg:person.014111764111.18 schema:affiliation https://www.grid.ac/institutes/grid.264580.d
102 schema:familyName Chen
103 schema:givenName Jui-Fa
104 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014111764111.18
105 rdf:type schema:Person
106 https://doi.org/10.1016/j.artmed.2007.04.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026754255
107 rdf:type schema:CreativeWork
108 https://doi.org/10.1016/j.ejvs.2007.12.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043666564
109 rdf:type schema:CreativeWork
110 https://doi.org/10.1016/j.ejvs.2008.03.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042033992
111 rdf:type schema:CreativeWork
112 https://doi.org/10.1016/j.eswa.2006.09.017 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037287183
113 rdf:type schema:CreativeWork
114 https://doi.org/10.1016/j.ijmedinf.2006.11.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047293305
115 rdf:type schema:CreativeWork
116 https://doi.org/10.1016/j.jbi.2007.06.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018735708
117 rdf:type schema:CreativeWork
118 https://doi.org/10.1016/j.jbi.2007.07.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031386906
119 rdf:type schema:CreativeWork
120 https://doi.org/10.1016/j.jvs.2006.10.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006102660
121 rdf:type schema:CreativeWork
122 https://doi.org/10.1016/j.neunet.2005.10.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027340947
123 rdf:type schema:CreativeWork
124 https://doi.org/10.1016/s0195-668x(02)00799-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1054625284
125 rdf:type schema:CreativeWork
126 https://doi.org/10.1016/s0967-2109(02)00081-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046387353
127 rdf:type schema:CreativeWork
128 https://doi.org/10.1109/tkde.2003.1245283 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061661217
129 rdf:type schema:CreativeWork
130 https://www.grid.ac/institutes/grid.264580.d schema:alternateName Tamkang University
131 schema:name Department of Computer Science and Information Engineering, Tamkang University, Taiwan, Republic of China
132 rdf:type schema:Organization
133 https://www.grid.ac/institutes/grid.412146.4 schema:alternateName National Taipei University of Nursing and Health Science
134 schema:name Department of Information Management, National Taipei University of Nursing and Health Sciences, Taiwan, Republic of China
135 rdf:type schema:Organization
136 https://www.grid.ac/institutes/grid.413846.c schema:alternateName Cheng Hsin General Hospital
137 schema:name Division of Cardiovascular Surgery, Department of Surgery Heart Center, Cheng-Hsin General Hospital, Taiwan, Republic of China
138 rdf:type schema:Organization
 




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


...