Mining Health Social Media with Sentiment Analysis View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2016-11

AUTHORS

Fu-Chen Yang, Anthony J.T. Lee, Sz-Chen Kuo

ABSTRACT

With the rapid development of the Internet, more and more users utilize health communities (known as forums) to find health-related information, share their medical stories and experiences, or interact with other people in the communities. In this paper, we propose a framework to analyze the user-generated contents in a health community. The proposed framework contains three phases. First, we extract medical terms, including conditions, symptoms, treatments, effectiveness and side effects to form a virtual document for each question in the community. Next, we modify Latent Dirichlet Allocation (LDA) by adding a weighted scheme, called conLDA, to cluster virtual documents with similar medical term distributions into a conditional topic (C-topic). Finally, we analyze the clustered C-topics by sentiment polarities, and physiological and psychological sentiment. The experiment results show that conLDA outperforms the original LDA, and can cluster relevant medical terms and relevant questions together. The C-topics clustered by conLDA are more thematic than those clustered by the original LDA. The results of sentiment analysis may provide a quick reference and valuable insights for patients, caregivers and doctors. More... »

PAGES

236

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10916-016-0604-4

DOI

http://dx.doi.org/10.1007/s10916-016-0604-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1025093220

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/27663246


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/2008/1117", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Public Health and Health Services", 
        "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"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Consumer Health Information", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Data Mining", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Social Media", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "National Taiwan University", 
          "id": "https://www.grid.ac/institutes/grid.19188.39", 
          "name": [
            "Department of Information Management, National Taiwan University, Taipei, Taiwan, Republic of China", 
            "Big Data Laboratory, Chunghwa Telcom Laboratories, Taipei, Taiwan, Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yang", 
        "givenName": "Fu-Chen", 
        "id": "sg:person.014274670066.81", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014274670066.81"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National Taiwan University", 
          "id": "https://www.grid.ac/institutes/grid.19188.39", 
          "name": [
            "Department of Information Management, National Taiwan University, Taipei, Taiwan, Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lee", 
        "givenName": "Anthony J.T.", 
        "id": "sg:person.012546425423.76", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012546425423.76"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National Taiwan University", 
          "id": "https://www.grid.ac/institutes/grid.19188.39", 
          "name": [
            "Department of Information Management, National Taiwan University, Taipei, Taiwan, Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kuo", 
        "givenName": "Sz-Chen", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s10916-012-9915-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002033958", 
          "https://doi.org/10.1007/s10916-012-9915-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/2337542.2337558", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006967306"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3322/caac.21254", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011682275"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10916-015-0333-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014975211", 
          "https://doi.org/10.1007/s10916-015-0333-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10916-013-9942-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016768383", 
          "https://doi.org/10.1007/s10916-013-9942-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1136/amiajnl-2013-002282", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020731937"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10916-015-0380-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021307017", 
          "https://doi.org/10.1007/s10916-015-0380-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.pec.2011.08.017", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022263443"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1197/jamia.m2215", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025518054"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10916-015-0201-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026987597", 
          "https://doi.org/10.1007/s10916-015-0201-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/1645953.1646003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030846598"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1046/j.1523-5394.2002.106005.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036413134"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/jncimonographs/lgt025", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041314220"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1468-0009.2012.00662.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045175257"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/2684822.2685324", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047575737"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-319-07617-1_15", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049772552", 
          "https://doi.org/10.1007/978-3-319-07617-1_15"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/2339530.2339552", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050577089"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2196/jmir.3187", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051492295"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/2433396.2433465", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053535400"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/taffc.2014.2315623", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061486949"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tkde.2011.48", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061662467"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2196/jmir.2752", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069286040"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2196/jmir.2934", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069286072"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/hicss.2013.216", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094468344"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/icct.2013.6820469", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094496995"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/hicss.2011.328", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094673317"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cse.2013.44", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095041596"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/passat/socialcom.2011.127", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095525679"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2016-11", 
    "datePublishedReg": "2016-11-01", 
    "description": "With the rapid development of the Internet, more and more users utilize health communities (known as forums) to find health-related information, share their medical stories and experiences, or interact with other people in the communities. In this paper, we propose a framework to analyze the user-generated contents in a health community. The proposed framework contains three phases. First, we extract medical terms, including conditions, symptoms, treatments, effectiveness and side effects to form a virtual document for each question in the community. Next, we modify Latent Dirichlet Allocation (LDA) by adding a weighted scheme, called conLDA, to cluster virtual documents with similar medical term distributions into a conditional topic (C-topic). Finally, we analyze the clustered C-topics by sentiment polarities, and physiological and psychological sentiment. The experiment results show that conLDA outperforms the original LDA, and can cluster relevant medical terms and relevant questions together. The C-topics clustered by conLDA are more thematic than those clustered by the original LDA. The results of sentiment analysis may provide a quick reference and valuable insights for patients, caregivers and doctors.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s10916-016-0604-4", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1088158", 
        "issn": [
          "0148-5598", 
          "1573-689X"
        ], 
        "name": "Journal of Medical Systems", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "11", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "40"
      }
    ], 
    "name": "Mining Health Social Media with Sentiment Analysis", 
    "pagination": "236", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "778b576da915ea753be5876c8bd92e19b07a92e584bc436956bcdbcbc9ab5eea"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "27663246"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "7806056"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s10916-016-0604-4"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1025093220"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s10916-016-0604-4", 
      "https://app.dimensions.ai/details/publication/pub.1025093220"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T12:24", 
    "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/v90/0000000362_0000000362/records_87097_00000000.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs10916-016-0604-4"
  }
]
 

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/s10916-016-0604-4'

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/s10916-016-0604-4'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10916-016-0604-4'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10916-016-0604-4'


 

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

189 TRIPLES      21 PREDICATES      61 URIs      25 LITERALS      13 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s10916-016-0604-4 schema:about N1b9eec92d46843178ba2825b7668d3f3
2 N80dbb452c08b4ce1b24a02e8146d31d6
3 Ndb808b7dff1847b7998a80c5b7e6165f
4 Neadae07d23994d98919f47bd8f84dfee
5 anzsrc-for:11
6 anzsrc-for:1117
7 schema:author N31c46cb8318a4b20a00a7d66ebcc2687
8 schema:citation sg:pub.10.1007/978-3-319-07617-1_15
9 sg:pub.10.1007/s10916-012-9915-2
10 sg:pub.10.1007/s10916-013-9942-7
11 sg:pub.10.1007/s10916-015-0201-y
12 sg:pub.10.1007/s10916-015-0333-0
13 sg:pub.10.1007/s10916-015-0380-6
14 https://doi.org/10.1016/j.pec.2011.08.017
15 https://doi.org/10.1046/j.1523-5394.2002.106005.x
16 https://doi.org/10.1093/jncimonographs/lgt025
17 https://doi.org/10.1109/cse.2013.44
18 https://doi.org/10.1109/hicss.2011.328
19 https://doi.org/10.1109/hicss.2013.216
20 https://doi.org/10.1109/icct.2013.6820469
21 https://doi.org/10.1109/passat/socialcom.2011.127
22 https://doi.org/10.1109/taffc.2014.2315623
23 https://doi.org/10.1109/tkde.2011.48
24 https://doi.org/10.1111/j.1468-0009.2012.00662.x
25 https://doi.org/10.1136/amiajnl-2013-002282
26 https://doi.org/10.1145/1645953.1646003
27 https://doi.org/10.1145/2337542.2337558
28 https://doi.org/10.1145/2339530.2339552
29 https://doi.org/10.1145/2433396.2433465
30 https://doi.org/10.1145/2684822.2685324
31 https://doi.org/10.1197/jamia.m2215
32 https://doi.org/10.2196/jmir.2752
33 https://doi.org/10.2196/jmir.2934
34 https://doi.org/10.2196/jmir.3187
35 https://doi.org/10.3322/caac.21254
36 schema:datePublished 2016-11
37 schema:datePublishedReg 2016-11-01
38 schema:description With the rapid development of the Internet, more and more users utilize health communities (known as forums) to find health-related information, share their medical stories and experiences, or interact with other people in the communities. In this paper, we propose a framework to analyze the user-generated contents in a health community. The proposed framework contains three phases. First, we extract medical terms, including conditions, symptoms, treatments, effectiveness and side effects to form a virtual document for each question in the community. Next, we modify Latent Dirichlet Allocation (LDA) by adding a weighted scheme, called conLDA, to cluster virtual documents with similar medical term distributions into a conditional topic (C-topic). Finally, we analyze the clustered C-topics by sentiment polarities, and physiological and psychological sentiment. The experiment results show that conLDA outperforms the original LDA, and can cluster relevant medical terms and relevant questions together. The C-topics clustered by conLDA are more thematic than those clustered by the original LDA. The results of sentiment analysis may provide a quick reference and valuable insights for patients, caregivers and doctors.
39 schema:genre research_article
40 schema:inLanguage en
41 schema:isAccessibleForFree false
42 schema:isPartOf N53acb12f95694f45ae75c5d51d114391
43 Nec541626875449349ea0a6906cc3c1e0
44 sg:journal.1088158
45 schema:name Mining Health Social Media with Sentiment Analysis
46 schema:pagination 236
47 schema:productId N18efa92519e84770b723b7fea9c99949
48 N5212671c08a440ad9f94f4b697e8e6a5
49 N9bce1146aa2b4b05ab41667f8f2f487f
50 Nc677b3776d2041b7b267fef837d1d3ed
51 Ncb1b755158b544aaa94f9b32a51ae7a2
52 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025093220
53 https://doi.org/10.1007/s10916-016-0604-4
54 schema:sdDatePublished 2019-04-11T12:24
55 schema:sdLicense https://scigraph.springernature.com/explorer/license/
56 schema:sdPublisher N53c6d2db3a724e6ba492e9f4b9716f09
57 schema:url https://link.springer.com/10.1007%2Fs10916-016-0604-4
58 sgo:license sg:explorer/license/
59 sgo:sdDataset articles
60 rdf:type schema:ScholarlyArticle
61 N18efa92519e84770b723b7fea9c99949 schema:name nlm_unique_id
62 schema:value 7806056
63 rdf:type schema:PropertyValue
64 N1b9eec92d46843178ba2825b7668d3f3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
65 schema:name Social Media
66 rdf:type schema:DefinedTerm
67 N31c46cb8318a4b20a00a7d66ebcc2687 rdf:first sg:person.014274670066.81
68 rdf:rest N5a5e6c80197449dda50a06ccb6401659
69 N3dff45c739924426bfc18d5bd4f9a8d7 rdf:first Na8200697eaea469b8d8315fc2de44064
70 rdf:rest rdf:nil
71 N5212671c08a440ad9f94f4b697e8e6a5 schema:name dimensions_id
72 schema:value pub.1025093220
73 rdf:type schema:PropertyValue
74 N53acb12f95694f45ae75c5d51d114391 schema:volumeNumber 40
75 rdf:type schema:PublicationVolume
76 N53c6d2db3a724e6ba492e9f4b9716f09 schema:name Springer Nature - SN SciGraph project
77 rdf:type schema:Organization
78 N5a5e6c80197449dda50a06ccb6401659 rdf:first sg:person.012546425423.76
79 rdf:rest N3dff45c739924426bfc18d5bd4f9a8d7
80 N80dbb452c08b4ce1b24a02e8146d31d6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
81 schema:name Consumer Health Information
82 rdf:type schema:DefinedTerm
83 N9bce1146aa2b4b05ab41667f8f2f487f schema:name pubmed_id
84 schema:value 27663246
85 rdf:type schema:PropertyValue
86 Na8200697eaea469b8d8315fc2de44064 schema:affiliation https://www.grid.ac/institutes/grid.19188.39
87 schema:familyName Kuo
88 schema:givenName Sz-Chen
89 rdf:type schema:Person
90 Nc677b3776d2041b7b267fef837d1d3ed schema:name doi
91 schema:value 10.1007/s10916-016-0604-4
92 rdf:type schema:PropertyValue
93 Ncb1b755158b544aaa94f9b32a51ae7a2 schema:name readcube_id
94 schema:value 778b576da915ea753be5876c8bd92e19b07a92e584bc436956bcdbcbc9ab5eea
95 rdf:type schema:PropertyValue
96 Ndb808b7dff1847b7998a80c5b7e6165f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
97 schema:name Data Mining
98 rdf:type schema:DefinedTerm
99 Neadae07d23994d98919f47bd8f84dfee schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
100 schema:name Humans
101 rdf:type schema:DefinedTerm
102 Nec541626875449349ea0a6906cc3c1e0 schema:issueNumber 11
103 rdf:type schema:PublicationIssue
104 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
105 schema:name Medical and Health Sciences
106 rdf:type schema:DefinedTerm
107 anzsrc-for:1117 schema:inDefinedTermSet anzsrc-for:
108 schema:name Public Health and Health Services
109 rdf:type schema:DefinedTerm
110 sg:journal.1088158 schema:issn 0148-5598
111 1573-689X
112 schema:name Journal of Medical Systems
113 rdf:type schema:Periodical
114 sg:person.012546425423.76 schema:affiliation https://www.grid.ac/institutes/grid.19188.39
115 schema:familyName Lee
116 schema:givenName Anthony J.T.
117 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012546425423.76
118 rdf:type schema:Person
119 sg:person.014274670066.81 schema:affiliation https://www.grid.ac/institutes/grid.19188.39
120 schema:familyName Yang
121 schema:givenName Fu-Chen
122 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014274670066.81
123 rdf:type schema:Person
124 sg:pub.10.1007/978-3-319-07617-1_15 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049772552
125 https://doi.org/10.1007/978-3-319-07617-1_15
126 rdf:type schema:CreativeWork
127 sg:pub.10.1007/s10916-012-9915-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002033958
128 https://doi.org/10.1007/s10916-012-9915-2
129 rdf:type schema:CreativeWork
130 sg:pub.10.1007/s10916-013-9942-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016768383
131 https://doi.org/10.1007/s10916-013-9942-7
132 rdf:type schema:CreativeWork
133 sg:pub.10.1007/s10916-015-0201-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1026987597
134 https://doi.org/10.1007/s10916-015-0201-y
135 rdf:type schema:CreativeWork
136 sg:pub.10.1007/s10916-015-0333-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014975211
137 https://doi.org/10.1007/s10916-015-0333-0
138 rdf:type schema:CreativeWork
139 sg:pub.10.1007/s10916-015-0380-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021307017
140 https://doi.org/10.1007/s10916-015-0380-6
141 rdf:type schema:CreativeWork
142 https://doi.org/10.1016/j.pec.2011.08.017 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022263443
143 rdf:type schema:CreativeWork
144 https://doi.org/10.1046/j.1523-5394.2002.106005.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1036413134
145 rdf:type schema:CreativeWork
146 https://doi.org/10.1093/jncimonographs/lgt025 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041314220
147 rdf:type schema:CreativeWork
148 https://doi.org/10.1109/cse.2013.44 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095041596
149 rdf:type schema:CreativeWork
150 https://doi.org/10.1109/hicss.2011.328 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094673317
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1109/hicss.2013.216 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094468344
153 rdf:type schema:CreativeWork
154 https://doi.org/10.1109/icct.2013.6820469 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094496995
155 rdf:type schema:CreativeWork
156 https://doi.org/10.1109/passat/socialcom.2011.127 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095525679
157 rdf:type schema:CreativeWork
158 https://doi.org/10.1109/taffc.2014.2315623 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061486949
159 rdf:type schema:CreativeWork
160 https://doi.org/10.1109/tkde.2011.48 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061662467
161 rdf:type schema:CreativeWork
162 https://doi.org/10.1111/j.1468-0009.2012.00662.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1045175257
163 rdf:type schema:CreativeWork
164 https://doi.org/10.1136/amiajnl-2013-002282 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020731937
165 rdf:type schema:CreativeWork
166 https://doi.org/10.1145/1645953.1646003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030846598
167 rdf:type schema:CreativeWork
168 https://doi.org/10.1145/2337542.2337558 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006967306
169 rdf:type schema:CreativeWork
170 https://doi.org/10.1145/2339530.2339552 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050577089
171 rdf:type schema:CreativeWork
172 https://doi.org/10.1145/2433396.2433465 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053535400
173 rdf:type schema:CreativeWork
174 https://doi.org/10.1145/2684822.2685324 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047575737
175 rdf:type schema:CreativeWork
176 https://doi.org/10.1197/jamia.m2215 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025518054
177 rdf:type schema:CreativeWork
178 https://doi.org/10.2196/jmir.2752 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069286040
179 rdf:type schema:CreativeWork
180 https://doi.org/10.2196/jmir.2934 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069286072
181 rdf:type schema:CreativeWork
182 https://doi.org/10.2196/jmir.3187 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051492295
183 rdf:type schema:CreativeWork
184 https://doi.org/10.3322/caac.21254 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011682275
185 rdf:type schema:CreativeWork
186 https://www.grid.ac/institutes/grid.19188.39 schema:alternateName National Taiwan University
187 schema:name Big Data Laboratory, Chunghwa Telcom Laboratories, Taipei, Taiwan, Republic of China
188 Department of Information Management, National Taiwan University, Taipei, Taiwan, Republic of China
189 rdf:type schema:Organization
 




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


...