A machine learning-based diagnostic model associated with knee osteoarthritis severity View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2020-09-25

AUTHORS

Soon Bin Kwon, Yunseo Ku, Hyuk-Soo Han, Myung Chul Lee, Hee Chan Kim, Du Hyun Ro

ABSTRACT

Knee osteoarthritis (KOA) is characterized by pain and decreased gait function. We aimed to find KOA-related gait features based on patient reported outcome measures (PROMs) and develop regression models using machine learning algorithms to estimate KOA severity. The study included 375 volunteers with variable KOA grades. The severity of KOA was determined using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). WOMAC scores were used to classify disease severity into three groups. A total of 1087 features were extracted from the gait data. An ANOVA and student’s t-test were performed and only features that were significant were selected for inclusion in the machine learning algorithm. Three WOMAC subscales (physical function, pain and stiffness) were further divided into three classes. An ANOVA was performed to determine which selected features were significantly related to the subscales. Both linear regression models and a random forest regression was used to estimate patient the WOMAC scores. Forty-three features were selected based on ANOVA and student’s t-test results. The following number of features were selected from each joint: 12 from hip, 1 feature from pelvic, 17 features from knee, 9 features from ankle, 1 feature from foot, and 3 features from spatiotemporal parameters. A significance level of < 0.0001 and < 0.00003 was set for the ANOVA and t-test, respectively. The physical function, pain, and stiffness subscales were related to 41, 10, and 16 features, respectively. Linear regression models showed a correlation of 0.723 and the machine learning algorithm showed a correlation of 0.741. The severity of KOA was predicted by gait analysis features, which were incorporated to develop an objective estimation model for KOA severity. The identified features may serve as a tool to guide rehabilitation and progress assessments. In addition, the estimation model presented here suggests an approach for clinical application of gait analysis data for KOA evaluation. More... »

PAGES

15743

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-020-72941-4

DOI

http://dx.doi.org/10.1038/s41598-020-72941-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1131167320

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/32978506


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/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing 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"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "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"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Aged", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Cross-Sectional Studies", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Early Diagnosis", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Female", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Gait Analysis", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Linear Models", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Machine Learning", 
        "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": "Osteoarthritis, Knee", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Pain Measurement", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Patient Reported Outcome Measures", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Severity of Illness Index", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, Korea", 
          "id": "http://www.grid.ac/institutes/grid.31501.36", 
          "name": [
            "Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kwon", 
        "givenName": "Soon Bin", 
        "id": "sg:person.013316503721.55", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013316503721.55"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Biomedical Engineering, College of Medicine, Chungnam National University, Daejeon, Korea", 
          "id": "http://www.grid.ac/institutes/grid.254230.2", 
          "name": [
            "Department of Biomedical Engineering, College of Medicine, Chungnam National University, Daejeon, Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ku", 
        "givenName": "Yunseo", 
        "id": "sg:person.01237602210.18", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01237602210.18"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Orthopedic Surgery, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, 110-744, Seoul, Korea", 
          "id": "http://www.grid.ac/institutes/grid.412484.f", 
          "name": [
            "Department of Orthopedic Surgery, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, 110-744, Seoul, Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Han", 
        "givenName": "Hyuk-Soo", 
        "id": "sg:person.011234154441.74", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011234154441.74"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Orthopedic Surgery, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, 110-744, Seoul, Korea", 
          "id": "http://www.grid.ac/institutes/grid.412484.f", 
          "name": [
            "Department of Orthopedic Surgery, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, 110-744, Seoul, Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lee", 
        "givenName": "Myung Chul", 
        "id": "sg:person.01337220140.25", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01337220140.25"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea", 
          "id": "http://www.grid.ac/institutes/grid.31501.36", 
          "name": [
            "Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, Korea", 
            "Institute of Medical & Biological Engineering, Medical Research Center, Seoul National University College of Medicine, Seoul, Korea", 
            "Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kim", 
        "givenName": "Hee Chan", 
        "id": "sg:person.01100214455.87", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01100214455.87"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Orthopedic Surgery, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, 110-744, Seoul, Korea", 
          "id": "http://www.grid.ac/institutes/grid.412484.f", 
          "name": [
            "Department of Orthopedic Surgery, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, 110-744, Seoul, Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ro", 
        "givenName": "Du Hyun", 
        "id": "sg:person.01322222131.16", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01322222131.16"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1023/a:1010933404324", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024739340", 
          "https://doi.org/10.1023/a:1010933404324"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2474-14-169", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005000418", 
          "https://doi.org/10.1186/1471-2474-14-169"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s13534-019-00094-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1111617253", 
          "https://doi.org/10.1007/s13534-019-00094-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10067-008-1021-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007022042", 
          "https://doi.org/10.1007/s10067-008-1021-y"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2020-09-25", 
    "datePublishedReg": "2020-09-25", 
    "description": "Knee osteoarthritis (KOA) is characterized by pain and decreased gait function. We aimed to find KOA-related gait features based on patient reported outcome measures (PROMs) and develop regression models using machine learning algorithms to estimate KOA severity. The study included 375 volunteers with variable KOA grades. The severity of KOA was determined using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). WOMAC scores were used to classify disease severity into three groups. A total of 1087 features were extracted from the gait data. An ANOVA and student\u2019s t-test were performed and only features that were significant were selected for inclusion in the machine learning algorithm. Three WOMAC subscales (physical function, pain and stiffness) were further divided into three classes. An ANOVA was performed to determine which selected features were significantly related to the subscales. Both linear regression models and a random forest regression was used to estimate patient the WOMAC scores. Forty-three features were selected based on ANOVA and student\u2019s t-test results. The following number of features were selected from each joint: 12 from hip, 1 feature from pelvic, 17 features from knee, 9 features from ankle, 1 feature from foot, and 3 features from spatiotemporal parameters. A significance level of\u2009<\u20090.0001 and\u2009<\u20090.00003 was set for the ANOVA and t-test, respectively. The physical function, pain, and stiffness subscales were related to 41, 10, and 16 features, respectively. Linear regression models showed a correlation of 0.723 and the machine learning algorithm showed a correlation of 0.741. The severity of KOA was predicted by gait analysis features, which were incorporated to develop an objective estimation model for KOA severity. The identified features may serve as a tool to guide rehabilitation and progress assessments. In addition, the estimation model presented here suggests an approach for clinical application of gait analysis data for KOA evaluation.", 
    "genre": "article", 
    "id": "sg:pub.10.1038/s41598-020-72941-4", 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1045337", 
        "issn": [
          "2045-2322"
        ], 
        "name": "Scientific Reports", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "10"
      }
    ], 
    "keywords": [
      "severity of KOA", 
      "knee osteoarthritis", 
      "WOMAC scores", 
      "KOA severity", 
      "McMaster Universities Osteoarthritis Index", 
      "t-test", 
      "regression models", 
      "gait analysis data", 
      "linear regression models", 
      "knee osteoarthritis severity", 
      "Student's t-test", 
      "WOMAC subscales", 
      "stiffness subscale", 
      "Osteoarthritis Index", 
      "gait function", 
      "physical function", 
      "outcome measures", 
      "Western Ontario", 
      "osteoarthritis severity", 
      "disease severity", 
      "spatiotemporal parameters", 
      "severity", 
      "pain", 
      "clinical application", 
      "gait data", 
      "Student's t-test results", 
      "subscales", 
      "scores", 
      "t-test results", 
      "ANOVA", 
      "significance level", 
      "gait features", 
      "patients", 
      "osteoarthritis", 
      "hip", 
      "ankle", 
      "knee", 
      "volunteers", 
      "diagnostic model", 
      "rehabilitation", 
      "total", 
      "foot", 
      "correlation", 
      "grade", 
      "group", 
      "regression", 
      "progress assessment", 
      "assessment", 
      "joints", 
      "function", 
      "index", 
      "features", 
      "data", 
      "levels", 
      "evaluation", 
      "study", 
      "Ontario", 
      "measures", 
      "inclusion", 
      "addition", 
      "model", 
      "estimation model", 
      "number of features", 
      "number", 
      "random forest regression", 
      "analysis features", 
      "results", 
      "forest regression", 
      "machine", 
      "algorithm", 
      "analysis data", 
      "tool", 
      "approach", 
      "parameters", 
      "class", 
      "applications"
    ], 
    "name": "A machine learning-based diagnostic model associated with knee osteoarthritis severity", 
    "pagination": "15743", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1131167320"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1038/s41598-020-72941-4"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "32978506"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1038/s41598-020-72941-4", 
      "https://app.dimensions.ai/details/publication/pub.1131167320"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-09-02T16:04", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220902/entities/gbq_results/article/article_840.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1038/s41598-020-72941-4"
  }
]
 

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.10.1038/s41598-020-72941-4'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/s41598-020-72941-4'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41598-020-72941-4'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41598-020-72941-4'


 

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

260 TRIPLES      21 PREDICATES      121 URIs      107 LITERALS      21 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1038/s41598-020-72941-4 schema:about N070ef570096f40f4ae00e003a4e25054
2 N1539e22fa980450585904ae5888fbf02
3 N1c83df5eaeec4b58bce883b005e6c510
4 N1c8f99146aa8475b950e7230299b4ca7
5 N252499dae4f64885aecd8bd48dd0ddbb
6 N37705a4766ef43f794c5e013255d99ec
7 N6f74f43d3c9844f09d68ed87be28a570
8 Na016f605063b48ed81d3a73de3ba17ab
9 Nacc3bb2bba72488380d48262bcf3dffe
10 Nb3057b54d79e4eacbf6c1476ab1e8674
11 Nc3270a747f604e95bd469762583461bb
12 Nc43c02690cd748159ec25f7ee077f3c2
13 Ncdca16a524794e5d8e3efe8bae2477c4
14 Nd1c765aefffb48338cf47001e37c8a60
15 anzsrc-for:08
16 anzsrc-for:0801
17 anzsrc-for:11
18 anzsrc-for:1103
19 schema:author N8b1e697a006841d08eafe5bd9cd9eedc
20 schema:citation sg:pub.10.1007/s10067-008-1021-y
21 sg:pub.10.1007/s13534-019-00094-z
22 sg:pub.10.1023/a:1010933404324
23 sg:pub.10.1186/1471-2474-14-169
24 schema:datePublished 2020-09-25
25 schema:datePublishedReg 2020-09-25
26 schema:description Knee osteoarthritis (KOA) is characterized by pain and decreased gait function. We aimed to find KOA-related gait features based on patient reported outcome measures (PROMs) and develop regression models using machine learning algorithms to estimate KOA severity. The study included 375 volunteers with variable KOA grades. The severity of KOA was determined using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). WOMAC scores were used to classify disease severity into three groups. A total of 1087 features were extracted from the gait data. An ANOVA and student’s t-test were performed and only features that were significant were selected for inclusion in the machine learning algorithm. Three WOMAC subscales (physical function, pain and stiffness) were further divided into three classes. An ANOVA was performed to determine which selected features were significantly related to the subscales. Both linear regression models and a random forest regression was used to estimate patient the WOMAC scores. Forty-three features were selected based on ANOVA and student’s t-test results. The following number of features were selected from each joint: 12 from hip, 1 feature from pelvic, 17 features from knee, 9 features from ankle, 1 feature from foot, and 3 features from spatiotemporal parameters. A significance level of < 0.0001 and < 0.00003 was set for the ANOVA and t-test, respectively. The physical function, pain, and stiffness subscales were related to 41, 10, and 16 features, respectively. Linear regression models showed a correlation of 0.723 and the machine learning algorithm showed a correlation of 0.741. The severity of KOA was predicted by gait analysis features, which were incorporated to develop an objective estimation model for KOA severity. The identified features may serve as a tool to guide rehabilitation and progress assessments. In addition, the estimation model presented here suggests an approach for clinical application of gait analysis data for KOA evaluation.
27 schema:genre article
28 schema:isAccessibleForFree true
29 schema:isPartOf Nab8f6f95c0104ffaba871aebb9668f17
30 Nc0b23660b6d24727a7f4cf998d60a1cd
31 sg:journal.1045337
32 schema:keywords ANOVA
33 KOA severity
34 McMaster Universities Osteoarthritis Index
35 Ontario
36 Osteoarthritis Index
37 Student's t-test
38 Student's t-test results
39 WOMAC scores
40 WOMAC subscales
41 Western Ontario
42 addition
43 algorithm
44 analysis data
45 analysis features
46 ankle
47 applications
48 approach
49 assessment
50 class
51 clinical application
52 correlation
53 data
54 diagnostic model
55 disease severity
56 estimation model
57 evaluation
58 features
59 foot
60 forest regression
61 function
62 gait analysis data
63 gait data
64 gait features
65 gait function
66 grade
67 group
68 hip
69 inclusion
70 index
71 joints
72 knee
73 knee osteoarthritis
74 knee osteoarthritis severity
75 levels
76 linear regression models
77 machine
78 measures
79 model
80 number
81 number of features
82 osteoarthritis
83 osteoarthritis severity
84 outcome measures
85 pain
86 parameters
87 patients
88 physical function
89 progress assessment
90 random forest regression
91 regression
92 regression models
93 rehabilitation
94 results
95 scores
96 severity
97 severity of KOA
98 significance level
99 spatiotemporal parameters
100 stiffness subscale
101 study
102 subscales
103 t-test
104 t-test results
105 tool
106 total
107 volunteers
108 schema:name A machine learning-based diagnostic model associated with knee osteoarthritis severity
109 schema:pagination 15743
110 schema:productId N0dd2abbf62764350b7f70039056b44aa
111 N95346df93cf84377ba40324ab709fe23
112 Ndcb1a8fe6ff54b7585329eada2c289e0
113 schema:sameAs https://app.dimensions.ai/details/publication/pub.1131167320
114 https://doi.org/10.1038/s41598-020-72941-4
115 schema:sdDatePublished 2022-09-02T16:04
116 schema:sdLicense https://scigraph.springernature.com/explorer/license/
117 schema:sdPublisher N74130b6968c1464e8bfd1e33356b3031
118 schema:url https://doi.org/10.1038/s41598-020-72941-4
119 sgo:license sg:explorer/license/
120 sgo:sdDataset articles
121 rdf:type schema:ScholarlyArticle
122 N070ef570096f40f4ae00e003a4e25054 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
123 schema:name Pain Measurement
124 rdf:type schema:DefinedTerm
125 N0dd2abbf62764350b7f70039056b44aa schema:name dimensions_id
126 schema:value pub.1131167320
127 rdf:type schema:PropertyValue
128 N1539e22fa980450585904ae5888fbf02 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
129 schema:name Aged
130 rdf:type schema:DefinedTerm
131 N1c83df5eaeec4b58bce883b005e6c510 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
132 schema:name Cross-Sectional Studies
133 rdf:type schema:DefinedTerm
134 N1c8f99146aa8475b950e7230299b4ca7 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
135 schema:name Severity of Illness Index
136 rdf:type schema:DefinedTerm
137 N252499dae4f64885aecd8bd48dd0ddbb schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
138 schema:name Patient Reported Outcome Measures
139 rdf:type schema:DefinedTerm
140 N2e120b12c95945368c3c963163b00d69 rdf:first sg:person.01337220140.25
141 rdf:rest Nfa40b64fc9764417b83b7f28ef7ce99e
142 N37705a4766ef43f794c5e013255d99ec schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
143 schema:name Female
144 rdf:type schema:DefinedTerm
145 N6f74f43d3c9844f09d68ed87be28a570 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
146 schema:name Osteoarthritis, Knee
147 rdf:type schema:DefinedTerm
148 N74130b6968c1464e8bfd1e33356b3031 schema:name Springer Nature - SN SciGraph project
149 rdf:type schema:Organization
150 N8b1e697a006841d08eafe5bd9cd9eedc rdf:first sg:person.013316503721.55
151 rdf:rest Ne46878e6270f4395adadf62ef6d3dc9d
152 N95346df93cf84377ba40324ab709fe23 schema:name doi
153 schema:value 10.1038/s41598-020-72941-4
154 rdf:type schema:PropertyValue
155 N9cdbfff399524fee87e6f08bda22b1df rdf:first sg:person.01322222131.16
156 rdf:rest rdf:nil
157 Na016f605063b48ed81d3a73de3ba17ab schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
158 schema:name Machine Learning
159 rdf:type schema:DefinedTerm
160 Nab8f6f95c0104ffaba871aebb9668f17 schema:volumeNumber 10
161 rdf:type schema:PublicationVolume
162 Nacc3bb2bba72488380d48262bcf3dffe schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
163 schema:name Linear Models
164 rdf:type schema:DefinedTerm
165 Nb3057b54d79e4eacbf6c1476ab1e8674 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
166 schema:name Early Diagnosis
167 rdf:type schema:DefinedTerm
168 Nc0b23660b6d24727a7f4cf998d60a1cd schema:issueNumber 1
169 rdf:type schema:PublicationIssue
170 Nc3270a747f604e95bd469762583461bb schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
171 schema:name Male
172 rdf:type schema:DefinedTerm
173 Nc43c02690cd748159ec25f7ee077f3c2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
174 schema:name Humans
175 rdf:type schema:DefinedTerm
176 Ncdca16a524794e5d8e3efe8bae2477c4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
177 schema:name Gait Analysis
178 rdf:type schema:DefinedTerm
179 Nd1c765aefffb48338cf47001e37c8a60 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
180 schema:name Middle Aged
181 rdf:type schema:DefinedTerm
182 Nd84eeaeec6fa43a28ffb8174cf05abdb rdf:first sg:person.011234154441.74
183 rdf:rest N2e120b12c95945368c3c963163b00d69
184 Ndcb1a8fe6ff54b7585329eada2c289e0 schema:name pubmed_id
185 schema:value 32978506
186 rdf:type schema:PropertyValue
187 Ne46878e6270f4395adadf62ef6d3dc9d rdf:first sg:person.01237602210.18
188 rdf:rest Nd84eeaeec6fa43a28ffb8174cf05abdb
189 Nfa40b64fc9764417b83b7f28ef7ce99e rdf:first sg:person.01100214455.87
190 rdf:rest N9cdbfff399524fee87e6f08bda22b1df
191 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
192 schema:name Information and Computing Sciences
193 rdf:type schema:DefinedTerm
194 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
195 schema:name Artificial Intelligence and Image Processing
196 rdf:type schema:DefinedTerm
197 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
198 schema:name Medical and Health Sciences
199 rdf:type schema:DefinedTerm
200 anzsrc-for:1103 schema:inDefinedTermSet anzsrc-for:
201 schema:name Clinical Sciences
202 rdf:type schema:DefinedTerm
203 sg:journal.1045337 schema:issn 2045-2322
204 schema:name Scientific Reports
205 schema:publisher Springer Nature
206 rdf:type schema:Periodical
207 sg:person.01100214455.87 schema:affiliation grid-institutes:grid.31501.36
208 schema:familyName Kim
209 schema:givenName Hee Chan
210 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01100214455.87
211 rdf:type schema:Person
212 sg:person.011234154441.74 schema:affiliation grid-institutes:grid.412484.f
213 schema:familyName Han
214 schema:givenName Hyuk-Soo
215 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011234154441.74
216 rdf:type schema:Person
217 sg:person.01237602210.18 schema:affiliation grid-institutes:grid.254230.2
218 schema:familyName Ku
219 schema:givenName Yunseo
220 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01237602210.18
221 rdf:type schema:Person
222 sg:person.01322222131.16 schema:affiliation grid-institutes:grid.412484.f
223 schema:familyName Ro
224 schema:givenName Du Hyun
225 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01322222131.16
226 rdf:type schema:Person
227 sg:person.013316503721.55 schema:affiliation grid-institutes:grid.31501.36
228 schema:familyName Kwon
229 schema:givenName Soon Bin
230 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013316503721.55
231 rdf:type schema:Person
232 sg:person.01337220140.25 schema:affiliation grid-institutes:grid.412484.f
233 schema:familyName Lee
234 schema:givenName Myung Chul
235 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01337220140.25
236 rdf:type schema:Person
237 sg:pub.10.1007/s10067-008-1021-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1007022042
238 https://doi.org/10.1007/s10067-008-1021-y
239 rdf:type schema:CreativeWork
240 sg:pub.10.1007/s13534-019-00094-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1111617253
241 https://doi.org/10.1007/s13534-019-00094-z
242 rdf:type schema:CreativeWork
243 sg:pub.10.1023/a:1010933404324 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024739340
244 https://doi.org/10.1023/a:1010933404324
245 rdf:type schema:CreativeWork
246 sg:pub.10.1186/1471-2474-14-169 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005000418
247 https://doi.org/10.1186/1471-2474-14-169
248 rdf:type schema:CreativeWork
249 grid-institutes:grid.254230.2 schema:alternateName Department of Biomedical Engineering, College of Medicine, Chungnam National University, Daejeon, Korea
250 schema:name Department of Biomedical Engineering, College of Medicine, Chungnam National University, Daejeon, Korea
251 rdf:type schema:Organization
252 grid-institutes:grid.31501.36 schema:alternateName Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea
253 Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, Korea
254 schema:name Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea
255 Institute of Medical & Biological Engineering, Medical Research Center, Seoul National University College of Medicine, Seoul, Korea
256 Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, Korea
257 rdf:type schema:Organization
258 grid-institutes:grid.412484.f schema:alternateName Department of Orthopedic Surgery, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, 110-744, Seoul, Korea
259 schema:name Department of Orthopedic Surgery, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, 110-744, Seoul, Korea
260 rdf:type schema:Organization
 




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


...