Effective detection of the evaluation and the outbreak of nosocomial infection surveillance data mining View Homepage


Ontology type: schema:MonetaryGrant     


Grant Info

YEARS

2006-2007

FUNDING AMOUNT

3100000 JPY

ABSTRACT

Health and Welfare students nosocomial infection control surveillance (JANIS)In business, data to understand the occurrence of nosocomial infection have been collected from all over the country dozens of medical facilities. However, clinical practice has not been performed reduction of specific practical information such as that sought, it is desired construction of epidemiological evaluation system therefor. The purpose of this study, by applying data mining to nosocomial infection surveillance, is to build an epidemiological evaluation system to achieve the effective detection of the evaluation and the outbreak of nosocomial infection surveillance. This year,JANIS事業のICUUsing a large-scale cohort data of the department, it was examined the application of statistical conventional methods and data mining method in the aggregate analysis of data collected by the hospital infection surveillance. Also, reducing the effort of information gathering site staff, in order to perform the nosocomial infection surveillance efficient, was examined utilizing the hospital information system. 1. Statistically conventional method using a neural network than (multiple logistic regression model) was constructed a model for predicting the outcome excellent prediction accuracy. (IMECS 2007Announcement) 2. It was to build a model to predict the outcome from the event of information up to the termination of the observation and information of the start of observation time using a neural network. (MEDINFO 2007Announcement) 3. On the basis of the 1 and 2 of the results of the above, are summarized on technique Ni'i to build a model to predict the outcome by applying the neural network to medical data in the chapter of the book. (Trends in Intelligent Systems and Computer Engineering, Springer,2008)4.It was investigated or can be calculated nosocomial infection incidence from the information in the hospital information system. (Environmental infection 2007) 5. Sampled by proposed a method to estimate the device mounting number of days in the denominator of nosocomial infection incidence was evaluated the prediction accuracy. Are summarized in (Japan environment infection Society 2008 announcement, the Japanese Society for Hygiene 2008 announcement) 6. Review the results of a series of studies. (Environmental Health Preventive Medicine,2008) More... »

URL

https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-18790406

Related SciGraph Publications

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/2008/2211", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/2208", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "type": "DefinedTerm"
      }
    ], 
    "amount": {
      "currency": "JPY", 
      "type": "MonetaryAmount", 
      "value": "3100000"
    }, 
    "description": "Health and Welfare students nosocomial infection control surveillance (JANIS)In business, data to understand the occurrence of nosocomial infection have been collected from all over the country dozens of medical facilities. However, clinical practice has not been performed reduction of specific practical information such as that sought, it is desired construction of epidemiological evaluation system therefor. The purpose of this study, by applying data mining to nosocomial infection surveillance, is to build an epidemiological evaluation system to achieve the effective detection of the evaluation and the outbreak of nosocomial infection surveillance. This year,JANIS\u4e8b\u696d\u306eICUUsing a large-scale cohort data of the department, it was examined the application of statistical conventional methods and data mining method in the aggregate analysis of data collected by the hospital infection surveillance. Also, reducing the effort of information gathering site staff, in order to perform the nosocomial infection surveillance efficient, was examined utilizing the hospital information system. 1. Statistically conventional method using a neural network than (multiple logistic regression model) was constructed a model for predicting the outcome excellent prediction accuracy. (IMECS 2007Announcement) 2. It was to build a model to predict the outcome from the event of information up to the termination of the observation and information of the start of observation time using a neural network. (MEDINFO 2007Announcement) 3. On the basis of the 1 and 2 of the results of the above, are summarized on technique Ni'i to build a model to predict the outcome by applying the neural network to medical data in the chapter of the book. (Trends in Intelligent Systems and Computer Engineering, Springer,2008)4.It was investigated or can be calculated nosocomial infection incidence from the information in the hospital information system. (Environmental infection 2007) 5. Sampled by proposed a method to estimate the device mounting number of days in the denominator of nosocomial infection incidence was evaluated the prediction accuracy. Are summarized in (Japan environment infection Society 2008 announcement, the Japanese Society for Hygiene 2008 announcement) 6. Review the results of a series of studies. (Environmental Health Preventive Medicine,2008)", 
    "endDate": "2007-12-31T00:00:00Z", 
    "funder": {
      "id": "https://www.grid.ac/institutes/grid.54432.34", 
      "type": "Organization"
    }, 
    "id": "sg:grant.5971908", 
    "identifier": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "5971908"
        ]
      }, 
      {
        "name": "kaken_id", 
        "type": "PropertyValue", 
        "value": [
          "18790406"
        ]
      }
    ], 
    "inLanguage": [
      "ja"
    ], 
    "keywords": [
      "nosocomial infections", 
      "health", 
      "occurrence", 
      "Computer Engineering", 
      "days", 
      "technique Ni'i", 
      "IMECS 2007Announcement", 
      "years", 
      "Welfare students", 
      "model", 
      "Springer,2008)4.It", 
      "outcome", 
      "evaluation", 
      "termination", 
      "statistical conventional methods", 
      "information gathering site staff", 
      "study", 
      "MEDINFO 2007Announcement", 
      "hospital infection surveillance", 
      "nosocomial infection surveillance", 
      "basis", 
      "medical facilities", 
      "neural network", 
      "Japanese Society", 
      "data", 
      "excellent prediction accuracy", 
      "observations", 
      "book", 
      "efforts", 
      "series", 
      "Environmental Health Preventive Medicine,2008", 
      "country dozens", 
      "Environmental infection 2007", 
      "nosocomial infection incidence", 
      "purpose", 
      "order", 
      "Hygiene 2008 announcement", 
      "application", 
      "conventional methods", 
      "data mining methods", 
      "specific practical information", 
      "number", 
      "denominator", 
      "results", 
      "effective detection", 
      "Department", 
      "chapter", 
      "prediction accuracy", 
      "epidemiological evaluation system therefor", 
      "trend", 
      "observation time", 
      "METHODS", 
      "nosocomial infection surveillance data mining", 
      "information", 
      "aggregate analysis", 
      "Intelligent Systems", 
      "start", 
      "nosocomial infection control surveillance", 
      "Japan environment infection Society 2008 announcement", 
      "data mining", 
      "epidemiological evaluation system", 
      "events", 
      "medical data", 
      "JANIS)In business", 
      "reduction", 
      "outbreak", 
      "hospital information system", 
      "clinical practice", 
      "multiple logistic regression model", 
      "desired construction", 
      "device", 
      "large-scale cohort data"
    ], 
    "name": "Effective detection of the evaluation and the outbreak of nosocomial infection surveillance data mining", 
    "recipient": [
      {
        "id": "https://www.grid.ac/institutes/grid.412764.2", 
        "type": "Organization"
      }
    ], 
    "sameAs": [
      "https://app.dimensions.ai/details/grant/grant.5971908"
    ], 
    "sdDataset": "grants", 
    "sdDatePublished": "2019-03-07T11:43", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com.uberresearch.data.processor/core_data/20181219_192338/projects/base/kaken_projects_21.xml.gz", 
    "startDate": "2006-01-01T00:00:00Z", 
    "type": "MonetaryGrant", 
    "url": "https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-18790406"
  }
]
 

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/grant.5971908'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/grant.5971908'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/grant.5971908'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/grant.5971908'


 

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

109 TRIPLES      19 PREDICATES      93 URIs      85 LITERALS      4 BLANK NODES

Subject Predicate Object
1 sg:grant.5971908 schema:about anzsrc-for:2208
2 anzsrc-for:2211
3 schema:amount Ne9d2ef838ae34ad6a0dd9c6c452f8850
4 schema:description Health and Welfare students nosocomial infection control surveillance (JANIS)In business, data to understand the occurrence of nosocomial infection have been collected from all over the country dozens of medical facilities. However, clinical practice has not been performed reduction of specific practical information such as that sought, it is desired construction of epidemiological evaluation system therefor. The purpose of this study, by applying data mining to nosocomial infection surveillance, is to build an epidemiological evaluation system to achieve the effective detection of the evaluation and the outbreak of nosocomial infection surveillance. This year,JANIS事業のICUUsing a large-scale cohort data of the department, it was examined the application of statistical conventional methods and data mining method in the aggregate analysis of data collected by the hospital infection surveillance. Also, reducing the effort of information gathering site staff, in order to perform the nosocomial infection surveillance efficient, was examined utilizing the hospital information system. 1. Statistically conventional method using a neural network than (multiple logistic regression model) was constructed a model for predicting the outcome excellent prediction accuracy. (IMECS 2007Announcement) 2. It was to build a model to predict the outcome from the event of information up to the termination of the observation and information of the start of observation time using a neural network. (MEDINFO 2007Announcement) 3. On the basis of the 1 and 2 of the results of the above, are summarized on technique Ni'i to build a model to predict the outcome by applying the neural network to medical data in the chapter of the book. (Trends in Intelligent Systems and Computer Engineering, Springer,2008)4.It was investigated or can be calculated nosocomial infection incidence from the information in the hospital information system. (Environmental infection 2007) 5. Sampled by proposed a method to estimate the device mounting number of days in the denominator of nosocomial infection incidence was evaluated the prediction accuracy. Are summarized in (Japan environment infection Society 2008 announcement, the Japanese Society for Hygiene 2008 announcement) 6. Review the results of a series of studies. (Environmental Health Preventive Medicine,2008)
5 schema:endDate 2007-12-31T00:00:00Z
6 schema:funder https://www.grid.ac/institutes/grid.54432.34
7 schema:identifier N093c885ef93a495490af63bf8d3c8d62
8 N2331ad7911b4467a95335b048c166497
9 schema:inLanguage ja
10 schema:keywords Computer Engineering
11 Department
12 Environmental Health Preventive Medicine,2008
13 Environmental infection 2007
14 Hygiene 2008 announcement
15 IMECS 2007Announcement
16 Intelligent Systems
17 JANIS)In business
18 Japan environment infection Society 2008 announcement
19 Japanese Society
20 MEDINFO 2007Announcement
21 METHODS
22 Springer,2008)4.It
23 Welfare students
24 aggregate analysis
25 application
26 basis
27 book
28 chapter
29 clinical practice
30 conventional methods
31 country dozens
32 data
33 data mining
34 data mining methods
35 days
36 denominator
37 desired construction
38 device
39 effective detection
40 efforts
41 epidemiological evaluation system
42 epidemiological evaluation system therefor
43 evaluation
44 events
45 excellent prediction accuracy
46 health
47 hospital infection surveillance
48 hospital information system
49 information
50 information gathering site staff
51 large-scale cohort data
52 medical data
53 medical facilities
54 model
55 multiple logistic regression model
56 neural network
57 nosocomial infection control surveillance
58 nosocomial infection incidence
59 nosocomial infection surveillance
60 nosocomial infection surveillance data mining
61 nosocomial infections
62 number
63 observation time
64 observations
65 occurrence
66 order
67 outbreak
68 outcome
69 prediction accuracy
70 purpose
71 reduction
72 results
73 series
74 specific practical information
75 start
76 statistical conventional methods
77 study
78 technique Ni'i
79 termination
80 trend
81 years
82 schema:name Effective detection of the evaluation and the outbreak of nosocomial infection surveillance data mining
83 schema:recipient https://www.grid.ac/institutes/grid.412764.2
84 schema:sameAs https://app.dimensions.ai/details/grant/grant.5971908
85 schema:sdDatePublished 2019-03-07T11:43
86 schema:sdLicense https://scigraph.springernature.com/explorer/license/
87 schema:sdPublisher N9c0b2fe5cfd34fdbb6cc44554c9a0920
88 schema:startDate 2006-01-01T00:00:00Z
89 schema:url https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-18790406
90 sgo:license sg:explorer/license/
91 sgo:sdDataset grants
92 rdf:type schema:MonetaryGrant
93 N093c885ef93a495490af63bf8d3c8d62 schema:name dimensions_id
94 schema:value 5971908
95 rdf:type schema:PropertyValue
96 N2331ad7911b4467a95335b048c166497 schema:name kaken_id
97 schema:value 18790406
98 rdf:type schema:PropertyValue
99 N9c0b2fe5cfd34fdbb6cc44554c9a0920 schema:name Springer Nature - SN SciGraph project
100 rdf:type schema:Organization
101 Ne9d2ef838ae34ad6a0dd9c6c452f8850 schema:currency JPY
102 schema:value 3100000
103 rdf:type schema:MonetaryAmount
104 anzsrc-for:2208 schema:inDefinedTermSet anzsrc-for:
105 rdf:type schema:DefinedTerm
106 anzsrc-for:2211 schema:inDefinedTermSet anzsrc-for:
107 rdf:type schema:DefinedTerm
108 https://www.grid.ac/institutes/grid.412764.2 schema:Organization
109 https://www.grid.ac/institutes/grid.54432.34 schema:Organization
 




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


...