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Name:
UMIN ID:

Recruitment status Preinitiation
Unique ID issued by UMIN UMIN000036064
Receipt No. R000041083
Scientific Title Comparison between manual analysis of medical records and analysis using machine learning
Date of disclosure of the study information 2019/04/30
Last modified on 2019/03/01

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Basic information
Public title Comparison between manual analysis of medical records and analysis using machine learning
Acronym Comparison between manual analysis of medical records and analysis using machine learning
Scientific Title Comparison between manual analysis of medical records and analysis using machine learning
Scientific Title:Acronym Comparison between manual analysis of medical records and analysis using machine learning
Region
Japan

Condition
Condition Headache
Classification by specialty
Medicine in general Emergency medicine
Classification by malignancy Others
Genomic information NO

Objectives
Narrative objectives1 Symbol sign determination is essential for constructing automatic diagnostic system using Bayes' theorem. Text mining technology has made it possible to process enormous amounts of information in a short time. Mechanical sign judgment of a medical record which is a natural language is said to be difficult due to the structure of Japanese. For the free description part in the medical record, compare the accuracy of the sign judgment of the symptom extracted comprehensively manually and the sign judgment extracted using the machine learning.
Basic objectives2 Efficacy
Basic objectives -Others
Trial characteristics_1
Trial characteristics_2
Developmental phase

Assessment
Primary outcomes Percentage of combinations that generated cross table
Sensitivity, specificity and likelihood ratio of each symptom
Key secondary outcomes

Base
Study type Observational

Study design
Basic design
Randomization
Randomization unit
Blinding
Control
Stratification
Dynamic allocation
Institution consideration
Blocking
Concealment

Intervention
No. of arms
Purpose of intervention
Type of intervention
Interventions/Control_1
Interventions/Control_2
Interventions/Control_3
Interventions/Control_4
Interventions/Control_5
Interventions/Control_6
Interventions/Control_7
Interventions/Control_8
Interventions/Control_9
Interventions/Control_10

Eligibility
Age-lower limit
20 years-old <=
Age-upper limit

Not applicable
Gender Male and Female
Key inclusion criteria Patients who visited the Tokyo Metropolitan Tama General Medical Center emergency outpatient for 2 months from May 1, 2014, with a headache complaint
Key exclusion criteria none
Target sample size 270

Research contact person
Name of lead principal investigator
1st name
Middle name
Last name Daiki Yokokawa
Organization Chiba University Hospital
Division name Department of General Medicine
Zip code
Address 1-8-1, Inohana, Chuo-ku, Chiba city
TEL 0432227171
Email dyokokawa6@chiba-u.jp

Public contact
Name of contact person
1st name
Middle name
Last name Daiki Yokokawa
Organization Chiba University Hospital
Division name Department of General Medicine
Zip code
Address 1-8-1, Inohana, Chuo-ku, Chiba city
TEL 0432227171
Homepage URL
Email dyokokawa6@chiba-u.jp

Sponsor
Institute Chiba University
Institute
Department

Funding Source
Organization None
Organization
Division
Category of Funding Organization Other
Nationality of Funding Organization

Other related organizations
Co-sponsor
Name of secondary funder(s)

IRB Contact (For public release)
Organization
Address
Tel
Email

Secondary IDs
Secondary IDs NO
Study ID_1
Org. issuing International ID_1
Study ID_2
Org. issuing International ID_2
IND to MHLW

Institutions
Institutions

Other administrative information
Date of disclosure of the study information
2019 Year 04 Month 30 Day

Related information
URL releasing protocol
Publication of results Unpublished

Result
URL related to results and publications
Number of participants that the trial has enrolled
Results
Results date posted
Results Delayed
Results Delay Reason
Date of the first journal publication of results
Baseline Characteristics
Participant flow
Adverse events
Outcome measures
Plan to share IPD
IPD sharing Plan description

Progress
Recruitment status Preinitiation
Date of protocol fixation
2019 Year 03 Month 01 Day
Date of IRB
Anticipated trial start date
2019 Year 04 Month 10 Day
Last follow-up date
Date of closure to data entry
Date trial data considered complete
Date analysis concluded

Other
Other related information Two physicians extracted all symptoms from the free entry in the medical record and signed it. Diagnosis was done using international headache classification 2nd edition, and a cross table of symptoms and diagnosis was prepared. Calculate the proportion of combinations that could produce a cross table and the sensitivity, specificity and likelihood ratio of each symptom.
Calculate the same indicator using machine learning, and compare them respectively.

Management information
Registered date
2019 Year 03 Month 01 Day
Last modified on
2019 Year 03 Month 01 Day


Link to view the page
URL(English) https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000041083

Research Plan
Registered date File name

Research case data specifications
Registered date File name

Research case data
Registered date File name


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