UMIN-CTR Clinical Trial
| Unique ID issued by UMIN | UMIN000036064 |
|---|---|
| Receipt number | 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 | 2024/09/03 09:42:49 |
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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 | Daiki |
| Middle name | |
| Last name | Yokokawa |
Organization
Chiba University Hospital
Division name
Department of General Medicine
Zip code
2608677
Address
1-8-1, Inohana, Chuo-ku, Chiba city
TEL
0432227171
dyokokawa6@chiba-u.jp
Public contact
Name of contact person
| 1st name | Daiki |
| Middle name | |
| Last name | Yokokawa |
Organization
Chiba University Hospital
Division name
Department of General Medicine
Zip code
2608677
Address
1-8-1, Inohana, Chuo-ku, Chiba city
TEL
0432227171
Homepage URL
dyokokawa6@chiba-u.jp
Sponsor or person
Institute
Chiba University
Institute
Department
Personal name
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
Chiba University Hospital
Address
1-8-1, Inohana, Chuo-ku, Chiba city
Tel
0432227171
dyokokawa6@chiba-u.jp
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
https://www.cureus.com/articles/231002-a-cross-sectional-study-on-whether-comprehensively-gathering-
Publication of results
Published
Result
URL related to results and publications
https://www.cureus.com/articles/231002-a-cross-sectional-study-on-whether-comprehensively-gathering-
Number of participants that the trial has enrolled
270
Results
Probability functions for the appearance of new unique keywords were modeled, and theoretical values were calculated. We extracted 623 unique keywords, 26 diagnoses, and 6,904 annotated keywords. Likelihood ratios could be calculated only for 276 combinations (1.70%), of which 24 (0.15%) exhibited significant differences. The power function+constant was the best fit for new unique keywords.
Results date posted
| 2024 | Year | 09 | Month | 03 | Day |
Results Delayed
Results Delay Reason
Date of the first journal publication of results
| 2024 | Year | 06 | Month | 04 | Day |
Baseline Characteristics
This was a single-center study. We retrospectively extracted the MRs of patients aged >=16 years whose chief complaint was experiencing headaches and who visited the emergency room (ER) at the Tokyo Metropolitan Tama Medical Center between May 1 and June 30, 2014. Approximately 150 patients experiencing headaches visit the hospital every month. The number of patients' records that could be annotated by reviewing all MRs was approximately 300.
Participant flow
Patients were included in the study if they visited the hospital on their own or by ambulance. In Japan, pediatricians treat patients under 16 years of age. The hospital under study does not have a pediatric department; therefore, patients under 16 years of age were excluded. Similarly, if patients were in a severe or critical condition, having experienced a stroke or shock based on vital signs and symptoms noted in the ER or ambulance, they were transferred to a critical emergency center, which became responsible for tertiary care, and thus were excluded from the study.
Adverse events
NA
Outcome measures
Diagnosis names and number of cases.
Frequency of unique keywords, annotated keywords, and calculated combinations
Operational characteristics
Plan to share IPD
IPD sharing Plan description
Progress
Recruitment status
Completed
Date of protocol fixation
| 2019 | Year | 03 | Month | 01 | Day |
Date of IRB
| 2019 | Year | 04 | Month | 22 | Day |
Anticipated trial start date
| 2019 | Year | 04 | Month | 22 | Day |
Last follow-up date
| 2020 | Year | 08 | Month | 31 | Day |
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
| 2024 | Year | 09 | Month | 03 | Day |
Link to view the page
Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000041083
