Unique ID issued by UMIN | UMIN000036064 |
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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 | 2020/09/01 09:59:29 |
Comparison between manual analysis of medical records and analysis using machine learning
Comparison between manual analysis of medical records and analysis using machine learning
Comparison between manual analysis of medical records and analysis using machine learning
Comparison between manual analysis of medical records and analysis using machine learning
Japan |
Headache
Medicine in general | Emergency medicine |
Others
NO
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.
Efficacy
Percentage of combinations that generated cross table
Sensitivity, specificity and likelihood ratio of each symptom
Observational
20 | years-old | <= |
Not applicable |
Male and Female
Patients who visited the Tokyo Metropolitan Tama General Medical Center emergency outpatient for 2 months from May 1, 2014, with a headache complaint
none
270
1st name | Daiki |
Middle name | |
Last name | Yokokawa |
Chiba University Hospital
Department of General Medicine
2608677
1-8-1, Inohana, Chuo-ku, Chiba city
0432227171
dyokokawa6@chiba-u.jp
1st name | Daiki |
Middle name | |
Last name | Yokokawa |
Chiba University Hospital
Department of General Medicine
2608677
1-8-1, Inohana, Chuo-ku, Chiba city
0432227171
dyokokawa6@chiba-u.jp
Chiba University
None
Other
Chiba University Hospital
1-8-1, Inohana, Chuo-ku, Chiba city
0432227171
dyokokawa6@chiba-u.jp
NO
2019 | Year | 04 | Month | 30 | Day |
Unpublished
Preinitiation
2019 | Year | 03 | Month | 01 | Day |
2019 | Year | 04 | Month | 10 | Day |
2020 | Year | 08 | Month | 31 | Day |
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.
2019 | Year | 03 | Month | 01 | Day |
2020 | Year | 09 | Month | 01 | Day |
Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000041083
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