A Study on decision-support system of clinical laboratory tests.
| Project/Area Number |
05454601
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| Research Category |
Grant-in-Aid for General Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
情報システム学(含情報図書館学)
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| Research Institution | Nagoya University |
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Principal Investigator |
YAMAUCHI Kazunobu Nagoya Univeristy, School of Medicine, Professor, 医学部, 教授 (90126912)
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| Co-Investigator(Kenkyū-buntansha) |
FUKATSU Toshiaki Nagoya Univeristy, School of Medicine, Asistant, 医学部, 助手 (60228864)
IKEDA Mitsuru Nagoya Univeristy, School of Medicine, Asistant Professor, 医学部, 助教授 (50184437)
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| Project Period (FY) |
1993 – 1995
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| Project Status |
Completed (Fiscal Year 1995)
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| Budget Amount *help |
¥5,900,000 (Direct Cost: ¥5,900,000)
Fiscal Year 1995: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 1994: ¥1,000,000 (Direct Cost: ¥1,000,000)
Fiscal Year 1993: ¥4,100,000 (Direct Cost: ¥4,100,000)
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| Keywords | neural metwork / expert system / in then rule / Fuzzy theory / hepatic disease / decision-tree logic / 心電図自動診断 / ルーチン検査 / ファジー理論 |
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| Research Abstract |
A study on decision-support system for liver disease using clinical laboratory tests was performed. The system was composed of two steps. One is a step to use an artificial neural network and the other is a step to use medical rule base, In the former, a back propagation neural network was designed to diagnose seven categories of chronic liver diseases : chronic inactive hepatitis, chronic active hepatitis, liver cirrhosis, hepatocellular carcinoma, fatty liver, alcoholic liver desease, and normal. The output of a neural network were input to the second step for a final diagnosis. Input data to the network were 26 items of hepatic laboratory data from 187 cases. The diagnostic accuracy of the system was 75.4% (141 of 187 cases) which was higher than the diagnostic sccuracy (63.0%) of 5 hapatologists. Yhis system was seemed to be effective as a decision-support system for chronic liver disease. When Fuzzy theory was used to diagnose liver disease, high sensitivity was obtained with low specificity.
Next, we tried categorization of ten disease groups of inflammatory disease, muscular or myocardial disease, anemia, malignant tumor, reno-urinary disease, hepatobiliary disease, diabetes mellitus, gastrointestinal disease, bone disease and hyperlipidemia from 30 sets of leboratory data using artificial neural network and dicision-tree logic. Ten-disease groups were automatically classified with 80.8% of diagnostic accuracy.This system will be helpful to infer the disease groups from basic laboratory data. In conclusion, neural network in very powerful to differentiate groups which shows overlapped limits of parameters.
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Report
(4 results)
Research Products
(8 results)
