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Titolo:
MACHINE LEARNING OF MOTOR-VEHICLE ACCIDENT CATEGORIES FROM NARRATIVE DATA
Autore:
LEHTO MR; SOROCK GS;
Indirizzi:
PURDUE UNIV,SCH IND ENGN W LAFAYETTE IN 47907 LIBERTY MUTUAL RES CTR SAFETY & HLTH HOPKINTON MA 00000
Titolo Testata:
Methods of information in medicine
fascicolo: 4-5, volume: 35, anno: 1996,
pagine: 309 - 316
SICI:
0026-1270(1996)35:4-5<309:MLOMAC>2.0.ZU;2-7
Fonte:
ISI
Lingua:
ENG
Soggetto:
PREVENTION; SYSTEM;
Keywords:
NARRATIVE TEXT; BAYESIAN METHODS; EPIDEMIOLOGY;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Science Citation Index Expanded
Science Citation Index Expanded
Citazioni:
19
Recensione:
Indirizzi per estratti:
Citazione:
M.R. Lehto e G.S. Sorock, "MACHINE LEARNING OF MOTOR-VEHICLE ACCIDENT CATEGORIES FROM NARRATIVE DATA", Methods of information in medicine, 35(4-5), 1996, pp. 309-316

Abstract

Bayesian inferencing as a machine learning technique was evaluated for identifying pre-crash activity and crash type from accident narratives describing 3,686 motor vehicle crashes. It was hypothesized that a Bayesian model could learn from a computer search for 63 keywords related to accident categories. Learning was described in terms of the ability to accurately classify previously unclassifiable narratives not containing the original keywords. When narratives contained keywords, the results obtained using both the Bayesian model and keyword search corresponded closely to expert ratings (P(detection) greater than or equal to 0.9, and P(false positive)less than or equal to 0.05). For narratives not containing keywords, when the threshold used by the Bayesian model was varied between p>0.5 and p>0.9, the overall probability ofdetecting a category assigned by the expert varied between 67% and 12%. False positives correspondingly varied between 32% and 3%. These latter results demonstrated that the Bayesian system learned from the results of the keyword searches.

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Documento generato il 01/10/20 alle ore 00:11:39