TY - CONF
T1 - Exploiting historical registers: Automatic methods for coding c19th and c20th cause of death descriptions to standard classifications
AU - Carson, Jamie Kirk
AU - Kirby, Graham Njal Cameron
AU - Dearle, Alan
AU - Williamson, Lee Emma Palmer
AU - Garrett, Eilidh
AU - Reid, Alice
AU - Dibben, Chris
PY - 2013/3/5
Y1 - 2013/3/5
N2 - The increasing availability of digitised registration records presents a significant opportunity for research. Returning to the original records allows researchers to classify descriptions, such as cause of death, to modern medical understandings of illness and disease, rather than relying on contemporary registrars’ classifications. Linkage of an individual’s records together also allows the production of sparse life-course micro-datasets. The further linkage of these into family units then presents the possibility of reconstructing family structures and producing multi-generational studies. We describe work to develop a method for automatically coding to standard classifications the causes of death from 8.3 million Scottish death certificates. We have evaluated a range of approaches using text processing and supervised machine learning, obtaining accuracy from 72%-96% on several test sets. We present results and speculate on further development that may be needed for classification of the full data set.
AB - The increasing availability of digitised registration records presents a significant opportunity for research. Returning to the original records allows researchers to classify descriptions, such as cause of death, to modern medical understandings of illness and disease, rather than relying on contemporary registrars’ classifications. Linkage of an individual’s records together also allows the production of sparse life-course micro-datasets. The further linkage of these into family units then presents the possibility of reconstructing family structures and producing multi-generational studies. We describe work to develop a method for automatically coding to standard classifications the causes of death from 8.3 million Scottish death certificates. We have evaluated a range of approaches using text processing and supervised machine learning, obtaining accuracy from 72%-96% on several test sets. We present results and speculate on further development that may be needed for classification of the full data set.
U2 - 10.2901/Eurostat.C2013.001
DO - 10.2901/Eurostat.C2013.001
M3 - Paper
SP - 598
EP - 607
ER -