TY - GEN
T1 - Separating wheat from chaff
T2 - 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Annual Conference on Innovative Applications of Artificial Intelligence, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019
AU - Nordon, Galia
AU - Koren, Gideon
AU - Shalev, Varda
AU - Horvitz, Eric
AU - Radinsky, Kira
N1 - Publisher Copyright:
© 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2019
Y1 - 2019
N2 - We present a system that jointly harnesses large-scale electronic health records data and a concept graph mined from the medical literature to guide drug repurposing-the process of applying known drugs in new ways to treat diseases. Our study is unique in methods and scope, per the scale of the concept graph and the quantity of data. We harness 10 years of nation-wide medical records of more than 1.5 million people and extract medical knowledge from all of PubMed, the world's largest corpus of online biomedical literature. We employ links on the concept graph to provide causal signals to prioritize candidate influences between medications and target diseases. We show results of the system on studies of drug repurposing for hypertension and diabetes. In both cases, we present drug families identified by the algorithm which were previously unknown. We verify the results via clinical expert opinion and by prospective clinical trials on hypertension.
AB - We present a system that jointly harnesses large-scale electronic health records data and a concept graph mined from the medical literature to guide drug repurposing-the process of applying known drugs in new ways to treat diseases. Our study is unique in methods and scope, per the scale of the concept graph and the quantity of data. We harness 10 years of nation-wide medical records of more than 1.5 million people and extract medical knowledge from all of PubMed, the world's largest corpus of online biomedical literature. We employ links on the concept graph to provide causal signals to prioritize candidate influences between medications and target diseases. We show results of the system on studies of drug repurposing for hypertension and diabetes. In both cases, we present drug families identified by the algorithm which were previously unknown. We verify the results via clinical expert opinion and by prospective clinical trials on hypertension.
UR - http://www.scopus.com/inward/record.url?scp=85090802309&partnerID=8YFLogxK
U2 - 10.1609/aaai.v33i01.33019565
DO - 10.1609/aaai.v33i01.33019565
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AN - SCOPUS:85090802309
T3 - 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019
SP - 9565
EP - 9572
BT - 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019
Y2 - 27 January 2019 through 1 February 2019
ER -