TY - GEN
T1 - Temporal Pattern and Association Discovery of Diagnosis Codes Using Deep Learning
AU - Mehrabi, Saaed
AU - Sohn, Sunghwan
AU - Li, Dingheng
AU - Pankratz, Joshua J.
AU - Therneau, Terry
AU - St Sauver, Jennifer L.
AU - Liu, Hongfang
AU - Palakal, Mathew
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/8
Y1 - 2015/12/8
N2 - Longitudinal health records contain data on patients' visits, condition, treatment, and test results representing progression of their health status over time. In poorly understood patient populations, such data are particularly helpful in characterizing disease progression and early detection. In this work we developed a deep learning algorithm for temporal pattern discovery over Rochester Epidemiology Project data. We modeled each patient's records as a matrix of temporal clinical events with ICD9 and HCUP CSS diagnosis codes as rows and years of diagnosis as columns. Patients aged 18 or younger at the time of diagnosis were selected. A deep Boltzmann machine network with three hidden layers was constructed with each patient's diagnosis matrix values as visible nodes. The final weights of the network model were analyzed as the common features among patients' records.
AB - Longitudinal health records contain data on patients' visits, condition, treatment, and test results representing progression of their health status over time. In poorly understood patient populations, such data are particularly helpful in characterizing disease progression and early detection. In this work we developed a deep learning algorithm for temporal pattern discovery over Rochester Epidemiology Project data. We modeled each patient's records as a matrix of temporal clinical events with ICD9 and HCUP CSS diagnosis codes as rows and years of diagnosis as columns. Patients aged 18 or younger at the time of diagnosis were selected. A deep Boltzmann machine network with three hidden layers was constructed with each patient's diagnosis matrix values as visible nodes. The final weights of the network model were analyzed as the common features among patients' records.
KW - Deep Learning
KW - Rochester Epidemiology Project
KW - Temporal Pattern Discovery
UR - http://www.scopus.com/inward/record.url?scp=84966421136&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84966421136&partnerID=8YFLogxK
U2 - 10.1109/ICHI.2015.58
DO - 10.1109/ICHI.2015.58
M3 - Conference contribution
AN - SCOPUS:84966421136
T3 - Proceedings - 2015 IEEE International Conference on Healthcare Informatics, ICHI 2015
SP - 408
EP - 416
BT - Proceedings - 2015 IEEE International Conference on Healthcare Informatics, ICHI 2015
A2 - Fu, Wai-Tat
A2 - Balakrishnan, Prabhakaran
A2 - Harabagiu, Sanda
A2 - Wang, Fei
A2 - Srivatsava, Jaideep
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE International Conference on Healthcare Informatics, ICHI 2015
Y2 - 21 October 2015 through 23 October 2015
ER -