TY - GEN
T1 - Maps of human disease
T2 - 1st International Workshop on Data Management and Analytics for Medicine and Healthcare, DMAH 2015 and Workshop on Big-Graphs Online Querying, Big-O(Q) 2015 held in conjunction with 41st International Conference on Very Large Data Bases, VLDB 2015
AU - Zhou, Naiyun
AU - Saltz, Joel
AU - Mueller, Klaus
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - We present a practical framework for visual exploration of co-morbidities between diseases. By utilizing high-quality multilevel layout and clustering algorithms, we have implemented an innovative two layer multiplex network of human diseases. Specifically, we extract the International Classification of Diseases, Ninth Revision (ICD9) codes from an Electronic Medical Records (EMRs) database to build our map of human diseases. In the lower layer, the abbreviated disease terms of ICD9 codes in the irregular regions look like cities in geographical maps. The connections represent the disease pairs co-morbidities, calculated by using co-occurrence. In the upper layer, we visualize multi-object profile of clinical information. For practical application, we propose an interactive system for users to define parameters of representations of the map (see a map representation example in Fig. 1). The demonstrated visualization method offer an opportunity to visually uncover the significant information in clinical data.
AB - We present a practical framework for visual exploration of co-morbidities between diseases. By utilizing high-quality multilevel layout and clustering algorithms, we have implemented an innovative two layer multiplex network of human diseases. Specifically, we extract the International Classification of Diseases, Ninth Revision (ICD9) codes from an Electronic Medical Records (EMRs) database to build our map of human diseases. In the lower layer, the abbreviated disease terms of ICD9 codes in the irregular regions look like cities in geographical maps. The connections represent the disease pairs co-morbidities, calculated by using co-occurrence. In the upper layer, we visualize multi-object profile of clinical information. For practical application, we propose an interactive system for users to define parameters of representations of the map (see a map representation example in Fig. 1). The demonstrated visualization method offer an opportunity to visually uncover the significant information in clinical data.
KW - Clinical information visualization
KW - EMRs
KW - Graph layout algorithm
KW - Human disease comorbidity
UR - https://www.scopus.com/pages/publications/84977477228
U2 - 10.1007/978-3-319-41576-5_4
DO - 10.1007/978-3-319-41576-5_4
M3 - Conference contribution
AN - SCOPUS:84977477228
SN - 9783319415758
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 47
EP - 60
BT - Biomedical Data Management and Graph Online Querying - VLDB 2015 Workshops, Big-O(Q) and DMAH, Revised Selected Papers
A2 - Khan, Arijit
A2 - Luo, Gang
A2 - Weng, Chunhua
A2 - Wang, Fusheng
A2 - Mitra, Prasenjit
A2 - Yu, Cong
PB - Springer Verlag
Y2 - 31 August 2015 through 4 September 2015
ER -