Abstract
In psychiatry, it is common to have multiple available instruments for a concept of interest. Different studies may choose to measure the same concept with different instruments for reasons such as time and resource constraints. This makes it difficult to compare findings between studies, since differences in findings may be due to the use of different instruments. In this paper, we described and demonstrated a two step process to identify a correspondence mapping between QLS and GAF, two quality of life instruments. Specifically, we applied a Markov Boundary discovery algorithm, HITON-MB, to identify the Markov Boundary of GAF using different QLS subscales and items as candidate Markov Boundary members. The mapping function is then derived by regressing the identified Markov Boundary members to GAF. We applied the two step procedure to two independ datasets to examine the QLS-GAF correspondence across different patient population and training conditions. We identified the intrapsychic foundation subscale of QLS to be a consistent Markov Boundary member of GAF. We also reported the quality of the derived mapping functions.
Original language | English (US) |
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Title of host publication | Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 |
Editors | Illhoi Yoo, Jinbo Bi, Xiaohua Tony Hu |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 2594-2598 |
Number of pages | 5 |
ISBN (Electronic) | 9781728118673 |
DOIs | |
State | Published - Nov 2019 |
Event | 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 - San Diego, United States Duration: Nov 18 2019 → Nov 21 2019 |
Publication series
Name | Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 |
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Conference
Conference | 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 |
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Country/Territory | United States |
City | San Diego |
Period | 11/18/19 → 11/21/19 |
Bibliographical note
Funding Information:ACKNOWLEDGMENT This research is supported by 1R03MH117254-01A1 and NCRR 1UL1TR002494-01.
Publisher Copyright:
© 2019 IEEE.