TY - JOUR
T1 - Deep into laboratory
T2 - An artificial intelligence approach to recommend laboratory tests
AU - Islam, Md Mohaimenul
AU - Poly, Tahmina Nasrin
AU - Yang, Hsuan Chia
AU - Li, Yu Chuan
N1 - Funding Information:
Funding Acknowledgement: This research is sponsored, in part, by the Ministry of Education (MOE) (Grant MOE DP2-110-21121-01-A-01) and the Ministry of Science and Technology (MOST) (Grant MOST 110-2321-B-038-002).
Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021
Y1 - 2021
N2 - Laboratory tests are performed to make effective clinical decisions. However, inappropriate laboratory test ordering hampers patient care and increases financial burden for healthcare. An automated laboratory test recommendation system can provide rapid and appropriate test selection, potentially improving the workflow to help physicians spend more time treating patients. The main objective of this study was to develop a deep learning-based automated system to recommend appropriate laboratory tests. A retrospective data collection was performed at the National Health Insurance database between 1 January 2013, and 31 December 2013. We included all prescriptions that had at least one laboratory test. A total of 1,463,837 prescriptions from 530,050 unique patients was included in our study. Of these patients, 296,541 were women (55.95%), the range of age was between 1 and 107 years. The deep learning (DL) model achieved a higher area under the receiver operating characteristics curve (AUROC micro = 0.98, and AUROC macro = 0.94). The findings of this study show that the DL model can accurately and efficiently identify laboratory tests. This model can be integrated into existing workflows to reduce under-and over-utilization problems.
AB - Laboratory tests are performed to make effective clinical decisions. However, inappropriate laboratory test ordering hampers patient care and increases financial burden for healthcare. An automated laboratory test recommendation system can provide rapid and appropriate test selection, potentially improving the workflow to help physicians spend more time treating patients. The main objective of this study was to develop a deep learning-based automated system to recommend appropriate laboratory tests. A retrospective data collection was performed at the National Health Insurance database between 1 January 2013, and 31 December 2013. We included all prescriptions that had at least one laboratory test. A total of 1,463,837 prescriptions from 530,050 unique patients was included in our study. Of these patients, 296,541 were women (55.95%), the range of age was between 1 and 107 years. The deep learning (DL) model achieved a higher area under the receiver operating characteristics curve (AUROC micro = 0.98, and AUROC macro = 0.94). The findings of this study show that the DL model can accurately and efficiently identify laboratory tests. This model can be integrated into existing workflows to reduce under-and over-utilization problems.
KW - Artificial intelligence
KW - Clinical decision support system
KW - Deep learning
KW - Laboratory test
KW - Recommendation system
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U2 - 10.3390/diagnostics11060990
DO - 10.3390/diagnostics11060990
M3 - Article
AN - SCOPUS:85107879111
SN - 2075-4418
VL - 11
JO - Diagnostics
JF - Diagnostics
IS - 6
M1 - 990
ER -