» Articles » PMID: 34072571

Deep into Laboratory: An Artificial Intelligence Approach to Recommend Laboratory Tests

Overview
Specialty Radiology
Date 2021 Jun 2
PMID 34072571
Citations 6
Authors
Affiliations
Soon will be listed here.
Abstract

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.

Citing Articles

Strengths-weaknesses-opportunities-threats analysis of artificial intelligence in anesthesiology and perioperative medicine.

Paiste H, Godwin R, Smith A, Berkowitz D, Melvin R Front Digit Health. 2024; 6:1316931.

PMID: 38444721 PMC: 10912557. DOI: 10.3389/fdgth.2024.1316931.


Artificial Intelligence in Dementia: A Bibliometric Study.

Wu C, Su C, Islam M, Liao M Diagnostics (Basel). 2023; 13(12).

PMID: 37371004 PMC: 10297057. DOI: 10.3390/diagnostics13122109.


The Use of Artificial Intelligence in the Diagnosis and Classification of Thyroid Nodules: An Update.

Ludwig M, Ludwig B, Mikula A, Biernat S, Rudnicki J, Kaliszewski K Cancers (Basel). 2023; 15(3).

PMID: 36765671 PMC: 9913834. DOI: 10.3390/cancers15030708.


Artificial intelligence and thyroid disease management: considerations for thyroid function tests.

Gruson D, Dabla P, Stankovic S, Homsak E, Gouget B, Bernardini S Biochem Med (Zagreb). 2022; 32(2):020601.

PMID: 35799984 PMC: 9195598. DOI: 10.11613/BM.2022.020601.


Rise of the Machines: The Inevitable Evolution of Medicine and Medical Laboratories Intertwining with Artificial Intelligence-A Narrative Review.

Cadamuro J Diagnostics (Basel). 2021; 11(8).

PMID: 34441333 PMC: 8392825. DOI: 10.3390/diagnostics11081399.


References
1.
Schumacher L, Jager L, Meier R, Rachamin Y, Senn O, Rosemann T . Trends and Between-Physician Variation in Laboratory Testing: A Retrospective Longitudinal Study in General Practice. J Clin Med. 2020; 9(6). PMC: 7355885. DOI: 10.3390/jcm9061787. View

2.
Castelvecchi D . Can we open the black box of AI?. Nature. 2016; 538(7623):20-23. DOI: 10.1038/538020a. View

3.
Delvaux N, Piessens V, De Burghgraeve T, Mamouris P, Vaes B, Vander Stichele R . Clinical decision support improves the appropriateness of laboratory test ordering in primary care without increasing diagnostic error: the ELMO cluster randomized trial. Implement Sci. 2020; 15(1):100. PMC: 7640389. DOI: 10.1186/s13012-020-01059-y. View

4.
Lippi G, Bovo C, Ciaccio M . Inappropriateness in laboratory medicine: an elephant in the room?. Ann Transl Med. 2017; 5(4):82. PMC: 5337217. DOI: 10.21037/atm.2017.02.04. View

5.
Yeh D . A clinician's perspective on laboratory utilization management. Clin Chim Acta. 2013; 427:145-50. DOI: 10.1016/j.cca.2013.09.023. View