Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Sep 7, 2020
Date Accepted: Sep 30, 2020
Artificial Intelligence-Based Automated Recommendation System for Clinical Laboratory Tests
ABSTRACT
Background:
Laboratory tests are considered as an essential part of patient safety as patients screening, diagnosis, and follow-up are solely based on laboratory tests. Diagnosis of patients could be wrong, missed, or delayed if laboratory tests erroneously performed. However, recognizing the value of correct laboratory test order is still underestimated by policymakers and clinicians. Nowadays, artificial intelligence (AI) such as machine learning (ML), deep learning (DL) has been extensively using as powerful tools for pattern recognition in large dataset. Therefore, developing an automated laboratory test recommendation tool using available data from the electronic health record could support current clinical practice.
Objective:
To develop an artificial intelligence-based automated model that can give laboratory test recommendations based on variables available in electronic health records.
Methods:
Retrospective analysis of national health insurance database between January 1, 2013, and December 31, 2013. We reviewed the record of all patients who visited the cardiology department and received laboratory tests. The dataset was split into training and testing sets (80:20) to develop the DL model. In the internal validation, 25 percent of data randomly selected from the training set to evaluate the performance of this current model.
Results:
We used the area under the receiver operating characteristic curve, precision, recall, hamming loss as comparative measures. A total of 129,938 prescriptions were used in our model. The DL-based automated recommendation system for laboratory tests achieved a significantly higher area under the receiver operating characteristic curve 0.85. Using low cut-off, the model identified appropriate laboratory tests with 99% sensitivity.
Conclusions:
The developed artificial intelligence based on DL, exhibited good discriminative capability for predicting laboratory tests using routinely collected electronic health record data. Utilization of DL approaches can facilitate optimal laboratory test selection for the patients which finally improves patient safety. However, future study is recommended to assess the cost-effectiveness for implementing it in real-world clinical settings. Clinical Trial: N/a
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