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Probo: Smart Approval System

Objective: The results of 60% of the tests performed in biochemistry labs were evaluated using machine learning to provide an additional expert opinion on the approval status.

Method: A two-pronged approach has been developed to ensure a comprehensive evaluation of patients’ health status. This approach involves the use of automatic and smart approval systems. The automatic approval system is rule-based and is mainly deployed to identify any malfunctions caused by devices. Once the automatic approval evaluation is complete, the approval status is evaluated a second time using Support Vector Machines (SVM), Multi-layer Perceptron (MLP), and Random Forest (RF) – traditional and convolutional machine learning methods – to determine the instantaneous status of the patient. The experiments conducted using these systems have shown a success rate of 99% across 27 tests.

Innovative Features

  • In the automated approval system, test results are scanned alongside patient information. Results with low compatibility trigger further action, such as retesting or referral to a specialist.
  • Reducing error rate
  • Increasing the speed of work in the laboratory
  • Eliminating differences in decision-making among experts ensures that standard decisions are made consistently.
  • Reducing the time spent on test results that do not require expertise and enabling the focus on test results that require domain knowledge and expertise.
  • LIMS integration


Sancak Mah. Sema Yazar Sok. No:10/1 Çankaya-ANKARA

Phone: 0312 265 00 80

Fax      :
 0312 265 00 83

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