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ToxNet: an artificial intelligence designed for decision support for toxin prediction.

Tobias Zellner¹, Katrin Romanek¹, Christian Rabe¹

¹Division of Clinical Toxicology, Department of Internal Medicine II, Poison Control Centre Munich, TUM School of Medicine, Technical University of Munich, Munich, Germany.

Clinical Toxicology (Philadelphia, Pa.)

|November 14, 2022

Summary

An artificial intelligence (AI) system called ToxNet was developed to predict poisonings using patient data. This AI significantly outperformed medical doctors in identifying toxins, showing promise for clinical toxicology applications.

Keywords:

Toxin prediction artificial intelligence disease classification graph convolutional networks representation learning

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Area of Science:

Medical Informatics
Toxicology
Artificial Intelligence

Background:

Artificial intelligences (AIs) are increasingly utilized in medical informatics, extending beyond medical imaging to diverse fields with large datasets.
Poison Control Centers (PCCs) manage extensive patient data, presenting opportunities for AI-driven insights.

Purpose of the Study:

To develop and validate an AI-based computer-aided diagnosis (CADx) system, named "ToxNet", for predicting poisons using PCC data.
To assess the accuracy of ToxNet and compare its performance against medical doctors (MDs).

Main Methods:

A CADx system was developed using a large dataset of 781,278 PCC calls (2001-2019) comprising patient symptoms and metadata.
The AI was initially trained on 10 substances and compared against various matching methods and neural networks, followed by comparison with MDs.
The system was subsequently expanded to 28 substances, with repeated predictions and comparisons.

Main Results:

In a pilot phase with 10 substances, ToxNet achieved a 0.66 F1 micro score, significantly outperforming other methods and experienced toxicologists.
In an extended dataset of 28 substances, ToxNet predicted toxins with an overall performance of 0.27 F1 micro score, again demonstrating superiority over comparative methods and MDs.

Conclusions:

An AI trained on a substantial PCC database demonstrates high efficacy in poison prediction.
ToxNet shows potential as a valuable tool for physicians in identifying unknown substances, marking a significant step towards AI integration in PCCs.