ToxNet : an artificial intelligence designed for decision support for toxin prediction
BACKGROUND: Artificial intelligences (AIs) are emerging in the field of medical informatics in many areas. They are mostly used for diagnosis support in medical imaging but have potential uses in many other fields of medicine where large datasets are available.
AIM: To develop an artificial intelligence (AI) "ToxNet", a machine-learning based computer-aided diagnosis (CADx) system, which aims to predict poisons based on patient's symptoms and metadata from our Poison Control Center (PCC) data. To prove its accuracy and compare it against medical doctors (MDs).
METHODS: The CADx system was developed and trained using data from 781,278 calls recorded in our PCC database from 2001 to 2019. All cases were mono-intoxications. Patient symptoms and meta-information (e.g., age group, sex, etiology, toxin point of entry, weekday, etc.) were provided. In the pilot phase, the AI was trained on 10 substances, the AI's prediction was compared to naïve matching, literature matching, a multi-layer perceptron (MLP), and the graph attention network (GAT). The trained AI's accuracy was then compared to 10 medical doctors in an individual and in an identical dataset. The dataset was then expanded to 28 substances and the predictions and comparisons repeated.
RESULTS: In the pilot, the prediction performance in a set of 8995 patients with 10 substances was 0.66 ± 0.01 (F1 micro score). Our CADx system was significantly superior to naïve matching, literature matching, MLP, and GAT (p < 0.005). It outperformed our physicians experienced in clinical toxicology in the individual and identical dataset. In the extended dataset, our CADx system was able to predict the correct toxin in a set of 36,033 patients with 28 substances with an overall performance of 0.27 ± 0.01 (F1 micro score), also significantly superior to naïve matching, literature matching, MLP, and GAT. It also outperformed our MDs.
CONCLUSION: Our AI trained on a large PCC database works well for poison prediction in these experiments. With further research, it might become a valuable aid for physicians in predicting unknown substances and might be the first step into AI use in PCCs.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:61 |
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| Enthalten in: |
Clinical toxicology (Philadelphia, Pa.) - 61(2023), 1 vom: 13. Jan., Seite 56-63 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zellner, Tobias [VerfasserIn] |
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| Links: |
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| Themen: |
Artificial intelligence |
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| Anmerkungen: |
Date Completed 07.02.2023 Date Revised 17.02.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1080/15563650.2022.2144345 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM348871147 |
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a BACKGROUND: Artificial intelligences (AIs) are emerging in the field of medical informatics in many areas. They are mostly used for diagnosis support in medical imaging but have potential uses in many other fields of medicine where large datasets are available | ||
| 520 | |a AIM: To develop an artificial intelligence (AI) "ToxNet", a machine-learning based computer-aided diagnosis (CADx) system, which aims to predict poisons based on patient's symptoms and metadata from our Poison Control Center (PCC) data. To prove its accuracy and compare it against medical doctors (MDs) | ||
| 520 | |a METHODS: The CADx system was developed and trained using data from 781,278 calls recorded in our PCC database from 2001 to 2019. All cases were mono-intoxications. Patient symptoms and meta-information (e.g., age group, sex, etiology, toxin point of entry, weekday, etc.) were provided. In the pilot phase, the AI was trained on 10 substances, the AI's prediction was compared to naïve matching, literature matching, a multi-layer perceptron (MLP), and the graph attention network (GAT). The trained AI's accuracy was then compared to 10 medical doctors in an individual and in an identical dataset. The dataset was then expanded to 28 substances and the predictions and comparisons repeated | ||
| 520 | |a RESULTS: In the pilot, the prediction performance in a set of 8995 patients with 10 substances was 0.66 ± 0.01 (F1 micro score). Our CADx system was significantly superior to naïve matching, literature matching, MLP, and GAT (p < 0.005). It outperformed our physicians experienced in clinical toxicology in the individual and identical dataset. In the extended dataset, our CADx system was able to predict the correct toxin in a set of 36,033 patients with 28 substances with an overall performance of 0.27 ± 0.01 (F1 micro score), also significantly superior to naïve matching, literature matching, MLP, and GAT. It also outperformed our MDs | ||
| 520 | |a CONCLUSION: Our AI trained on a large PCC database works well for poison prediction in these experiments. With further research, it might become a valuable aid for physicians in predicting unknown substances and might be the first step into AI use in PCCs | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Toxin prediction | |
| 650 | 4 | |a artificial intelligence | |
| 650 | 4 | |a disease classification | |
| 650 | 4 | |a graph convolutional networks | |
| 650 | 4 | |a representation learning | |
| 700 | 1 | |a Romanek, Katrin |e verfasserin |4 aut | |
| 700 | 1 | |a Rabe, Christian |e verfasserin |4 aut | |
| 700 | 1 | |a Schmoll, Sabrina |e verfasserin |4 aut | |
| 700 | 1 | |a Geith, Stefanie |e verfasserin |4 aut | |
| 700 | 1 | |a Heier, Eva-Carina |e verfasserin |4 aut | |
| 700 | 1 | |a Stich, Raphael |e verfasserin |4 aut | |
| 700 | 1 | |a Burwinkel, Hendrik |e verfasserin |4 aut | |
| 700 | 1 | |a Keicher, Matthias |e verfasserin |4 aut | |
| 700 | 1 | |a Bani-Harouni, David |e verfasserin |4 aut | |
| 700 | 1 | |a Navab, Nassir |e verfasserin |4 aut | |
| 700 | 1 | |a Ahmadi, Seyed-Ahmad |e verfasserin |4 aut | |
| 700 | 1 | |a Eyer, Florian |e verfasserin |4 aut | |
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