Personalized anti-tumor drug efficacy prediction based on clinical data
© 2024 Published by Elsevier Ltd..
Anti-tumor drug efficacy prediction poses an unprecedented challenge to realizing personalized medicine. This paper proposes to predict personalized anti-tumor drug efficacy based on clinical data. Specifically, we encode the clinical text as numeric vectors featured with hidden topics for patients using Latent Dirichlet Allocation model. Then, to classify patients into two classes, responsive or non-responsive to a drug, drug efficacy predictors are established by machine learning based on the Latent Dirichlet Allocation topic representation. To evaluate the proposed method, we collected and collated clinical records of lung and bowel cancer patients treated with platinum. Experimental results on the data sets show the efficacy and effectiveness of the proposed method, suggesting the potential value of clinical data in cancer precision medicine. We hope that it will promote the research of drug efficacy prediction based on clinical data.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:10 |
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| Enthalten in: |
Heliyon - 10(2024), 6 vom: 30. März, Seite e27300 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Xie, Xinping [VerfasserIn] |
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| Links: |
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| Themen: |
Clinical data |
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| Anmerkungen: |
Date Revised 20.03.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1016/j.heliyon.2024.e27300 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM369906438 |
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| 520 | |a Anti-tumor drug efficacy prediction poses an unprecedented challenge to realizing personalized medicine. This paper proposes to predict personalized anti-tumor drug efficacy based on clinical data. Specifically, we encode the clinical text as numeric vectors featured with hidden topics for patients using Latent Dirichlet Allocation model. Then, to classify patients into two classes, responsive or non-responsive to a drug, drug efficacy predictors are established by machine learning based on the Latent Dirichlet Allocation topic representation. To evaluate the proposed method, we collected and collated clinical records of lung and bowel cancer patients treated with platinum. Experimental results on the data sets show the efficacy and effectiveness of the proposed method, suggesting the potential value of clinical data in cancer precision medicine. We hope that it will promote the research of drug efficacy prediction based on clinical data | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Clinical data | |
| 650 | 4 | |a Clinical decision-making | |
| 650 | 4 | |a Drug efficacy prediction | |
| 650 | 4 | |a Text mining | |
| 700 | 1 | |a Li, Dandan |e verfasserin |4 aut | |
| 700 | 1 | |a Pei, Yangyang |e verfasserin |4 aut | |
| 700 | 1 | |a Zhu, Weiwei |e verfasserin |4 aut | |
| 700 | 1 | |a Du, Xiaodong |e verfasserin |4 aut | |
| 700 | 1 | |a Jiang, Xiaodong |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Lei |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Hong-Qiang |e verfasserin |4 aut | |
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