Machine learning approaches for predicting the onset time of the adverse drug events in oncology
Predicting the onset time of adverse drug events can substantially lessen the negative impact on the prognosis of cancer patients who are often subject of aggressive and highly toxic treatment regimens. However, the laboratory verification of each patient case to study the mechanics of adverse drug...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-09-01
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| Series: | Machine Learning with Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666827022000615 |
| _version_ | 1798004263321862144 |
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| author | Mohan Timilsina Meera Tandan Vít Nováček |
| author_facet | Mohan Timilsina Meera Tandan Vít Nováček |
| author_sort | Mohan Timilsina |
| collection | DOAJ |
| description | Predicting the onset time of adverse drug events can substantially lessen the negative impact on the prognosis of cancer patients who are often subject of aggressive and highly toxic treatment regimens. However, the laboratory verification of each patient case to study the mechanics of adverse drug events requires costly, time-intensive research. Thus, to alleviate the efforts required to tackle this problem, using computational models is highly desirable. To provide a suite of such applicable models, we used openly available adverse drug event data resources called FAERS and explored various machine learning paradigms to assess their performance in predicting adverse effect onset days (since the beginning of the treatment). Among various machine learning approaches, we observed that the graph-based embedding model, particularly ComplEx, performed better than other, more traditional machine learning approaches. The embedding learned from the ComplEX trained with k-NN regression for the downstream predictive task obtained the lowest root mean square error, which we consider very promising for further research. |
| first_indexed | 2024-04-11T12:21:54Z |
| format | Article |
| id | doaj.art-cc2e6e3b1fa44ef89ea49b02db6531df |
| institution | Directory Open Access Journal |
| issn | 2666-8270 |
| language | English |
| last_indexed | 2024-04-11T12:21:54Z |
| publishDate | 2022-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Machine Learning with Applications |
| spelling | doaj.art-cc2e6e3b1fa44ef89ea49b02db6531df2022-12-22T04:24:05ZengElsevierMachine Learning with Applications2666-82702022-09-019100367Machine learning approaches for predicting the onset time of the adverse drug events in oncologyMohan Timilsina0Meera Tandan1Vít Nováček2Data Science Institute, Insight Centre for Data Analytics, National University of Ireland Galway, Ireland; Corresponding author.Cecil G Sheps Center for Health Service Research, University of North Carolina, Chapel Hill, USAData Science Institute, Insight Centre for Data Analytics, National University of Ireland Galway, Ireland; Faculty of Informatics, Masaryk University Brno, Czech Republic; Masaryk Memorial Cancer Institute, Brno, Czech RepublicPredicting the onset time of adverse drug events can substantially lessen the negative impact on the prognosis of cancer patients who are often subject of aggressive and highly toxic treatment regimens. However, the laboratory verification of each patient case to study the mechanics of adverse drug events requires costly, time-intensive research. Thus, to alleviate the efforts required to tackle this problem, using computational models is highly desirable. To provide a suite of such applicable models, we used openly available adverse drug event data resources called FAERS and explored various machine learning paradigms to assess their performance in predicting adverse effect onset days (since the beginning of the treatment). Among various machine learning approaches, we observed that the graph-based embedding model, particularly ComplEx, performed better than other, more traditional machine learning approaches. The embedding learned from the ComplEX trained with k-NN regression for the downstream predictive task obtained the lowest root mean square error, which we consider very promising for further research.http://www.sciencedirect.com/science/article/pii/S2666827022000615RegressionOnsetDrugsSupervisedEmbedding |
| spellingShingle | Mohan Timilsina Meera Tandan Vít Nováček Machine learning approaches for predicting the onset time of the adverse drug events in oncology Machine Learning with Applications Regression Onset Drugs Supervised Embedding |
| title | Machine learning approaches for predicting the onset time of the adverse drug events in oncology |
| title_full | Machine learning approaches for predicting the onset time of the adverse drug events in oncology |
| title_fullStr | Machine learning approaches for predicting the onset time of the adverse drug events in oncology |
| title_full_unstemmed | Machine learning approaches for predicting the onset time of the adverse drug events in oncology |
| title_short | Machine learning approaches for predicting the onset time of the adverse drug events in oncology |
| title_sort | machine learning approaches for predicting the onset time of the adverse drug events in oncology |
| topic | Regression Onset Drugs Supervised Embedding |
| url | http://www.sciencedirect.com/science/article/pii/S2666827022000615 |
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