Predicting patients’ sentiments about medications using artificial intelligence techniques
Abstract The increasing development of technology has led to the increase of digital data in various fields, such as medication-related texts. Sentiment Analysis (SA) in medication is essential to give clinicians insights into patients’ feedback about the treatment procedure. Therefore, this study i...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-83222-9 |
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| author | Amir Sorayaie Azar Samin Babaei Rikan Amin Naemi Jamshid Bagherzadeh Mohasefi Uffe Kock Wiil |
| author_facet | Amir Sorayaie Azar Samin Babaei Rikan Amin Naemi Jamshid Bagherzadeh Mohasefi Uffe Kock Wiil |
| author_sort | Amir Sorayaie Azar |
| collection | DOAJ |
| description | Abstract The increasing development of technology has led to the increase of digital data in various fields, such as medication-related texts. Sentiment Analysis (SA) in medication is essential to give clinicians insights into patients’ feedback about the treatment procedure. Therefore, this study intends to develop Artificial Intelligence (AI) models to predict patients’ sentiments. This study used a large medication review dataset to perform a SA of medications. Three scenarios were considered for classification, including two, three, and ten classes. The Word2Vec algorithm and pre-trained word embeddings, including the general and clinical domains, were utilized in model development. Seven Machine Learning (ML) and Deep Learning (DL) models were developed for various scenarios. The best hyperparameters for all models were fine-tuned. Moreover, two ensemble learning models were developed from the proposed ML and DL models. For the first time, a technique was implemented to interpret the results for explainability and interpretability. The results showed that the developed deep ensemble model (DL_ENS), using PubMed and PMC, as pre-trained word embedding representation, achieved the best results, with accuracy and F1-Score of 92.96% and 92.27% in two classes, 92.18% and 88.50 in three classes, and 90.31% and 67.07% in ten classes, respectively. Combining DL models and developing a DL_ENS with clinical domain pre-trained word embedding representation can accurately predict classes and scores of patients’ sentiments about medications compared to previous studies on the same dataset. Due to the transparency in decision-making, our DL_ENS model can be used as an auxiliary tool to help clinicians prescribe medications. |
| format | Article |
| id | doaj-art-a7eee301b3324bbda44746245648b98d |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-a7eee301b3324bbda44746245648b98d2025-01-05T12:26:03ZengNature PortfolioScientific Reports2045-23222024-12-0114112310.1038/s41598-024-83222-9Predicting patients’ sentiments about medications using artificial intelligence techniquesAmir Sorayaie Azar0Samin Babaei Rikan1Amin Naemi2Jamshid Bagherzadeh Mohasefi3Uffe Kock Wiil4SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern DenmarkDepartment of Computer Engineering, Urmia UniversitySDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern DenmarkSDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern DenmarkSDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern DenmarkAbstract The increasing development of technology has led to the increase of digital data in various fields, such as medication-related texts. Sentiment Analysis (SA) in medication is essential to give clinicians insights into patients’ feedback about the treatment procedure. Therefore, this study intends to develop Artificial Intelligence (AI) models to predict patients’ sentiments. This study used a large medication review dataset to perform a SA of medications. Three scenarios were considered for classification, including two, three, and ten classes. The Word2Vec algorithm and pre-trained word embeddings, including the general and clinical domains, were utilized in model development. Seven Machine Learning (ML) and Deep Learning (DL) models were developed for various scenarios. The best hyperparameters for all models were fine-tuned. Moreover, two ensemble learning models were developed from the proposed ML and DL models. For the first time, a technique was implemented to interpret the results for explainability and interpretability. The results showed that the developed deep ensemble model (DL_ENS), using PubMed and PMC, as pre-trained word embedding representation, achieved the best results, with accuracy and F1-Score of 92.96% and 92.27% in two classes, 92.18% and 88.50 in three classes, and 90.31% and 67.07% in ten classes, respectively. Combining DL models and developing a DL_ENS with clinical domain pre-trained word embedding representation can accurately predict classes and scores of patients’ sentiments about medications compared to previous studies on the same dataset. Due to the transparency in decision-making, our DL_ENS model can be used as an auxiliary tool to help clinicians prescribe medications.https://doi.org/10.1038/s41598-024-83222-9Medication reviewsPatients’ sentiment analysisMachine learningDeep learningEnsemble learningExplainable artificial intelligence |
| spellingShingle | Amir Sorayaie Azar Samin Babaei Rikan Amin Naemi Jamshid Bagherzadeh Mohasefi Uffe Kock Wiil Predicting patients’ sentiments about medications using artificial intelligence techniques Scientific Reports Medication reviews Patients’ sentiment analysis Machine learning Deep learning Ensemble learning Explainable artificial intelligence |
| title | Predicting patients’ sentiments about medications using artificial intelligence techniques |
| title_full | Predicting patients’ sentiments about medications using artificial intelligence techniques |
| title_fullStr | Predicting patients’ sentiments about medications using artificial intelligence techniques |
| title_full_unstemmed | Predicting patients’ sentiments about medications using artificial intelligence techniques |
| title_short | Predicting patients’ sentiments about medications using artificial intelligence techniques |
| title_sort | predicting patients sentiments about medications using artificial intelligence techniques |
| topic | Medication reviews Patients’ sentiment analysis Machine learning Deep learning Ensemble learning Explainable artificial intelligence |
| url | https://doi.org/10.1038/s41598-024-83222-9 |
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