Drug Recommendation System for Healthcare Professionals’ Decision-Making Using Opinion Mining and Machine Learning
Keikhosrokiani, Pantea; Balasubramaniam, Katheeravan; Isomursu, Minna (2024-05-05)
Springer
05.05.2024
Keikhosrokiani, P., Balasubramaniam, K., Isomursu, M. (2024). Drug Recommendation System for Healthcare Professionals’ Decision-Making Using Opinion Mining and Machine Learning. In: Särestöniemi, M., et al. Digital Health and Wireless Solutions. NCDHWS 2024. Communications in Computer and Information Science, vol 2084. Springer, Cham. https://doi.org/10.1007/978-3-031-59091-7_15
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© 2024 The Author(s). This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
https://creativecommons.org/licenses/by/4.0/
© 2024 The Author(s). This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202405063161
https://urn.fi/URN:NBN:fi:oulu-202405063161
Tiivistelmä
Abstract
The concern has been raised regarding errors in drugs prescription and medical diagnostics that need to be carefully thought through. Both patient diagnosis and medication prescription are the responsibilities of healthcare providers. As the number of people with health issues rises, the healthcare professionals’ burden is increased. Medical errors may occur in the healthcare sector as a result of healthcare professionals prescribing drugs medicines based on inadequate information related to patient history and drug side effects. Therefore, this study aims to propose a drug recommender system to assist healthcare providers in decision making when prescribing drugs for patients depending on their diagnoses. Drug reviews sentiments are analyzed to find the drug effectiveness among the users. Furthermore, the most suitable recommender algorithm for recommending drugs based on the data from healthcare professionals are selected for this study. Opinion mining is applied on drug reviews, and a hybrid method is implemented to overcome the limitations of content-based and collaborative filtering methods, such as the cold start problem and increasing client preference. The system is developed and tested successfully. The proposed system can assist healthcare professionals in drug decision making and sustain the whole digital care pathway for various diseases.
The concern has been raised regarding errors in drugs prescription and medical diagnostics that need to be carefully thought through. Both patient diagnosis and medication prescription are the responsibilities of healthcare providers. As the number of people with health issues rises, the healthcare professionals’ burden is increased. Medical errors may occur in the healthcare sector as a result of healthcare professionals prescribing drugs medicines based on inadequate information related to patient history and drug side effects. Therefore, this study aims to propose a drug recommender system to assist healthcare providers in decision making when prescribing drugs for patients depending on their diagnoses. Drug reviews sentiments are analyzed to find the drug effectiveness among the users. Furthermore, the most suitable recommender algorithm for recommending drugs based on the data from healthcare professionals are selected for this study. Opinion mining is applied on drug reviews, and a hybrid method is implemented to overcome the limitations of content-based and collaborative filtering methods, such as the cold start problem and increasing client preference. The system is developed and tested successfully. The proposed system can assist healthcare professionals in drug decision making and sustain the whole digital care pathway for various diseases.
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