scholarly journals A Critical Review On Predicting Drug-Drug Reactions Using Machine Learning Techniques

2021 ◽  
pp. 119-121
Author(s):  
A. Saran Kumar ◽  
Keyword(s):  
Machine Learning ◽  
Critical Review ◽  
Machine Learning Techniques ◽  
Drug Reactions ◽  
Learning Techniques

A Pharmaco-Cybernetics Approach to Patient Safety

2016 ◽  
pp. 1445-1464
Author(s):  
Kevin Yi-Lwern Yap
Keyword(s):  
Machine Learning ◽  
Patient Safety ◽  
Cancer Patients ◽  
Adverse Drug Reactions ◽  
Principal Component ◽  
Machine Learning Techniques ◽  
Drug Reactions ◽  
Internet Applications ◽  
Case Examples ◽  
Learning Techniques

Pharmaco-cybernetics is an upcoming interdisciplinary field that supports our use of medicines and drugs through the combined use of computational technologies and techniques with human-computer-environment interactions to reduce or prevent drug-related problems. The advent of pharmaco-cybernetics has led to the development of various software, tools, and Internet applications that can be used by healthcare practitioners to deliver optimum pharmaceutical care and health-related outcomes. Patients are becoming more informed through health information on the Internet, which empowers them to better participate in the management of their own conditions. Focusing on patients with cancer, this chapter describes the use of a pharmaco-cybernetics approach to identify clinically relevant predictors of two debilitating adverse drug reactions, which are a cause of patient safety – chemotherapy-induced nausea and vomiting and febrile neutropenia. The early identification of such clinical predictors enables clinicians to prevent or reduce the occurrence of adverse drug reactions in cancer patients undergoing chemotherapy through appropriate management strategies. The computational methods used in this approach involve two unsupervised machine-learning techniques – principal component and multiple correspondence analyses. Using two case examples, this chapter shows the potential of machine-learning techniques for identifying patients who are at greater risks of these adverse drug reactions, thus enhancing patient safety. This chapter also aims to increase the awareness among healthcare professionals and clinician-scientists about the usefulness of such techniques in clinical patient populations, so that these can be considered as part of clinical care pathways to enhance patient safety and effectively manage cancer patients on chemotherapy.

A Pharmaco-Cybernetics Approach to Patient Safety

2014 ◽  
pp. 179-197
Author(s):  
Kevin Yi-Lwern Yap
Keyword(s):  
Machine Learning ◽  
Patient Safety ◽  
Cancer Patients ◽  
Adverse Drug Reactions ◽  
Principal Component ◽  
Machine Learning Techniques ◽  
Drug Reactions ◽  
Internet Applications ◽  
Case Examples ◽  
Learning Techniques

Pharmaco-cybernetics is an upcoming interdisciplinary field that supports our use of medicines and drugs through the combined use of computational technologies and techniques with human-computer-environment interactions to reduce or prevent drug-related problems. The advent of pharmaco-cybernetics has led to the development of various software, tools, and Internet applications that can be used by healthcare practitioners to deliver optimum pharmaceutical care and health-related outcomes. Patients are becoming more informed through health information on the Internet, which empowers them to better participate in the management of their own conditions. Focusing on patients with cancer, this chapter describes the use of a pharmaco-cybernetics approach to identify clinically relevant predictors of two debilitating adverse drug reactions, which are a cause of patient safety – chemotherapy-induced nausea and vomiting and febrile neutropenia. The early identification of such clinical predictors enables clinicians to prevent or reduce the occurrence of adverse drug reactions in cancer patients undergoing chemotherapy through appropriate management strategies. The computational methods used in this approach involve two unsupervised machine-learning techniques – principal component and multiple correspondence analyses. Using two case examples, this chapter shows the potential of machine-learning techniques for identifying patients who are at greater risks of these adverse drug reactions, thus enhancing patient safety. This chapter also aims to increase the awareness among healthcare professionals and clinician-scientists about the usefulness of such techniques in clinical patient populations, so that these can be considered as part of clinical care pathways to enhance patient safety and effectively manage cancer patients on chemotherapy.

A critical review on predicting drug-drug reactions using machine learning techniques

2021 ◽  
Vol 16 (10) ◽  
pp. 186-188
Author(s):  
A. Saran Kumar ◽  
R. Rekha
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Computational Techniques ◽  
Learning Approaches ◽  
Drug Reactions ◽  
Advantages And Disadvantages ◽  
Learning Techniques ◽  
Comprehensive Survey

Drug-Drug interaction (DDI) refers to change in the reaction of a drug when the person consumes other drug. It is the main cause of avertable bad drug reactions causing major issues on the patient’s health and the information systems. Many computational techniques have been used to predict the adverse effects of drug-drug interactions. However, these methods do not provide adequate information required for the prediction of DDI. Machine learning algorithms provide a set of methods which can increase the accuracy and success rate for well-defined issues with abundant data. This study provides a comprehensive survey on most popular machine learning and deep learning algorithms used by the researchers to predict DDI. In addition, the advantages and disadvantages of various machine learning approaches have also been discussed here.

A Pharmaco-Cybernetics Approach to Patient Safety

2017 ◽  
pp. 1291-1310
Author(s):  
Kevin Yi-Lwern Yap
Keyword(s):  
Machine Learning ◽  
Patient Safety ◽  
Cancer Patients ◽  
Adverse Drug Reactions ◽  
Principal Component ◽  
Machine Learning Techniques ◽  
Drug Reactions ◽  
Internet Applications ◽  
Case Examples ◽  
Learning Techniques

Pharmaco-cybernetics is an upcoming interdisciplinary field that supports our use of medicines and drugs through the combined use of computational technologies and techniques with human-computer-environment interactions to reduce or prevent drug-related problems. The advent of pharmaco-cybernetics has led to the development of various software, tools, and Internet applications that can be used by healthcare practitioners to deliver optimum pharmaceutical care and health-related outcomes. Patients are becoming more informed through health information on the Internet, which empowers them to better participate in the management of their own conditions. Focusing on patients with cancer, this chapter describes the use of a pharmaco-cybernetics approach to identify clinically relevant predictors of two debilitating adverse drug reactions, which are a cause of patient safety – chemotherapy-induced nausea and vomiting and febrile neutropenia. The early identification of such clinical predictors enables clinicians to prevent or reduce the occurrence of adverse drug reactions in cancer patients undergoing chemotherapy through appropriate management strategies. The computational methods used in this approach involve two unsupervised machine-learning techniques – principal component and multiple correspondence analyses. Using two case examples, this chapter shows the potential of machine-learning techniques for identifying patients who are at greater risks of these adverse drug reactions, thus enhancing patient safety. This chapter also aims to increase the awareness among healthcare professionals and clinician-scientists about the usefulness of such techniques in clinical patient populations, so that these can be considered as part of clinical care pathways to enhance patient safety and effectively manage cancer patients on chemotherapy.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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