Abstract
This work is intended for providing a review of reallife practical applications of Artificial Intelligence (AI) methods. We focus on the use of Machine Learning (ML) methods applied to rather real problems than synthetic problems with standard and controlled environment. In particular, we will describe the following problems in next sections: • Optimization of Erythropoietin (EPO) dosages in anaemic patients undergoing Chronic Renal Failure (CRF). • Optimization of a recommender system for citizen web portal users. • Optimization of a marketing campaign. The choice of these problems is due to their relevance and their heterogeneity. This heterogeneity shows the capabilities and versatility of ML methods to solve real-life problems in very different fields of knowledge. The following methods will be mentioned during this work: • Artificial Neural Networks (ANNs): Multilayer Perceptron (MLP), Finite Impulse Response (FIR) Neural Network, Elman Network, Self-Oganizing Maps (SOMs) and Adaptive Resonance Theory (ART). • Other clustering algorithms: K-Means, Expectation- Maximization (EM) algorithm, Fuzzy C-Means (FCM), Hierarchical Clustering Algorithms (HCA). • Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH). • Support Vector Regression (SVR). • Collaborative filtering techniques. • Reinforcement Learning (RL) methods.
TopIntroduction
This work is intended for providing a review of real-life practical applications of Artificial Intelligence (AI) methods. We focus on the use of Machine Learning (ML) methods applied to rather real problems than synthetic problems with standard and controlled environment. In particular, we will describe the following problems in next sections:
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Optimization of Erythropoietin (EPO) dosages in anaemic patients undergoing Chronic Renal Failure (CRF).
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Optimization of a recommender system for citizen web portal users.
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Optimization of a marketing campaign.
The choice of these problems is due to their relevance and their heterogeneity. This heterogeneity shows the capabilities and versatility of ML methods to solve real-life problems in very different fields of knowledge. The following methods will be mentioned during this work:
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Artificial Neural Networks (ANNs): Multilayer Perceptron (MLP), Finite Impulse Response (FIR) Neural Network, Elman Network, Self-Oganizing Maps (SOMs) and Adaptive Resonance Theory (ART).
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Other clustering algorithms: K-Means, Expectation-Maximization (EM) algorithm, Fuzzy C-Means (FCM), Hierarchical Clustering Algorithms (HCA).
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Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH).
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Support Vector Regression (SVR).
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Collaborative filtering techniques.
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Reinforcement Learning (RL) methods.
TopBackground
The aim of this communication is to emphasize the capabilities of ML methods to deliver practical and effective solutions in difficult real-world applications. In order to make the work easy to read we focus on each of the three separate domains, namely, Pharmacokinetics (PK), Web Recommender Systems and Marketing.
Pharmacokinetics
Clinical decision-making support systems have used Artificial Intelligence (AI) methods since the end of the fifties. Nevertheless, it was only during the nineties that decision support systems were routinely used in clinical practice on a significant scale. In particular, ANNs have been widely used in medical applications the last two decades (Lisboa, 2002). One of the first relevant studies involving ANNs and Therapeutic Drug Monitoring was (Gray, Ash, Jacobi, & Michel, 1991). In this work, an ANN-based drug interaction warning system was developed with a computerized real-time entry medical records system. A reference work in this field is found in (Brier, Zurada, & Aronoff, 1995), in which the capabilities of ANNs and NONMEN are benchmarked.
Focusing on problems that are closer to the real-life application that will be described in next section, there are also a number of recent works involving the use of ML for drug delivery in kidney disease. For instance, a comparison of renal-related adverse drug reactions between rofecoxib and celecoxib, based on the WHO/Uppsala Monitoring Centre safety database, was carried out by (Zhao, Reynolds, Lejkowith, Whelton, & Arellano, 2001). Disproportionality in the association between a particular drug and renal-related adverse drug reactions was evaluated using a Bayesian confidence propagation neural network method. A study of prediction of cyclosporine dosage in patients after kidney transplantation using neural networks and kernel-based methods was carried out in (Camps et al., 2003). In (Gaweda, Jacobs, Brier, & Zurada, 2003), a pharmacodynamic population analysis in CRF patients using ANNs was performed. Such models allow for adjusting the dosing regime. Finally, in (Martín et al., 2003), the use of neural networks was proposed for the optimization of EPO dosage in patients undergoing anaemia connected with CRF.
Key Terms in this Chapter
Specificity: Success rate measure in a classification problem. If there are two classes (namely, positive and negative), specificity measures the ratio of negatives that are correctly classified by the model.
Reward: In RL terms, the immediate reward is the value returned by the environment to the agent depending on the taken action. The long-term reward is the sum of all the immediate rewards throughout a complete decision process.
Sensitivity: Similar measure that offers the ratio of positives that are correctly classified by the model. (Refer to Specificity.)
Life-Time Value: It is a measure widely used in marketing applications that offers the long-term result that has to be maximized.
Agent: In RL terms, it is the responsible of making decisions according to observations of its environment.
Exploration-Explotation Dilemma: It is a classical RL dilemma, in which a trade-off solution must be achieved. Exploration means random search of new actions in order to achieve a likely (but yet unknown) better reward than all the known ones, while explotation is focused on exploiting the current knowledge for the maximization of the reward (greedy approach).
Environment: In RL terms, it is every external condition to the agent.