The ROC curve for determining prediction performance.

<p>The ROC curve shows the tradeoff between sensitivity and specificity (any increase in sensitivity will be accompanied by a decrease in specificity). The closer the curve is to the minimum false alarm rate (<i>x</i>-axis) and the maximum sensitivity (<i>y</i>-axis), the more accurate the test. As the ROC curve approaches <i>y = x</i>, the less accurate the test becomes. The intersection point of the ROC curve with the line <i>y = −x</i> is defined as the optimum operation point. In this ROC curve, the optimum operation point had an 80% true positive rate, with a 20% false positive rate.</p

Performance metrics.

<p>Typical performance metrics table that reports the number of True Negatives, False Positives, False Negatives, True Positives, Sensitivity and Specificity.</p

Representative <i>Giardia</i> killing curves.

<p>Killing curves generated from the average of three replicates per dose. The cell counts were compared to the control, which is typical of killing curves. Note that the general trend is towards an exponential decrease over the time interval of the study, 18 hours. In the lowest dose tested, 1.9 µg/ml, an obvious plateau is reached at 18 hours indicating an ineffective dosing, while at the other doses, the trend indicates a decline.</p

A Data-Driven Predictive Approach for Drug Delivery Using Machine Learning Techniques

<div><p>In drug delivery, there is often a trade-off between effective killing of the pathogen, and harmful side effects associated with the treatment. Due to the difficulty in testing every dosing scenario experimentally, a computational approach will be helpful to assist with the prediction of effective drug delivery methods. In this paper, we have developed a data-driven predictive system, using machine learning techniques, to determine, <em>in silico</em>, the effectiveness of drug dosing. The system framework is scalable, autonomous, robust, and has the ability to predict the effectiveness of the current drug treatment and the subsequent drug-pathogen dynamics. The system consists of a dynamic model incorporating both the drug concentration and pathogen population into distinct states. These states are then analyzed using a temporal model to describe the drug-cell interactions over time. The dynamic drug-cell interactions are learned in an adaptive fashion and used to make sequential predictions on the effectiveness of the dosing strategy. Incorporated into the system is the ability to adjust the sensitivity and specificity of the learned models based on a threshold level determined by the operator for the specific application. As a proof-of-concept, the system was validated experimentally using the pathogen <em>Giardia lamblia</em> and the drug metronidazole <em>in vitro</em>.</p> </div

Example of second order PSA built from the state sequence, .

<p>This state transition diagram shows the probability of transitioning between states. The sum of all possible states transitions equals one, indicating that a state transition must occur.</p

The Markov model state transition diagram built from an ineffective drug delivery.

<p>The “start” and “end” states are added for illustrative purpose. In this example of ineffective delivery, the model has only two transition states. When the concentration of drug is low and the current population is below the starting population, the system is more likely to remain in this state for several iterations. Eventually, however, a transition out of this state will occur resulting in a low drug concentration and a larger current population. Once in this state it is impossible to leave this state, and eventually an end state will be reached.</p

Diagram of the proposed machine learning procedure.

<p>The top part of the diagram shows the training procedure, which contains the clustering module and temporal analysis module. The bottom part shows the testing procedure.</p

The Markov model state transition diagram built from the 15 effective drug delivery trials.

<p>The “start” and “end” states are added for illustrative purposes. In the effective delivery strategy, it is possible to transition between three states. From the high drug state it is only possible to remain in that state, or transition to the medium drug state. Similarly, once in the medium drug state it is not possible to transition back to the high drug state, it is only possible to remain in that state or transition to the low drug state. Once in the low drug state, the system will remain in the state for various iterations before finally ending.</p

The prediction performance of 3-fold cross-validation for all doses.

<p>The averaged prediction accuracies for variable sequence lengths for all dosing methods. (control, 1.9 µg/ml, 6.19 µg/ml, 10.95 µg/ml, 16.67 µg/ml, 20.4 µg/ml, 32 µg/ml, and 50 µg/ml).</p

The average prediction accuracies of 3-fold cross-validation for each dose.

<p>The prediction accuracy based on sequence length shows a similar trend observed when all of the curves were averaged. As the sequence length increases for each does, the prediction accuracy increases. The only exception is the dose of 6.19 µg/ml, where the accuracy is 100% because this dose does not transition out of the initial state. In cases with more complex transitions, a larger sequence length is needed for accurate predictions.</p