Identifying Opioid and Illicit Drug Use from Adverse Event Reported Outcomes Using Machine Learning

Earlier identification of opioid and illicit drug use may be used as a powerful tool for better guiding treatment strategies as well as appropriate triage of suspected illicit drug overdose patients. In this study, a machine learning model was used to distinguish patient drug use based solely on reported physiological events. For training and testing sets, data were derived from AEOLUS, a database of curated adverse drug reports based on the US Food and Drug Administration (FDA) Adverse Event Reporting System. Google’s TensorFlow library was used to build, train, and test the linear regression model. The positive results of this study suggest that machine learning approaches can be used to identify drugs based on reported outcomes. Introduction Toxicology screening with patient urine and blood samples has become a standard of care for patients suspected of illicit or opioid drug use [1]. Absent essential information about whether illicit or opioid drugs are involved in the manifestation of symptoms can lead to challenges in developing strategies for patient treatment. Improvements in rapidly identifying illicit drugs and opioids based on presentation of symptoms have the potential to change practice patterns, especially in acute care environments. The steady increase in drug overdose deaths since the 2000s[2] therefore calls for improved drug screening methods in the clinical setting. In emergency situations, rapid detection of exact prescription or illicit drug use can be crucial for determining proper care delivery. The availability of drug adverse event reporting provides an opportunity to build predictive models for detection of drug use. This study uses data from the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS), a database containing quarterly reports on adverse event and medication error reports submitted to FDA [3]. In the FAERS dataset, combinations of drugs are reported with combinations of resulting adverse outcomes [3], providing information linking drugs with certain physiological effects. Combined with current machine learning techniques, this information can be used to create algorithms correlating certain physiological factors with known drug outcomes. These correlations can then be used to construct predictive algorithms that determine drug use. Machine learning involves the use of data to create predictive models that can learn and improve without the aid of explicit programming. Two steps are involved in the creation of a machine learning algorithm: (1) training and (2) testing. Training involves performing statistics iteratively on a set of data until the predictions made by the model reach a certain level of accuracy. Afterwards, testing is done to further improve the model and determine the final accuracy. Machine learning techniques have been used previously in clinical settings to improve viral testing [4], graft failure prediction [5], and clinical decision making for breast cancer drug therapies[6]. Given the breadth of clinical data available, machine learning provides techniques to standardize and improve clinical care. In this study, machine learning techniques were used to explore the potential to identify opioid and illicit drug intake based on electronically captured FAERS data. Opioids and illicit drugs were tested specifically given the rise in illicit drug and opioid overdose cases nationwide[7], making drug detection increasingly important in the clinical setting. Utilizing machine learning tools provided for by TensorFlow[8], Google’s machine learning library, the goal of this study was to explore the potential to develop a predictive modeling system that does not require full patient history and makes classifications based on a patient’s current physiological state. Specific focus of this study was on four commonly prescribed opioids: (1) oxycodone; (2) hydrocodone; (3) fentanyl; and (4) morphine and three commonly abused drugs: (1) cocaine; (2) heroine; and (3) methamphetamine. The promising findings suggest that the machine learning approach employed in this study can indeed be used to rapidly identify individuals who may be at high risk of illicit or opioid drug use. Materials and Methods FAERS The FDA Adverse Event Reporting System (FAERS) is a database containing information on adverse event and medication error reports submitted to the FDA [3]. Event reports are submitted quarterly by health professionals and consumers and evaluated by clinical reviewers in the Center for Drug Evaluation and Research. Information is presented as reports stating all drugs taken by patients followed by all outcomes presented. No causal links are recorded between product and outcome. Using FAERS for mining drug-effect associations has been an active area of research and multiple data mining algorithms have been created for this purpose [9-11]. To date, there has been no reported use of predictive machine learning models for determining possible drugs based upon given events. AEOLUS From the community efforts of the Observational Health Data Science and Informatics (OHDSI) initiative, the FAERS and LAERS (Legacy Adverse Event Reporting System, which contains adverse event reports before 2012) datasets were reprocessed, cleaned, and standardized to form the Adverse Event Open Learning through Universal Standard (AEOLUS) database[15]. Single missing value imputation was first performed followed by case de-duplication. Every case was then given a primaryid or isr number, which indicate FAERS or LAERS cases respectively. Linked to each case are reported outcomes, given in OHDSI outcome concepts, and associated drugs, standardized to RxNorm Concept Unique Identifiers. Data are organized in AEOLUS into seven different MySQL tables, two of which were used in this study: one listing case ids with reported outcome concept ids and another listing case ids with standard drug concept ids. There were a total of 4245 distinct drug IDs and 17,710 distinct outcome IDs. y = Wx + b drug = W ∗ event + b Figure 1: Overview of linear regression model employed by TensorFlow. en represents event n and dm represents drug m. Wm,n represents the weight multiplied to event n for drug m and bm represents the bias for drug m. The bottom half of the figure shows the resulting matrix multiplication. Machine Learning Model A linear regression model, similar to that employed with the Mixed National Institute of Standards and Technology (MNIST), was used for training and testing. MNIST is a large database of handwritten digits that have been subjected to various machine learning methods to identify systems or approaches that achieve near-human performance. The model follows a y=Wx+b equation (Figure 1), with drugs as the dependent variable and physiological events as the independent variable. The linear model also accounted for biases (b) and contained a matrix for weights (W). The drugs and outcomes were given Boolean 0 or 1 values depending on if the drug or outcome was present in the tested case (Figure 2). For example, following Figure 1, e1 would be a Boolean for the presence of event A and would be 0 or 1 depending on if event A was present in the test case. d1 would be a Boolean for the presence of drug B and would be 0 or 1 depending on if drug B was present in the test case. W1,1 is the weight multiplied to e1 for the calculation of the probability d1 was present (Figure 1). In this study, this was implemented using TensorFlow, which is Google’s open-source software for deep neural networks, and provides a platform for accurate, large-scale machine learning research [8]. In TensorFlow, the data are vectorized into tensors and used to construct a data-flow graph. The graph is altered as more training data are deployed, adjusting the weights of the neural network with each iteration. In this study, TensorFlow was used to follow this machine learning model, iteratively adjusting weights and biases using softmax regression and loss functions. Conditions Tested This study focused on evaluating the ability to develop a prediction model using TensorFlow for effectiveness in classifying based on drug class, identifying the presence of specific drugs, and distinguishing between individual drugs. For drug class, opioids were chosen given the rising opioid epidemic [7] and four commonly prescribed opioids were specifically analyzed: (1) oxycodone; (2) hydrocodone; (3) fentanyl; and (4) morphine. For individual drug identification, three of the most common illicit drugs reported in AEOLUS were used: (1) cocaine, (2) methamphetamine, and (3) heroin. Evaluation of the model thus came from three tested conditions: (1) classification of opioid versus non-opioid; (2) prediction of illicit drug presence versus absence; (3) identification between different illicit drugs. Developing Outcome and Drug Arrays Arrays of Outcomes and Drugs were generated based on cases reported in AEOLUS. For identifying opioids, the total set of outcomes considered was narrowed down to the 35 outcomes with the highest numbers of cases. In other words, each drug-associated outcome was required to occur in a certain number of cases for each drug, and only the 35 outcomes with the highest number of cases were selected for the final outcome array (Table 1). For illicit drugs, the 20 outcomes with the highest number of cases for each illicit drug were used given the smaller number of total outcomes for illicit drugs (Table 2). Given some of the outcomes coded in the AEOLUS dataset were non-physiological, another round of testing was performed removing all non-physiological outcomes and all cases containing only non-physiological outcomes for the illicit drugs. The twenty highest physiological outcomes with the highest number of cases were used for the outcome arrays. The differences in outcome are shown in Table 3. A total of 5000 cases were used as training and test cases for each condition tested except for the between drug