Opioid dispensing 2008-18: a Queensland perspective.

Objective This study provides an overview of opioid dispensing in Queensland from 2008 to 2018 by recipient age, drug, oral morphine equivalent and remoteness. Methods Data were obtained from the Queensland Monitoring of Drugs of Dependence System database for 2008-18 and analysed using data from the Australian Bureau of Statistics to account for population growth. Opioid dispensing by age, drug, oral morphine equivalent and remoteness were assessed. Results The number of prescriptions for Schedule 8 opioid medicines dispensed in Queensland increased from 190 to 430 per 1000 population over the study period (2.3-fold increase). Oxycodone had the largest increase in dispensing over the study period of 3.1-fold, with tapentadol increasing rapidly since initial Pharmaceutical Benefits Scheme listing in 2013 to the third most dispensed opioid by 2018. By 2018, opioid dispensing among the oldest Queenslanders, those aged 85+ years, occurred at triple the rate for those aged 65-84 years. When adjusted to report oral morphine equivalents (OME) in milligrams (mg), there has been an increase of approximately 1.9-fold over the study period. Results were also presented by geographical area, including a heatmap and analysis by remoteness. Prescriptions dispensed per 1000 population were 416 for major cities, 551 for inner regional and 445 for outer regional, and highlight that inner and outer regional areas have higher rates of prescriptions when compared to major cities (32 and 7% higher, respectively). Conclusion This study highlights changes in opioid prescription dispensing by drug and OME, as well as the variation in dispensing rates when accounting for remoteness. Further studies to link statewide databases, and to better understand drivers for differences in dispensing by location, will provide valuable insights to further inform policy and service provision.

Predicting alcohol dependence treatment outcomes: a prospective comparative study of clinical psychologists versus 'trained' machine learning models.

BACKGROUND AND AIMS: Clinical staff are typically poor at predicting alcohol dependence treatment outcomes. Machine learning (ML) offers the potential to model complex clinical data more effectively. This study tested the predictive accuracy of ML algorithms demonstrated to be effective in predicting alcohol dependence outcomes, compared with clinical judgement and traditional linear regression. DESIGN: Prospective study. ML models were trained on 1016 previously treated patients (training-set) who attended a hospital-based alcohol and drug clinic. ML models (n = 27), clinical psychologists (n = 10) and a 'traditional' logistic regression model (n = 1) predicted treatment outcome during the initial treatment session of an alcohol dependence programme. SETTING: A 12-week cognitive behavioural therapy (CBT)-based abstinence programme for alcohol dependence in a hospital-based alcohol and drug clinic in Australia. PARTICIPANTS: Prospective predictions were made for 220 new patients (test-set; 70.91% male, mean age = 35.78 years, standard deviation = 9.19). Sixty-nine (31.36%) patients successfully completed treatment. MEASUREMENTS: Treatment success was the primary outcome variable. The cross-validated training-set accuracy of ML models was used to determine optimal parameters for selecting models for prospective prediction. Accuracy, sensitivity, specificity, area under the receiver operator curve (AUC), Brier score and calibration curves were calculated and compared across predictions. FINDINGS: The mean aggregate accuracy of the ML models (63.06%) was higher than the mean accuracy of psychologist predictions (56.36%). The most accurate ML model achieved 70% accuracy, as did logistic regression. Both were more accurate than psychologists (P < 0.05) and had superior calibration. The high specificity for the selected ML (79%) and logistic regression (90%) meant they were significantly (P < 0.001) more effective than psychologists (50%) at correctly identifying patients whose treatment was unsuccessful. For ML and logistic regression, high specificity came at the expense of sensitivity (26 and 31%, respectively), resulting in poor prediction of successful patients. CONCLUSIONS: Machine learning models and logistic regression appear to be more accurate than psychologists at predicting treatment outcomes in an abstinence programme for alcohol dependence, but sensitivity is low.

Network Analysis and Visualisation of Opioid Prescribing Data.

In many countries around the world (including Australia), the prescribing of opioid analgesic drugs is an increasing trend associated with significant increases in drug-related patient harm such as abuse, overdose, and death. In Australia, the Medicines Regulation and Quality Unit within Queensland Health maintains a database recording opioid analgesic drug prescriptions dispensed across the State (population 4.703 million). In this work, we propose the use of network visualisation and analysis as a tool for improved understanding of these data. Prescribing data for Fentanyl patches, a strong opioid with high potential for misuse and subsequent harm, across Queensland, Australia from 2011 to 2018 is analysed as an example of using network analysis, where prescribing patterns are viewed as a dynamic, bipartite graph of the interactions between patients and prescribers over time. The technique provides a global view of a large state-wide prescribing dataset, including the distribution of subgraph structures present. Local analysis is also carried out to demonstrate the clinical utility of the technique, including the dynamics of the graph structure over time. A variety of network statistics that measure network structural and dynamic properties are presented to reveal the characteristics and trends of drug seeking and prescribing behaviours. This approach has been recognised by healthcare professionals at Queensland Health as leading to new and useful insights on the relationship between patients and prescribers and supporting their advisory role to reduce patient harm from inappropriate use of prescription drugs.

Machine learning vs addiction therapists: A pilot study predicting alcohol dependence treatment outcome from patient data in behavior therapy with adjunctive medication.

BACKGROUND AND OBJECTIVES: Clinical staff providing addiction treatment predict patient outcome poorly. Prognoses based on linear statistics are rarely replicated. Addiction is a complex non-linear behavior. Incorporating non-linear models, Machine Learning (ML) has successfully predicted treatment outcome when applied in other areas of medicine. Using identical assessment data across the two groups, this study compares the accuracy of ML models versus clinical staff to predict alcohol dependence treatment outcome in behavior therapy using patient data only. METHODS: Machine learning models (n = 28) were constructed ('trained') using demographic and psychometric assessment data from 780 previously treated patients who had undertaken a 12 week, abstinence-based Cognitive Behavioral Therapy program for alcohol dependence. Independent predictions applying assessment data for an additional 50 consecutive patients were obtained from 10 experienced addiction therapists and the 28 trained ML models. The predictive accuracy of the ML models and the addiction therapists was then compared with further investigation of the 10 best models selected by cross-validated accuracy on the training-set. Variables selected as important for prediction by staff and the most accurate ML model were examined. RESULTS: The most accurate ML model (Fuzzy Unordered Rule Induction Algorithm, 74%) was significantly more accurate than the four least accurate clinical staff (51%-40%). However, the robustness of this finding may be limited by the moderate area under the receiver operator curve (AUC = 0.49). There was no significant difference in mean aggregate predictive accuracy between 10 clinical staff (56.1%) and the 28 best models (58.57%). Addiction therapists favoured demographic and consumption variables compared with the ML model using more questionnaire subscales. CONCLUSIONS: The majority of staff and ML models were not more accurate than suggested by chance. However, the best performing prediction models may provide useful adjunctive information to standard clinically available prognostic data to more effectively target treatment approaches in clinical settings.

Direct Feature Evaluation in Black-Box Optimization Using Problem Transformations.

Exploratory Landscape Analysis provides sample-based methods to calculate features of black-box optimization problems in a quantitative and measurable way. Many problem features have been proposed in the literature in an attempt to provide insights into the structure of problem landscapes and to use in selecting an effective algorithm for a given optimization problem. While there has been some success, evaluating the utility of problem features in practice presents some significant challenges. Machine learning models have been employed as part of the evaluation process, but they may require additional information about the problems as well as having their own hyper-parameters, biases and experimental variability. As a result, extra layers of uncertainty and complexity are added into the experimental evaluation process, making it difficult to clearly assess the effect of the problem features. In this article, we propose a novel method for the evaluation of problem features which can be applied directly to individual or groups of features and does not require additional machine learning techniques or confounding experimental factors. The method is based on the feature's ability to detect a prior ranking of similarity in a set of problems. Analysis of Variance (ANOVA) significance tests are used to determine if the feature has successfully distinguished the successive problems in the set. Based on ANOVA test results, a percentage score is assigned to each feature for different landscape characteristics. Experimental results for twelve different features on four problem transformations demonstrate the method and provide quantitative evidence about the ability of different problem features to detect specific properties of problem landscapes.

A Model-based Framework for Black-box Problem Comparison Using Gaussian Processes.

An important challenge in black-box optimization is to be able to understand the relative performance of different algorithms on problem instances. This challenge has motivated research in exploratory landscape analysis and algorithm selection, leading to a number of frameworks for analysis. However, these procedures often involve significant assumptions, or rely on information not typically available. In this paper we propose a new, model-based framework for the characterization of black-box optimization problems using Gaussian Process regression. The framework allows problem instances to be compared to each other in a relatively simple way. The model-based approach also allows us to assess the goodness of fit and Gaussian Processes lead to an efficient means of model comparison. The implementation of the framework is described and validated on several test sets as one benchmark problem is slowly transformed into another.

Detecting contaminated birthdates using generalized additive models.

BACKGROUND: Erroneous patient birthdates are common in health databases. Detection of these errors usually involves manual verification, which can be resource intensive and impractical. By identifying a frequent manifestation of birthdate errors, this paper presents a principled and statistically driven procedure to identify erroneous patient birthdates. RESULTS: Generalized additive models (GAM) enabled explicit incorporation of known demographic trends and birth patterns. With false positive rates controlled, the method identified birthdate contamination with high accuracy. In the health data set used, of the 58 actual incorrect birthdates manually identified by the domain expert, the GAM-based method identified 51, with 8 false positives (resulting in a positive predictive value of 86.0% (51/59) and a false negative rate of 12.0% (7/58)). These results outperformed linear time-series models. CONCLUSIONS: The GAM-based method is an effective approach to identify systemic birthdate errors, a common data quality issue in both clinical and administrative databases, with high accuracy.

Estimating the intensity of ward admission and its effect on emergency department access block.

Emergency department access block is an urgent problem faced by many public hospitals today. When access block occurs, patients in need of acute care cannot access inpatient wards within an optimal time frame. A widely held belief is that access block is the end product of a long causal chain, which involves poor discharge planning, insufficient bed capacity, and inadequate admission intensity to the wards. This paper studies the last link of the causal chain-the effect of admission intensity on access block, using data from a metropolitan hospital in Australia. We applied several modern statistical methods to analyze the data. First, we modeled the admission events as a nonhomogeneous Poisson process and estimated time-varying admission intensity with penalized regression splines. Next, we established a functional linear model to investigate the effect of the time-varying admission intensity on emergency department access block. Finally, we used functional principal component analysis to explore the variation in the daily time-varying admission intensities. The analyses suggest that improving admission practice during off-peak hours may have most impact on reducing the number of ED access blocks.

Using Gaussian process with test rejection to detect T-cell epitopes in pathogen genomes.

A major challenge in the development of peptide-based vaccines is finding the right immunogenic element, with efficient and long-lasting immunization effects, from large potential targets encoded by pathogen genomes. Computer models are convenient tools for scanning pathogen genomes to preselect candidate immunogenic peptides for experimental validation. Current methods predict many false positives resulting from a low prevalence of true positives. We develop a test reject method based on the prediction uncertainty estimates determined by Gaussian process regression. This method filters false positives among predicted epitopes from a pathogen genome. The performance of stand-alone Gaussian process regression is compared to other state-of-the-art methods using cross validation on 11 benchmark data sets. The results show that the Gaussian process method has the same accuracy as the top performing algorithms. The combination of Gaussian process regression with the proposed test reject method is used to detect true epitopes from the Vaccinia virus genome. The test rejection increases the prediction accuracy by reducing the number of false positives without sacrificing the method's sensitivity. We show that the Gaussian process in combination with test rejection is an effective method for prediction of T-cell epitopes in large and diverse pathogen genomes, where false positives are of concern.

Population-based continuous optimization, probabilistic modelling and mean shift.

Evolutionary algorithms perform optimization using a population of sample solution points. An interesting development has been to view population-based optimization as the process of evolving an explicit, probabilistic model of the search space. This paper investigates a formal basis for continuous, population-based optimization in terms of a stochastic gradient descent on the Kullback-Leibler divergence between the model probability density and the objective function, represented as an unknown density of assumed form. This leads to an update rule that is related and compared with previous theoretical work, a continuous version of the population-based incremental learning algorithm, and the generalized mean shift clustering framework. Experimental results are presented that demonstrate the dynamics of the new algorithm on a set of simple test problems.