Predicting opioid dependence from electronic health records with machine learning

Artificial intelligence-based analysis of behavior and brain images in cocaine-self-administered marmosets

BACKGROUND

The sophisticated behavioral and cognitive repertoires of non-human primates (NHPs) make them suitable subjects for studies involving cocaine self-administration (SA) schedules. However, ethical considerations, adherence to the 3Rs principle (replacement, reduction and refinement), and other factors make it challenging to obtain NHPs individuals for research. Consequently, there is a need for methods that can comprehensively analyze small datasets using artificial intelligence (AI).

NEW METHODS

We employed AI to identify cocaine dependence patterns from collected data. First, we collected behavioral data from cocaine SA marmosets (Callithrix jacchus) to develop a dependence prediction model. SHapley Additive exPlanations (SHAP) values were used to demonstrate the importance of various variables. Additionally, we collected positron emission tomographic (PET) images showing dopamine transporter (DAT) binding potential and developed an algorithm for PET image segmentation.

RESULTS

The prediction model indicated that the Random Forest (RF) algorithm performed best, with an area under the curve (AUC) of 0.92. The top five variables influencing the model were identified using SHAP values. The PET image segmentation model achieved an accuracy of 0.97, a mean squared error of 0.02, an intersection over union (IoU) of 0.845, and a Dice coefficient of 0.913.

COMPARISON WITH EXISTING METHODS AND CONCLUSION

Utilizing data from the marmoset SA experiment, we developed an ML-based dependence prediction model and analyzed variable importance rankings using SHAP. AI-based imaging segmentation methods offer a valuable tool for evaluating DAT availability in NHPs following chronic cocaine administration.

Outcome prediction of methadone poisoning in the United States: implications of machine learning in the National Poison Data System (NPDS)

Methadone is an opioid receptor agonist with a high potential for abuse. The current study aimed to compare different machine learning models to predict the outcomes following methadone poisoning. This six-year retrospective longitudinal study utilizes National Poison Data System (NPDS) data. The severity of outcomes was derived from the NPDS Coding Manual. Our database was divided into training (70%) and test (30%) sets. We used a light gradient boosting machine (LGBM), extreme gradient boosting (XGBoost), random forest (RF), and logistic regression (LR) to predict the outcomes of methadone poisoning. A total of 3847 patients with methadone exposures were included. Our results demonstrated that machine learning models conferred high accuracy and reliability in determining the outcomes of methadone poisoning cases. The performance evaluation showed all models had high accuracy, precision, specificity, recall, and F1-score values. All models could reach high specificity (more than 96%) and high precision (80% or more) for predicting major outcomes. The models could also achieve a high sensitivity to predict minor outcomes. Finally, the accuracy of all models was about 75%. However, XGBoost and LGBM models achieved the best performance among all models. This study showcased the accuracy and reliability of machine learning models in the outcome prediction of methadone poisoning.

Mitigating Sociodemographic Bias in Opioid Use Disorder Prediction: Fairness-Aware Machine Learning Framework

BACKGROUND

Opioid use disorder (OUD) is a critical public health crisis in the United States, affecting >5.5 million Americans in 2021. Machine learning has been used to predict patient risk of incident OUD. However, little is known about the fairness and bias of these predictive models.

OBJECTIVE

The aims of this study are two-fold: (1) to develop a machine learning bias mitigation algorithm for sociodemographic features and (2) to develop a fairness-aware weighted majority voting (WMV) classifier for OUD prediction.

METHODS

We used the 2020 National Survey on Drug and Health data to develop a neural network (NN) model using stochastic gradient descent (SGD; NN-SGD) and an NN model using Adam (NN-Adam) optimizers and evaluated sociodemographic bias by comparing the area under the curve values. A bias mitigation algorithm, based on equality of odds, was implemented to minimize disparities in specificity and recall. Finally, a WMV classifier was developed for fairness-aware prediction of OUD. To further analyze bias detection and mitigation, we did a 1-N matching of OUD to non-OUD cases, controlling for socioeconomic variables, and evaluated the performance of the proposed bias mitigation algorithm and WMV classifier.

RESULTS

Our bias mitigation algorithm substantially reduced bias with NN-SGD, by 21.66% for sex, 1.48% for race, and 21.04% for income, and with NN-Adam by 16.96% for sex, 8.87% for marital status, 8.45% for working condition, and 41.62% for race. The fairness-aware WMV classifier achieved a recall of 85.37% and 92.68% and an accuracy of 58.85% and 90.21% using NN-SGD and NN-Adam, respectively. The results after matching also indicated remarkable bias reduction with NN-SGD and NN-Adam, respectively, as follows: sex (0.14% vs 0.97%), marital status (12.95% vs 10.33%), working condition (14.79% vs 15.33%), race (60.13% vs 41.71%), and income (0.35% vs 2.21%). Moreover, the fairness-aware WMV classifier achieved high performance with a recall of 100% and 85.37% and an accuracy of 73.20% and 89.38% using NN-SGD and NN-Adam, respectively.

CONCLUSIONS

The application of the proposed bias mitigation algorithm shows promise in reducing sociodemographic bias, with the WMV classifier confirming bias reduction and high performance in OUD prediction.

Prediction of sustained opioid use in children and adolescents using machine learning

BACKGROUND

Opioid misuse in the paediatric population is understudied. This study aimed to develop a machine learning classifier to differentiate between occasional and sustained opioid users among children and adolescents in outpatient settings.

METHODS

Data for 29,335 patients under 19 yr with recorded opioid purchases were collected from medical records. Machine learning methods were applied to predict sustained opioid use within 1, 2, or 3 yr after first opioid use, using sociodemographic information, medical history, and healthcare usage variables collected near the time of first prescription fulfilment. The models' performance was evaluated with classification and calibration metrics, and a decision curve analysis. An online tool was deployed for model self-exploration and visualisation.

RESULTS

The models demonstrated good performance, with a 1-yr follow-up model achieving a sensitivity of 0.772, a specificity of 0.703, and an ROC-AUC of 0.792 on an independent test set, with calibration intercept and slope of 0.00 and 1.02, respectively. Decision curve analysis revealed the clinical benefit of using the model relative to other strategies. SHAP analysis (SHapley Additive exPlanations) identified influential variables, including the number of diagnoses, medical images, laboratory tests, and type of opioid used.

CONCLUSIONS

Our model showed promising performance in predicting sustained opioid use among paediatric patients. The online risk prediction tool can facilitate compliance to such tools by clinicians. This study presents the potential of machine learning in identifying at-risk paediatric populations for sustained opioid use, potentially contributing to secondary prevention of opioid abuse.

Development and validation of machine-learning algorithms predicting retention, overdoses, and all-cause mortality among US military veterans treated with buprenorphine for opioid use disorder

BACKGROUND

Buprenorphine for opioid use disorder (B-MOUD) is essential to improving patient outcomes; however, retention is essential.

OBJECTIVE

To develop and validate machine-learning algorithms predicting retention, overdoses, and all-cause mortality among US military veterans initiating B-MOUD.

METHODS

Veterans initiating B-MOUD from fiscal years 2006-2020 were identified. Veterans' B-MOUD episodes were randomly divided into training (80%;n = 45,238) and testing samples (20%;n = 11,309). Candidate algorithms [multiple logistic regression, least absolute shrinkage and selection operator regression, random forest (RF), gradient boosting machine (GBM), and deep neural network (DNN)] were used to build and validate classification models to predict six binary outcomes: 1) B-MOUD retention, 2) any overdose, 3) opioid-related overdose, 4) overdose death, 5) opioid overdose death, and 6) all-cause mortality. Model performance was assessed using standard classification statistics [e.g., area under the receiver operating characteristic curve (AUC-ROC)].

RESULTS

Episodes in the training sample were 93.0% male, 78.0% White, 72.3% unemployed, and 48.3% had a concurrent drug use disorder. The GBM model slightly outperformed others in predicting B-MOUD retention (AUC-ROC = 0.72). RF models outperformed others in predicting any overdose (AUC-ROC = 0.77) and opioid overdose (AUC-ROC = 0.77). RF and GBM outperformed other models for overdose death (AUC-ROC = 0.74 for both), and RF and DNN outperformed other models for opioid overdose death (RF AUC-ROC = 0.79; DNN AUC-ROC = 0.78). RF and GBM also outperformed other models for all-cause mortality (AUC-ROC = 0.76 for both). No single predictor accounted for >3% of the model's variance.

CONCLUSIONS

Machine-learning algorithms can accurately predict OUD-related outcomes with moderate predictive performance; however, prediction of these outcomes is driven by many characteristics.

Predictive Models to Assess Risk of Persistent Opioid Use, Opioid Use Disorder, and Overdose

BACKGROUND

This systematic review summarizes the development, accuracy, quality, and clinical utility of predictive models to assess the risk of opioid use disorder (OUD), persistent opioid use, and opioid overdose.

METHODS

In accordance with Preferred Reporting Items for a Systematic Review and Meta-analysis guidelines, 8 electronic databases were searched for studies on predictive models and OUD, overdose, or persistent use in adults until June 25, 2023. Study selection and data extraction were completed independently by 2 reviewers. Risk of bias of included studies was assessed independently by 2 reviewers using the Prediction model Risk of Bias ASsessment Tool (PROBAST).

RESULTS

The literature search yielded 3130 reports; after removing 199 duplicates, excluding 2685 studies after abstract review, and excluding 204 studies after full-text review, the final sample consisted of 41 studies that developed more than 160 predictive models. Primary outcomes included opioid overdose (31.6% of studies), OUD (41.4%), and persistent opioid use (17%). The most common modeling approach was regression modeling, and the most common predictors included age, sex, mental health diagnosis history, and substance use disorder history. Most studies reported model performance via the c statistic, ranging from 0.507 to 0.959; gradient boosting tree models and neural network models performed well in the context of their own study. One study deployed a model in real time. Risk of bias was predominantly high; concerns regarding applicability were predominantly low.

CONCLUSIONS

Models to predict opioid-related risks are developed using diverse data sources and predictors, with a wide and heterogenous range of accuracy metrics. There is a need for further research to improve their accuracy and implementation.

Assessment of potential transthyretin amyloid cardiomyopathy cases in the Brazilian public health system using a machine learning model

OBJECTIVES

To identify and describe the profile of potential transthyretin cardiac amyloidosis (ATTR-CM) cases in the Brazilian public health system (SUS), using a predictive machine learning (ML) model.

METHODS

This was a retrospective descriptive database study that aimed to estimate the frequency of potential ATTR-CM cases in the Brazilian public health system using a supervised ML model, from January 2015 to December 2021. To build the model, a list of ICD-10 codes and procedures potentially related with ATTR-CM was created based on literature review and validated by experts.

RESULTS

From 2015 to 2021, the ML model classified 262 hereditary ATTR-CM (hATTR-CM) and 1,581 wild-type ATTR-CM (wtATTR-CM) potential cases. Overall, the median age of hATTR-CM and wtATTR-CM patients was 66.8 and 59.9 years, respectively. The ICD-10 codes most presented as hATTR-CM and wtATTR-CM were related to heart failure and arrythmias. Regarding the therapeutic itinerary, 13% and 5% of hATTR-CM and wtATTR-CM received treatment with tafamidis meglumine, respectively, while 0% and 29% of hATTR-CM and wtATTR-CM were referred to heart transplant.

CONCLUSION

Our findings may be useful to support the development of health guidelines and policies to improve diagnosis, treatment, and to cover unmet medical needs of patients with ATTR-CM in Brazil.

Opioid prescription and opioid disorders in burns: A large database analysis from 1990 to 2019

BACKGROUND

Opioids remain crucial in the management of burn pain. A comprehensive analysis of opioid use in burns and their complications has not been investigated.

METHODS

Data were collected from TriNetX, a large multicenter database with de-identified patient information. The population included patients prescribed opioids on or following burn injury from January 1st, 1990, to December 31st, 2019. Opioid prescription use was analyzed after cohort stratification by decades: 1990-1999, 2000-2009, and 2010-2019. Outcomes for opioid-related disorders, opioid dependence, opioid abuse, intentional self-harm, and mental and behavioral disorders from psychoactive substance use were investigated.

RESULTS

Hydrocodone was the most frequently prescribed opioid in 1990-1999 and 2000-2009, with oxycodone taking the lead in 2010-2019 (p < 0.0001). During 1990-1999, patients had a decreased risk of recorded opioid-related disorders (RR=0.52), opioid dependence (RR=0.46), opioid abuse (RR=0.55), mental and behavioral disorders (RR=0.88), and intentional self-harm (RR=0.37) when compared to 2000-2009. A comparison of the 2000-2009-2010-2019 cohorts showed an increased risk of recorded opioid-related disorders (RR= 1.91), opioid dependence (RR=1.56), opioid abuse (RR=1.67), mental and behavioral disorders (RR =1.73), and intentional self-harm (RR=2.02).

CONCLUSIONS

The risk of opioid-related disorders has nearly doubled since the year 2000 warranting precautions when prescribing pain medications to burn patients.

Providers' Perceptions of the Effectiveness of Electronic Health Records in Identifying Opioid Misuse

GOAL

This study aimed to understand prescribing providers' perceptions of electronic health record (EHR) effectiveness in enabling them to identify and prevent opioid misuse and addiction.

METHODS

We used a cross-sectional survey designed and administered by KLAS Research to examine healthcare providers' perceptions of their experiences with EHR systems. Univariate analysis and mixed-effects logistic regression analysis with organization-level random effects were performed.

PRINCIPAL FINDINGS

A total of 17,790 prescribing providers responded to the survey question related to this article's primary outcome about opioid misuse prevention. Overall, 34% of respondents believed EHRs helped prevent opioid misuse and addiction. Advanced practice providers were more likely than attending physicians and trainees to believe EHRs were effective in reducing opioid misuse, as were providers with fewer than 5 years of experience.

PRACTICAL APPLICATIONS

Understanding providers' perceptions of EHR effectiveness is critical as the health outcome of reducing opioid misuse depends upon their willingness to adopt and apply new technology to their standardized routines. Healthcare managers can enhance providers' use of EHRs to facilitate the prevention of opioid misuse with ongoing training related to advanced EHR system features.

Process, structural, and outcome quality indicators to support perioperative opioid stewardship: a rapid review

Opioids are effective analgesics but can cause harm. Opioid stewardship is key to ensuring that opioids are used effectively and safely. There is no agreed set of quality indicators relating to the use of opioids perioperatively. This work is part of the Yorkshire Cancer Research Bowel Cancer Quality Improvement programme and aims to develop useful quality indicators for the improvement of care and patient outcomes at all stages of the perioperative journey.A rapid review was performed to identify original research and reviews in which quality indicators for perioperative opioid use are described. A data tool was developed to enable reliable and reproducible extraction of opioid quality indicators.A review of 628 abstracts and 118 full-text publications was undertaken. Opioid quality indicators were identified from 47 full-text publications. In total, 128 structure, process and outcome quality indicators were extracted. Duplicates were merged, with the final extraction of 24 discrete indicators. These indicators are based on five topics: patient education, clinician education, pre-operative optimization, procedure, and patient-specific prescribing and de-prescribing and opioid-related adverse drug events.The quality indicators are presented as a toolkit to contribute to practical opioid stewardship. Process indicators were most commonly identified and contribute most to quality improvement. Fewer quality indicators relating to intraoperative and immediate recovery stages of the patient journey were identified. An expert clinician panel will be convened to agree which of the quality indicators identified will be most valuable in our region for the management of patients undergoing surgery for bowel cancer.

Opioid-related harms and care impacts of conventional and AI-based prescription management strategies: insights from leveraging agent-based modeling and machine learning

Introduction

Like its counterpart to the south, Canada ranks among the top five countries with the highest rates of opioid prescriptions. With many suffering from opioid use disorder first having encountered opioids via prescription routes, practitioners and health systems have an enduring need to identify and effectively respond to the problematic use of opioid prescription. There are strong challenges to successfully addressing this need: importantly, the patterns of prescription fulfillment that signal opioid abuse can be subtle and difficult to recognize, and overzealous enforcement can deprive those with legitimate pain management needs the appropriate care. Moreover, injudicious responses risk shifting those suffering from early-stage abuse of prescribed opioids to illicitly sourced street alternatives, whose varying dosage, availability, and the risk of adulteration can pose grave health risks.

Methods

This study employs a dynamic modeling and simulation to evaluate the effectiveness of prescription regimes employing machine learning monitoring programs to identify the patients who are at risk of opioid abuse while being treated with prescribed opioids. To this end, an agent-based model was developed and implemented to examine the effect of reduced prescribing and prescription drug monitoring programs on overdose and escalation to street opioids among patients, and on the legitimacy of fulfillments of opioid prescriptions over a 5-year time horizon. A study released by the Canadian Institute for Health Information was used to estimate the parameter values and assist in the validation of the existing agent-based model.

Results and discussion

The model estimates that lowering the prescription doses exerted the most favorable impact on the outcomes of interest over 5 years with a minimum burden on patients with a legitimate need for pharmaceutical opioids. The accurate conclusion about the impact of public health interventions requires a comprehensive set of outcomes to test their multi-dimensional effects, as utilized in this research. Finally, combining machine learning and agent-based modeling can provide significant advantages, particularly when using the latter to gain insights into the long-term effects and dynamic circumstances of the former.

Outcome class imbalance and rare events: An underappreciated complication for overdose risk prediction modeling

BACKGROUND AND AIMS

Low outcome prevalence, often observed with opioid-related outcomes, poses an underappreciated challenge to accurate predictive modeling. Outcome class imbalance, where non-events (i.e. negative class observations) outnumber events (i.e. positive class observations) by a moderate to extreme degree, can distort measures of predictive accuracy in misleading ways, and make the overall predictive accuracy and the discriminatory ability of a predictive model appear spuriously high. We conducted a simulation study to measure the impact of outcome class imbalance on predictive performance of a simple SuperLearner ensemble model and suggest strategies for reducing that impact.

DESIGN, SETTING, PARTICIPANTS

Using a Monte Carlo design with 250 repetitions, we trained and evaluated these models on four simulated data sets with 100 000 observations each: one with perfect balance between events and non-events, and three where non-events outnumbered events by an approximate factor of 10:1, 100:1, and 1000:1, respectively.

MEASUREMENTS

We evaluated the performance of these models using a comprehensive suite of measures, including measures that are more appropriate for imbalanced data.

FINDINGS

Increasing imbalance tended to spuriously improve overall accuracy (using a high threshold to classify events vs non-events, overall accuracy improved from 0.45 with perfect balance to 0.99 with the most severe outcome class imbalance), but diminished predictive performance was evident using other metrics (corresponding positive predictive value decreased from 0.99 to 0.14).

CONCLUSION

Increasing reliance on algorithmic risk scores in consequential decision-making processes raises critical fairness and ethical concerns. This paper provides broad guidance for analytic strategies that clinical investigators can use to remedy the impacts of outcome class imbalance on risk prediction tools.

Machine learning for predicting opioid use disorder from healthcare data: A systematic review

INTRODUCTION

The US opioid epidemic has been one of the leading causes of injury-related deaths according to the CDC Injury Center. The increasing availability of data and tools for machine learning (ML) resulted in more researchers creating datasets and models to help analyze and mitigate the crisis. This review investigates peer-reviewed journal papers that applied ML models to predict opioid use disorder (OUD). The review is split into two parts. The first part summarizes the current research in OUD prediction with ML. The second part evaluates how ML techniques and processes were used to achieve these results and suggests improvements to refine further attempts to use ML for OUD prediction.

METHODS

The review includes peer-reviewed journal papers published on or after 2012 that use healthcare data to predict OUD. We searched Google Scholar, Semantic Scholar, PubMed, IEEE Xplore, and Science.gov in September of 2022. Data extracted includes the study's goal, dataset used, cohort selected, types of ML models created, model evaluation metrics, and the details of the ML tools and techniques used to create the models.

RESULTS

The review analyzed 16 papers. Three papers created their dataset, five used a publicly available dataset, and the remaining eight used a private dataset. Cohort size ranged from the low hundreds to over half a million. Six papers used one type of ML model, and the remaining ten used up to five different ML models. The reported ROC AUC was higher than 0.8 for all but one of the papers. Five papers used only non-interpretable models, and the other 11 used interpretable models exclusively or in combination with non-interpretable ones. The interpretable models were the highest or second-highest ROC AUC values. Most papers did not sufficiently describe the ML techniques and tools used to produce their results. Only three papers published their source code.

CONCLUSIONS

We found that while there are indications that ML methods applied to OUD prediction may be valuable, the lack of details and transparency in creating the ML models limits their usefulness. We end the review with recommendations to improve studies on this critical healthcare subject.

A deep learning method to detect opioid prescription and opioid use disorder from electronic health records

OBJECTIVE

As the opioid epidemic continues across the United States, methods are needed to accurately and quickly identify patients at risk for opioid use disorder (OUD). The purpose of this study is to develop two predictive algorithms: one to predict opioid prescription and one to predict OUD.

MATERIALS AND METHODS

We developed an informatics algorithm that trains two deep learning models over patient Electronic Health Records (EHRs) using the MIMIC-III database. We utilize both the structured and unstructured parts of the EHR and show that it is possible to predict both challenging outcomes.

RESULTS

Our deep learning models incorporate elements from EHRs to predict opioid prescription with an F1-score of 0.88 ± 0.003 and an AUC-ROC of 0.93 ± 0.002. We also constructed a model to predict OUD diagnosis achieving an F1-score of 0.82 ± 0.05 and AUC-ROC of 0.94 ± 0.008.

DISCUSSION

Our model for OUD prediction outperformed prior algorithms for specificity, F1 score and AUC-ROC while achieving equivalent sensitivity. This demonstrates the importance of a) deep learning approaches in predicting OUD and b) incorporating both structured and unstructured data for this prediction task. No prediction models for opioid prescription as an outcome were found in the literature and therefore our model is the first to predict opioid prescribing behavior.

CONCLUSION

Algorithms such as those described in this paper will become increasingly important to understand the drivers underlying this national epidemic.

Predicting Patient Satisfaction With Medications for Treating Opioid Use Disorder: Case Study Applying Natural Language Processing to Reviews of Methadone and Buprenorphine/Naloxone on Health-Related Social Media

Background

Medication-assisted treatment (MAT) is an effective method for treating opioid use disorder (OUD), which combines behavioral therapies with one of three Food and Drug Administration-approved medications: methadone, buprenorphine, and naloxone. While MAT has been shown to be effective initially, there is a need for more information from the patient perspective about the satisfaction with medications. Existing research focuses on patient satisfaction with the entirety of the treatment, making it difficult to determine the unique role of medication and overlooking the views of those who may lack access to treatment due to being uninsured or concerns over stigma. Studies focusing on patients' perspectives are also limited by the lack of scales that can efficiently collect self-reports across domains of concerns.

Objective

A broad survey of patients' viewpoints can be obtained through social media and drug review forums, which are then assessed using automated methods to discover factors associated with medication satisfaction. Because the text is unstructured, it may contain a mix of formal and informal language. The primary aim of this study was to use natural language processing methods on text posted on health-related social media to detect patients' satisfaction with two well-studied OUD medications: methadone and buprenorphine/naloxone.

Methods

We collected 4353 patient reviews of methadone and buprenorphine/naloxone from 2008 to 2021 posted on WebMD and Drugs.com. To build our predictive models for detecting patient satisfaction, we first employed different analyses to build four input feature sets using the vectorized text, topic models, duration of treatment, and biomedical concepts by applying MetaMap. We then developed six prediction models: logistic regression, Elastic Net, least absolute shrinkage and selection operator, random forest classifier, Ridge classifier, and extreme gradient boosting to predict patients' satisfaction. Lastly, we compared the prediction models' performance over different feature sets.

Results

Topics discovered included oral sensation, side effects, insurance, and doctor visits. Biomedical concepts included symptoms, drugs, and illnesses. The F-score of the predictive models across all methods ranged from 89.9% to 90.8%. The Ridge classifier model, a regression-based method, outperformed the other models.

Conclusions

Assessment of patients' satisfaction with opioid dependency treatment medication can be predicted using automated text analysis. Adding biomedical concepts such as symptoms, drug name, and illness, along with the duration of treatment and topic models, had the most benefits for improving the prediction performance of the Elastic Net model compared to other models. Some of the factors associated with patient satisfaction overlap with domains covered in medication satisfaction scales (eg, side effects) and qualitative patient reports (eg, doctors' visits), while others (insurance) are overlooked, thereby underscoring the value added from processing text on online health forums to better understand patient adherence.

Individualized Prospective Prediction of Opioid Use Disorder

OBJECTIVE

Opioid use disorder (OUD) is a chronic relapsing disorder with a problematic pattern of opioid use, affecting nearly 27 million people worldwide. Machine learning (ML)-based prediction of OUD may lead to early detection and intervention. However, most ML prediction studies were not based on representative data sources and prospective validations, limiting their potential to predict future new cases. In the current study, we aimed to develop and prospectively validate an ML model that could predict individual OUD cases based on representative large-scale health data.

METHOD

We present an ensemble machine-learning model trained on a cross-linked Canadian administrative health data set from 2014 to 2018 (n  =  699,164), with validation of model-predicted OUD cases on a hold-out sample from 2014 to 2018 (n  =  174,791) and prospective prediction of OUD cases on a non-overlapping sample from 2019 (n  =  316,039). We used administrative records of OUD diagnosis for each subject based on International Classification of Diseases (ICD) codes.

RESULTS

With 6409 OUD cases in 2019 (mean [SD], 45.34 [14.28], 3400 males), our model prospectively predicted OUD cases at a high accuracy (balanced accuracy, 86%, sensitivity, 93%; specificity 79%). In accord with prior findings, the top risk factors for OUD in this model were opioid use indicators and a history of other substance use disorders.

CONCLUSION

Our study presents an individualized prospective prediction of OUD cases by applying ML to large administrative health datasets. Such prospective predictions based on ML would be essential for potential future clinical applications in the early detection of OUD.

Using machine learning to study the effect of medication adherence in Opioid Use Disorder

BACKGROUND

Opioid Use Disorder (OUD) and opioid overdose (OD) impose huge social and economic burdens on society and health care systems. Research suggests that Medication for Opioid Use Disorder (MOUD) is effective in the treatment of OUD. We use machine learning to investigate the association between patient's adherence to prescribed MOUD along with other risk factors in patients diagnosed with OUD and potential OD following the treatment.

METHODS

We used longitudinal Medicaid claims for two selected US states to subset a total of 26,685 patients with OUD diagnosis and appropriate Medicaid coverage between 2015 and 2018. We considered patient age, sex, region level socio-economic data, past comorbidities, MOUD prescription type and other selected prescribed medications along with the Proportion of Days Covered (PDC) as a proxy for adherence to MOUD as predictive variables for our model, and overdose events as the dependent variable. We applied four different machine learning classifiers and compared their performance, focusing on the importance and effect of PDC as a variable. We also calculated results based on risk stratification, where our models separate high risk individuals from low risk, to assess usefulness in clinical decision-making.

RESULTS

Among the selected classifiers, the XGBoost classifier has the highest AUC (0.77) closely followed by the Logistic Regression (LR). The LR has the best stratification result: patients in the top 10% of risk scores account for 35.37% of overdose events over the next 12 month observation period. PDC score calculated over the treatment window is one of the most important features, with better PDC lowering risk of OD, as expected. In terms of risk stratification results, of the 35.37% of overdose events that the predictive model could detect within the top 10% of risk scores, 72.3% of these cases were non-adherent in terms of their medication (PDC <0.8). Targeting the top 10% outcome of the predictive model could decrease the total number of OD events by 10.4%.

CONCLUSIONS

The best performing models allow identification of, and focus on, those at high risk of opioid overdose. With MOUD being included for the first time as a factor of interest, and being identified as a significant factor, outreach activities related to MOUD can be targeted at those at highest risk.

Machine Learning Approaches to Define Candidates for Ambulatory Single Level Laminectomy Surgery

STUDY DESIGN

retrospective cohort study.

OBJECTIVES

To test and compare 2 machine learning algorithms to define characteristics associated with candidates for ambulatory same day laminectomy surgery.

METHODS

The American College of Surgeons National Surgical Quality Improvement Program database was queried for patients who underwent single level laminectomy in 2017 and 2018. The main outcome was ambulatory same day discharge. Study variables of interest included demographic information, comorbidities, preoperative laboratory values, and intra-operative information. Two machine learning predictive modeling algorithms, artificial neural network (ANN) and random forest, were trained to predict same day discharge. The quality of models was evaluated with area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) measures.

RESULTS

Among 35,644 patients, 13,230 (37.1%) were discharged on the day of surgery. Both ANN and RF demonstrated a satisfactory model quality in terms of AUC (0.77 and 0.77), accuracy (0.69 and 0.70), sensitivity (0.83 and 0.58), specificity (0.55 and 0.80), PPV (0.77 and 0.69), and NPV (0.64 and 0.70). Both models highlighted several important predictive variables, including age, duration of operation, body mass index and preoperative laboratory values including, hematocrit, platelets, white blood cells, and alkaline phosphatase.

CONCLUSION

Machine learning approaches provide a promising tool to identify candidates for ambulatory laminectomy surgery. Both machine learning algorithms highlighted the as yet unrecognized importance of preoperative laboratory testing on patient pathway design.

Prescription quantity and duration predict progression from acute to chronic opioid use in opioid-naïve Medicaid patients

Opiates used for acute pain are an established risk factor for chronic opioid use (COU). Patient characteristics contribute to progression from acute opioid use to COU, but most are not clinically modifiable. To develop and validate machine-learning algorithms that use claims data to predict progression from acute to COU in the Medicaid population, Adult opioid naïve Medicaid patients from 6 anonymized states who received an opioid prescription between 2015 and 2019 were included. Five machine learning (ML) Models were developed, and model performance assessed by area under the receiver operating characteristic curve (auROC), precision and recall. In the study, 29.9% (53820/180000) of patients transitioned from acute opioid use to COU. Initial opioid prescriptions in COU patients had increased morphine milligram equivalents (MME) (33.2 vs. 23.2), tablets per prescription (45.6 vs. 36.54), longer prescriptions (26.63 vs 24.69 days), and higher proportions of tramadol (16.06% vs. 13.44%) and long acting oxycodone (0.24% vs 0.04%) compared to non- COU patients. The top performing model was XGBoost that achieved average precision of 0.87 and auROC of 0.63 in testing and 0.55 and 0.69 in validation, respectively. Top-ranking prescription-related features in the model included quantity of tablets per prescription, prescription length, and emergency department claims. In this study, the Medicaid population, opioid prescriptions with increased tablet quantity and days supply predict increased risk of progression from acute to COU in opioid-naïve patients. Future research should evaluate the effects of modifying these risk factors on COU incidence.

Aiding the prescriber: developing a machine learning approach to personalized risk modeling for chronic opioid therapy amongst US Army soldiers

The opioid epidemic is a major policy concern. The widespread availability of opioids, which is fueled by physician prescribing patterns, medication diversion, and the interaction with potential illicit opioid use, has been implicated as proximal cause for subsequent opioid dependence and mortality. Risk indicators related to chronic opioid therapy (COT) at the point of care may influence physicians' prescribing decisions, potentially reducing rates of dependency and abuse. In this paper, we investigate the performance of machine learning algorithms for predicting the risk of COT. Using data on over 12 million observations of active duty US Army soldiers, we apply machine learning models to predict the risk of COT in the initial months of prescription. We use the area under the curve (AUC) as an overall measure of model performance, and we focus on the positive predictive value (PPV), which reflects the models' ability to accurately target military members for intervention. Of the many models tested, AUC ranges between 0.83 and 0.87. When we focus on the top 1% of members at highest risk, we observe a PPV value of 8.4% and 20.3% for months 1 and 3, respectively. We further investigate the performance of sparse models that can be implemented in sparse data environments. We find that when the goal is to identify patients at the highest risk of chronic use, these sparse linear models achieve a performance similar to models trained on hundreds of variables. Our predictive models exhibit high accuracy and can alert prescribers to the risk of COT for the highest risk patients. Optimized sparse models identify a parsimonious set of factors to predict COT: initial supply of opioids, the supply of opioids in the month being studied, and the number of prescriptions for psychotropic medications. Future research should investigate the possible effects of these tools on prescriber behavior (e.g., the benefit of clinician nudging at the point of care in outpatient settings).

Integration of Artificial Intelligence and Blockchain Technology in Healthcare and Agriculture

Over the last decade, the healthcare sector has accelerated its digitization and electronic health records (EHRs). As information technology progresses, the notion of intelligent health also gathers popularity. By combining technologies such as the internet of things (IoT) and artificial intelligence (AI), innovative healthcare modifies and enhances traditional medical systems in terms of efficiency, service, and personalization. On the other side, intelligent healthcare systems are incredibly vulnerable to data breaches and other malicious assaults. Recently, blockchain technology has emerged as a potentially transformative option for enhancing data management, access control, and integrity inside healthcare systems. Integrating these advanced approaches in agriculture is critical for managing food supply chains, drug supply chains, quality maintenance, and intelligent prediction. This study reviews the literature, formulates a research topic, and analyzes the applicability of blockchain to the agriculture/food industry and healthcare, with a particular emphasis on AI and IoT. This article summarizes research on the newest blockchain solutions paired with AI technologies for strengthening and inventing new technological standards for the healthcare ecosystems and food industry.

Artificial Intelligence in Emergency Medicine: Benefits, Risks, and Recommendations

BACKGROUND

Artificial intelligence (AI) can be described as the use of computers to perform tasks that formerly required human cognition. The American Medical Association prefers the term 'augmented intelligence' over 'artificial intelligence' to emphasize the assistive role of computers in enhancing physician skills as opposed to replacing them. The integration of AI into emergency medicine, and clinical practice at large, has increased in recent years, and that trend is likely to continue.

DISCUSSION

AI has demonstrated substantial potential benefit for physicians and patients. These benefits are transforming the therapeutic relationship from the traditional physician-patient dyad into a triadic doctor-patient-machine relationship. New AI technologies, however, require careful vetting, legal standards, patient safeguards, and provider education. Emergency physicians (EPs) should recognize the limits and risks of AI as well as its potential benefits.

CONCLUSIONS

EPs must learn to partner with, not capitulate to, AI. AI has proven to be superior to, or on a par with, certain physician skills, such as interpreting radiographs and making diagnoses based on visual cues, such as skin cancer. AI can provide cognitive assistance, but EPs must interpret AI results within the clinical context of individual patients. They must also advocate for patient confidentiality, professional liability coverage, and the essential role of specialty-trained EPs.

Identifying Predictors of Opioid Overdose Death at a Neighborhood Level With Machine Learning

Predictors of opioid overdose death in neighborhoods are important to identify, both to understand characteristics of high-risk areas and to prioritize limited prevention and intervention resources. Machine learning methods could serve as a valuable tool for identifying neighborhood-level predictors. We examined statewide data on opioid overdose death from Rhode Island (log-transformed rates for 2016-2019) and 203 covariates from the American Community Survey for 742 US Census block groups. The analysis included a least absolute shrinkage and selection operator (LASSO) algorithm followed by variable importance rankings from a random forest algorithm. We employed double cross-validation, with 10 folds in the inner loop to train the model and 4 outer folds to assess predictive performance. The ranked variables included a range of dimensions of socioeconomic status, including education, income and wealth, residential stability, race/ethnicity, social isolation, and occupational status. The R2 value of the model on testing data was 0.17. While many predictors of overdose death were in established domains (education, income, occupation), we also identified novel domains (residential stability, racial/ethnic distribution, and social isolation). Predictive modeling with machine learning can identify new neighborhood-level predictors of overdose in the continually evolving opioid epidemic and anticipate the neighborhoods at high risk of overdose mortality.

Using machine learning to classify patients on opioid use

Objectives

High-frequent opioid use tends to increase an individual's risk of opioid use disorder, overdose and death. Thus, it is important to predict an individuals' opioid use frequency to improve opioid prescription utilization outcomes.

Methods

Individuals receiving at least one opioid prescription from 2016 to 2018 in the national representative data, Medical Expenditure Panel Survey, were included. This study applied five machine learning (ML) techniques, including support vector machine, random forest, neural network, gradient boosting and XGBoost (extreme gradient boosting), to predict opioid use frequency. This study compared the performance of these ML models with penalized logistic regression. The study outcome was whether an individual lied in the upper 10% of the opioid prescription distribution. Predictors were selected based on Gelberg-Andersen's Behavioral Model of Health Services Utilization. The prediction performance was assessed using the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC) in the test data. Patient characteristics as predictors for high-frequency use of opioids were ranked by the relative importance in prediction in the test data.

Key findings

Random forest and gradient boosting achieved the top values of both AUROC and AUPRC, outperforming logistic regression and three other ML methods. In the best performing model, the random forest, the following characteristics had high predictive power in the frequency of opioid use: age, number of chronic conditions, public insurance and self-perceived health status.

Conclusions

The results of this study demonstrate that ML techniques can be a promising and powerful technique in predicting the frequency of opioid use and health outcomes.

Ensemble learning to predict opioid-related overdose using statewide prescription drug monitoring program and hospital discharge data in the state of Tennessee

Objective

To develop and validate algorithms for predicting 30-day fatal and nonfatal opioid-related overdose using statewide data sources including prescription drug monitoring program data, Hospital Discharge Data System data, and Tennessee (TN) vital records. Current overdose prevention efforts in TN rely on descriptive and retrospective analyses without prognostication.

Materials and Methods

Study data included 3 041 668 TN patients with 71 479 191 controlled substance prescriptions from 2012 to 2017. Statewide data and socioeconomic indicators were used to train, ensemble, and calibrate 10 nonparametric “weak learner” models. Validation was performed using area under the receiver operating curve (AUROC), area under the precision recall curve, risk concentration, and Spiegelhalter z-test statistic.

Results

Within 30 days, 2574 fatal overdoses occurred after 4912 prescriptions (0.0069%) and 8455 nonfatal overdoses occurred after 19 460 prescriptions (0.027%). Discrimination and calibration improved after ensembling (AUROC: 0.79–0.83; Spiegelhalter P value: 0–.12). Risk concentration captured 47–52% of cases in the top quantiles of predicted probabilities.

Discussion

Partitioning and ensembling enabled all study data to be used given computational limits and helped mediate case imbalance. Predicting risk at the prescription level can aggregate risk to the patient, provider, pharmacy, county, and regional levels. Implementing these models into Tennessee Department of Health systems might enable more granular risk quantification. Prospective validation with more recent data is needed.

Conclusion

Predicting opioid-related overdose risk at statewide scales remains difficult and models like these, which required a partnership between an academic institution and state health agency to develop, may complement traditional epidemiological methods of risk identification and inform public health decisions.

Ancestry may confound genetic machine learning: Candidate-gene prediction of opioid use disorder as an example

BACKGROUND

Machine learning (ML) models are beginning to proliferate in psychiatry, however machine learning models in psychiatric genetics have not always accounted for ancestry. Using an empirical example of a proposed genetic test for OUD, and exploring a similar test for tobacco dependence and a simulated binary phenotype, we show that genetic prediction using ML is vulnerable to ancestral confounding.

METHODS

We utilize five ML algorithms trained with 16 brain reward-derived "candidate" SNPs proposed for commercial use and examine their ability to predict OUD vs. ancestry in an out-of-sample test set (N = 1000, stratified into equal groups of n = 250 cases and controls each of European and African ancestry). We rerun analyses with 8 random sets of allele-frequency matched SNPs. We contrast findings with 11 genome-wide significant variants for tobacco smoking. To document generalizability, we generate and test a random phenotype.

RESULTS

None of the 5 ML algorithms predict OUD better than chance when ancestry was balanced but were confounded with ancestry in an out-of-sample test. In addition, the algorithms preferentially predicted admixed subpopulations. Random sets of variants matched to the candidate SNPs by allele frequency produced similar bias. Genome-wide significant tobacco smoking variants were also confounded by ancestry. Finally, random SNPs predicting a random simulated phenotype show that the bias attributable to ancestral confounding could impact any ML-based genetic prediction.

CONCLUSIONS

Researchers and clinicians are encouraged to be skeptical of claims of high prediction accuracy from ML-derived genetic algorithms for polygenic traits like addiction, particularly when using candidate variants.

The leading neighborhood-level predictors of drug overdose: A mixed machine learning and spatial approach

BACKGROUND

Drug overdose is a leading cause of unintentional death in the United States and has contributed significantly to a decline in life expectancy during recent years. To combat this health issue, this study aims to identify the leading neighborhood-level predictors of drug overdose and develop a model to predict areas at the highest risk of drug overdose using geographic information systems and machine learning (ML) techniques.

METHOD

Neighborhood-level (block group) predictors were grouped into three domains: socio-demographic factors, drug use variables, and protective resources. We explored different ML algorithms, accounting for spatial dependency, to identify leading predictors in each domain. Using geographically weighted regression and the best-performing ML algorithm, we combined the output prediction of three domains to produce a final ensemble model. The model performance was validated using classification evaluation metrics, spatial cross-validation, and spatial autocorrelation testing.

RESULTS

The variables contributing most to the predictive model included the proportion of households with food stamps, households with an annual income below $35,000, opioid prescription rate, smoking accessories expenditures, and accessibility to opioid treatment programs and hospitals. Compared to the error estimated from normal cross-validation, the generalized error of the model did not increase considerably in spatial cross-validation. The ensemble model using ML outperformed the GWR method.

CONCLUSION

This study identified strong neighborhood-level predictors that place a community at risk of experiencing drug overdoses, as well as protective factors. Our findings may shed light on several specific avenues for targeted intervention in neighborhoods at risk for high drug overdose burdens.

Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review

Adverse drug events (ADEs) represent one of the most prevalent types of health-care-related harm, and there is substantial room for improvement in the way that they are currently predicted and detected. We conducted a scoping review to identify key use cases in which artificial intelligence (AI) could be leveraged to reduce the frequency of ADEs. We focused on modern machine learning techniques and natural language processing. 78 articles were included in the scoping review. Studies were heterogeneous and applied various AI techniques covering a wide range of medications and ADEs. We identified several key use cases in which AI could contribute to reducing the frequency and consequences of ADEs, through prediction to prevent ADEs and early detection to mitigate the effects. Most studies (73 [94%] of 78) assessed technical algorithm performance, and few studies evaluated the use of AI in clinical settings. Most articles (58 [74%] of 78) were published within the past 5 years, highlighting an emerging area of study. Availability of new types of data, such as genetic information, and access to unstructured clinical notes might further advance the field.

Healthcare resource utilisation and cost analysis associated with opioid analgesic use for non-cancer pain: A case-control, retrospective study between 2005 and 2015

Objective

To examine differences in healthcare utilisation and costs associated with opioid prescriptions for non-cancer pain issued in primary care.

Method

A longitudinal, case-control study retrospectively examined Welsh healthcare data for the period 1 January 2005–31 December 2015. Data were extracted from the Secure Anonymised Information Linkage (SAIL) databank. Subjects, aged 18 years and over, were included if their primary care record contained at least one of six overarching pain diagnoses during the study period. Subjects were excluded if their record also contained a cancer diagnosis in that time or the year prior to the study period. Case subjects also received at least one prescription for an opioid analgesic. Controls were matched by gender, age, pain-diagnosis and socioeconomic deprivation. Healthcare use included primary care visits, emergency department (ED) and outpatient (OPD) attendances, inpatient (IP) admissions and length of stay. Cost analysis for healthcare utilisation used nationally derived unit costs for 2015. Differences between case and control subjects for resource use and costs were analysed and further stratified by gender, prescribing persistence (PP) and deprivation.

Results

Data from 3,286,215 individuals were examined with 657,243 receiving opioids. Case subjects averaged 5 times more primary care visits, 2.8 times more OPD attendances, 3 times more ED visits and twice as many IN admissions as controls. Prescription persistence over 6 months and greater deprivation were associated with significantly greater utilisation of healthcare resources. Opioid prescribing was associated with 69% greater average healthcare costs than in control subjects. National Health Service (NHS) healthcare service costs for people with common, pain-associated diagnoses, receiving opioid analgesics were estimated to be £0.9billion per year between 2005 and 2015.

Conclusion

Receipt of opioid prescriptions was associated with significantly greater healthcare utilisation and accompanying costs in all sectors. Extended prescribing durations are particularly important to address and should be considered at the point of initiation.

Two data-driven approaches to identifying the spectrum of problematic opioid use: A pilot study within a chronic pain cohort

BACKGROUND

Although electronic health records (EHR) have significant potential for the study of opioid use disorders (OUD), detecting OUD in clinical data is challenging. Models using EHR data to predict OUD often rely on case/control classifications focused on extreme opioid use. There is a need to expand this work to characterize the spectrum of problematic opioid use.

METHODS

Using a large academic medical center database, we developed 2 data-driven methods of OUD detection: (1) a Comorbidity Score developed from a Phenome-Wide Association Study of phenotypes associated with OUD and (2) a Text-based Score using natural language processing to identify OUD-related concepts in clinical notes. We evaluated the performance of both scores against a manual review with correlation coefficients, Wilcoxon rank sum tests, and area-under the receiver operating characteristic curves. Records with the highest Comorbidity and Text-based scores were re-evaluated by manual review to explore discrepancies.

RESULTS

Both the Comorbidity and Text-based OUD risk scores were significantly elevated in the patients judged as High Evidence for OUD in the manual review compared to those with No Evidence (p = 1.3E-5 and 1.3E-6, respectively). The risk scores were positively correlated with each other (rho = 0.52, p < 0.001). AUCs for the Comorbidity and Text-based scores were high (0.79 and 0.76, respectively). Follow-up manual review of discrepant findings revealed strengths of data-driven methods over manual review, and opportunities for improvement in risk assessment.

CONCLUSION

Risk scores comprising comorbidities and text offer differing but synergistic insights into characterizing problematic opioid use. This pilot project establishes a foundation for more robust work in the future.

A machine learning model for identifying patients at risk for wild-type transthyretin amyloid cardiomyopathy

Transthyretin amyloid cardiomyopathy, an often unrecognized cause of heart failure, is now treatable with a transthyretin stabilizer. It is therefore important to identify at-risk patients who can undergo targeted testing for earlier diagnosis and treatment, prior to the development of irreversible heart failure. Here we show that a random forest machine learning model can identify potential wild-type transthyretin amyloid cardiomyopathy using medical claims data. We derive a machine learning model in 1071 cases and 1071 non-amyloid heart failure controls and validate the model in three nationally representative cohorts (9412 cases, 9412 matched controls), and a large, single-center electronic health record-based cohort (261 cases, 39393 controls). We show that the machine learning model performs well in identifying patients with cardiac amyloidosis in the derivation cohort and all four validation cohorts, thereby providing a systematic framework to increase the suspicion of transthyretin cardiac amyloidosis in patients with heart failure.

Identifying risk of opioid use disorder for patients taking opioid medications with deep learning

OBJECTIVE

The United States is experiencing an opioid epidemic. In recent years, there were more than 10 million opioid misusers aged 12 years or older annually. Identifying patients at high risk of opioid use disorder (OUD) can help to make early clinical interventions to reduce the risk of OUD. Our goal is to develop and evaluate models to predict OUD for patients on opioid medications using electronic health records and deep learning methods. The resulting models help us to better understand OUD, providing new insights on the opioid epidemic. Further, these models provide a foundation for clinical tools to predict OUD before it occurs, permitting early interventions.

METHODS

Electronic health records of patients who have been prescribed with medications containing active opioid ingredients were extracted from Cerner's Health Facts database for encounters between January 1, 2008, and December 31, 2017. Long short-term memory models were applied to predict OUD risk based on five recent prior encounters before the target encounter and compared with logistic regression, random forest, decision tree, and dense neural network. Prediction performance was assessed using F1 score, precision, recall, and area under the receiver-operating characteristic curve.

RESULTS

The long short-term memory (LSTM) model provided promising prediction results which outperformed other methods, with an F1 score of 0.8023 (about 0.016 higher than dense neural network (DNN)) and an area under the receiver-operating characteristic curve (AUROC) of 0.9369 (about 0.145 higher than DNN).

CONCLUSIONS

LSTM-based sequential deep learning models can accurately predict OUD using a patient's history of electronic health records, with minimal prior domain knowledge. This tool has the potential to improve clinical decision support for early intervention and prevention to combat the opioid epidemic.

Epistasis Analysis: Classification Through Machine Learning Methods

Complex disease is different from Mendelian disorders. Its development usually involves the interaction of multiple genes or the interaction between genes and the environment (i.e. epistasis). Although the high-throughput sequencing technologies for complex diseases have produced a large amount of data, it is extremely difficult to analyze the data due to the high feature dimension and the combination in the epistasis analysis. In this work, we introduce machine learning methods to effectively reduce the gene dimensionality, retain the key epistatic effects, and effectively characterize the relationship between epistatic effects and complex diseases.

Predicting opioid overdose risk of patients with opioid prescriptions using electronic health records based on temporal deep learning

The US is experiencing an opioid epidemic, and opioid overdose is causing more than 100 deaths per day. Early identification of patients at high risk of Opioid Overdose (OD) can help to make targeted preventative interventions. We aim to build a deep learning model that can predict the patients at high risk for opioid overdose and identify most relevant features. The study included the information of 5,231,614 patients from the Health Facts database with at least one opioid prescription between January 1, 2008 and December 31, 2017. Potential predictors (n = 1185) were extracted to build a feature matrix for prediction. Long Short-Term Memory (LSTM) based models were built to predict overdose risk in the next hospital visit. Prediction performance was compared with other machine learning methods assessed using machine learning metrics. Our sequential deep learning models built upon LSTM outperformed the other methods on opioid overdose prediction. LSTM with attention mechanism achieved the highest F-1 score (F-1 score: 0.7815, AUCROC: 0.8449). The model is also able to reveal top ranked predictive features by permutation important method, including medications and vital signs. This study demonstrates that a temporal deep learning based predictive model can achieve promising results on identifying risk of opioid overdose of patients using the history of electronic health records. It provides an alternative informatics-based approach to improving clinical decision support for possible early detection and intervention to reduce opioid overdose.

A spatiotemporal analysis of opioid poisoning mortality in Ohio from 2010 to 2016

Opioid-related deaths have severely increased since 2000 in the United States. This crisis has been declared a public health emergency, and among the most affected states is Ohio. We used statewide vital statistic data from the Ohio Department of Health (ODH) and demographics data from the U.S. Census Bureau to analyze opioid-related mortality from 2010 to 2016. We focused on the characterization of the demographics from the population of opioid-related fatalities, spatiotemporal pattern analysis using Moran's statistics at the census-tract level, and comorbidity analysis using frequent itemset mining and association rule mining. We found higher rates of opioid-related deaths in white males aged 25-54 compared to the rest of Ohioans. Deaths tended to increasingly cluster around Cleveland, Columbus and Cincinnati and away from rural regions as time progressed. We also found relatively high co-occurrence of cardiovascular disease, anxiety or drug abuse history, with opioid-related mortality. Our results demonstrate that state-wide spatiotemporal and comorbidity analysis of the opioid epidemic could provide novel insights into how the demographic characteristics, spatiotemporal factors, and/or health conditions may be associated with opioid-related deaths in the state of Ohio.

Identifying factors associated with opioid cessation in a biracial sample using machine learning

Aim

Racial disparities in opioid use disorder (OUD) management exist, however, and there is limited research on factors that influence opioid cessation in different population groups.

Methods

We employed multiple machine learning prediction algorithms least absolute shrinkage and selection operator, random forest, deep neural network, and support vector machine to assess factors associated with ceasing opioid use in a sample of 1,192 African Americans (AAs) and 2,557 individuals of European ancestry (EAs) who met Diagnostic and Statistical Manual of Mental Disorders, 5th Edition criteria for OUD. Values for nearly 4,000 variables reflecting demographics, alcohol and other drug use, general health, non-drug use behaviors, and diagnoses for other psychiatric disorders, were obtained for each participant from the Semi-Structured Assessment for Drug Dependence and Alcoholism, a detailed semi-structured interview.

Results

Support vector machine models performed marginally better on average than other machine learning methods with maximum prediction accuracies of 75.4% in AAs and 79.4% in EAs. Subsequent stepwise regression considered the 83 most highly ranked variables across all methods and models and identified less recent cocaine use (AAs: odds ratio (OR) = 1.82, P = 9.19 × 10^-5; EAs: OR = 1.91, P = 3.30 × 10^-15), shorter duration of opioid use (AAs: OR = 0.55, P = 5.78 × 10^-6; EAs: OR = 0.69, P = 3.01 × 10^-7), and older age (AAs: OR = 2.44, P = 1.41 × 10^-12; EAs: OR = 2.00, P = 5.74 × 10^-9) as the strongest independent predictors of opioid cessation in both AAs and EAs. Attending self-help groups for OUD was also an independent predictor (P < 0.05) in both population groups, while less gambling severity (OR = 0.80, P = 3.32 × 10^-2) was specific to AAs and post-traumatic stress disorder recovery (OR = 1.93, P = 7.88 × 10^-5), recent antisocial behaviors (OR = 0.64, P = 2.69 × 10^-3), and atheism (OR = 1.45, P = 1.34 × 10^-2) were specific to EAs. Factors related to drug use comprised about half of the significant independent predictors in both AAs and EAs, with other predictors related to non-drug use behaviors, psychiatric disorders, overall health, and demographics.

Conclusions

These proof-of-concept findings provide avenues for hypothesis-driven analysis, and will lead to further research on strategies to improve OUD management in EAs and AAs.

Classification of Opioid Usage Through Semi-Supervised Learning for Total Joint Replacement Patients

Opioid misuse and overdose have become a public health hazard and caused drug addiction and death in the United States due to rapid increase in prescribed and non-prescribed opioid usage. The misuse and overdose are highly related to opioid over-prescription for chronic and acute pain treatment, where a one-size-fits-all prescription plan is often adopted but can lead to substantial leftovers for patients who only consume a few. To reduce over-prescription and opioid overdose, each patient's opioid usage pattern should be taken into account. As opioids are often prescribed for patients after total joint replacement surgeries, this study introduces a machine learning model to predict each patient's opioid usage level in the first 2 weeks after discharge. Specifically, the electronic health records, patient prescription history, and consumption survey data are collected to investigate the level of short-term opioid usage after joint replacement surgeries. However, there are a considerable number of answers missing in the surveys, which degrades data quality. To overcome this difficulty, a semi-supervised learning model that assigns pseudo labels via Bayesian regression is proposed. Using this model, the missing survey answers of opioids amount taken by the patients are predicted first. Then, based on the prediction, pseudo labels are assigned to those patients to improve classification performance. Extensive experiments indicate that such a semi-supervised learning model has shown a better performance in the resulting patients classification. It is expected that by using such a model the providers can adjust the amount of prescribed opioids to meet each patient's actual need, which can benefit the management of opioid prescription and pain intervention.

Potential predictors of type-2 diabetes risk: machine learning, synthetic data and wearable health devices

BACKGROUND

The aim of a recent research project was the investigation of the mechanisms involved in the onset of type 2 diabetes in the absence of familiarity. This has led to the development of a computational model that recapitulates the aetiology of the disease and simulates the immunological and metabolic alterations linked to type-2 diabetes subjected to clinical, physiological, and behavioural features of prototypical human individuals.

RESULTS

We analysed the time course of 46,170 virtual subjects, experiencing different lifestyle conditions. We then set up a statistical model able to recapitulate the simulated outcomes.

CONCLUSIONS

The resulting machine learning model adequately predicts the synthetic dataset and can, therefore, be used as a computationally-cheaper version of the detailed mathematical model, ready to be implemented on mobile devices to allow self-assessment by informed and aware individuals. The computational model used to generate the dataset of this work is available as a web-service at the following address: http://kraken.iac.rm.cnr.it/T2DM .

Development of a machine learning algorithm for early detection of opioid use disorder

BACKGROUND

Opioid use disorder (OUD) affects an estimated 16 million people worldwide. The diagnosis of OUD is commonly delayed or missed altogether. We aimed to test the utility of machine learning in creating a prediction model and algorithm for early diagnosis of OUD.

SUBJECTS AND METHODS

We analyzed data gathered in a commercial claim database from January 1, 2006, to December 31, 2018 of 10 million medical insurance claims from 550 000 patient records. We compiled 436 predictor candidates, divided to six feature groups - demographics, chronic conditions, diagnosis and procedures features, medication features, medical costs, and episode counts. We employed the Word2Vec algorithm and the Gradient Boosting trees algorithm for the analysis.

RESULTS

The c-statistic for the model was 0.959, with a sensitivity of 0.85 and specificity of 0.882. Positive Predictive Value (PPV) was 0.362 and Negative Predictive Value (NPV) was 0.998. Significant differences between positive OUD- and negative OUD- controls were in the mean annual amount of opioid use days, number of overlaps in opioid prescriptions per year, mean annual opioid prescriptions, and annual benzodiazepine and muscle relaxant prescriptions. Notable differences were the count of intervertebral disc disorder-related complaints per year, post laminectomy syndrome diagnosed per year, and pain disorders diagnosis per year. Significant differences were also found in the episodes and costs categories.

CONCLUSIONS

The new algorithm offers a mean 14.4 months reduction in time to diagnosis of OUD, at potential saving in further morbidity, medical cost, addictions and mortality.

Using machine learning to predict risk of incident opioid use disorder among fee-for-service Medicare beneficiaries: A prognostic study

OBJECTIVE

To develop and validate a machine-learning algorithm to improve prediction of incident OUD diagnosis among Medicare beneficiaries with ≥1 opioid prescriptions.

METHODS

This prognostic study included 361,527 fee-for-service Medicare beneficiaries, without cancer, filling ≥1 opioid prescriptions from 2011-2016. We randomly divided beneficiaries into training, testing, and validation samples. We measured 269 potential predictors including socio-demographics, health status, patterns of opioid use, and provider-level and regional-level factors in 3-month periods, starting from three months before initiating opioids until development of OUD, loss of follow-up or end of 2016. The primary outcome was a recorded OUD diagnosis or initiating methadone or buprenorphine for OUD as proxy of incident OUD. We applied elastic net, random forests, gradient boosting machine, and deep neural network to predict OUD in the subsequent three months. We assessed prediction performance using C-statistics and other metrics (e.g., number needed to evaluate to identify an individual with OUD [NNE]). Beneficiaries were stratified into subgroups by risk-score decile.

RESULTS

The training (n = 120,474), testing (n = 120,556), and validation (n = 120,497) samples had similar characteristics (age ≥65 years = 81.1%; female = 61.3%; white = 83.5%; with disability eligibility = 25.5%; 1.5% had incident OUD). In the validation sample, the four approaches had similar prediction performances (C-statistic ranged from 0.874 to 0.882); elastic net required the fewest predictors (n = 48). Using the elastic net algorithm, individuals in the top decile of risk (15.8% [n = 19,047] of validation cohort) had a positive predictive value of 0.96%, negative predictive value of 99.7%, and NNE of 104. Nearly 70% of individuals with incident OUD were in the top two deciles (n = 37,078), having highest incident OUD (36 to 301 per 10,000 beneficiaries). Individuals in the bottom eight deciles (n = 83,419) had minimal incident OUD (3 to 28 per 10,000).

CONCLUSIONS

Machine-learning algorithms improve risk prediction and risk stratification of incident OUD in Medicare beneficiaries.

Engineering Approaches for Addressing Opioid Use Disorder in the Community

Many communities in the United States are struggling to deal with the negative consequences of illicit opioid use. Effectively addressing this epidemic requires the coordination and support of community stakeholders in a change process with common goals and objectives, continuous engagement with individuals with opioid use disorder (OUD) through their treatment and recovery journeys, application of systems engineering principles to drive process change and sustain it, and use of a formal evaluation process to support a learning community that continuously adapts. This review presents strategies to improve OUD treatment and recovery with a focus on engineering approaches grounded in systems thinking.

Machine Learning Based Opioid Overdose Prediction Using Electronic Health Records

Opioid addiction in the United States has come to national attention as opioid overdose (OD) related deaths have risen at alarming rates. Combating opioid epidemic becomes a high priority for not only governments but also healthcare providers. This depends on critical knowledge to understand the risk of opioid overdose of patients. In this paper, we present our work on building machine learning based prediction models to predict opioid overdose of patients based on the history of patients' electronic health records (EHR). We performed two studies using New York State claims data (SPARCS) with 440,000 patients and Cerner's Health Facts database with 110,000 patients. Our experiments demonstrated that EHR based prediction can achieve best recall with random forest method (precision: 95.3%, recall: 85.7%, F1 score: 90.3%), best precision with deep learning (precision: 99.2%, recall: 77.8%, F1 score: 87.2%). We also discovered that clinical events are among critical features for the predictions.