Heterogeneous Causal Effect of Polysubstance Usage on Drug Overdose
- URL: http://arxiv.org/abs/2105.07224v1
- Date: Sat, 15 May 2021 13:52:20 GMT
- Title: Heterogeneous Causal Effect of Polysubstance Usage on Drug Overdose
- Authors: Vaishali Mahipal, Mohammad Arif Ul Alam
- Abstract summary: We propose a system to estimate heterogeneous concurrent drug usage effects on overdose estimation.
We apply our framework to answer a critical question, "can concurrent usage of benzodiazepines and opioids has heterogeneous causal effects on opioid overdose epidemic?"
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper, we propose a system to estimate heterogeneous concurrent drug
usage effects on overdose estimation, that consists of efficient co-variate
selection, sub-group selection, generation of and heterogeneous causal effect
estimation. Although, there has been several association studies have been
proposed in the state-of-art methods, heterogeneous causal effects have never
been studied in concurrent drug usage and drug overdose problem. We apply our
framework to answer a critical question, "can concurrent usage of
benzodiazepines and opioids has heterogeneous causal effects on opioid overdose
epidemic?" Using Truven MarketScan claim data collected from 2001 to 2013 have
shown significant promise of our proposed framework's efficacy. Our efficient
causal inference model estimated that the causal effect is higher (19%) than
the regression studies (15%) to estimate the risks associated with the
concurrent usage of opioid and benzodiazepines on opioid overdose.
Related papers
- Identifying Self-Disclosures of Use, Misuse and Addiction in Community-based Social Media Posts [26.161892748901252]
We present a corpus of 2500 opioid-related posts from various subreddits labeled with six different phases of opioid use.
For every post, we annotate span-level explanations and crucially study their role both in annotation quality and model development.
arXiv Detail & Related papers (2023-11-15T16:05:55Z) - The Blessings of Multiple Treatments and Outcomes in Treatment Effect
Estimation [53.81860494566915]
Existing studies leveraged proxy variables or multiple treatments to adjust for confounding bias.
In many real-world scenarios, there is greater interest in studying the effects on multiple outcomes.
We show that parallel studies of multiple outcomes involved in this setting can assist each other in causal identification.
arXiv Detail & Related papers (2023-09-29T14:33:48Z) - Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment
Effect Estimation [137.3520153445413]
A notable gap exists in the evaluation of causal discovery methods, where insufficient emphasis is placed on downstream inference.
We evaluate seven established baseline causal discovery methods including a newly proposed method based on GFlowNets.
The results of our study demonstrate that some of the algorithms studied are able to effectively capture a wide range of useful and diverse ATE modes.
arXiv Detail & Related papers (2023-07-11T02:58:10Z) - Neural Bandits for Data Mining: Searching for Dangerous Polypharmacy [63.135687276599114]
Some polypharmacies, deemed inappropriate, may be associated with adverse health outcomes such as death or hospitalization.
We propose the OptimNeuralTS strategy to efficiently mine claims datasets and build a predictive model of the association between drug combinations and health outcomes.
Our method can detect up to 72% of PIPs while maintaining an average precision score of 99% using 30 000 time steps.
arXiv Detail & Related papers (2022-12-10T03:43:23Z) - Understanding the factors driving the opioid epidemic using machine
learning [10.021195517057462]
U.S. has experienced an opioid epidemic with an unprecedented number of drugs overdose deaths.
In this study we apply machine learning based techniques to identify opioid risks of neighborhoods in Delaware.
arXiv Detail & Related papers (2021-08-16T18:08:56Z) - Patterns of Routes of Administration and Drug Tampering for Nonmedical
Opioid Consumption: Data Mining and Content Analysis of Reddit Discussions [0.0]
We used a semiautomatic information retrieval algorithm to identify subreddits discussing nonmedical opioid consumption.
We modeled the preferences of adoption of substances and routes of administration, estimating their prevalence and temporal unfolding.
We found evidence of understudied abusive behaviors like chewing fentanyl patches and dissolving buprenorphine sublingually.
arXiv Detail & Related papers (2021-02-22T18:14:48Z) - A standardized framework for risk-based assessment of treatment effect
heterogeneity in observational healthcare databases [60.07352590494571]
The aim of this study was to extend this approach to the observational setting using a standardized scalable framework.
We demonstrate our framework by evaluating the effect of angiotensin-converting enzyme (ACE) inhibitors versus beta blockers on three efficacy and six safety outcomes.
arXiv Detail & Related papers (2020-10-13T14:48:31Z) - Discovering Drug-Drug and Drug-Disease Interactions Inducing Acute
Kidney Injury Using Deep Rule Forests [0.0]
Drug-drug interactions and drug-disease interactions are critical issues for Acute Kidney Injury (AKI)
We propose a novel learning algorithm, Deep Rule Forests (DRF), which discovers rules from multilayer tree models as the combinations of drug usages and disease indications.
Our experimental results also show that the DRF model performs comparatively better than typical tree-based and other state-of-the-art algorithms in terms of prediction accuracy and model interpretability.
arXiv Detail & Related papers (2020-07-04T14:10:28Z) - Enabling Counterfactual Survival Analysis with Balanced Representations [64.17342727357618]
Survival data are frequently encountered across diverse medical applications, i.e., drug development, risk profiling, and clinical trials.
We propose a theoretically grounded unified framework for counterfactual inference applicable to survival outcomes.
arXiv Detail & Related papers (2020-06-14T01:15:00Z) - Learning for Dose Allocation in Adaptive Clinical Trials with Safety
Constraints [84.09488581365484]
Phase I dose-finding trials are increasingly challenging as the relationship between efficacy and toxicity of new compounds becomes more complex.
Most commonly used methods in practice focus on identifying a Maximum Tolerated Dose (MTD) by learning only from toxicity events.
We present a novel adaptive clinical trial methodology that aims at maximizing the cumulative efficacies while satisfying the toxicity safety constraint with high probability.
arXiv Detail & Related papers (2020-06-09T03:06:45Z) - A Node Embedding Framework for Integration of Similarity-based Drug
Combination Prediction [7.4517333921953215]
We propose a Network Embedding framework in Multiplex Networks (NEMN) to predict synthetic drug combinations.
Based on a multiplex drug similarity network, we offered alternative methods to integrate useful information from different aspects.
For Drug combination prediction, we found seven novel drug combinations which have been validated by external sources.
arXiv Detail & Related papers (2020-02-25T02:24:47Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.