Dataset Information

High accuracy machine learning identification of fentanyl-relevant molecular compound classification via constituent functional group analysis.

ABSTRACT: Fentanyl is an anesthetic with a high bioavailability and is the leading cause of drug overdose death in the U.S. Fentanyl and its derivatives have a low lethal dose and street drugs which contain such compounds may lead to death of the user and simultaneously pose hazards for first responders. Rapid identification methods of both known and emerging opioid fentanyl substances is crucial. In this effort, machine learning (ML) is applied in a systematic manner to identify fentanyl-related functional groups in such compounds based on their observed spectral properties. In our study, accurate infrared (IR) spectra of common organic molecules which contain functional groups that are constituents of fentanyl is determined by investigating the structure-property relationship. The average accuracy rate of correctly identifying the functional groups of interest is 92.5% on our testing data. All the IR spectra of 632 organic molecules are from National Institute of Standards and Technology (NIST) database as the training set and are assessed. Results from this work will provide Artificial Intelligence (AI) based tools and algorithms increased confidence, which serves as a basis to detect fentanyl and its derivatives.

SUBMITTER: Xu M

PROVIDER: S-EPMC7419312 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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High accuracy machine learning identification of fentanyl-relevant molecular compound classification via constituent functional group analysis.

Xu Mengyu M Wang Chun-Hung CH Terracciano Anthony C AC Masunov Artem E AE Vasu Subith S SS

Scientific reports 20200811 1

Fentanyl is an anesthetic with a high bioavailability and is the leading cause of drug overdose death in the U.S. Fentanyl and its derivatives have a low lethal dose and street drugs which contain such compounds may lead to death of the user and simultaneously pose hazards for first responders. Rapid identification methods of both known and emerging opioid fentanyl substances is crucial. In this effort, machine learning (ML) is applied in a systematic manner to identify fentanyl-related function ...[more]

PMID: 32782338

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Project description:BackgroundPoor functional status is a key marker of morbidity, yet is not routinely captured in clinical encounters. We developed and evaluated the accuracy of a machine learning algorithm that leveraged electronic health record (EHR) data to provide a scalable process for identification of functional impairment.MethodsWe identified a cohort of patients with an electronically captured screening measure of functional status (Older Americans Resources and Services ADL/IADL) between 2018 and 2020 (N = 6484). Patients were classified using unsupervised learning K means and t-distributed Stochastic Neighbor Embedding into normal function (NF), mild to moderate functional impairment (MFI), and severe functional impairment (SFI) states. Using 11 EHR clinical variable domains (832 variable input features), we trained an Extreme Gradient Boosting supervised machine learning algorithm to distinguish functional status states, and measured prediction accuracies. Data were randomly split into training (80%) and test (20%) sets. The SHapley Additive Explanations (SHAP) feature importance analysis was used to list the EHR features in rank order of their contribution to the outcome.ResultsMedian age was 75.3 years, 62% female, 60% White. Patients were classified as 53% NF (n = 3453), 30% MFI (n = 1947), and 17% SFI (n = 1084). Summary of model performance for identifying functional status state (NF, MFI, SFI) was AUROC (area under the receiving operating characteristic curve) 0.92, 0.89, and 0.87, respectively. Age, falls, hospitalization, home health use, labs (e.g., albumin), comorbidities (e.g., dementia, heart failure, chronic kidney disease, chronic pain), and social determinants of health (e.g., alcohol use) were highly ranked features in predicting functional status states.ConclusionA machine learning algorithm run on EHR clinical data has potential utility for differentiating functional status in the clinical setting. Through further validation and refinement, such algorithms can complement traditional screening methods and result in a population-based strategy for identifying patients with poor functional status who need additional health resources.

Dataset Information

High accuracy machine learning identification of fentanyl-relevant molecular compound classification via constituent functional group analysis.

Publications

High accuracy machine learning identification of fentanyl-relevant molecular compound classification via constituent functional group analysis.

Similar Datasets

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