Dataset Information

Analysis of substance use and its outcomes by machine learning: II. Derivation and prediction of the trajectory of substance use severity.

ABSTRACT:

Background

This longitudinal study explored the utility of machine learning (ML) methodology in predicting the trajectory of severity of substance use from childhood to thirty years of age using a set of psychological and health characteristics.

Design

Boys (N?=?494) and girls (N?=?206) were recruited using a high-risk paradigm at 10-12 years of age and followed up at 12-14, 16, 19, 22, 25 and 30 years of age.

Measurements

At each visit, the subjects were administered a comprehensive battery to measure psychological makeup, health status, substance use and psychiatric disorder, and their overall harmfulness of substance consumption was quantified according to the multidimensional criteria (physical, dependence, and social) developed by Nutt et al. (2007). Next, high- and low- substance use severity trajectories were derived differentially associated with probability of segueing to substance use disorder (SUD). ML methodology was employed to predict trajectory membership.

Findings

The high-severity trajectory group had a higher probability of leading to SUD than the low-severity trajectory (89.0% vs 32.4%; odds ratio?=?16.88, p?ConclusionThese findings demonstrate the applicability of the machine learning methodology for detecting membership in a substance use trajectory with high probability of culminating in SUD, potentially informing primary and secondary prevention.

SUBMITTER: Hu Z

PROVIDER: S-EPMC7476073 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Analysis of substance use and its outcomes by machine learning: II. Derivation and prediction of the trajectory of substance use severity.

Hu Ziheng Z Jing Yankang Y Xue Ying Y Fan Peihao P Wang Lirong L Vanyukov Michael M Kirisci Levent L Wang Junmei J Tarter Ralph E RE Xie Xiang-Qun XQ

Drug and alcohol dependence 20191001

<h4>Background</h4>This longitudinal study explored the utility of machine learning (ML) methodology in predicting the trajectory of severity of substance use from childhood to thirty years of age using a set of psychological and health characteristics.<h4>Design</h4>Boys (N = 494) and girls (N = 206) were recruited using a high-risk paradigm at 10-12 years of age and followed up at 12-14, 16, 19, 22, 25 and 30 years of age.<h4>Measurements</h4>At each visit, the subjects were administered a com ...[more]

PMID: 31615693

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Project description:Expression of CCR5 and its cognate ligands have been implicated in COVID-19 pathogenesis, consequently therapeutics directed against CCR5 are being investigated. Here, we explored the role of CCR5 and its ligands across the immunologic spectrum of COVID-19. We used a bioinformatics approach to predict and model the immunologic phases of COVID so that effective treatment strategies can be devised and monitored. We investigated 224 individuals including healthy controls and patients spanning the COVID-19 disease continuum. We assessed the plasma and isolated peripheral blood mononuclear cells (PBMCs) from 29 healthy controls, 26 Mild-Moderate COVID-19 individuals, 48 Severe COVID-19 individuals, and 121 individuals with post-acute sequelae of COVID-19 (PASC) symptoms. Immune subset profiling and a 14-plex cytokine panel were run on all patients from each group. B-cells were significantly elevated compared to healthy control individuals (P<0.001) as was the CD14+, CD16+, CCR5+ monocytic subset (P<0.001). CD4 and CD8 positive T-cells expressing PD-1 as well as T-regulatory cells were significantly lower than healthy controls (P<0.001 and P=0.01 respectively). CCL5/RANTES, IL-2, IL-4, CCL3, IL-6, IL-10, IFN-γ, and VEGF were all significantly elevated compared to healthy controls (all P<0.001). Conversely GM-CSF and CCL4 were in significantly lower levels than healthy controls (P=0.01). Data were further analyzed and the classes were balanced using SMOTE. With a balanced working dataset, we constructed 3 random forest classifiers: a multi-class predictor, a Severe disease group binary classifier and a PASC binary classifier. Models were also analyzed for feature importance to identify relevant cytokines to generate a disease score. Multi-class models generated a score specific for the PASC patients and defined as S1 = (IFN-γ + IL-2)/CCL4-MIP-1β. Second, a score for the Severe COVID-19 patients was defined as S2 = (IL-6+sCD40L/1000 + VEGF/10 + 10*IL-10)/(IL-2 + IL-8). Severe COVID-19 patients are characterized by excessive inflammation and dysregulated T cell activation, recruitment, and counteracting activities. While PASC patients are characterized by a profile able to induce the activation of effector T cells with pro-inflammatory properties and the capacity of generating an effective immune response to eliminate the virus but without the proper recruitment signals to attract activated T cells.

Dataset Information

Analysis of substance use and its outcomes by machine learning: II. Derivation and prediction of the trajectory of substance use severity.

Background

Design

Measurements

Findings

Publications

Analysis of substance use and its outcomes by machine learning: II. Derivation and prediction of the trajectory of substance use severity.

Similar Datasets

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