Dataset Information

Identifying schizophrenia stigma on Twitter: a proof of principle model using service user supervised machine learning.

ABSTRACT: Stigma has negative effects on people with mental health problems by making them less likely to seek help. We develop a proof of principle service user supervised machine learning pipeline to identify stigmatising tweets reliably and understand the prevalence of public schizophrenia stigma on Twitter. A service user group advised on the machine learning model evaluation metric (fewest false negatives) and features for machine learning. We collected 13,313 public tweets on schizophrenia between January and May 2018. Two service user researchers manually identified stigma in 746 English tweets; 80% were used to train eight models, and 20% for testing. The two models with fewest false negatives were compared in two service user validation exercises, and the best model used to classify all extracted public English tweets. Tweets classed as stigmatising by service users were more negative in sentiment (t (744) = 12.02, p < 0.001 [95% CI: 0.196-0.273]). Our linear Support Vector Machine was the best performing model with fewest false negatives and higher service user validation. This model identified public stigma in 47% of English tweets (n5,676) which were more negative in sentiment (t (12,143) = 64.38, p < 0.001 [95% CI: 0.29-0.31]). Machine learning can identify stigmatising tweets at large scale, with service user involvement. Given the prevalence of stigma, there is an urgent need for education and online campaigns to reduce it. Machine learning can provide a real time metric on their success.

SUBMITTER: Jilka S

PROVIDER: S-EPMC8821670 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BACKGROUND:Chronic spinal pain conditions affect millions of US adults and carry a high healthcare cost burden, both direct and indirect. Conservative interventions for spinal pain conditions, including chiropractic care, have been associated with lower healthcare costs and improvements in pain status in different clinical populations, including veterans. Little is currently known about predicting healthcare service utilization in the domain of conservative interventions for spinal pain conditions, including the frequency of use of chiropractic services. The purpose of this retrospective cohort study was to explore the use of supervised machine learning approaches to predicting one-year chiropractic service utilization by veterans receiving VA chiropractic care. METHODS:We included 19,946 veterans who entered the Musculoskeletal Diagnosis Cohort between October 1, 2003 and September 30, 2013 and utilized VA chiropractic services within one year of cohort entry. The primary outcome was one-year chiropractic service utilization following index chiropractic visit, split into quartiles represented by the following classes: 1 visit, 2 to 3 visits, 4 to 6 visits, and 7 or greater visits. We compared the performance of four multiclass classification algorithms (gradient boosted classifier, stochastic gradient descent classifier, support vector classifier, and artificial neural network) in predicting visit quartile using 158 sociodemographic and clinical features. RESULTS:The selected algorithms demonstrated poor prediction capabilities. Subset accuracy was 42.1% for the gradient boosted classifier, 38.6% for the stochastic gradient descent classifier, 41.4% for the support vector classifier, and 40.3% for the artificial neural network. The micro-averaged area under the precision-recall curve for each one-versus-rest classifier was 0.43 for the gradient boosted classifier, 0.38 for the stochastic gradient descent classifier, 0.43 for the support vector classifier, and 0.42 for the artificial neural network. Performance of each model yielded only a small positive shift in prediction probability (approximately 15%) compared to naïve classification. CONCLUSIONS:Using supervised machine learning to predict chiropractic service utilization remains challenging, with only a small shift in predictive probability over naïve classification and limited clinical utility. Future work should examine mechanisms to improve model performance.

Project description:In this work we demonstrate a novel method of methylation detection that utilises immunoaffinity to detect the presence of methylated DNA hybridised to a cDNA microarray. We use a monoclonal antibody specific to 5 methylcytosine to detect the presence of 5 methylcytosine in genomic DNA from human fibroblasts bearing the karyotype 45 XO. We report that over 2900 genes show the presence of methylation in this condition. We also report that 165 genes are consistently methylated in all replicates of these experiments. The methylated genes show a uniform distribution over all the chromosomes. The gene ontology of these also indicates no functional correlation between the genes that are methylated. We detect the presence of methylation in IGF2, an imprinted gene and thus known to harbour DNA methylation. The method is extremely specific and offers a quick and efficient way to analyse the methylation landscape on a high throughput scale. This method uses existing technology to assess methylation and thus can integrate very efficiently into any platform used. A method that detects DNA methylation that is not restricted to specific sequences would provide a useful platform to analyse methylation distribution among the genes in any given phenotypic condition.Availability of additional approaches to examine DNA methylation would lead to increase in our understanding of the methylome and would help define the intrinsic and extrinsic factors which govern it. Microarray based high throughput methods have been extensively used for gene expression analysis. The method described uses a microarray to analyse DNA methylation levels in a gene/cDNA/oligo specific manner. Here, genomic DNA is fragmented and used for hybridization to microarray slides. The presence of DNA cytosine methylation in the hybridized DNA is detected by employing Cy3 labelled anti 5mC( 5-methyl cytidine) antibody (monoclonal). The resolution provided by this method is dependent upon the microarray platform used. This method can be adapted to any platform thus enabling seamless integration with existing technology of gene expression analysis

Dataset Information

Identifying schizophrenia stigma on Twitter: a proof of principle model using service user supervised machine learning.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets