Utilization of sentiment analysis to assess and compare negative finding reporting in veterinary and human literature.

Publication bias and the decreased publication of trials with negative or non-significant results is a well-recognized problem in human and veterinary medical publications. These biases may present an incomplete picture of evidence-based clinical care and negatively impact medical practices. The purpose of this study was to utilize a novel sentiment analysis tool as a quantitative measure for assessing clinical trial reporting trends in human and veterinary medical literature. Abstracts from 177,617 clinical trials in human medical journals and 8684 in veterinary medical journals published in the PubMed database from 1995 to 2020. Abstracts were analyzed using the GAN-BioBERT sentiment classifier for both general trends and percentage of neutral/negative publications. Sentiment was defined on a - 1 (highly negative) to 1 (highly positive) scale. Human-based clinical trial publications were less likely to feature positive findings (OR 0.87, P < 0.001) and more likely to include neutral findings (OR 1.18, P < 0.001) relative to veterinary clinical trials. No difference was found in reporting of negative sentiment trials (OR 1.007, P = 0.83). In both groups, the published sentiment of clinical trials increased over time. Using sentiment analysis to evaluate large publication datasets and compare publication trends within and between groups, this study is significant in its detection of significant publication differences between human and veterinary medicine clinical trials and a continued unbalanced positive sentiment in the published literature. The implications of this unbiased reporting have important clinical and research implications that require consideration.

Validating GAN-BioBERT: A Methodology for Assessing Reporting Trends in Clinical Trials.

Background: The aim of this study was to validate a three-class sentiment classification model for clinical trial abstracts combining adversarial learning and the BioBERT language processing model as a tool to assess trends in biomedical literature in a clearly reproducible manner. We then assessed the model's performance for this application and compared it to previous models used for this task. Methods: Using 108 expert-annotated clinical trial abstracts and 2,000 unlabeled abstracts this study develops a three-class sentiment classification algorithm for clinical trial abstracts. The model uses a semi-supervised model based on the Bidirectional Encoder Representation from Transformers (BERT) model, a much more advanced and accurate method compared to previously used models based upon traditional machine learning methods. The prediction performance was compared to those previous studies. Results: The algorithm was found to have a classification accuracy of 91.3%, with a macro F1-Score of 0.92, significantly outperforming previous studies used to classify sentiment in clinical trial literature, while also making the sentiment classification finer grained with greater reproducibility. Conclusion: We demonstrate an easily applied sentiment classification model for clinical trial abstracts that significantly outperforms previous models with greater reproducibility and applicability to large-scale study of reporting trends.

A performance based feature selection technique for subject independent MI based BCI.

PURPOSE: Significant research has been conducted in the field of brain computer interface (BCI) algorithm development, however, many of the resulting algorithms are both complex, and specific to a particular user as the most successful methodology can vary between individuals and sessions. The objective of this study was to develop a simple yet effective method of feature selection to improve the accuracy of a subject independent BCI algorithm and streamline the process of BCI algorithm development. Over the past several years, several high precision features have been suggested by researchers to classify different motor imagery tasks. This research applies fourteen of these features as a feature pool that can be used as a reference for future researchers. Additionally, we look for the most efficient feature or feature set with four different classifiers that best differentiates several motor imagery tasks. In this work we have successfully employed a feature fusion method to obtain the best sub-set of features. We have proposed a novel computer aided feature selection method to determine the best set of features for distinguishing between motor imagery tasks in lieu of the manual feature selection that has been performed in past studies. The features selected by this method were then fed into a Linear Discriminant Analysis, K-nearest neighbor, decision tree, or support vector machine classifier for classification to determine the overall performance. METHODS: The methods used were a novel performance based additive feature fusion algorithm working in conjunction with machine learning in order to classify the motor imagery signals into particular states. The data used for this study was collected from BCI competition III dataset IVa. RESULT: The result of this algorithm was a classification accuracy of 99% for a subject independent algorithm with less computation cost compared to traditional methods, in addition to multiple feature/classifier combinations that outperform current subject independent methods. CONCLUSION: The conclusion of this study and its significance is that it developed a viable methodology for simple, efficient feature selection and BCI algorithm development, which leads to an overall increase in algorithm classification accuracy.