Objective: To establish if the BElfast Retinal Tear and detachment Score (BERT Score) can be used in triaging patients presenting with vitreous haemorrhage to allow safe differentiation between those with retinal tears and detachments, versus haemorrhagic posterior vitreous detachments.
Methods: Retrospective audit of 122 patients presenting to eye casualty with vitreous haemorrhage excluding trauma and vascular causes. Twenty-two patients were excluded from the study as they had no follow-up. The BERT Score was applied to the remaining 100 patients.
Results: Vitreous haemorrhages with a BERT score ≥4 points were more likely to have a retinal tear or detachment (P = 0.0056). The sensitivity was 84.6% (confidence interval (CI) 65.0-100.0%), specificity 34.5% (CI 24.5-44.5%), positive predictive value 16.2% (CI 7.4-24.9%) and negative predictive value 94% (CI 85.4-100.0%).
Conclusions: The BERT is a reliable scoring system to risk stratify patients with vitreous haemorrhage. Its high sensitivity and negative predictive value can help clinicians to detect high-risk patients.
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http://dx.doi.org/10.1038/s41433-023-02660-3 | DOI Listing |
JMIR Med Inform
January 2025
Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, China.
Background: Machine learning models can reduce the burden on doctors by converting medical records into International Classification of Diseases (ICD) codes in real time, thereby enhancing the efficiency of diagnosis and treatment. However, it faces challenges such as small datasets, diverse writing styles, unstructured records, and the need for semimanual preprocessing. Existing approaches, such as naive Bayes, Word2Vec, and convolutional neural networks, have limitations in handling missing values and understanding the context of medical texts, leading to a high error rate.
View Article and Find Full Text PDFPLoS Comput Biol
January 2025
School of Biomedical Engineering, Anhui Medical University, Hefei, China.
Synonymous mutations, once considered neutral, are now understood to have significant implications for a variety of diseases, particularly cancer. It is indispensable to identify these driver synonymous mutations in human cancers, yet current methods are constrained by data limitations. In this study, we initially investigate the impact of sequence-based features, including DNA shape, physicochemical properties and one-hot encoding of nucleotides, and deep learning-derived features from pre-trained chemical molecule language models based on BERT.
View Article and Find Full Text PDFFront Psychol
December 2024
Department of Critical Care Medicine, Sir Run Run Shaw Hospital, Hangzhou, Zhejiang, China.
Objective: This study proposes an emotion correlation-enhanced sentiment analysis model (ECO-SAM), a sentiment correlation modeling-based multi-label sentiment analysis model.
Methods: The ECO-SAM utilizes a pre-trained BERT encoder to obtain semantic embedding of input texts and then leverages a self-attention mechanism to model the semantic correlation between emotions. Additionally, it utilizes a text emotion matching neural network to make sentiment analysis for input texts.
Comput Biol Med
January 2025
LMA Laboratory, University of Bejaia, Bejaia 06000, Algeria. Electronic address:
Social networks are increasingly taking over daily life, creating a volume of unsecured data and making it very difficult to capture safe data, especially in times of crisis. This study aims to use a Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM)-based hybrid model for health monitoring and health crisis forecasting. It consists of efficiently retrieving safe content from multiple social media sources.
View Article and Find Full Text PDFJMIR Serious Games
December 2024
Department of Psychology, Lund University, Lund, Sweden.
Background: Words are a natural way to describe mental states in humans, while numerical values are a convenient and effective way to carry out quantitative psychological research. With the growing interest of researchers in gaming disorder, the number of screening tools is growing. However, they all require self-quantification of mental states.
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