enhancing-fairness-large-language-models-for-clinical-artificial

Enhancing Fairness in Large Language Models for Clinical Artificial Intelligence Applications Through Fine-Tuning and Prompting

Large Language Models (LLMs) are increasingly being applied to sensitive domains such as medical care, which pose multiple technical and ethical ...

Delhi Technological University

COVID-19 vaccine on board ships: current and future implications of seafarers.

Consequences of COVID-19 pandemic on global maritime trade industry.

Diabetes mellitus is a long-term chronicle disorder with a high prevalence rate worldwide. Continuous blood glucose and lifestyle monitoring enabled the control of blood glucose dynamics through machine learning applications using data created by various popular sensors. This survey aims to assess various classical time series, neural networks and state-of-the-art regression models based on a wide variety of machine learning techniques to predict blood glucose and hyper/hypoglycemia in Type 1 diabetic patients. The analysis covers blood glucose prediction modeling, regression, hyper/hypoglycemia alerts, diabetes diagnosis, monitoring, and management. However, the primary focus is on evaluating models for the prediction of Type 1 diabetes. A wide variety of machine learning algorithms have been explored to implement precision medicine by clinicians and provide patients with an early warning system. The automated pancreas may benefit from predictions and alerts of hyper and hypoglycemia.

Multivariate Models of Blood Glucose Prediction in Type1 Diabetes: A Survey of the State-of-the-art.

The latest buzzword in today's world is fake news. The circulation of false information influences elections, public health, brand reputations, and violence. Hence, the severity of the threat of fake news is increasing. The danger for fake news exists everywhere globally and is not specific to one language or nation. The creators of fake news layer the facts in the news with misinformation to confuse the readers. Hence, a need arises for creating a model for detecting fake news in multiple languages. This paper proposes a unified attention-based model Mul-FaD to detect fake news in various languages. We have created our dataset with around 40000 articles in English, German, and French. This paper also shows an exploratory analysis of the dataset created. In this paper, we perform experiments from a multilingual perspective in which we use an altered hierarchical attention-based network to detect fake news. Our model is able to achieve an accuracy of 93.73 and an F1 score of 92.9 for the combined corpus of the three languages.

Article	Year
COVID-19 vaccine on board ships: current and future implications of seafarers. International maritime health\| PubMed ID: 33829476	2021
Consequences of COVID-19 pandemic on global maritime trade industry. International maritime health\| PubMed ID: 33829480	2021
Multivariate Models of Blood Glucose Prediction in Type1 Diabetes: A Survey of the State-of-the-art. Current pharmaceutical biotechnology\| PubMed ID: 35657050	2023
Mul-FaD: attention based detection of multiLingual fake news. Journal of ambient intelligence and humanized computing\| PubMed ID: 36684482	2023

Shailender Kumar

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