Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Bhatti, Anubhav"'
Time Series Foundation Models (TSFMs) have recently garnered attention for their ability to model complex, large-scale time series data across domains such as retail, finance, and transportation. However, their application to sensitive, domain-specif
Externí odkaz:
http://arxiv.org/abs/2409.11302
Open-source, multilingual medical large language models (LLMs) have the potential to serve linguistically diverse populations across different regions. Adapting generic LLMs for healthcare often requires continual pretraining, but this approach is co
Externí odkaz:
http://arxiv.org/abs/2409.05732
Low-Rank Adaptation (LoRA) is a widely used technique for fine-tuning large pre-trained or foundational models across different modalities and tasks. However, its application to time series data, particularly within foundational models, remains under
Externí odkaz:
http://arxiv.org/abs/2405.10216
Sepsis is a leading cause of mortality in intensive care units (ICUs), representing a substantial medical challenge. The complexity of analyzing diverse vital signs to predict sepsis further aggravates this issue. While deep learning techniques have
Externí odkaz:
http://arxiv.org/abs/2405.01714
Autor:
Bhatti, Anubhav, Angkan, Prithila, Behinaein, Behnam, Mahmud, Zunayed, Rodenburg, Dirk, Braund, Heather, Mclellan, P. James, Ruberto, Aaron, Harrison, Geoffery, Wilson, Daryl, Szulewski, Adam, Howes, Dan, Etemad, Ali, Hungler, Paul
We present a novel multimodal dataset for Cognitive Load Assessment in REaltime (CLARE). The dataset contains physiological and gaze data from 24 participants with self-reported cognitive load scores as ground-truth labels. The dataset consists of fo
Externí odkaz:
http://arxiv.org/abs/2404.17098
We are introducing SM70, a 70 billion-parameter Large Language Model that is specifically designed for SpassMed's medical devices under the brand name 'JEE1' (pronounced as G1 and means 'Life'). This large language model provides more accurate and sa
Externí odkaz:
http://arxiv.org/abs/2312.06974
Autor:
Bhatti, Anubhav, Liu, Yuwei, Dan, Chen, Shen, Bingjie, Lee, San, Kim, Yonghwan, Kim, Jang Yong
Sepsis and septic shock are a critical medical condition affecting millions globally, with a substantial mortality rate. This paper uses state-of-the-art deep learning (DL) architectures to introduce a multi-step forecasting system to predict vital s
Externí odkaz:
http://arxiv.org/abs/2311.04770
Autor:
Bhatti, Anubhav, Thangavelu, Naveen, Hassan, Marium, Kim, Choongmin, Lee, San, Kim, Yonghwan, Kim, Jang Yong
Detecting and predicting septic shock early is crucial for the best possible outcome for patients. Accurately forecasting the vital signs of patients with sepsis provides valuable insights to clinicians for timely interventions, such as administering
Externí odkaz:
http://arxiv.org/abs/2306.14016
Autor:
Angkan, Prithila, Behinaein, Behnam, Mahmud, Zunayed, Bhatti, Anubhav, Rodenburg, Dirk, Hungler, Paul, Etemad, Ali
Through this paper, we introduce a novel driver cognitive load assessment dataset, CL-Drive, which contains Electroencephalogram (EEG) signals along with other physiological signals such as Electrocardiography (ECG) and Electrodermal Activity (EDA) a
Externí odkaz:
http://arxiv.org/abs/2304.04273
We propose cross-modal attentive connections, a new dynamic and effective technique for multimodal representation learning from wearable data. Our solution can be integrated into any stage of the pipeline, i.e., after any convolutional layer or block
Externí odkaz:
http://arxiv.org/abs/2206.04625