Zobrazeno 1 - 10
of 319
pro vyhledávání: '"Kamaleswaran, Rishikesan"'
Autor:
Wu, Alan, Choudhary, Tilendra, Upadhyaya, Pulakesh, Ali, Ayman, Yang, Philip, Kamaleswaran, Rishikesan
Sepsis-induced acute respiratory failure (ARF) is a serious complication with a poor prognosis. This paper presents a deep representation learningbased phenotyping method to identify distinct groups of clinical trajectories of septic patients with AR
Externí odkaz:
http://arxiv.org/abs/2405.02563
Autor:
Kobara, Seibi, Rafiei, Alireza, Nateghi, Masoud, Bozkurt, Selen, Kamaleswaran, Rishikesan, Sarker, Abeed
Breast cancer is a significant public health concern and is the leading cause of cancer-related deaths among women. Despite advances in breast cancer treatments, medication non-adherence remains a major problem. As electronic health records do not ty
Externí odkaz:
http://arxiv.org/abs/2403.00821
Autor:
Rafiei, Alireza, Moore, Ronald, Choudhary, Tilendra, Marshall, Curtis, Smith, Geoffrey, Roback, John D., Patel, Ravi M., Josephson, Cassandra D., Kamaleswaran, Rishikesan
Objective: Blood transfusions, crucial in managing anemia and coagulopathy in ICU settings, require accurate prediction for effective resource allocation and patient risk assessment. However, existing clinical decision support systems have primarily
Externí odkaz:
http://arxiv.org/abs/2401.00972
With the growing prevalence of machine learning and artificial intelligence-based medical decision support systems, it is equally important to ensure that these systems provide patient outcomes in a fair and equitable fashion. This paper presents an
Externí odkaz:
http://arxiv.org/abs/2312.02959
Autor:
Arora, Mehak, Mortagy, Hassan, Dwarshius, Nathan, Gupta, Swati, Holder, Andre L., Kamaleswaran, Rishikesan
Machine learning (ML) models are increasingly pivotal in automating clinical decisions. Yet, a glaring oversight in prior research has been the lack of proper processing of Electronic Medical Record (EMR) data in the clinical context for errors and o
Externí odkaz:
http://arxiv.org/abs/2308.10781
As a subset of machine learning, meta-learning, or learning to learn, aims at improving the model's capabilities by employing prior knowledge and experience. A meta-learning paradigm can appropriately tackle the conventional challenges of traditional
Externí odkaz:
http://arxiv.org/abs/2308.02877
We present a Transfer Causal Learning (TCL) framework when target and source domains share the same covariate/feature spaces, aiming to improve causal effect estimation accuracy in limited data. Limited data is very common in medical applications, wh
Externí odkaz:
http://arxiv.org/abs/2305.09126
We present a generalized linear structural causal model, coupled with a novel data-adaptive linear regularization, to recover causal directed acyclic graphs (DAGs) from time series. By leveraging a recently developed stochastic monotone Variational I
Externí odkaz:
http://arxiv.org/abs/2301.11197
Sepsis is a deadly condition affecting many patients in the hospital. Recent studies have shown that patients diagnosed with sepsis have significant mortality and morbidity, resulting from the body's dysfunctional host response to infection. Clinicia
Externí odkaz:
http://arxiv.org/abs/2212.06364
Autor:
Xu, Yanbo, Khare, Alind, Matlin, Glenn, Ramadoss, Monish, Kamaleswaran, Rishikesan, Zhang, Chao, Tumanov, Alexey
Machine Learning (ML) research has focused on maximizing the accuracy of predictive tasks. ML models, however, are increasingly more complex, resource intensive, and costlier to deploy in resource-constrained environments. These issues are exacerbate
Externí odkaz:
http://arxiv.org/abs/2210.15056