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Machine learning derivation of two cardiac arrest subphenotypes with distinct responses to treatment.
J Transl Med
January 2025
Fourth Clinical Medical College of Zhejiang Chinese Medical University, Zhejiang, 310006, Hangzhou, China.
Introduction: Cardiac arrest (CA), characterized by its heterogeneity, poses challenges in patient management. This study aimed to identify clinical subphenotypes in CA patients to aid in patient classification, prognosis assessment, and treatment decision-making.
Methods: For this study, comprehensive data were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) 2.
Prediction of ECG signals from ballistocardiography using deep learning for the unconstrained measurement of heartbeat intervals.
Sci Rep
January 2025
Department of Biosystems Engineering, Graduate School of Science and Engineering, Yamagata University (emeritus), Yonezawa, Japan.
We developed a deep learning-based extraction of electrocardiographic (ECG) waves from ballistocardiographic (BCG) signals and explored their use in R-R interval (RRI) estimation. Preprocessed BCG and reference ECG signals were inputted into the bidirectional long short-term memory network to train the model to minimize the loss function of the mean squared error between the predicted ECG (pECG) and genuine ECG signals. Using a dataset acquired with polyvinylidene fluoride and ECG sensors in different recumbent positions from 18 participants, we generated pECG signals from preprocessed BCG signals using the learned model and evaluated the RRI estimation performance by comparing the predicted RRI with the reference RRI obtained from the ECG signal using a leave-one-subject-out cross-validation scheme.
View Article and Find Full Text PDFLeveraging cancer mutation data to inform the pathogenicity classification of germline missense variants.
PLoS Genet
January 2025
Program in Genetics and Genome Biology, SickKids Research Institute, Toronto, Ontario, Canada.
Innovative and easy-to-implement strategies are needed to improve the pathogenicity assessment of rare germline missense variants. Somatic cancer driver mutations identified through large-scale tumor sequencing studies often impact genes that are also associated with rare Mendelian disorders. The use of cancer mutation data to aid in the interpretation of germline missense variants, regardless of whether the gene is associated with a hereditary cancer predisposition syndrome or a non-cancer-related developmental disorder, has not been systematically assessed.
View Article and Find Full Text PDFDevelopment and Validation of a Model Based on Circulating Biomarkers for Discriminating Symptomatic Spontaneous Intracranial Artery Dissection.
Transl Stroke Res
January 2025
Department of Neurosurgery, Shanxi Provincial People's Hospital, Taiyuan, Shanxi, China.
Spontaneous intracranial artery dissection (sIAD) is the leading cause of stroke in young individuals. Identifying high-risk sIAD cases that exhibit symptoms and are likely to progress is crucial for treatment decision-making. This study aimed to develop a model relying on circulating biomarkers to discriminate symptomatic sIADs.
View Article and Find Full Text PDFPrediction of Cisplatin-Induced Acute Kidney Injury Using an Interpretable Machine Learning Model and Electronic Medical Record Information.
Clin Transl Sci
January 2025
Department of Regulatory Science, Nagoya City University Graduate School of Pharmaceutical Sciences, Nagoya, Japan.
Predicting cisplatin-induced acute kidney injury (Cis-AKI) before its onset is important. We aimed to develop a predictive model for Cis-AKI using patient clinical information based on an interpretable machine learning algorithm. This single-center retrospective study included hospitalized patients aged ≥ 18 years who received the first course of cisplatin chemotherapy from January 1, 2011, to December 31, 2020, at Nagoya City University Hospital.
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