Application of machine learning in predicting survival outcomes involving real-world data: a scoping review
Background Despite the interest in machine learning (ML) algorithms for analyzing real-
world data (RWD) in healthcare, the use of ML in predicting time-to-event data, a common …
world data (RWD) in healthcare, the use of ML in predicting time-to-event data, a common …
Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques
Objective The proper handling of missing values is critical to delivering reliable estimates
and decisions, especially in high-stakes fields such as clinical research. In response to the …
and decisions, especially in high-stakes fields such as clinical research. In response to the …
Bottom-up and top-down paradigms of artificial intelligence research approaches to healthcare data science using growing real-world big data
Objectives As the real-world electronic health record (EHR) data continue to grow
exponentially, novel methodologies involving artificial intelligence (AI) are becoming …
exponentially, novel methodologies involving artificial intelligence (AI) are becoming …
RadioLOGIC, a healthcare model for processing electronic health records and decision-making in breast disease
Digital health data used in diagnostics, patient care, and oncology research continue to
accumulate exponentially. Most medical information, and particularly radiology results, are …
accumulate exponentially. Most medical information, and particularly radiology results, are …
Global analysis and prediction scenario of infectious outbreaks by recurrent dynamic model and machine learning models: A case study on COVID-19
SA Rakhshan, MS Nejad, M Zaj, FH Ghane - Computers in Biology and …, 2023 - Elsevier
It is essential to evaluate patient outcomes at an early stage when dealing with a pandemic
to provide optimal clinical care and resource management. Many methods have been …
to provide optimal clinical care and resource management. Many methods have been …
Machine and deep learning methods for clinical outcome prediction based on physiological data of COVID-19 patients: a scoping review
Introduction Since the beginning of the COVID-19 pandemic, numerous machine and deep
learning (MDL) methods have been proposed in the literature to analyze patient …
learning (MDL) methods have been proposed in the literature to analyze patient …
[HTML][HTML] PK-RNN-V E: A deep learning model approach to vancomycin therapeutic drug monitoring using electronic health record data
Vancomycin is a commonly used antimicrobial in hospitals, and therapeutic drug monitoring
(TDM) is required to optimize its efficacy and avoid toxicities. Bayesian models are currently …
(TDM) is required to optimize its efficacy and avoid toxicities. Bayesian models are currently …
TADA: Temporal-aware Adversarial Domain Adaptation for patient outcomes forecasting
Patient outcomes forecasting (POF) has been shown to be an effective diagnostic assistant
that can be used to predict disease progression and patient status in advance. In practice …
that can be used to predict disease progression and patient status in advance. In practice …
[HTML][HTML] Transcriptomics secondary analysis of severe human infection with SARS-CoV-2 identifies gene expression changes and predicts three transcriptional …
J Clancy, CS Hoffmann, BE Pickett - Computational and Structural …, 2023 - Elsevier
SARS-CoV-2 is the causative agent of COVID-19, which has greatly affected human health
since it first emerged. Defining the human factors and biomarkers that differentiate severe …
since it first emerged. Defining the human factors and biomarkers that differentiate severe …
Large-scale deep learning analysis to identify adult patients at risk for combined and common variable immunodeficiencies
Background Primary immunodeficiency (PI) is a group of heterogeneous disorders resulting
from immune system defects. Over 70% of PI is undiagnosed, leading to increased mortality …
from immune system defects. Over 70% of PI is undiagnosed, leading to increased mortality …