Intensivists

A deep learning system for heart failure mortality prediction.

Heart failure (HF) is the final stage of the various heart diseases developing. The mortality rates of prognosis HF patients are highly variable, ranging from 5% to 75%. Evaluating the all-cause mortality of HF patients is an important means to avoid death and positively affect the health of patients. But in fact, machine learning models are difficult to gain good results on missing values, high d...

Deep learning-based open set multi-source domain adaptation with complementary transferability metric for mechanical fault diagnosis.

Intelligent fault diagnosis aims to build robust mechanical condition recognition models with limited dataset. At this stage, fault diagnosis faces two practical challenges: (1) the variability of mechanical working conditions makes the collected data distribution inconsistent, which brings about the domain shift; (2) some unpredictable unknown fault modes that do not observe in the training datas...

Exploring a global interpretation mechanism for deep learning networks when predicting sepsis.

The purpose of this study is to identify additional clinical features for sepsis detection through the use of a novel mechanism for interpreting black...

Improving Intensive Care Unit Early Readmission Prediction Using Optimized and Explainable Machine Learning.

It is of great interest to develop and introduce new techniques to automatically and efficiently analyze the enormous amount of data generated in toda...

[Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extensionDiretrizes para relatórios de ensaios clínicos com intervenções que utilizam inteligência artificial: a extensão CONSORT-AI].

The CONSORT 2010 statement provides minimum guidelines for reporting randomized trials. Its widespread use has been instrumental in ensuring transpare...

Logistic regression technique is comparable to complex machine learning algorithms in predicting cognitive impairment related to post intensive care syndrome.

To evaluate the performance of machine learning (ML) models and to compare it with logistic regression (LR) technique in predicting cognitive impairme...

[Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extensionDiretrizes para protocolos de ensaios clínicos com intervenções que utilizam inteligência artificial: a extensão SPIRIT-AI].

The SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the mi...

Compressed Sensing Data with Performing Audio Signal Reconstruction for the Intelligent Classification of Chronic Respiratory Diseases.

Chronic obstructive pulmonary disease (COPD) concerns the serious decline of human lung functions. These have emerged as one of the most concerning he...

Image augmentation and automated measurement of endotracheal-tube-to-carina distance on chest radiographs in intensive care unit using a deep learning model with external validation.

BACKGROUND: Chest radiographs are routinely performed in intensive care unit (ICU) to confirm the correct position of an endotracheal tube (ETT) relat...

Evaluations on supervised learning methods in the calibration of seven-hole pressure probes.

Machine learning method has become a popular, convenient and efficient computing tool applied to many industries at present. Multi-hole pressure probe...

Knowledge Graph Embeddings for ICU readmission prediction.

BACKGROUND: Intensive Care Unit (ICU) readmissions represent both a health risk for patients,with increased mortality rates and overall health deterio...

Two-Step Approach for Occupancy Estimation in Intensive Care Units Based on Bayesian Optimization Techniques.

Due to the high occupational pressure suffered by intensive care units (ICUs), a correct estimation of the patients' length of stay (LoS) in the ICU i...

A comparison of total thoracoscopic versus robotic approach for cardiac myxoma resection: a single-center retrospective study.

Advances in instrumentation and technique have facilitated minimally invasive surgeries for cardiac myxoma treatment. This study aims to compare the c...

Machine Learning Models for the Automatic Detection of Exercise Thresholds in Cardiopulmonary Exercising Tests: From Regression to Generation to Explanation.

The cardiopulmonary exercise test (CPET) constitutes a gold standard for the assessment of an individual's cardiovascular fitness. A trend is emerging...

Clustering of critically ill patients using an individualized learning approach enables dose optimization of mobilization in the ICU.

BACKGROUND: While early mobilization is commonly implemented in intensive care unit treatment guidelines to improve functional outcome, the characteri...

Database-based machine learning in sepsis deserves attention.

Artificial intelligence-based multi-omics analysis fuels cancer precision medicine.

With biotechnological advancements, innovative omics technologies are constantly emerging that have enabled researchers to access multi-layer informat...

OnAI-Comp: An Online AI Experts Competing Framework for Early Sepsis Detection.

Sepsis is a major public concern due to its high mortality, morbidity, and financial cost. There are many existing works of early sepsis prediction us...

Artificial Intelligence for Clinical Interpretation of Bedside Chest Radiographs.

Background Supine chest radiography for bedridden patients in intensive care units (ICUs) is one of the most frequently ordered imaging studies worldw...

3D CT-Inclusive Deep-Learning Model to Predict Mortality, ICU Admittance, and Intubation in COVID-19 Patients.

Chest CT is a useful initial exam in patients with coronavirus disease 2019 (COVID-19) for assessing lung damage. AI-powered predictive models could b...