Intensivists

Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension.

The SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to un...

Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension.

The SPIRIT 2013 (The Standard Protocol Items: Recommendations for Interventional Trials) statement aims to improve the completeness of clinical trial protocol reporting, by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there is a growing recognition ...

Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension.

The CONSORT 2010 (Consolidated Standards of Reporting Trials) statement provides minimum guidelines for reporting randomised trials. Its widespread us...

Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension.

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

Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension.

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

Machine learning based refined differential gene expression analysis of pediatric sepsis.

BACKGROUND: Differential expression (DE) analysis of transcriptomic data enables genome-wide analysis of gene expression changes associated with biolo...

A Single-Center Evaluation of Extended Infusion Piperacillin/Tazobactam for Empiric Treatment in the Intensive Care Unit.

Piperacillin/tazobactam (PTZ) extended infusion (EI) is often used empirically in the intensive care unit (ICU). Gram-negative (GN) organisms with PT...

Integrating multi-omics data by learning modality invariant representations for improved prediction of overall survival of cancer.

Breast and ovarian cancers are the second and the fifth leading causes of cancer death among women. Predicting the overall survival of breast and ovar...

Detection of Bacteremia in Surgical In-Patients Using Recurrent Neural Network Based on Time Series Records: Development and Validation Study.

BACKGROUND: Detecting bacteremia among surgical in-patients is more obscure than other patients due to the inflammatory condition caused by the surger...

Validation of the usefulness of artificial neural networks for risk prediction of adverse drug reactions used for individual patients in clinical practice.

Artificial neural networks are the main tools for data mining and were inspired by the human brain and nervous system. Studies have demonstrated their...

Machine Learning Algorithms Identify Pathogen-Specific Biomarkers of Clinical and Metabolomic Characteristics in Septic Patients with Bacterial Infections.

Sepsis is a high-mortality disease that is infected by bacteria, but pathogens in individual patients are difficult to diagnosis. Metabolomic changes ...

Supervised machine learning for the early prediction of acute respiratory distress syndrome (ARDS).

PURPOSE: Acute respiratory distress syndrome (ARDS) is a serious respiratory condition with high mortality and associated morbidity. The objective of ...

Novel application of an automated-machine learning development tool for predicting burn sepsis: proof of concept.

Sepsis is the primary cause of burn-related mortality and morbidity. Traditional indicators of sepsis exhibit poor performance when used in this uniqu...

Automated design and optimization of multitarget schizophrenia drug candidates by deep learning.

Complex neuropsychiatric diseases such as schizophrenia require drugs that can target multiple G protein-coupled receptors (GPCRs) to modulate complex...

Benchmarking machine learning models on multi-centre eICU critical care dataset.

Progress of machine learning in critical care has been difficult to track, in part due to absence of public benchmarks. Other fields of research (such...

The development an artificial intelligence algorithm for early sepsis diagnosis in the intensive care unit.

BACKGROUND: Severe sepsis and septic shock are still the leading causes of death in Intensive Care Units (ICUs), and timely diagnosis is crucial for t...

Deep Multi-Critic Network for accelerating Policy Learning in multi-agent environments.

Humans live among other humans, not in isolation. Therefore, the ability to learn and behave in multi-agent environments is essential for any autonomo...

Exploration of critical care data by using unsupervised machine learning.

BACKGROUND AND OBJECTIVE: Identification of subgroups may be useful to understand the clinical characteristics of ICU patients. The purposes of this s...

Bio-inspired multi-scale fusion.

We reveal how implementing the homogeneous, multi-scale mapping frameworks observed in the mammalian brain's mapping systems radically improves the pe...

Implementation of an Artificial Intelligence Algorithm for sepsis detection.

OBJECTIVES: to present the nurses' experience with technological tools to support the early identification of sepsis.