Intensivists

Transfer learning with chest X-rays for ER patient classification.

One of the challenges with urgent evaluation of patients with acute respiratory distress syndrome (ARDS) in the emergency room (ER) is distinguishing between cardiac vs infectious etiologies for their pulmonary findings. We conducted a retrospective study with the collected data of 171 ER patients. ER patient classification for cardiac and infection causes was evaluated with clinical data and ches...

Risk factors and socio-economic burden in pancreatic ductal adenocarcinoma operation: a machine learning based analysis.

BACKGROUND: Surgical resection is the major way to cure pancreatic ductal adenocarcinoma (PDAC). However, this operation is complex, and the peri-operative risk is high, making patients more likely to be admitted to the intensive care unit (ICU). Therefore, establishing a risk model that predicts admission to ICU is meaningful in preventing patients from post-operation deterioration and potentiall...

A Customizable Analysis Flow in Integrative Multi-Omics.

The number of researchers using multi-omics is growing. Though still expensive, every year it is cheaper to perform multi-omic studies, often exponent...

Analytics with artificial intelligence to advance the treatment of acute respiratory distress syndrome.

Artificial intelligence (AI) has found its way into clinical studies in the era of big data. Acute respiratory distress syndrome (ARDS) or acute lung ...

Deep learning-based clustering robustly identified two classes of sepsis with both prognostic and predictive values.

BACKGROUND: Sepsis is a heterogenous syndrome and individualized management strategy is the key to successful treatment. Genome wide expression profil...

Artificial Intelligence in the Intensive Care Unit.

The diffusion of electronic health records collecting large amount of clinical, monitoring, and laboratory data produced by intensive care units (ICUs...

Validation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals.

BACKGROUND: Severe sepsis and septic shock are among the leading causes of death in the United States and sepsis remains one of the most expensive con...

Classification of aortic stenosis using conventional machine learning and deep learning methods based on multi-dimensional cardio-mechanical signals.

This paper introduces a study on the classification of aortic stenosis (AS) based on cardio-mechanical signals collected using non-invasive wearable i...

Utilization of Deep Learning for Subphenotype Identification in Sepsis-Associated Acute Kidney Injury.

BACKGROUND AND OBJECTIVES: Sepsis-associated AKI is a heterogeneous clinical entity. We aimed to agnostically identify sepsis-associated AKI subphenot...

Not All Sepsis-Associated Acute Kidney Injury Is the Same: There May Be an App for That.

Clinical Predictive Models for COVID-19: Systematic Study.

BACKGROUND: COVID-19 is a rapidly emerging respiratory disease caused by SARS-CoV-2. Due to the rapid human-to-human transmission of SARS-CoV-2, many ...

Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU.

BACKGROUND: Early and accurate identification of sepsis patients with high risk of in-hospital death can help physicians in intensive care units (ICUs...

Predicting acute kidney injury in critically ill patients using comorbid conditions utilizing machine learning.

BACKGROUND: Acute kidney injury (AKI) carries a poor prognosis. Its incidence is increasing in the intensive care unit (ICU). Our purpose in this stud...

AI in the Intensive Care Unit: Up-to-Date Review.

AI is the latest technologic trend that likely will have a huge impact in medicine. AI's potential lies in its ability to process large volumes of dat...

Acute kidney disease and long-term outcomes in critically ill acute kidney injury patients with sepsis: a cohort analysis.

BACKGROUND: Acute kidney injury (AKI) is frequent during hospitalization and may contribute to adverse short- and long-term consequences. Acute kidney...

Application of machine learning to the prediction of postoperative sepsis after appendectomy.

BACKGROUND: We applied various machine learning algorithms to a large national dataset to model the risk of postoperative sepsis after appendectomy to...

Supervised classification techniques for prediction of mortality in adult patients with sepsis.

BACKGROUND: Sepsis mortality is still unacceptably high and an appropriate prognostic tool may increase the accuracy for clinical decisions.

Data-driven ICU management: Using Big Data and algorithms to improve outcomes.

The digitalization of the Intensive Care Unit (ICU) led to an increasing amount of clinical data being collected at the bedside. The term "Big Data" c...

Unsupervised Clustering of Missense Variants in HNF1A Using Multidimensional Functional Data Aids Clinical Interpretation.

Exome sequencing in diabetes presents a diagnostic challenge because depending on frequency, functional impact, and genomic and environmental contexts...

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

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