Sepsis

Machine learning applications for the prediction of surgical site infection in neurological operations.

OBJECTIVE: Surgical site infection (SSI) following a neurosurgical operation is a complication that impacts morbidity, mortality, and economics. Currently, machine learning (ML) algorithms are used for outcome prediction in various neurosurgical aspects. The implementation of ML algorithms to learn from medical data may help in obtaining prognostic information on diseases, especially SSIs. The pur...

Application of Machine Learning to Prediction of Surgical Site Infection.

Surgical site infections are an important health concern, particularly in low-resource areas, where there is poor access to clinical facilities or trained clinical staff. As an application of machine learning, we present results from a study conducted in rural Rwanda for the purpose of predicting infection in Cesarean section wounds, which is a leading cause of maternal mortality. Questionnaire an...

New Phenotypes for Sepsis: The Promise and Problem of Applying Machine Learning and Artificial Intelligence in Clinical Research.

Supervised machine learning for the prediction of infection on admission to hospital: a prospective observational cohort study.

BACKGROUND: Infection diagnosis can be challenging, relying on clinical judgement and non-specific markers of infection. We evaluated a supervised mac...

High-throughput screening and Bayesian machine learning for copper-dependent inhibitors of Staphylococcus aureus.

One potential source of new antibacterials is through probing existing chemical libraries for copper-dependent inhibitors (CDIs), i.e., molecules with...

Heart rate variability based machine learning models for risk prediction of suspected sepsis patients in the emergency department.

Early identification of high-risk septic patients in the emergency department (ED) may guide appropriate management and disposition, thereby improving...

Precision immunoprofiling to reveal diagnostic signatures for latent tuberculosis infection and reactivation risk stratification.

Latent tuberculosis infection (LTBI) is estimated in nearly one quarter of the world's population, and of those immunocompetent and infected ~10% will...

Photographic LVAD Driveline Wound Infection Recognition Using Deep Learning.

The steady increase in the number of patients equipped with mechanical heart support implants, such as left ventricular assist devices (LVAD), along w...

New Computational Tool Based on Machine-learning Algorithms for the Identification of Rhinovirus Infection-Related Genes.

BACKGROUND: Human rhinovirus has different identified serotypes and is the most common cause of cold in humans. To date, many genes have been discover...

Three Dimensional Analysis of SPECT Images for Diagnosing Early Parkinson's Disease using Radial Basis Function Kernel - Extreme Learning Machine.

BACKGROUND: Parkinson's Disease (PD) is caused by the deficiency of dopamine, the neurotransmitter that has an effect on specific uptake region of the...

Principi di farmacodinamica e farmacocinetica nello switch tra antipsicotici: focus su cariprazina.

Cariprazina {RGH-188; trans-N- [4- [2- [4- (2,3-diclorofenil) piperazin-1-il] etil] cicloesil] -N_, N_-dimetilurea cloridrato} è un antipsicotico atip...

Artificial Intelligence Tools for Sepsis and Cancer.

Hydrocortisone, Vitamin C and thiamine for the treatment of sepsis and septic shock following cardiac surgery.

BACKGROUND AND AIMS: The effect of vitamin C on vasopressor requirement in critically ill patients have been evaluated previously. We aimed to evaluat...

The role of presepsin in the diagnosis and assessment of severity of sepsis and severe pneumonia.

AIM: The aim of this study was to evaluate marker of inflammation presepsin to improve diagnosis of severe pneumonia, sepsis.

Applying Artificial Intelligence to Identify Physiomarkers Predicting Severe Sepsis in the PICU.

OBJECTIVES: We used artificial intelligence to develop a novel algorithm using physiomarkers to predict the onset of severe sepsis in critically ill c...

Artificial intelligence in the diagnosis of Parkinson's disease from ioflupane-123 single-photon emission computed tomography dopamine transporter scans using transfer learning.

OBJECTIVE: The objective of this study was to identify the extent to which artificial intelligence could be used in the diagnosis of Parkinson's disea...

Early Prediction of Sepsis in EMR Records Using Traditional ML Techniques and Deep Learning LSTM Networks.

Sepsis is a life-threatening condition caused by infection and subsequent overreaction by the immune system. Physicians effectively treat sepsis with ...

[Comparison of machine learning method and logistic regression model in prediction of acute kidney injury in severely burned patients].

To build risk prediction models for acute kidney injury (AKI) in severely burned patients, and to compare the prediction performance of machine learn...

Predicting Corticosteroid-Free Biologic Remission with Vedolizumab in Crohn's Disease.

BACKGROUND AND AIMS: Vedolizumab (VDZ) is effective for Crohn's disease (CD) but costly and is slow to produce remission. Early knowledge of whether v...

An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU.

OBJECTIVES: Sepsis is among the leading causes of morbidity, mortality, and cost overruns in critically ill patients. Early intervention with antibiot...