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Machine learning applied to near-infrared spectra for clinical pleural effusion classification.

Scientific reports
Lung cancer patients with malignant pleural effusions (MPE) have a particular poor prognosis. It is crucial to distinguish MPE from benign pleural effusion (BPE). The present study aims to develop a rapid, convenient and economical diagnostic method ...

Automated Diagnosis of COVID-19 Using Deep Features and Parameter Free BAT Optimization.

IEEE journal of translational engineering in health and medicine
Accurate and fast diagnosis of COVID-19 is very important to manage the medical conditions of affected persons. The task is challenging owing to shortage and ineffectiveness of clinical testing kits. However, the existing problems can be improved by...

Upper urinary tract surgery and radical prostatectomy with Senhance robotic system: Single center experience-First 100 cases.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: The Senhance robotic surgery system is a novel robotic platform used in several European and World centres. We present our experience in urologic surgery using this platform.

Diagnostic and prognostic capabilities of a biomarker and EMR-based machine learning algorithm for sepsis.

Clinical and translational science
Sepsis is a major cause of mortality among hospitalized patients worldwide. Shorter time to administration of broad-spectrum antibiotics is associated with improved outcomes, but early recognition of sepsis remains a major challenge. In a two-center ...

Multitask fMRI and machine learning approach improve prediction of differential brain activity pattern in patients with insomnia disorder.

Scientific reports
We investigated the differential spatial covariance pattern of blood oxygen level-dependent (BOLD) responses to single-task and multitask functional magnetic resonance imaging (fMRI) between patients with psychophysiological insomnia (PI) and healthy...

A Machine Learning Model Approach to Risk-Stratify Patients With Gastrointestinal Cancer for Hospitalization and Mortality Outcomes.

International journal of radiation oncology, biology, physics
PURPOSE: Patients with gastrointestinal (GI) cancer frequently experience unplanned hospitalizations, but predictive tools to identify high-risk patients are lacking. We developed a machine learning model to identify high-risk patients.

Volume of β-Bursts, But Not Their Rate, Predicts Successful Response Inhibition.

The Journal of neuroscience : the official journal of the Society for Neuroscience
In humans, impaired response inhibition is characteristic of a wide range of psychiatric diseases and of normal aging. It is hypothesized that the right inferior frontal cortex (rIFC) plays a key role by inhibiting the motor cortex via the basal gang...

Computational medication regimen for Parkinson's disease using reinforcement learning.

Scientific reports
Our objective is to derive a sequential decision-making rule on the combination of medications to minimize motor symptoms using reinforcement learning (RL). Using an observational longitudinal cohort of Parkinson's disease patients, the Parkinson's P...