Machine learning modelling for predicting the utilization of invasive and non-invasive ventilation throughout the ICU duration.

The goal of this work is to develop a Machine Learning model to predict the need for both invasive and non-invasive mechanical ventilation in intensive care unit (ICU) patients. Using the Philips eICU Research Institute (ERI) database, 2.6 million ICU patient data from 2010 to 2019 were analyzed.

Machine Learning for Benchmarking Critical Care Outcomes.

Objectives: Enhancing critical care efficacy involves evaluating and improving system functioning. Benchmarking, a retrospective comparison of results against standards, aids risk-adjusted assessment and helps healthcare providers identify areas for improvement based on observed and predicted outcomes. The last two decades have seen the development of several models using machine learning (ML) for clinical outcome prediction.

Machine learning prediction of the total duration of invasive and non-invasive ventilation During ICU Stay.

Predicting the duration of ventilation in the ICU helps in assessing the risk of ventilator-induced lung injury, ensuring sufficient oxygenation, and optimizing resource allocation. Prior models provided a prediction of total duration without distinguishing between invasive and non-invasive ventilation. This work proposes two independent gradient boosting regression models for predicting the duration of invasive and non-invasive ventilation based on commonly available ICU features.

Imaging-AMARETTO: An Imaging Genomics Software Tool to Interrogate Multiomics Networks for Relevance to Radiography and Histopathology Imaging Biomarkers of Clinical Outcomes.

Purpose: The availability of increasing volumes of multiomics, imaging, and clinical data in complex diseases such as cancer opens opportunities for the formulation and development of computational imaging genomics methods that can link multiomics, imaging, and clinical data.

Methods: Here, we present the Imaging-AMARETTO algorithms and software tools to systematically interrogate regulatory networks derived from multiomics data within and across related patient studies for their relevance to radiography and histopathology imaging features predicting clinical outcomes.

Results: To demonstrate its utility, we applied Imaging-AMARETTO to integrate three patient studies of brain tumors, specifically, multiomics with radiography imaging data from The Cancer Genome Atlas (TCGA) glioblastoma multiforme (GBM) and low-grade glioma (LGG) cohorts and transcriptomics with histopathology imaging data from the Ivy Glioblastoma Atlas Project (IvyGAP) GBM cohort.

Visuomotor control of ankle joint using position vs. force.

Ankle joint plays a critical role in daily activities involving interactions with environment using force and position control. Neuromechanical dysfunctions (e.g.

Combined Analysis of Metabolomes, Proteomes, and Transcriptomes of Hepatitis C Virus-Infected Cells and Liver to Identify Pathways Associated With Disease Development.

Background & Aims: The mechanisms of hepatitis C virus (HCV) infection, liver disease progression, and hepatocarcinogenesis are only partially understood. We performed genomic, proteomic, and metabolomic analyses of HCV-infected cells and chimeric mice to learn more about these processes.

Methods: Huh7.

An examination of muscle force control in individuals with a functionally unstable ankle.

Previous studies suggest that functional ankle instability (FAI) may be associated with deficits in the ability to sense muscle forces. We tested individuals with FAI to determine if they have reduced ability to control ankle muscle forces, which is a function of force sense. Our test was performed isometrically to minimize the involvement of joint position sense and kinesthesia.

An Open-Source Feature Extraction Tool for the Analysis of Peripheral Physiological Data.

Electrocardiogram, electrodermal activity, electromyogram, continuous blood pressure, and impedance cardiography are among the most commonly used peripheral physiological signals (biosignals) in psychological studies and healthcare applications, including health tracking, sleep quality assessment, disease early-detection/diagnosis, and understanding human emotional and affective phenomena. This paper presents the development of a biosignal-specific processing toolbox (Bio-SP tool) for preprocessing and feature extraction of these physiological signals according to the state-of-the-art studies reported in the scientific literature and feedback received from the field experts. Our open-source Bio-SP tool is intended to assist researchers in affective computing, digital and mobile health, and telemedicine to extract relevant physiological patterns (i.

Patient-specific optimization of mechanical ventilation for patients with acute respiratory distress syndrome using quasi-static pulmonary P-V data.

Quasi-static, pulmonary pressure-volume (P-V) curves were combined with a respiratory system model to analyze tidal pressure cycles, simulating mechanical ventilation of patients with acute respiratory distress syndrome (ARDS). Two important quantities including 1) tidal recruited volume and 2) tidal hyperinflated volume were analytically computed by integrating the distribution of alveolar elements over the affected pop-open pressure range. We analytically predicted the tidal recruited volume of four canine subjects and compared our results with similar experimental measurements on canine models for the validation.

Quasi-static pulmonary P-V curves of patients with ARDS, Part I: Characterization.

Quasi-static, pulmonary pressure-volume (P-V) curves are combined with a respiratory system model to analyze characteristics of patients with acute respiratory distress syndrome (ARDS). It is shown that there exist distinct differences between healthy- and injured-respiratory system in the order of magnitudes of parameters of their P-V model equation. Four stages of ARDS (Early ARDS, Deep knee, Advanced ARDS and Baby lung) are defined quantitatively in terms of these parameters.

An examination of lower limb asymmetry in ankle isometric force control.

While asymmetries have been observed between the dominant and non-dominant legs, it is unclear whether they have different abilities in isometric force control (IFC). The purpose of this study was to compare ankle IFC between the legs. IFC is important for stabilization rather than object manipulation, and people typically use their non-dominant leg for stabilization tasks.

Position versus force control: using the 2-DOF robotic ankle trainer to assess ankle's motor control.

An estimated of 2,000,000 acute ankle sprains occur annually in the United States. Furthermore, ankle disabilities are caused by neurological impairments such as traumatic brain injury, cerebral palsy and stroke. The virtually interfaced robotic ankle and balance trainer (vi-RABT) was introduced as a cost-effective platform-based rehabilitation robot to improve overall ankle/balance strength, mobility and control.