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Machine learning in predicting outcomes for stroke patients following rehabilitation treatment: A systematic review
Discussion and conclusionThere remains much room for improvement in future modeling studies, such as high-quality data sources and model analysis. Reliable predictive models should be developed to improve the efficacy of rehabilitation treatment by clinicians.
Source: PLoS One - June 28, 2023 Category: Biomedical Science Authors: Wanting Zu Source Type: research

Improvement of predictive accuracies of functional outcomes after subacute stroke inpatient rehabilitation by machine learning models
ConclusionsThis study suggested that the machine learning models outperformed SLR for predicting FIM prognosis. The machine learning models used only patients ’ background characteristics and FIM scores at admission and more accurately predicted FIM gain than previous studies. ANN, SVR, and GPR outperformed RT and EL. GPR could have the best predictive accuracy for FIM prognosis.
Source: PLoS One - May 26, 2023 Category: Biomedical Science Authors: Yuta Miyazaki Source Type: research

Effect of alteplase, benzodiazepines and beta-blocker on post-stroke pneumonia: Exploration of VISTA-Acute
by Thanh G. Phan, Richard Beare, Philip M. Bath, Svitlana Ievlieva, Stella Ho, John Ly, Amanda G. Thrift, Velandai K. Srikanth, Henry Ma, on behalf of the VISTA-Acute Collaborators BackgroundPost-stroke pneumonia is a frequent complication of stroke and is associated with high mortality. Investigators have described its associations with beta-blocker. However, there has been no evaluation of the role of recombinant tissue plasminogen activator (RTPA). We postulate that RTPA may modify the effect of stroke on pneumonia by reducing stroke disability. We explore this using data from neuroprotection trials in Virtual Internati...
Source: PLoS One - May 1, 2023 Category: Biomedical Science Authors: Thanh G. Phan Source Type: research

Identification of key predictors of hospital mortality in critically ill patients with embolic stroke using machine learning
This study aimed to use machine learning (ML) to identify the key predictors of mortality for ES patients in the intensive care unit (ICU). Data were extracted from two large ICU databases: MIMIC-IV for training and internal validation, and eICU-CRD for external validation. We developed predictive models of ES mortality based on 15 ML algorithms. We relied on the synthetic minority oversampling technique (SMOTE) to address class imbalance. Our main performance metric was area under the ROC (AUROC). We adopted recursive feature elimination (RFE) for feature selection. We assessed model performance using three disease-severi...
Source: Bioscience Reports - August 22, 2022 Category: Biomedical Science Authors: Wei Liu Wei Ma Na Bai Chun-Yan Li Kuangpin Liu Jinwei Yang Sijia Zhang Kewei Zhu Qiang Zhou Hua Liu Jianhui Guo Liyan Li Source Type: research

A machine learning approach to identifying important features for achieving step thresholds in individuals with chronic stroke
ConclusionsAddressing physical capacity isnecessary but not sufficient to achieve important step thresholds, asancillary characteristics, such as readiness to change activity behavior and physical health may also need to be targeted. This delineation may explain heterogeneity across studies examining predictors of stepping activity in stroke.
Source: PLoS One - June 17, 2022 Category: Biomedical Science Authors: Allison E. Miller Source Type: research

Can training on ex-vivo models increase neurointerventionalists ’ subjective self-confidence in the operating room?
by Nathalie Mathern, Johanna Sandmann, Thorsten Sichtermann, Hani Ridwan, Alexander Riabikin, Andrea Stockero, Omid Nikoubashman, Martin Wiesmann, German Stroke School Group In a changing learning environment where young neurointerventionalists spend less time in the operating room, computer simulators have been established as a new training model. Our aim was the comparison of silicone models and computer simulators, and the evaluation of their influence on subjectiv e self-confidence of operators. Pre- and postquestionnaires of 27 participants and 9 tutors were evaluated after the participation in a three-days intervent...
Source: PLoS One - February 22, 2022 Category: Biomedical Science Authors: Nathalie Mathern Source Type: research

Automatic Classification of the Korean Triage Acuity Scale in Simulated Emergency Rooms Using Speech Recognition and Natural Language Processing: a Proof of Concept Study
CONCLUSION: We demonstrated the potential of an automatic KTAS classification system using speech recognition models, machine learning and BERT-based classifiers.PMID:34254471 | DOI:10.3346/jkms.2021.36.e175
Source: Journal of Korean Medical Science - July 13, 2021 Category: Biomedical Science Authors: Dongkyun Kim Jaehoon Oh Heeju Im Myeongseong Yoon Jiwoo Park Joohyun Lee Source Type: research

A comparative study on machine learning-based classification to find photothrombotic lesion in histological rabbit brain images
In this study, we established machine learning-based algorithms to detect photothrombotic lesions in histological images of photothrombosis-induced rabbit brains. Six machine learning-based algorithms for binary classification were applied, and the accuracies were compared to classify normal tissues and photothrombotic lesions. The lesion classification model consisting of a 3-layered neural network with a rectified linear unit (ReLU) activation function, Xavier initialization, and Adam optimization using datasets with a unit size of [math] pixels yielded the highest accuracy (0.975). In the validation using the tested his...
Source: Journal of Innovative Optical Health Sciences - July 2, 2021 Category: Biomedical Science Authors: Sang Hee Jo Yoonhee Kim Yoon Bum Lee Sung Suk Oh Jong-ryul Choi Source Type: research

Development and validation of a nomogram for predicting stroke risk in rheumatoid arthritis patients
In conclusion, the nomogram can be used for individualized preoperative prediction of stroke risk in RA patients.PMID:34081620 | DOI:10.18632/aging.203071
Source: Aging - June 3, 2021 Category: Biomedical Science Authors: Fangran Xin Lingyu Fu Bowen Yang Haina Liu Tingting Wei Cunlu Zou Bingqing Bai Source Type: research

Kinetic analysis of ASIC1a delineates conformational signaling from proton-sensing domains to the channel gate
Acid-sensing ion channels (ASICs) are neuronal Na+ channels that are activated by a drop in pH. Their established physiological and pathological roles, involving fear behaviors, learning, pain sensation and neurodegeneration after stroke, make them promising targets for future drugs. Currently, the ASIC activation mechanism is not understood. Here we used voltage-clamp fluorometry (VCF) combined with fluorophore-quencher pairing to determine the kinetics and direction of movements. We show that conformational changes with the speed of channel activation occur close to the gate and in more distant extracellular sites, where...
Source: eLife - March 17, 2021 Category: Biomedical Science Tags: Neuroscience Structural Biology and Molecular Biophysics Source Type: research

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