scholarly journals Computer Vision and Abnormal Patient Gait: A Comparison of Methods

2020 ◽  
Vol 6 (1) ◽  
pp. 29-34
Author(s):  
Jasmin Hundal ◽  
Benson A. Babu
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Machine Learning Techniques ◽  
High Morbidity ◽  
Support Tool ◽  
Gait Energy Image ◽  
Abnormal Gait ◽  
Gait Abnormalities

Abnormal gait, falls and its associated complications have high morbidity and mortality. Computer vision detects, predicts gait abnormalities, assesses fall risk, and serves as a clinical decision support tool for physicians. This paper performs a systematic review of computer vision, machine learning techniques to analyse abnormal gait. This literature outlines the use of different machine learning and poses estimation algorithms in gait analysis that includes partial affinity fields, pictorial structures model, hierarchical models, sequential-prediction-framework-based approaches, convolutional pose machines, gait energy image, 2-Directional 2-dimensional principles component analysis ((2D) 2PCA) and 2G (2D) 2PCA) Enhanced Gait Energy Image (EGEI), SVM, ANN, K-Star, Random Forest, KNN, to perform the image classification of the features extracted inpatient gait abnormalities.

A Machine-Learning Clinical Decision Support Tool for Myocardial Infarction Diagnosis

2021 ◽  
Vol 28 ◽  
pp. S13
Author(s):  
Saarang Panchavati ◽  
Carson Lam ◽  
Anurag Garikipati ◽  
Nicole Zelin ◽  
Emily Pellegrini ◽  
...  
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Support Tool ◽  
Clinical Decision Support Tool

scholarly journals A MACHINE LEARNING CLINICAL DECISION SUPPORT TOOL FOR MYOCARDIAL INFARCTION DIAGNOSIS

2021 ◽  
Vol 77 (18) ◽  
pp. 653
Author(s):  
Emily Pellegrini ◽  
Saarang Panchavati ◽  
Carson Lam ◽  
Anurag Garikipati ◽  
Nicole Zelin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Support Tool ◽  
Clinical Decision Support Tool

scholarly journals Evaluation of ML-Based Clinical Decision Support Tool to Replace an Existing Tool in an Academic Health System: Lessons Learned

2020 ◽  
Vol 10 (3) ◽  
pp. 104
Author(s):  
Myung Woo ◽  
Brooke Alhanti ◽  
Sam Lusk ◽  
Felicia Dunston ◽  
Stephen Blackwelder ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Care ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Learning Tools ◽  
Modest Improvement ◽  
Support Tool ◽  
Clinical Decision Support Tool

There is increasing application of machine learning tools to problems in healthcare, with an ultimate goal to improve patient safety and health outcomes. When applied appropriately, machine learning tools can augment clinical care provided to patients. However, even if a model has impressive performance characteristics, prospectively evaluating and effectively implementing models into clinical care remains difficult. The primary objective of this paper is to recount our experiences and challenges in comparing a novel machine learning-based clinical decision support tool to legacy, non-machine learning tools addressing potential safety events in the hospitals and to summarize the obstacles which prevented evaluation of clinical efficacy of tools prior to widespread institutional use. We collected and compared safety events data, specifically patient falls and pressure injuries, between the standard of care approach and machine learning (ML)-based clinical decision support (CDS). Our assessment was limited to performance of the model rather than the workflow due to challenges in directly comparing both approaches. We did note a modest improvement in falls with ML-based CDS; however, it was not possible to determine that overall improvement was due to model characteristics.

A traumatic brain injury prognostic model to support in-hospital triage in a low-income country: a machine learning–based approach

2020 ◽  
Vol 132 (6) ◽  
pp. 1961-1969 ◽  
Author(s):  
Thiago Augusto Hernandes Rocha ◽  
Cyrus Elahi ◽  
Núbia Cristina da Silva ◽  
Francis M. Sakita ◽  
Anthony Fuller ◽  
...  
Keyword(s):  
Machine Learning ◽  
Traumatic Brain Injury ◽  
Decision Support ◽  
Brain Injury ◽  
Clinical Decision Support ◽  
Prognostic Model ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Support Tool ◽  
Clinical Decision Support Tool

OBJECTIVETraumatic brain injury (TBI) is a leading cause of death and disability worldwide, with a disproportionate burden of this injury on low- and middle-income countries (LMICs). Limited access to diagnostic technologies and highly skilled providers combined with high patient volumes contributes to poor outcomes in LMICs. Prognostic modeling as a clinical decision support tool, in theory, could optimize the use of existing resources and support timely treatment decisions in LMICs. The objective of this study was to develop a machine learning–based prognostic model using data from Kilimanjaro Christian Medical Centre in Moshi, Tanzania.METHODSThis study is a secondary analysis of a TBI data registry including 3138 patients. The authors tested nine different machine learning techniques to identify the prognostic model with the greatest area under the receiver operating characteristic curve (AUC). Input data included demographics, vital signs, injury type, and treatment received. The outcome variable was the discharge score on the Glasgow Outcome Scale–Extended.RESULTSThe AUC for the prognostic models varied from 66.2% (k-nearest neighbors) to 86.5% (Bayesian generalized linear model). An increasing Glasgow Coma Scale score, increasing pulse oximetry values, and undergoing TBI surgery were predictive of a good recovery, while injuries suffered from a motor vehicle crash and increasing age were predictive of a poor recovery.CONCLUSIONSThe authors developed a TBI prognostic model with a substantial level of accuracy in a low-resource setting. Further research is needed to externally validate the model and test the algorithm as a clinical decision support tool.

scholarly journals Reinforcement Learning for Precision Oncology

Cancers ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 4624
Author(s):  
Jan-Niklas Eckardt ◽  
Karsten Wendt ◽  
Martin Bornhäuser ◽  
Jan Moritz Middeke
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Decision Support ◽  
Molecular Mechanisms ◽  
Decision Support Tool ◽  
Autonomous Driving ◽  
Machine Learning Techniques ◽  
Precision Oncology ◽  
Treatment Pathways ◽  
Support Tool

Precision oncology is grounded in the increasing understanding of genetic and molecular mechanisms that underly malignant disease and offer different treatment pathways for the individual patient. The growing complexity of medical data has led to the implementation of machine learning techniques that are vastly applied for risk assessment and outcome prediction using either supervised or unsupervised learning. Still largely overlooked is reinforcement learning (RL) that addresses sequential tasks by exploring the underlying dynamics of an environment and shaping it by taking actions in order to maximize cumulative rewards over time, thereby achieving optimal long-term outcomes. Recent breakthroughs in RL demonstrated remarkable results in gameplay and autonomous driving, often achieving human-like or even superhuman performance. While this type of machine learning holds the potential to become a helpful decision support tool, it comes with a set of distinctive challenges that need to be addressed to ensure applicability, validity and safety. In this review, we highlight recent advances of RL focusing on studies in oncology and point out current challenges and pitfalls that need to be accounted for in future studies in order to successfully develop RL-based decision support systems for precision oncology.

scholarly journals Challenges involved in establishing a web-based clinical decision support tool in community health centers

Healthcare ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 100488
Author(s):  
Rachel Gold ◽  
Mary Middendorf ◽  
John Heintzman ◽  
Joan Nelson ◽  
Patrick O'Connor ◽  
...  
Keyword(s):  
Decision Support ◽  
Community Health ◽  
Clinical Decision Support ◽  
Community Health Centers ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Health Centers ◽  
Web Based ◽  
Support Tool ◽  
Clinical Decision Support Tool
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