scholarly journals A Novel Machine Learning Sepsis Prediction Algorithm for Intended ICU Use (NAVOY® Sepsis): A Proof-of-Concept Study (Preprint)

10.2196/28000 ◽  
2021 ◽  
Author(s):  
Inger Persson ◽  
Andreas Östling ◽  
Martin Arlbrandt ◽  
Joakim Söderberg ◽  
David Becedas
Keyword(s):  
Machine Learning ◽  
Prediction Algorithm ◽  
Proof Of Concept ◽  
Concept Study

scholarly journals A Proof of Concept Study of Using Machine-Learning in Artificial Aortic Valve Design: From Leaflet Design to Stress Analysis

2019 ◽  
Vol 6 (4) ◽  
pp. 104 ◽  
Author(s):  
Liang Liang ◽  
Bill Sun
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Aortic Valve ◽  
Stress Analysis ◽  
Heart Valves ◽  
Deep Neural Networks ◽  
Design Parameters ◽  
Attractive Alternative ◽  
Proof Of Concept ◽  
Concept Study

Artificial heart valves, used to replace diseased human heart valves, are life-saving medical devices. Currently, at the device development stage, new artificial valves are primarily assessed through time-consuming and expensive benchtop tests or animal implantation studies. Computational stress analysis using the finite element (FE) method presents an attractive alternative to physical testing. However, FE computational analysis requires a complex process of numeric modeling and simulation, as well as in-depth engineering expertise. In this proof of concept study, our objective was to develop machine learning (ML) techniques that can estimate the stress and deformation of a transcatheter aortic valve (TAV) from a given set of TAV leaflet design parameters. Two deep neural networks were developed and compared: the autoencoder-based ML-models and the direct ML-models. The ML-models were evaluated through Monte Carlo cross validation. From the results, both proposed deep neural networks could accurately estimate the deformed geometry of the TAV leaflets and the associated stress distributions within a second, with the direct ML-models (ML-model-d) having slightly larger errors. In conclusion, although this is a proof-of-concept study, the proposed ML approaches have demonstrated great potential to serve as a fast and reliable tool for future TAV design.

Machine learning algorithm for the diagnosis of pulmonary embolism: a proof of concept study

2020 ◽  
Author(s):  
Konstantinos Exarchos ◽  
Agapi Aggelopoulou ◽  
Konstantinos Bartziokas ◽  
Elpida Tsina ◽  
Christos Tagkas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pulmonary Embolism ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Proof Of Concept ◽  
Concept Study

scholarly journals Predicting pain among female survivors of recent interpersonal violence: A proof-of-concept machine-learning approach

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0255277
Author(s):  
Edward Lannon ◽  
Francisco Sanchez-Saez ◽  
Brooklynn Bailey ◽  
Natalie Hellman ◽  
Kerry Kinney ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chronic Pain ◽  
Young Women ◽  
Interpersonal Violence ◽  
Predictive Performance ◽  
The United States ◽  
Pain Severity ◽  
Proof Of Concept ◽  
Concept Study

Interpersonal violence (IPV) is highly prevalent in the United States and is a major public health problem. The emergence and/or worsening of chronic pain are known sequelae of IPV; however, not all those who experience IPV develop chronic pain. To mitigate its development, it is critical to identify the factors that are associated with increased risk of pain after IPV. This proof-of-concept study used machine-learning strategies to predict pain severity and interference in 47 young women, ages 18 to 30, who experienced an incident of IPV (i.e., physical and/or sexual assault) within three months of their baseline assessment. Young women are more likely than men to experience IPV and to subsequently develop posttraumatic stress disorder (PTSD) and chronic pain. Women completed a comprehensive assessment of theory-driven cognitive and neurobiological predictors of pain severity and pain-related interference (e.g., pain, coping, disability, psychiatric diagnosis/symptoms, PTSD/trauma, executive function, neuroendocrine, and physiological stress response). Gradient boosting machine models were used to predict symptoms of pain severity and pain-related interference across time (Baseline, 1-,3-,6- follow-up assessments). Models showed excellent predictive performance for pain severity and adequate predictive performance for pain-related interference. This proof-of-concept study suggests that machine-learning approaches are a useful tool for identifying predictors of pain development in survivors of recent IPV. Baseline measures of pain, family life impairment, neuropsychological function, and trauma history were of greatest importance in predicting pain and pain-related interference across a 6-month follow-up period. Present findings support the use of machine-learning techniques in larger studies of post-IPV pain development and highlight theory-driven predictors that could inform the development of targeted early intervention programs. However, these results should be replicated in a larger dataset with lower levels of missing data.

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