scholarly journals Deep Learning of Markov Model Based Machines for Determination of Better Treatment Option Decisions for Infertile Women

2019 ◽  
Author(s):  
Arni S.R. Srinivasa Rao ◽  
Michael P. Diamond
Keyword(s):  
Mathematical Modeling ◽  
Machine Learning ◽  
Deep Learning ◽  
Clinical Decision Making ◽  
Treatment Options ◽  
Clinical Decision ◽  
Infertile Women ◽  
Learning Techniques ◽  
Technical Article

AbstractIn this technical article, we are proposing ideas those we have been developing of how machine learning and deep learning techniques can potentially assist obstetricians / gynecologists in better clinical decision making using infertile women in their treatment options in combination with mathematical modeling in pregnant women as examples.

scholarly journals COVID Mortality Prediction with Machine Learning Methods: A Systematic Review and Critical Appraisal

2021 ◽  
Vol 11 (9) ◽  
pp. 893
Author(s):  
Francesca Bottino ◽  
Emanuela Tagliente ◽  
Luca Pasquini ◽  
Alberto Di Napoli ◽  
Martina Lucignani ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Mortality Prediction ◽  
Machine Learning Techniques ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Learning Techniques ◽  
Ventilation Modes

More than a year has passed since the report of the first case of coronavirus disease 2019 (COVID), and increasing deaths continue to occur. Minimizing the time required for resource allocation and clinical decision making, such as triage, choice of ventilation modes and admission to the intensive care unit is important. Machine learning techniques are acquiring an increasingly sought-after role in predicting the outcome of COVID patients. Particularly, the use of baseline machine learning techniques is rapidly developing in COVID mortality prediction, since a mortality prediction model could rapidly and effectively help clinical decision-making for COVID patients at imminent risk of death. Recent studies reviewed predictive models for SARS-CoV-2 diagnosis, severity, length of hospital stay, intensive care unit admission or mechanical ventilation modes outcomes; however, systematic reviews focused on prediction of COVID mortality outcome with machine learning methods are lacking in the literature. The present review looked into the studies that implemented machine learning, including deep learning, methods in COVID mortality prediction thus trying to present the existing published literature and to provide possible explanations of the best results that the studies obtained. The study also discussed challenging aspects of current studies, providing suggestions for future developments.

scholarly journals Automated Classification of Significant Prostate Cancer on MRI: A Systematic Review on the Performance of Machine Learning Applications

Cancers ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1606
Author(s):  
Jose M. Castillo T. ◽  
Muhammad Arif ◽  
Wiro J. Niessen ◽  
Ivo G. Schoots ◽  
Jifke F. Veenland
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Clinical Decision Making ◽  
External Validation ◽  
Clinical Decision ◽  
Interquartile Range ◽  
Added Value ◽  
Receiver Operating Curve ◽  
Learning Approaches

Significant prostate carcinoma (sPCa) classification based on MRI using radiomics or deep learning approaches has gained much interest, due to the potential application in assisting in clinical decision-making. Objective: To systematically review the literature (i) to determine which algorithms are most frequently used for sPCa classification, (ii) to investigate whether there exists a relation between the performance and the method or the MRI sequences used, (iii) to assess what study design factors affect the performance on sPCa classification, and (iv) to research whether performance had been evaluated in a clinical setting Methods: The databases Embase and Ovid MEDLINE were searched for studies describing machine learning or deep learning classification methods discriminating between significant and nonsignificant PCa on multiparametric MRI that performed a valid validation procedure. Quality was assessed by the modified radiomics quality score. We computed the median area under the receiver operating curve (AUC) from overall methods and the interquartile range. Results: From 2846 potentially relevant publications, 27 were included. The most frequent algorithms used in the literature for PCa classification are logistic regression (22%) and convolutional neural networks (CNNs) (22%). The median AUC was 0.79 (interquartile range: 0.77–0.87). No significant effect of number of included patients, image sequences, or reference standard on the reported performance was found. Three studies described an external validation and none of the papers described a validation in a prospective clinical trial. Conclusions: To unlock the promising potential of machine and deep learning approaches, validation studies and clinical prospective studies should be performed with an established protocol to assess the added value in decision-making.

Management of thoracic aortic lesions - the future is endovascular

VASA ◽  
2012 ◽  
Vol 41 (3) ◽  
pp. 163-176 ◽  
Author(s):  
Weidenhagen ◽  
Bombien ◽  
Meimarakis ◽  
Geisler ◽  
A. Koeppel
Keyword(s):  
Thoracic Aorta ◽  
Clinical Decision Making ◽  
Treatment Options ◽  
Clinical Decision ◽  
Clinical Results ◽  
Treatment Modalities ◽  
Traumatic Rupture ◽  
Descending Thoracic Aorta ◽  
New Treatment ◽  
Aneurysm Dissection

Open surgical repair of lesions of the descending thoracic aorta, such as aneurysm, dissection and traumatic rupture, has been the “state-of-the-art” treatment for many decades. However, in specialized cardiovascular centers, thoracic endovascular aortic repair and hybrid aortic procedures have been implemented as novel treatment options. The current clinical results show that these procedures can be performed with low morbidity and mortality rates. However, due to a lack of randomized trials, the level of reliability of these new treatment modalities remains a matter of discussion. Clinical decision-making is generally based on the experience of the vascular center as well as on individual factors, such as life expectancy, comorbidity, aneurysm aetiology, aortic diameter and morphology. This article will review and discuss recent publications of open surgical, hybrid thoracic aortic (in case of aortic arch involvement) and endovascular repair in complex pathologies of the descending thoracic aorta.

scholarly journals Explaining deep neural networks for knowledge discovery in electrocardiogram analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Steven A. Hicks ◽  
Jonas L. Isaksen ◽  
Vajira Thambawita ◽  
Jonas Ghouse ◽  
Gustav Ahlberg ◽  
...  
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Clinical Decision Making ◽  
Medical Knowledge ◽  
Clinical Decision ◽  
Medical Data ◽  
Medical Doctors ◽  
Medical Tests ◽  
Deep Learning Model

AbstractDeep learning-based tools may annotate and interpret medical data more quickly, consistently, and accurately than medical doctors. However, as medical doctors are ultimately responsible for clinical decision-making, any deep learning-based prediction should be accompanied by an explanation that a human can understand. We present an approach called electrocardiogram gradient class activation map (ECGradCAM), which is used to generate attention maps and explain the reasoning behind deep learning-based decision-making in ECG analysis. Attention maps may be used in the clinic to aid diagnosis, discover new medical knowledge, and identify novel features and characteristics of medical tests. In this paper, we showcase how ECGradCAM attention maps can unmask how a novel deep learning model measures both amplitudes and intervals in 12-lead electrocardiograms, and we show an example of how attention maps may be used to develop novel ECG features.

Machine learning in clinical decision making

Med ◽  
2021 ◽  
Author(s):  
Lorenz Adlung ◽  
Yotam Cohen ◽  
Uria Mor ◽  
Eran Elinav
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision
Sign in / Sign up

Export Citation Format

Share Document