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Multiple Organ Failure Diagnosis Using Adverse Events and Neural Networks
[chapter]
Enterprise Information Systems VI
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In this work, Neural Networks are applied to the prediction of organ dysfunction in Intensive Care Units. ...
The novelty of this approach comes from the use of adverse events, which are triggered from four bedside alarms, being achieved an overall predictive accuracy of 70%. ...
In this work, these techniques were applied for organ failure diagnosis of ICU patients. ...
doi:10.1007/1-4020-3675-2_15
dblp:conf/iceis/Silva0SG004
fatcat:qcig7umlv5eqdnjz3l3h52eaiy
CT Image Feature under Intelligent Algorithm in the Evaluation of Continuous Blood Purification in the Treatment and Nursing of Pulmonary Infection-Caused Severe Sepsis
2021
Computational and Mathematical Methods in Medicine
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Convolutional neural network algorithm was used to segment CT images of severe sepsis caused by pulmonary infection. ...
time, malnutrition inflammation score (MIS), and incidence of adverse events were compared between the two groups before and after treatment. ...
organ failure [16] [17] [18] . ...
doi:10.1155/2021/2281327
pmid:34876921
pmcid:PMC8645405
fatcat:hhu6juq5czbqlhli2jd6kjytyy
Representation learning in intraoperative vital signs for heart failure risk prediction
2019
BMC Medical Informatics and Decision Making
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The probability of heart failure during the perioperative period is 2% on average and it is as high as 17% when accompanied by cardiovascular diseases in China. ...
There are major practical and technical barriers to understand perioperative complications. ...
used for early diagnosis and prediction, the early clinical diagnosis of adverse events of heart failure still relies on the clinical experience of anesthesiologists and physicians. ...
doi:10.1186/s12911-019-0978-6
pmid:31818298
pmcid:PMC6902523
fatcat:nkenjnnbzvg6vpospuhxiskwoq
DEEP LEARNING PREDICTION OF ADVERSE DRUG REACTION ANALYSIS USING ARTIFICIAL NEURAL NETWORK MODEL
2022
Zenodo
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Neural network based classification is the proposed system to classify drug reaction based on data set columns. ...
It used the Proportionality Reporting Ratio (PRR) along with ChiSquare test equations to find out the different relationships between drug and symptoms called the drugADR association. ...
Thiscomposition uses the case study of a case with multiple adverse medicine events to clarify crucial terms, similar asadverse event, adverse medicine response, adverse medicine event, drug error, and ...
doi:10.5281/zenodo.6410023
fatcat:tgde2afihnf5fdswonahmycf6q
Artificial intelligence, machine learning, and deep learning in women's health nursing
2020
Yeoseong Geon-gang Ganho Hakoeji
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Deep learning is a subset of machine learning that uses artificial neural networks consisting of multiple hidden layers. ...
The standard curriculum should be organized by the nursing society. ...
Deep learning is a model of machine learning using artificial neural networks that consist of multiple hidden layers, which is why these neural networks are known as "deep" neural networks and the framework ...
doi:10.4069/kjwhn.2020.03.11
fatcat:cfkzbq2ji5ex3gvzobdvah7vr4
Artificial Intelligence in Medicine and Radiation Oncology
2018
Cureus
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It was concluded that AI could greatly improve the efficiency and accuracy of radiation oncology operations. ...
The improvement of process efficiencies and the prevention of errors were found to be the most significant contributions of AI to radiation oncology. ...
The concurrent use of thermography and artificial neural networks (ANN) for the diagnosis of breast cancer was used in a study by Ng et al. [7] . ...
doi:10.7759/cureus.2475
pmid:29904616
pmcid:PMC5999390
fatcat:apqe5uad4bc3fhpudvrptxstga
Activation of Functional Brain Networks in Children With Psychogenic Non-epileptic Seizures
2020
Frontiers in Human Neuroscience
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While this shift in functional organization may confer a short-term adaptive advantage-one that facilitates neural communication and the child's capacity to respond self-protectively in the face of stressful ...
Compared to controls, they also had higher levels of autonomic arousal (e.g., lower heart variability); more anxiety, depression, and stress on the Depression Anxiety and Stress Scales; and more adverse ...
Moreover, given that PNES occur in the context of high arousal and exposure to adverse childhood experiences, arousaldecreasing interventions on multiple system levels are likely to help the child's neural ...
doi:10.3389/fnhum.2020.00339
pmid:33192376
pmcid:PMC7477327
fatcat:gcpkuq3i5vd2znrvk2ohurbjhm
Prognostic and diagnostic monitoring of complex systems for product lifecycle management: Challenges and opportunities
2005
Computers and Chemical Engineering
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Businesses and federal organizations are increasingly required to manage their entire products' life cycles to avoid costly failure or degradation in performance through service/maintenance, more robust ...
These interactions thrive in complex systems when the combined effects of uncertainty and operational adversity are not properly addressed either in design or in operation. ...
usefulness of neural networks for fault diagnosis. ...
doi:10.1016/j.compchemeng.2005.02.026
fatcat:5k5r3bfmrnaxdi3vh7o4xjzvvu
Editorial Biologically Learned/Inspired Methods for Sensing, Control, and Decision
2022
IEEE Transactions on Neural Networks and Learning Systems
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artificial neural networks and biological neural networks. ...
The authors demonstrate improved performance in dealing with signal dropping by using a weighted multiple neural network voting (WMV) approach. In [A23] , Wang et al. ...
doi:10.1109/tnnls.2022.3161003
fatcat:4e6v2kclcbb5pgkqqsyyaiwzjy
Artificial Intelligence (AI) and Cardiovascular Diseases: An Unexpected Alliance
2020
Cardiology Research and Practice
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Cardiovascular disease (CVD), despite the significant advances in the diagnosis and treatments, still represents the leading cause of morbidity and mortality worldwide. ...
Likewise, the use of emerging communication and information technologies is becoming pivotal to create a pervasive healthcare service through which elderly and chronic disease patients can receive medical ...
Conflicts of Interest e authors declare no potential conflicts of interest with respect to the research, authorship and/or publication of this article. ...
doi:10.1155/2020/4972346
pmid:32676206
pmcid:PMC7336209
fatcat:bsas334w75co7a33avqkmrcp7m
Adverse Event Profile of Tigecycline: Data Mining of the Public Version of the U.S. Food and Drug Administration Adverse Event Reporting System
2012
Biological and Pharmaceutical Bulletin
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, the information component given by a Bayesian confidence propagation neural network, and the empirical Bayes geometric mean. ...
Adverse events with a relatively high frequency included nausea, vomiting, pancreatitis, hepatic failure, hypoglycemia, and increase in levels of alanine aminotransferase, bilirubin, alkaline phosphatase ...
For example, death, multiple-organ failure, sepsis, renal failure, and respiratory failure listed in Table 1 were expected to be due to infection rather than to tigecycline. ...
doi:10.1248/bpb.35.967
pmid:22687540
fatcat:lkptxhq4kffr3numpenm3xudtq
Artificial Intelligence in Pharmacoepidemiology: A Systematic Review. Part 1—Overview of Knowledge Discovery Techniques in Artificial Intelligence
2020
Frontiers in Pharmacology
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The top three used techniques were artificial neural networks, random forest, and support vector machines models. ...
Clinical trials, meta-analyses, narrative/systematic review, and observational studies using (or mentioning articles using) artificial intelligence techniques were eligible. ...
MS, DL, MA, MK, and AK analyzed or interpreted the data. MS, DL, MA, MK, and AK wrote the paper. ...
doi:10.3389/fphar.2020.01028
pmid:32765261
pmcid:PMC7378532
fatcat:mk7vvat5e5cqjizfbbgqq6pgsq
DeepCompete : A deep learning approach to competing risks in continuous time domain
2021
AMIA Annual Symposium Proceedings
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Clinicians need new tools to quantify the relative risk of an adverse event due to each competing disease and prioritize treatment among various diseases affecting a patient. ...
A large percentage of this population is afflicted with multiple acute diseases (multi-morbidity). ...
Also, we use a flexible loss function that takes multiple risks as well as censoring into account. Our network has multiple components, but we train the network end to end by back-propagating losses. ...
pmid:33936389
pmcid:PMC8075516
fatcat:m6wcxh3dwrcdpb4qwylgun5m3i
The Role of Artificial Intelligence (AI) in Condition Monitoring and Diagnostic Engineering Management (COMADEM): A Literature Survey
2021
American Journal of Artificial Intelligence
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Organizations have more data than ever, so it's crucial to ensure that the analytics team should differentiate between Interesting Data and Useful Data. ...
AI techniques such as, knowledge based systems, expert systems, artificial neural networks, genetic algorithms, fuzzy logic, casebased reasoning and any combination of these techniques (hybrid systems) ...
complex networks, neural networks, fuzzy systems, neuro-fuzzy systems, deep learning, real world applications, self-organizing, emerging or bioinspired systems, global optimization, meta-heuristics and ...
doi:10.11648/j.ajai.20210501.12
fatcat:gvplqmpqubdw3pquik5eavux5u
IN SEARCH FOR BIOMARKERS, ENDOPHENOTYPES OR BIOSIGNATURES OF PTSD: WHAT HAVE WE LEARNED FROM THE SOUTH EAST EUROPEAN STUDY
2019
Psychiatria Danubina
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The puzzle how brain function enables the resilience to adversity and how brain dysfunctions lead to vulnerability to stress and development of PTSD and other stress-related disorders is still awaiting ...
The development of PTSD is influenced by a tangled and complicated interaction of inborn or acquired predisposition or vulnerability and environmental adversity which alters gene regulation producing effects ...
) and future (the expectation of suffering and failure). ...
doi:10.24869/psyd.2019.282
fatcat:6pn66zffvjarfornucbbfjdy7q
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