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Multiple Organ Failure Diagnosis Using Adverse Events and Neural Networks [chapter]

Álvaro Silva, Paulo Cortez, Manuel Santos, Lopes Gomes, José Neves
Enterprise Information Systems VI  
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

Liping Liu, Yanyan Liu, Aimin Xing, Siyu Chen, Mingli Gu, Osamah Ibrahim Khalaf
2021 Computational and Mathematical Methods in Medicine  
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

Yuwen Chen, Baolian Qi
2019 BMC Medical Informatics and Decision Making  
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

Mrs. K.E. Eswari, M.C.A., M.Phil., M.E., SET., R. Sarathkumar
2022 Zenodo  
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

Geum Hee Jeong
2020 Yeoseong Geon-gang Ganho Hakoeji  
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

Vincent Weidlich, Georg A. Weidlich
2018 Cureus  
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

Mohammadreza Radmanesh, Mahdi Jalili, Kasia Kozlowska
2020 Frontiers in Human Neuroscience  
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

Venkat Venkatasubramanian
2005 Computers and Chemical Engineering  
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

Yongduan Song, Jennie Si, Sonya Coleman, Dermot Kerr
2022 IEEE Transactions on Neural Networks and Learning Systems  
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

Silvia Romiti, Mattia Vinciguerra, Wael Saade, Iñaki Anso Cortajarena, Ernesto Greco
2020 Cardiology Research and Practice  
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

Kaori Kadoyama, Toshiyuki Sakaeda, Akiko Tamon, Yasushi Okuno
2012 Biological and Pharmaceutical Bulletin  
, 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

Maurizio Sessa, Abdul Rauf Khan, David Liang, Morten Andersen, Murat Kulahci
2020 Frontiers in Pharmacology  
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

Aastha, Pengyu Huang, Yan Liu
2021 AMIA Annual Symposium Proceedings  
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

B. K. Nagaraja Rao
2021 American Journal of Artificial Intelligence  
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

Miro Jakovljevic, Department of Psychiatry and Psychological Medicine, University Hospital Centre Zagreb, Zagreb, Croatia
2019 Psychiatria Danubina  
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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