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Mortality assessment in intensive care units via adverse events using artificial neural networks

Álvaro Silva, Paulo Cortez, Manuel Filipe Santos, Lopes Gomes, José Neves
2006 Artificial Intelligence in Medicine  
Objective: This work presents a novel approach for the prediction of mortality in intensive care units (ICUs) based on the use of adverse events, which are defined from four bedside alarms, and artificial  ...  neural networks (ANNs).  ...  Acknowledgments We thank FRICE and the BIOMED project BMH4-CT96-0817 for the provision of part of the EURICUS II data and support for this study, which is integrated in a PhD program, developed at Instituto  ... 
doi:10.1016/j.artmed.2005.07.006 pmid:16213693 fatcat:l5urwjg2jjgindllvuhbedaufu

Rating organ failure via adverse events using data mining in the intensive care unit

Álvaro Silva, Paulo Cortez, Manuel Filipe Santos, Lopes Gomes, José Neves
2008 Artificial Intelligence in Medicine  
Conclusion: Adverse events, taken from bedside monitored data, are important 28. intermediate outcomes, contributing to a timely recognition of organ dysfunction 29. and failure during ICU length of stay  ...  The obtained results show that is possible to 30. use DM methods to get knowledge from easy obtainable data, thus opening room 31. for the development of intelligent clinical alarm monitoring. 32.  ...  One of the most promising recent developments in intensive care consists in the 52. use of artificial intelligence/data mining techniques [1, 10] .  ... 
doi:10.1016/j.artmed.2008.03.010 pmid:18486459 fatcat:x5qmr7yjkjantdloba56uepzuu

Advances in artificial neural networks as a disease prediction tool

Taylor MA, Bennett CL, Schoen MW, Hoque S
2021 Journal of Cancer Research & Therapy  
Artificial neural networks (ANNs) have demonstrated increased application due to their versatility and ability to learn from large datasets.  ...  ANN prediction studies dominate in fields such as cardiovascular disease, neurologic disease, and osteoporosis. Neural networks consistently show higher predictive accuracy than industry standards.  ...  The machine learning model was unable to account for asthmatic patients with pneumonia being directly admitted to intensive care units, which resulted in higher levels of care and lower incidence of complications  ... 
doi:10.14312/2052-4994.2021-1 fatcat:tzfjpd3fnjgqjp5ueq65ycmyge

Patient length of stay and mortality prediction: A survey

Aya Awad, Mohamed Bader–El–Den, James McNicholas
2017 Health Services Management Research  
Over the past few years, there has been increased interest in data mining and machine learning methods to improve hospital performance, in particular hospitals want to improve their intensive care unit  ...  This paper examines a range of length of stay and mortality prediction applications in acute medicine and the critical care unit.  ...  Also, artificial neural networks, decision trees and ensemble methods are used in developing an intelligent decision support system-INTCare for intensive medicine in the ICU of the Hospital Santo Antonio  ... 
doi:10.1177/0951484817696212 pmid:28539083 fatcat:iplxgixvkvgvloqxpf6w7jel2u

The use of a neural network for studying the relationship between air pollution and asthma-related emergency room visits

A. Nutman, Y. Solomon, S. Mendel, J. Nutman, E. Hines, M. Topilsky, S. Kivity
1998 Respiratory Medicine  
The study findings demonstrated that ordinary network technology can be used for learning the effect of air pollution ER visits and, although limited in accuracy, to also predict future ER visits.  ...  To establish the relationship between air pollution levels and bronchial asthma-associated emergency room (ER) visits, we adapted artificial network technology to conduct this study which focused on three  ...  The ability to predict increases in asthma morbidity can assist the health care system and the patient in improved management of the disease.  ... 
doi:10.1016/s0954-6111(98)90421-8 pmid:9926149 fatcat:4p7tu7lglba5hmn7zt2etuz43i

Current and Future Applications of Artificial Intelligence in Coronary Artery Disease

Nitesh Gautam, Prachi Saluja, Abdallah Malkawi, Mark G. Rabbat, Mouaz H. Al-Mallah, Gianluca Pontone, Yiye Zhang, Benjamin C. Lee, Subhi J. Al'Aref
2022 Healthcare  
Coronary artery disease, as one disease entity under the CVDs umbrella, had a prevalence of 7.2% among adults in the United States and incurred a financial burden of 360 billion US dollars in the years  ...  From automatic interpretations of heart rhythm disorders via smartwatches, to assisting in complex decision-making, AI has quickly expanded its realms in medicine and has demonstrated itself as a promising  ...  diagnosis to the first potential adverse event (time to adverse event, TAE).  ... 
doi:10.3390/healthcare10020232 pmid:35206847 pmcid:PMC8872080 fatcat:ahltma4hpzblxcsedcz4noa6y4

Analyzing Patient Trajectories With Artificial Intelligence

Ahmed Allam, Stefan Feuerriegel, Michael Rebhan, Michael Krauthammer
2021 Journal of Medical Internet Research  
However, digital medicine often uses only limited patient data, consisting of health events from only a single or small number of time points while ignoring additional information encoded in patient trajectories  ...  In digital medicine, patient data typically record health events over time (eg, through electronic health records, wearables, or other sensing technologies) and thus form unique patient trajectories.  ...  The opinions expressed in this manuscript are those of the authors' and do not necessarily reflect those of the Novartis Institutes for Biomedical Research.  ... 
doi:10.2196/29812 pmid:34870606 pmcid:PMC8686456 fatcat:o52qxkwudbfflk5tqcvrfgfvae

Machine Learning and Artificial Intelligence in Neurocritical Care: a Specialty-Wide Disruptive Transformation or a Strategy for Success

Fawaz Al-Mufti, Michael Kim, Vincent Dodson, Tolga Sursal, Christian Bowers, Chad Cole, Corey Scurlock, Christian Becker, Chirag Gandhi, Stephan A. Mayer
2019 Current Neurology and Neuroscience Reports  
Neurocritical care combines the complexity of both medical and surgical disease states with the inherent limitations of assessing patients with neurologic injury.  ...  Artificial intelligence (AI) has garnered interest in the basic management of these complicated patients as data collection becomes increasingly automated.  ...  Artificial neural networks have also been used to detect seizure activity [50, 51] .  ... 
doi:10.1007/s11910-019-0998-8 pmid:31720867 fatcat:hzoofsqsvbfv7j4htcjfq5kexa

Decision Support Systems in Medicine - Anesthesia, Critical Care and Intensive Care Medicine [chapter]

Thomas M., Fabrizio Cirillo, Shantale Cyr
2012 Decision Support Systems  
"nother general DSS for intensive care unit is RHE" [ ] like "CUDES, it collects data from patients and gets information about adverse events and nosocomial infection risk for each patient.  ...  "n example of a DSS for in-hospital emergencies is an artificial neural network designed to diagnose acute myocardial infarction [ ].  ... 
doi:10.5772/51756 fatcat:azhuyi6arjapplcmomvq65i33u

Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review

Avishek Choudhury, Onur Asan
2020 JMIR Medical Informatics  
Future work is still needed for robust validation of these systems in prospective and real-world clinical environments to understand how well AI can predict safety outcomes in health care settings.  ...  We focused on quantitative studies that reported positive, negative, or intermediate changes in patient safety outcomes using AI apps, specifically those based on machine-learning algorithms and natural  ...  h ICU: intensive care unit. i MMD: multimodal section. j DT: decision tree. k r w ff Table 2 . 2 Performance of artificial intelligence.  ... 
doi:10.2196/18599 pmid:32706688 fatcat:dtwcdsgcdva4tkravsacf342ji

Predicting Cardiac Arrest and Respiratory Failure Using Feasible Artificial Intelligence with Simple Trajectories of Patient Data

Jeongmin Kim, Myunghun Chae, Hyuk-Jae Chang, Young-Ah Kim, Eunjeong Park
2019 Journal of Clinical Medicine  
Data from the trajectory of 29,181 patients in intensive care units of two hospitals includes periodic vital signs, a history of treatment, current health status, and recent surgery.  ...  We introduce a Feasible Artificial Intelligence with Simple Trajectories for Predicting Adverse Catastrophic Events (FAST-PACE) solution for preparing immediate intervention in emergency situations.  ...  ICU = intensive care unit; FAST-PACE = Feasible Artificial intelligence with Simple Trajectories for Predicting Adverse Catastrophic Events.  ... 
doi:10.3390/jcm8091336 pmid:31470543 pmcid:PMC6780058 fatcat:5d4skq53frcxfpbyrswx4k7ixa

Deep learning in pharmacogenomics: from gene regulation to patient stratification

Alexandr A Kalinin, Gerald A Higgins, Narathip Reamaroon, Sayedmohammadreza Soroushmehr, Ari Allyn-Feuer, Ivo D Dinov, Kayvan Najarian, Brian D Athey
2018 Pharmacogenomics (London)  
We anticipate that in the future deep learning will be widely used to predict personalized drug response and optimize medication selection and dosing, using knowledge extracted from large and complex molecular  ...  improvements on a wide range of tasks in biomedicine.  ...  Their associated outcomes having been predicted from the publicly available MIMIC II intensive care unit database [103] .  ... 
doi:10.2217/pgs-2018-0008 pmid:29697304 fatcat:tkhmrqkevjfqxdty6ttbw33jam

2020 Index IEEE Journal of Biomedical and Health Informatics Vol. 24

2020 IEEE journal of biomedical and health informatics  
., Ballistocardiography Can Estimate Beat-to-Beat Heart Rate Accurately at Night in Patients After Vascular Intervention; JBHI Aug. 2020 2230-2237 Hoogi, A., Mishra, A., Gimenez, F., Dong, J., and Rubin  ...  ., +, JBHI April 2020 1070-1079 IRIS: A Modular Platform for Continuous Monitoring and Caretaker Notification in the Intensive Care Unit.  ...  ., +, JBHI May 2020 1528-1536 IRIS: A Modular Platform for Continuous Monitoring and Caretaker Noti- fication in the Intensive Care Unit.  ... 
doi:10.1109/jbhi.2020.3048808 fatcat:iifrkwtzazdmboabdqii7x5ukm

A Review on the Use of Artificial Intelligence in Spinal Diseases

Parisa Azimi, Taravat Yazdanian, Edward C. Benzel, Hossein Nayeb Aghaei, Shirzad Azhari, Sohrab Sadeghi, Ali Montazeri
2020 Asian Spine Journal  
Artificial neural networks (ANNs) have been used in a wide variety of real-world applications and it emerges as a promising field across various branches of medicine.  ...  The search strategy was set as the combinations of the following keywords: "artificial neural networks," "spine," "back pain," "prognosis," "grading," "classification," "prediction," "segmentation," "biomechanics  ...  Acknowledgments The authors thank the staff of the Neurosurgery Unit at Imam-Hossain Hospital, Tehran, Iran.  ... 
doi:10.31616/asj.2020.0147 pmid:32326672 pmcid:PMC7435304 fatcat:cxdxp3jpurcgzp2hjne5mrj5qu

Artificial intelligence in personalized cardiovascular medicine and cardiovascular imaging

Ikram-Ul Haq, Iqraa Haq, Bo Xu
2021 Cardiovascular Diagnosis and Therapy  
A 2016 multicentre trial compared ML models with logistical regression models in predicting adverse outcomes in hospitals, such as cardiac arrest, intensive care unit transfer, or death (48) .  ...  Table 4 4 Selected applications of ML algorithms applied to electronic health records (EHR) AUC, area under curve; HF, heart failure; MI, myocardial infarction; ICU, intensive care unit.  ... 
doi:10.21037/cdt.2020.03.09 fatcat:hbproigrrvafnadiwzqtizi2gu
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