ENVISION – Improve intensive care of COVID-19 patients with artificial intelligence

Edge computing is a fast-growing and much needed technology in healthcare. The problem of implementing artificial intelligence on edge devices is the complexity and high resource intensity of the most known neural network data analysis methods and algorithms. The difficulty of implementing these methods on low-power microcontrollers with small memory size calls for the development of new effective algorithms for neural networks. This study presents a new method for analyzing medical data based on the LogNNet neural network, which uses chaotic mappings to transform input information. The method effectively solves classification problems and calculates risk factors for the presence of a disease in a patient according to a set of medical health indicators. The efficiency of LogNNet in assessing perinatal risk is illustrated on cardiotocogram data obtained from the UC Irvine machine learning repository. The classification accuracy reaches ~91% with the~3–10 kB of RAM used on the Arduino microcontroller. Using the LogNNet network trained on a publicly available database of the Israeli Ministry of Health, a service concept for COVID-19 express testing is provided. A classification accuracy of ~95% is achieved, and~0.6 kB of RAM is used. In all examples, the model is tested using standard classification quality metrics: precision, recall, and F1-measure. The LogNNet architecture allows the implementation of artificial intelligence on medical peripherals of the Internet of Things with low RAM resources and can be used in clinical decision support systems.

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Medical Outcome Prediction for Intensive Care Unit Patients

Innovations in Data Methodologies and Computational Algorithms for Medical Applications ◽

10.4018/978-1-4666-0282-3.ch015 ◽

2012 ◽

pp. 251-262

Author(s):

Simone A. Ludwig ◽

Stefanie Roos ◽

Monique Frize ◽

Nicole Yu

Keyword(s):

Intensive Care Unit ◽

Intensive Care ◽

Decision Support ◽

Decision Support Systems ◽

Support Systems ◽

Medical Decision ◽

Adult Intensive Care Unit ◽

Medical Decision Support ◽

Elimination Algorithm ◽

Medical Decision Support Systems

The rate of people dying from medical errors in hospitals each year is very high. Errors that frequently occur during the course of providing health care are adverse drug events and improper transfusions, surgical injuries and wrong-site surgery, suicides, restraint-related injuries or death, falls, burns, pressure ulcers, and mistaken patient identities. Medical decision support systems play an increasingly important role in medical practice. By assisting physicians in making clinical decisions, medical decision support systems improve the quality of medical care. Two approaches have been investigated for the prediction of medical outcomes: “hours of ventilation” and the “mortality rate” in the adult intensive care unit. The first approach is based on neural networks with the weight-elimination algorithm, and the second is based on genetic programming. Both approaches are compared to commonly used machine learning algorithms. Results show that both algorithms developed score well for the outcomes selected.

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Medical Outcome Prediction for Intensive Care Unit Patients

Computer Engineering ◽

10.4018/978-1-61350-456-7.ch419 ◽

2012 ◽

pp. 1068-1079

Author(s):

Simone A. Ludwig ◽

Stefanie Roos ◽

Monique Frize ◽

Nicole Yu

Keyword(s):

Intensive Care Unit ◽

Intensive Care ◽

Decision Support ◽

Decision Support Systems ◽

Support Systems ◽

Medical Decision ◽

Adult Intensive Care Unit ◽

Medical Decision Support ◽

Elimination Algorithm ◽

Medical Decision Support Systems

The rate of people dying from medical errors in hospitals each year is very high. Errors that frequently occur during the course of providing health care are adverse drug events and improper transfusions, surgical injuries and wrong-site surgery, suicides, restraint-related injuries or death, falls, burns, pressure ulcers, and mistaken patient identities. Medical decision support systems play an increasingly important role in medical practice. By assisting physicians in making clinical decisions, medical decision support systems improve the quality of medical care. Two approaches have been investigated for the prediction of medical outcomes: “hours of ventilation” and the “mortality rate” in the adult intensive care unit. The first approach is based on neural networks with the weight-elimination algorithm, and the second is based on genetic programming. Both approaches are compared to commonly used machine learning algorithms. Results show that both algorithms developed score well for the outcomes selected.

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Predicting Mortality in the Surgical Intensive Care Unit Using Artificial Intelligence and Natural Language Processing of Physician Documentation

The American Surgeon ◽

10.1177/000313481808400736 ◽

2018 ◽

Vol 84 (7) ◽

pp. 1190-1194 ◽

Cited By ~ 3

Author(s):

Joshua Parreco ◽

Antonio Hidalgo ◽

Robert Kozol ◽

Nicholas Namias ◽

Rishi Rattan

Keyword(s):

Artificial Intelligence ◽

Intensive Care Unit ◽

Intensive Care ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Severity Of Illness ◽

Surgical Intensive Care Unit ◽

Surgical Intensive Care ◽

Severity Of Injury

The purpose of this study was to use natural language processing of physician documentation to predict mortality in patients admitted to the surgical intensive care unit (SICU). The Multiparameter Intelligent Monitoring in Intensive Care III database was used to obtain SICU stays with six different severity of illness scores. Natural language processing was performed on the physician notes. Classifiers for predicting mortality were created. One classifier used only the physician notes, one used only the severity of illness scores, and one used the physician notes with severity of injury scores. There were 3838 SICU stays identified during the study period and 5.4 per cent ended with mortality. The classifier trained with physician notes with severity of injury scores performed with the highest area under the curve (0.88 ± 0.05) and accuracy (94.6 ± 1.1%). The most important variable was the Oxford Acute Severity of Illness Score (16.0%). The most important terms were “dilated” (4.3%) and “hemorrhage” (3.7%). This study demonstrates the novel use of artificial intelligence to process physician documentation to predict mortality in the SICU. The classifiers were able to detect the subtle nuances in physician vernacular that predict mortality. These nuances provided improved performance in predicting mortality over physiologic parameters alone.

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