1,477 research outputs found
The role of Artificial Intelligence in Management of Critical COVID-19 patients
Background: the COVID-19 outbreak has created a great challenge for the healthcare system worldwide. One of the most critical points of this challenge is the management of COVID-19 patients needing acute and/or critical respiratory care. This study was performed to discover an AI based model to improve the critical care of the COVID-19 patients.Material and methods: in a descriptive study, all the published research available in PubMed, Web of Science, Google scholar and other databases were retrieved. Based on these studies, a three stage model of input, process and output was created.Results: the three stage model of AI application in ICU was completed. Input included Clinical, Paraclinical, Personalized Medicine (OMICS) and Epidemiologic data. The process included Artificial Intelligence (i.e. Artificial Neural Network, Machine Learning, Deep Learning and Expert Systems). The output which was ICU Decision Making included Diagnosis, Treatment, Risk Stratification, Prognosis and Management.Conclusion: the efforts of the healthcare system to defeat COVID-19 could be supported by an AI-based decision-making system which would double them up and help manage these patients much more efficiently, especially those in COVID-19 IC
Timing of antibiotic therapy in the ICU
Severe or life threatening infections are common among patients in the intensive care unit (ICU). Most infections in the ICU are bacterial or fungal in origin and require antimicrobial therapy for clinical resolution. Antibiotics are the cornerstone of therapy for infected critically ill patients. However, antibiotics are often not optimally administered resulting in less favorable patient outcomes including greater mortality. The timing of antibiotics in patients with life threatening infections including sepsis and septic shock is now recognized as one of the most important determinants of survival for this population. Individuals who have a delay in the administration of antibiotic therapy for serious infections can have a doubling or more in their mortality. Additionally, the timing of an appropriate antibiotic regimen, one that is active against the offending pathogens based on in vitro susceptibility, also influences survival. Thus not only is early empiric antibiotic administration important but the selection of those agents is crucial as well. The duration of antibiotic infusions, especially for β-lactams, can also influence antibiotic efficacy by increasing antimicrobial drug exposure for the offending pathogen. However, due to mounting antibiotic resistance, aggressive antimicrobial de-escalation based on microbiology results is necessary to counterbalance the pressures of early broad-spectrum antibiotic therapy. In this review, we examine time related variables impacting antibiotic optimization as it relates to the treatment of life threatening infections in the ICU. In addition to highlighting the importance of antibiotic timing in the ICU we hope to provide an approach to antimicrobials that also minimizes the unnecessary use of these agents. Such approaches will increasingly be linked to advances in molecular microbiology testing and artificial intelligence/machine learning. Such advances should help identify patients needing empiric antibiotic therapy at an earlier time point as well as the specific antibiotics required in order to avoid unnecessary administration of broad-spectrum antibiotics
Impact of Analytics Applying Artificial Intelligence and Machine Learning on Enhancing Intensive Care Unit: A Narrative Review
Introduction. The intensive care unit (ICU) plays a pivotal role in providing specialized care to patients with severe illnesses or injuries. As a critical aspect of healthcare, ICU admissions demand immediate attention and skilled care from healthcare professionals. However, the intricacies involved in this process necessitate analytical solutions to ensure effective management and optimal patient outcomes.
Aim. The aim of this review was to highlight the enhancement of the ICUs through the application of analytics, artificial intelligence, and machine learning.
Methods. The review approach was carried out through databases such as MEDLINE, Embase, Web of Science, Scopus, Taylor & Francis, Sage, ProQuest, Science Direct, CINAHL, and Google Scholar. These databases were chosen due to their potential to offer pertinent and comprehensive coverage of the topic while reducing the likelihood of overlooking certain publications. The studies for this review involved the period from 2016 to 2023.
Results. Artificial intelligence and machine learning have been instrumental in benchmarking and identifying effective practices to enhance ICU care. These advanced technologies have demonstrated significant improvements in various aspects.
Conclusions. Artificial intelligence, machine learning, and data analysis techniques significantly improved critical care, patient outcomes, and healthcare delivery
Pediatric Blood Calculator
This paper outlines an expert system based solution for calculating the optimal amount of blood draw from infants to carry out critical tests requested by the attending clinicians. The solution is a hand-held device with a user-friendly interface that allows a meaningful two-way conversation between the clinician and the pathology office. Based on the tests being requested, the calculator determines the minimum amount of blood required in the different vials based on a smart expert system. This removes the uncertainty that is prevalent today in the amount of blood required to do all the tests, since in some cases there is not enough blood for all the requested tests by the attending clinicians. The expert-based solution would be a stand-alone hand-held device, but have the ability to interface with the hospital electronic record systems to ensure all compliances and easy transference of the information
Streaming Feature Grouping and Selection (Sfgs) For Big Data Classification
Real-time data has always been an essential element for organizations when the quickness of data delivery is critical to their businesses. Today, organizations understand the importance of real-time data analysis to maintain benefits from their generated data. Real-time data analysis is also known as real-time analytics, streaming analytics, real-time streaming analytics, and event processing. Stream processing is the key to getting results in real-time. It allows us to process the data stream in real-time as it arrives. The concept of streaming data means the data are generated dynamically, and the full stream is unknown or even infinite. This data becomes massive and diverse and forms what is known as a big data challenge. In machine learning, streaming feature selection has always been a preferred method in the preprocessing of streaming data. Recently, feature grouping, which can measure the hidden information between selected features, has begun gaining attention. This dissertation’s main contribution is in solving the issue of the extremely high dimensionality of streaming big data by delivering a streaming feature grouping and selection algorithm. Also, the literature review presents a comprehensive review of the current streaming feature selection approaches and highlights the state-of-the-art algorithms trending in this area. The proposed algorithm is designed with the idea of grouping together similar features to reduce redundancy and handle the stream of features in an online fashion. This algorithm has been implemented and evaluated using benchmark datasets against state-of-the-art streaming feature selection algorithms and feature grouping techniques. The results showed better performance regarding prediction accuracy than with state-of-the-art algorithms
Effects of COVID-19 on Global Research in STEM
A global public health emergency like the Coronavirus Infectious Disease 2019 (COVID-19) pandemic requires accurate and timely data collection in the research community. High impact research in science, technology, engineering, and mathematics (STEM) has been prioritized in the fight against COVID-19. The present study investigated the impact of COVID-19 on STEM research and the collaboration between global research institutions and industries. It was noted that COVID-19 had caused significant delays in non-COVID-19-related research projects and the onset of several remote studies. Most importantly, researchers in the STEM fields directed their attention and expertise to help mitigate virus transmission, treat patients, and implement appropriate public health interventions. Innovations are being integrated in several fields of technological and engineering research to provide optimal patient care and enhance physical distancing measures. Global research platforms are also designed to encourage accelerated research, especially in potential medicinal treatment. Collaboration amongst different disciplines and countries has enabled remarkable progress in the dissemination of scientific knowledge and appropriate responses to address the consequences of this pandemic on worldwide research in STEM
- …