Artificial intelligence in intensive care medicine

International Journal of Reconfigurable and Embedded Systems (IJRES), 2023

Intensive care unit deals with data that are dynamic in nature like real time measurement of health condition to laboratory test data that are continuously changes accordingly with time. Artificial intelligence (AI's) potential ability to perform complex pattern analyses using large volumes of data. Generated pattern discovers the new symptoms of the disease in the Intensive care units (ICUs), helps the doctors to prescribe the new drug discovery which is helpful to intelligent use. Currently research work has been focused in the ICU making more efficient clinical workflow by generation of high-risk patterns from improved high volumes of data. Emerging area of AI in the ICU includes mortality prediction, uses of powerful sensors, new drug discovery, prediction of length of stay and legal role in uses of drugs for severity of disease. This review focuses latest application of AI drugs and other relevant issues for the ICU.

Finnish Journal of eHealth and eWelfare, 2021

The Envision project aims at developing artificial intelligence-based tools for supporting the treatment of critically ill COVID-19 patients in the intensive care unit. Twelve European hospitals participate in the collection of patient data for the development and validation of the artificial intelligence tools. Ten potential use cases have been identified as development targets. Data analysis and results from expert interviews are applied to define the clinically most relevant parameters and functional use cases to be used in providing decision support for the clinicians in the intensive care units for this patient group. The resulting artificial intelligence-based tool may be beneficial in the management of the next similar epidemics, as well.

Intensive Care Medicine, 2020

ArXiv, 2018

Currently, many critical care indices are repetitively assessed and recorded by overburdened nurses, e.g. physical function or facial pain expressions of nonverbal patients. In addition, many essential information on patients and their environment are not captured at all, or are captured in a non-granular manner, e.g. sleep disturbance factors such as bright light, loud background noise, or excessive visitations. In this pilot study, we examined the feasibility of using pervasive sensing technology and artificial intelligence for autonomous and granular monitoring of critically ill patients and their environment in the Intensive Care Unit (ICU). As an exemplar prevalent condition, we also characterized delirious and non-delirious patients and their environment. We used wearable sensors, light and sound sensors, and a high-resolution camera to collected data on patients and their environment. We analyzed collected data using deep learning and statistical analysis. Our system performe...

Journal of Intensive Care …, 2004

Health care information systems have the potential to enable better care of patients in much the same manner as the widespread use of the automobile and telephone did in the early 20th century. The car and phone were rapidly accepted and embraced throughout ...

Critical care medicine, 2011

Objective We sought to develop an intensive care unit research database applying automated techniques to aggregate high-resolution diagnostic and therapeutic data from a large, diverse population of adult intensive care unit patients. This freely available database is intended to support epidemiologic research in critical care medicine and serve as a resource to evaluate new clinical decision support and monitoring algorithms. Design

Critical Care Clinics, 1996

The computer and its accouterments are changing the standard of critical care. Like previous technologic advances, the computer began as a curiosity, and enjoyed both unalloyed but overly quick praise and disdain, eventually emerging as a necessary tool. Like other technologic advances, computer use raises a cluster of new ethical issues. This is true in critical care medicine in particular, but it is also the case throughout medicine and the other health sciences. These ethical issues are addressed infrequently, and only recently has there been any movement toward a sustained disc~ssion.'~ All major technologic advances raise ethical issues. From hemodialysis and mechanical ventilation to artificial organs and genetic engineering, the recent history of medicine has been fueled by the engines of invention and scrutinized by the tools of ethics. This article offers some of this scrutiny in the domain of critical care computing. Not all of the ethical issues that arise in critical care computing are unique to it. Many of these issues surface in other areas as well. For instance, safeguarding data from inappropriate access and predicting outcomes are issues that have emerged throughout the field of medical informatics. Furthermore, critical care units (CCUs) rarely are autonomous entities. Links between and among CCUs, emergency departments, trauma centers, and other departments reveal extensive networks

IEEE Access

Mean arterial pressure (MAP) is an important clinical parameter to evaluate the health of critically ill patients in intensive care units. Thus, the real time clinical decision support systems detecting anomalies and deviations in MAP enable early interventions and prevent serious complications. The stateof-the-art decision support systems are based on a three-phase method that applies offline training, transfer learning, and retraining at the bedside. Their applicability in critical care units is challenging with delay and inaccuracy. In this article, we propose a real time clinical decision support system forecasting the MAP status at the bedside using a new machine learning structure. The proposed system works in real time at the bedside without requiring the offline phase for training using large datasets. It thereby enables timely interventions and improved healthcare services. The proposed machine learning structure includes two stages. Stage I applies online learning using hierarchical temporal memory (HTM) to enable real time stream processing and provides unsupervised predictions. To the best of our knowledge, this is the first time it is applied to medical signals. Stage II is a long short-term memory (LSTM) classifier that forecasts the status of the patient's MAP ahead of time based on Stage I stream predictions. We perform a thorough performance evaluation of the proposed system and compare it with the state-of-the-art systems employing logistic regression (LR). The comparison shows the proposed system outperforms LR in terms of the classification accuracy, recall, precision, and area under the receiver operation curve (AUROC). INDEX TERMS Clinical decision support, classification, hierarchical temporal memory (HTM), long short-term memory (LSTM), machine learning, real time prediction.

Critical care medicine, 2011

Informatics, 2021

Modern Intensive Care Units (ICUs) provide continuous monitoring of critically ill patients susceptible to many complications affecting morbidity and mortality. ICU settings require a high staff-to-patient ratio and generates a sheer volume of data. For clinicians, the real-time interpretation of data and decision-making is a challenging task. Machine Learning (ML) techniques in ICUs are making headway in the early detection of high-risk events due to increased processing power and freely available datasets such as the Medical Information Mart for Intensive Care (MIMIC). We conducted a systematic literature review to evaluate the effectiveness of applying ML in the ICU settings using the MIMIC dataset. A total of 322 articles were reviewed and a quantitative descriptive analysis was performed on 61 qualified articles that applied ML techniques in ICU settings using MIMIC data. We assembled the qualified articles to provide insights into the areas of application, clinical variables u...