Machine Learning for Pulmonary and Critical Care Medicine: A Narrative Review

Mlodzinski, Eric; Stone, David J.; Celi, Leo Anthony

doi:10.1007/s41030-020-00110-z

Cited by 33 publications

(35 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Our results indicate that most of the topics proposed (eg, prediction of mortality, prediction of complications, or proposal of guidelines) were seen as potential use cases for CDSS by ICU staff. For these and several other instances, algorithms already exist that could be adjusted for real-time data [ 34 ].…”

Section: Discussionmentioning

confidence: 99%

Improvements in Patient Monitoring in the Intensive Care Unit: Survey Study

Poncette¹,

Mosch²,

Spies³

et al. 2020

J Med Internet Res

View full text Add to dashboard Cite

Background Due to demographic change and, more recently, coronavirus disease (COVID-19), the importance of modern intensive care units (ICU) is becoming apparent. One of the key components of an ICU is the continuous monitoring of patients' vital parameters. However, existing advances in informatics, signal processing, or engineering that could alleviate the burden on ICUs have not yet been applied. This could be due to the lack of user involvement in research and development. Objective This study focused on the satisfaction of ICU staff with current patient monitoring and their suggestions for future improvements. We aimed to identify aspects of monitoring that interrupt patient care, display devices for remote monitoring, use cases for artificial intelligence (AI), and whether ICU staff members are willing to improve their digital literacy or contribute to the improvement of patient monitoring. We further aimed to identify differences in the responses of different professional groups. Methods This survey study was performed with ICU staff from 4 ICUs of a German university hospital between November 2019 and January 2020. We developed a web-based 36-item survey questionnaire, by analyzing a preceding qualitative interview study with ICU staff, about the clinical requirements of future patient monitoring. Statistical analyses of questionnaire results included median values with their bootstrapped 95% confidence intervals, and chi-square tests to compare the distributions of item responses of the professional groups. Results In total, 86 of the 270 ICU physicians and nurses completed the survey questionnaire. The majority stated they felt confident using the patient monitoring equipment, but that high rates of false-positive alarms and the many sensor cables interrupted patient care. Regarding future improvements, respondents asked for wireless sensors, a reduction in the number of false-positive alarms, and hospital standard operating procedures for alarm management. Responses to the display devices proposed for remote patient monitoring were divided. Most respondents indicated it would be useful for earlier alerting or when they were responsible for multiple wards. AI for ICUs would be useful for early detection of complications and an increased risk of mortality; in addition, the AI could propose guidelines for therapy and diagnostics. Transparency, interoperability, usability, and staff training were essential to promote the use of AI. The majority wanted to learn more about new technologies for the ICU and required more time for learning. Physicians had fewer reservations than nurses about AI-based intelligent alarm management and using mobile phones for remote monitoring. Conclusions This survey study of ICU staff revealed key improvements for patient monitoring in intensive care medicine. Hospital providers and medical device manufacturers should focus on reducing false alarms, implementing hospital alarm standard operating procedures, introducing wireless sensors, preparing for the use of AI, and enhancing the digital literacy of ICU staff. Our results may contribute to the user-centered transfer of digital technologies into practice to alleviate challenges in intensive care medicine. Trial Registration ClinicalTrials.gov NCT03514173; https://clinicaltrials.gov/ct2/show/NCT03514173

show abstract

Section: Discussionmentioning

confidence: 99%

Improvements in Patient Monitoring in the Intensive Care Unit: Survey Study

Poncette¹,

Mosch²,

Spies³

et al. 2020

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…Furthermore, most healthcare systems worldwide may not have enough capacity to successfully integrate AI into the current workflow. Decision-making and predictive models do not yet match the currently known healthcare systems, and a lot of improvements are needed to successfully integrate these innovations ( 50 ).…”

Section: The Safety and Challenges Of Using Ai In Sepsismentioning

confidence: 99%

Artificial Intelligence for Clinical Decision Support in Sepsis

et al. 2021

Front. Med.

View full text Add to dashboard Cite

Sepsis is one of the main causes of death in critically ill patients. Despite the continuous development of medical technology in recent years, its morbidity and mortality are still high. This is mainly related to the delay in starting treatment and non-adherence of clinical guidelines. Artificial intelligence (AI) is an evolving field in medicine, which has been used to develop a variety of innovative Clinical Decision Support Systems. It has shown great potential in predicting the clinical condition of patients and assisting in clinical decision-making. AI-derived algorithms can be applied to multiple stages of sepsis, such as early prediction, prognosis assessment, mortality prediction, and optimal management. This review describes the latest literature on AI for clinical decision support in sepsis, and outlines the application of AI in the prediction, diagnosis, subphenotyping, prognosis assessment, and clinical management of sepsis. In addition, we discussed the challenges of implementing and accepting this non-traditional methodology for clinical purposes.

show abstract

“…AI, particularly pattern recognition using deep and machine learning, has numerous potential applications in pulmonary medicine, whether in image analysis, decision-making or prognosis prediction [ 5 – 7 ]. In this section, we provide examples of how AI is also used for computer vision in medical imaging, predictive modelling with machine learning, and in battling the novel SARS-CoV-2 pandemic ( table 3 ).…”

Section: Applications In Clinical Pulmonary Medicinementioning

confidence: 99%

“…increase compared to only 171 articles found in a similar search for the years 2010-2014. Furthermore, our query for full-text, English language systematic and narrative reviews published from 2018 onward using the search terms "machine learning in respiratory medicine" identified 32 scientific papers: 14 pertained to cardiac or critical care medicine, 14 were dedicated to specific lung disorders, and four described the role of machine learning in general pulmonary medicine [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence in pulmonary medicine: computer vision, predictive model and COVID-19

Khemasuwan

Sorensen²,

Colt

2020

Eur Respir Rev

View full text Add to dashboard Cite

Artificial intelligence (AI) is transforming healthcare delivery. The digital revolution in medicine and healthcare information is prompting a staggering growth of data intertwined with elements from many digital sources such as genomics, medical imaging and electronic health records. Such massive growth has sparked the development of an increasing number of AI-based applications that can be deployed in clinical practice. Pulmonary specialists who are familiar with the principles of AI and its applications will be empowered and prepared to seize future practice and research opportunities. The goal of this review is to provide pulmonary specialists and other readers with information pertinent to the use of AI in pulmonary medicine. First, we describe the concept of AI and some of the requisites of machine learning and deep learning. Next, we review some of the literature relevant to the use of computer vision in medical imaging, predictive modelling with machine learning, and the use of AI for battling the novel severe acute respiratory syndrome-coronavirus-2 pandemic. We close our review with a discussion of limitations and challenges pertaining to the further incorporation of AI into clinical pulmonary practice.

show abstract

Machine Learning for Pulmonary and Critical Care Medicine: A Narrative Review

Cited by 33 publications

References 39 publications

Improvements in Patient Monitoring in the Intensive Care Unit: Survey Study

Improvements in Patient Monitoring in the Intensive Care Unit: Survey Study

Artificial Intelligence for Clinical Decision Support in Sepsis

Artificial intelligence in pulmonary medicine: computer vision, predictive model and COVID-19

Contact Info

Product

Resources

About