Generalizable deep temporal models for predicting episodes of sudden hypotension in critically ill patients: a personalized approach

Chan, Barry; Chen, Brian; Sedghi, Alireza; Laird, Philip; Maslove, David; Mousavi, Parvin

doi:10.1038/s41598-020-67952-0

Cited by 13 publications

(16 citation statements)

References 34 publications

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“…Similar monitoring of telemetry changes has shown that machine learning and AI techniques can be applied to telemetry waveforms to also predict sepsis (Bravi et al, 2012 ). Preliminary studies have also applied AI models to predict hypotension (Chan et al, 2020 ). Sequential monitoring of variables used in a risk score may provide better mortality prediction than a single snapshot assessment.…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

Mortality Prediction in Cardiac Intensive Care Unit Patients: A Systematic Review of Existing and Artificial Intelligence Augmented Approaches

Rafie

Jentzer

Noseworthy

et al. 2022

Front. Artif. Intell.

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The medical complexity and high acuity of patients in the cardiac intensive care unit make for a unique patient population with high morbidity and mortality. While there are many tools for predictions of mortality in other settings, there is a lack of robust mortality prediction tools for cardiac intensive care unit patients. The ongoing advances in artificial intelligence and machine learning also pose a potential asset to the advancement of mortality prediction. Artificial intelligence algorithms have been developed for application of electrocardiogram interpretation with promising accuracy and clinical application. Additionally, artificial intelligence algorithms applied to electrocardiogram interpretation have been developed to predict various variables such as structural heart disease, left ventricular systolic dysfunction, and atrial fibrillation. These variables can be used and applied to new mortality prediction models that are dynamic with the changes in the patient's clinical course and may lead to more accurate and reliable mortality prediction. The application of artificial intelligence to mortality prediction will fill the gaps left by current mortality prediction tools.

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Section: Limitations and Future Directionsmentioning

confidence: 99%

Mortality Prediction in Cardiac Intensive Care Unit Patients: A Systematic Review of Existing and Artificial Intelligence Augmented Approaches

Rafie

Jentzer

Noseworthy

et al. 2022

Front. Artif. Intell.

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“…Больные с идентифицированным гипервоспалительным либо гиповоспалительным фенотипом имеют существенно различающиеся клинические исходы и дифферен-цированный ответ на ИВЛ, инфузионную терапию и симвастатин [41]. Гипервоспалительный субфенотип ассоциирован с шоковым состоянием, метаболическим ацидозом и худшим клиническим исходом [42][43][44].…”

Section: внедрение принципов пм в медицину критических состоянийunclassified

Personalized Critical Care Medicine (Review)

Голубев

2022

Obŝaâ reanimatologiâ

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Personalized medicine (PM) is a major trend in health care development in the 21st century. This area includes studying risk factors for disease development (prediction), interventions for preventing diseases (prophylaxis), individualization of diagnosis and treatment (personalization), informing the patient on disease prevention and treatment (participation). In the recent years, an intense research to introduce the personalized medicine principles into the management of critically ill patients, has been under way. This includes identification of patient groups based on genomic research, development of diagnostic tests using molecular markers, creation of novel classes of drugs based on individual patient characteristics.The aim of the review is to summarize the available data on the implementation of the principles of PM in the routine practice of critical care institutions.We analyzed more than 300 sources of literature from the Pubmed and Scopus databases, as well as the RSCI database. Eighty five most relevant sources were selected for the review. The paper reports data on the organization and results of implementation of PM principles and advanced technologies, such as Emergency Medicine Sample Bank (EMSB), in the daily activity of clinics providing emergency critical care. The formation of the novel PM concept focused on the treatment of critically ill patients has been discussed. The review contains detailed data on the patterns of development of specific critical illnesses such as acute cerebrovascular events, acute respiratory distress syndrome, traumatic brain injury, shock, myocardial infarction, cardiac rhythm and conduction disturbances. Medication eﬃcacy in view of individual genetic patient characteristics has also been highlighted. No research limitations on the subject were identified.Conclusion. The analysis of literature has demonstrated positive results of implementing PM principles in prevention, diagnosis and treatment of critically ill patients. Creation of Biobanks, development of training programs and regulatory documentation, advancing the scientific research, introduction of new methods of diagnosis and treatment will contribute to the implementation of PM principles in practical healthcare.

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“…Patients who are admitted to ICU are very sick and therefore they tend to have a higher mortality rate than the average patients on the wards. With the development of artificial intelligence, many researchers have applied machine learning methods to study severe illnesses, such as sepsis [2][3][4][5][6][7], Acute kidney injury (AKI) [8][9][10][11], hypotension [12][13][14], etc. Early prediction of patients at high risk of deterioration within a short period of time would trigger immediate attention and intervention and thus would reduce the mortality rate at ICU, especially in low-and middle-income countries with limited health care resources [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Dynamic prediction of life-threatening events for patients in intensive care unit

Kang

Xu³

et al. 2022

BMC Med Inform Decis Mak

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Background Early prediction of patients’ deterioration is helpful in early intervention for patients at greater risk of deterioration in Intensive Care Unit (ICU). This study aims to apply machine learning approaches to heterogeneous clinical data for predicting life-threatening events of patients in ICU. Methods We collected clinical data from a total of 3151 patients admitted to the Medical Intensive Care Unit of Peking Union Medical College Hospital in China from January 1st, 2014, to October 1st, 2019. After excluding the patients who were under 18 years old or stayed less than 24 h at the ICU, a total of 2170 patients were enrolled in this study. Multiple machine learning approaches were utilized to predict life-threatening events (i.e., death) in seven 24-h windows (day 1 to day 7) and their performance was compared. Results Light Gradient Boosting Machine showed the best performance. We found that life-threatening events during the short-term windows can be better predicted than those in the medium-term windows. For example, death in 24 h can be predicted with an Area Under Curve of 0.905. Features like infusion pump related fluid input were highly related to life-threatening events. Furthermore, the prediction power of static features such as age and cardio-pulmonary function increased with the extended prediction window. Conclusion This study demonstrates that the integration of machine learning approaches and large-scale high-quality clinical data in ICU could accurately predict life-threatening events for ICU patients for early intervention.

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Generalizable deep temporal models for predicting episodes of sudden hypotension in critically ill patients: a personalized approach

Cited by 13 publications

References 34 publications

Mortality Prediction in Cardiac Intensive Care Unit Patients: A Systematic Review of Existing and Artificial Intelligence Augmented Approaches

Mortality Prediction in Cardiac Intensive Care Unit Patients: A Systematic Review of Existing and Artificial Intelligence Augmented Approaches

Personalized Critical Care Medicine (Review)

Dynamic prediction of life-threatening events for patients in intensive care unit

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