Machine Learning-based Early Warning Systems for Clinical Deterioration: A Systematic Scoping Review (Preprint)

BACKGROUND Timely identification of patients at a high risk of clinical deterioration is key to prioritizing care, allocating resources effectively and preventing adverse outcomes. Vital signs-based aggregate-weighted Early Warning Systems are commonly used to predict the risk of outcomes related to cardiorespiratory instability and sepsis, which are strong predictors of poor outcomes and mortality. Machine learning models, which can incorporate trends and capture relationships among parameters that aggregate-weighted models cannot, have recently been showing promising results. OBJECTIVE To identify, summarize, and evaluate the available research, current state of utility and challenges with machine learning based early warning systems using vital signs to predict the risk of physiological deterioration in acutely ill patients, across acute and ambulatory care settings. METHODS PubMed, CINAHL, Cochrane Library, Web of Science, Embase, and Google Scholar were searched for peer-reviewed, original studies with keywords related to “vital signs”, “clinical deterioration”, and “machine learning”. Included studies used patient vital signs along with demographics and described a machine learning model for predicting an outcome in acute and ambulatory care settings. Data were extracted following PRISMA, TRIPOD, and Cochrane Collaboration guidelines. RESULTS 24 peer-reviewed studies were identified for inclusion from 417 articles. 23 studies were retrospective, while 1 was prospective in nature. Care settings included general wards, ICUs, emergency departments, step-down units, medical assessment units, post-anesthetic wards, and home care. Machine learning models including logistic regression, tree-based methods, kernel-based methods and neural networks were most commonly used to predict the risk of deterioration. The area under the curve for models ranged from 0.57 to 0.97. CONCLUSIONS In studies that compared performance, reported results suggest that machine learning based early warning systems can achieve greater accuracy than aggregate weighted early warning systems but several areas for further research were identified. While these models have the potential to provide clinical decision support, there is a need for standardized outcome measures to allow for rigorous evaluation of performance across models. Further research needs to address the interpretability of model outputs by clinicians, clinical efficacy of these systems through prospective study design, and their potential impact in different clinical settings. CLINICALTRIAL

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Flood Early Warning Systems Using Machine Learning Techniques: The Case of the Tomebamba Catchment at the Southern Andes of Ecuador

Hydrology ◽

10.3390/hydrology8040183 ◽

2021 ◽

Vol 8 (4) ◽

pp. 183

Author(s):

Paul Muñoz ◽

Johanna Orellana-Alvear ◽

Jörg Bendix ◽

Jan Feyen ◽

Rolando Célleri

Keyword(s):

Machine Learning ◽

Early Warning ◽

Early Warning Systems ◽

Data Representation ◽

Machine Learning Techniques ◽

Lead Times ◽

Warning Systems ◽

Tropical Andes ◽

K Nearest Neighbors ◽

Learning Techniques

Worldwide, machine learning (ML) is increasingly being used for developing flood early warning systems (FEWSs). However, previous studies have not focused on establishing a methodology for determining the most efficient ML technique. We assessed FEWSs with three river states, No-alert, Pre-alert and Alert for flooding, for lead times between 1 to 12 h using the most common ML techniques, such as multi-layer perceptron (MLP), logistic regression (LR), K-nearest neighbors (KNN), naive Bayes (NB), and random forest (RF). The Tomebamba catchment in the tropical Andes of Ecuador was selected as a case study. For all lead times, MLP models achieve the highest performance followed by LR, with f1-macro (log-loss) scores of 0.82 (0.09) and 0.46 (0.20) for the 1 h and 12 h cases, respectively. The ranking was highly variable for the remaining ML techniques. According to the g-mean, LR models correctly forecast and show more stability at all states, while the MLP models perform better in the Pre-alert and Alert states. The proposed methodology for selecting the optimal ML technique for a FEWS can be extrapolated to other case studies. Future efforts are recommended to enhance the input data representation and develop communication applications to boost the awareness of society of floods.

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Enhancing the reliability of landslide early warning systems by machine learning

Landslides ◽

10.1007/s10346-020-01453-z ◽

2020 ◽

Vol 17 (9) ◽

pp. 2231-2246

Author(s):

Hemalatha Thirugnanam ◽

Maneesha Vinodini Ramesh ◽

Venkat P. Rangan

Keyword(s):

Machine Learning ◽

Early Warning ◽

Early Warning Systems ◽

Warning Systems ◽

Landslide Early Warning

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Flood Early Warning Systems using Machine Learning Techniques. Case the Tomebamba Catchment at the Southern Andes of Ecuador

10.20944/preprints202111.0510.v1 ◽

2021 ◽

Author(s):

Paul Muñoz ◽

Johanna Orellana-Alvear ◽

Jörg Bendix ◽

Jan Feyen ◽

Rolando Célleri

Keyword(s):

Machine Learning ◽

Early Warning ◽

Early Warning Systems ◽

Data Representation ◽

Machine Learning Techniques ◽

Lead Times ◽

Warning Systems ◽

Tropical Andes ◽

K Nearest Neighbors ◽

Learning Techniques

Flood Early Warning Systems (FEWSs) using Machine Learning (ML) has gained worldwide popularity. However, determining the most efficient ML technique is still a bottleneck. We assessed FEWSs with three river states, No-alert, Pre-alert, and Alert for flooding, for lead times between 1 to 12 hours using the most common ML techniques, such as Multi-Layer Perceptron (MLP), Logistic Regression (LR), K-Nearest Neighbors (KNN), Naive Bayes (NB), and Random Forest (RF). The Tomebamba catchment in the tropical Andes of Ecuador was selected as case study. For all lead times, MLP models achieve the highest performance followed by LR, with f1-macro (log-loss) scores of 0.82 (0.09) and 0.46 (0.20) for the 1- and 12-hour cases, respectively. The ranking was highly variable for the remaining ML techniques. According to the g-mean, LR models correctly forecast and show more stability at all states, while the MLP models perform better in the Pre-alert and Alert states. Future efforts are recommended to enhance the input data representation and develop communication applications to boost the awareness of the society for floods.

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