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

2020 ◽  
Author(s):  
Sankavi Muralitharan ◽  
Walter Nelson ◽  
Shuang Di ◽  
Michael McGillion ◽  
PJ Devereaux ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ambulatory Care ◽  
Early Warning ◽  
Vital Signs ◽  
Early Warning Systems ◽  
Clinical Deterioration ◽  
Cochrane Library ◽  
Warning Systems ◽  
Learning Models ◽  
Machine Learning Models

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

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

10.2196/25187 ◽  
2020 ◽  
Author(s):  
Sankavi Muralitharan ◽  
Walter Nelson ◽  
Shuang Di ◽  
Michael McGillion ◽  
PJ Devereaux ◽  
...  
Keyword(s):  
Machine Learning ◽  
Scoping Review ◽  
Early Warning ◽  
Early Warning Systems ◽  
Clinical Deterioration ◽  
Warning Systems

scholarly journals Forecasting Bank Failure with Machine Learning Models: A study on Turkish Banks

2021 ◽  
Vol 3 (2) ◽  
pp. 51-59
Author(s):  
Safa SEN ◽  
Sara Almeida de Figueiredo
Keyword(s):  
Machine Learning ◽  
Early Warning Systems ◽  
Bank Failure ◽  
Bank Failures ◽  
Warning Systems ◽  
Learning Models ◽  
Economic Prosperity ◽  
Economic Activities ◽  
Failure Data ◽  
Machine Learning Models

Forecasting bank failures has been an essential study in the literature due to their significant impact on the economic prosperity of a country. Acting as an intermediary player, banks channel funds from those with surplus capital to those who require capital to carry out their economic activities. Therefore, it is essential to generate early warning systems that could warn banks and stakeholders in case of financial turbulence. In this paper, three machine learning models named as GLMBoost, XGBoost, and SMO were used to forecast bank failures. We used commercial bank failure data of Turkey between 1997 and 2001, where we have 17 failed and 20 healthy banks. Our results show that the Sequential Minimal Optimization and GLMBoost provide the same performance when classifying failed banks, while GLMBoost performs better in AUC and SMO when considering total classification success. Lastly, XGBoost, one of the most recent and robust classification models, surprisingly underperformed in all three metrics we used in research.

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

Hydrology ◽  
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.

Enhancing the reliability of landslide early warning systems by machine learning

Landslides ◽  
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

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

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.

scholarly journals Machine learning methods to predict mechanical ventilation and mortality in patients with COVID-19

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249285
Author(s):  
Limin Yu ◽  
Alexandra Halalau ◽  
Bhavinkumar Dalal ◽  
Amr E. Abbas ◽  
Felicia Ivascu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mechanical Ventilation ◽  
Hospital Mortality ◽  
Emergency Room ◽  
Vital Signs ◽  
High Accuracy ◽  
Gradient Boosting ◽  
Learning Models ◽  
Increased Risk ◽  
Machine Learning Models

Background The Coronavirus disease 2019 (COVID-19) pandemic has affected millions of people across the globe. It is associated with a high mortality rate and has created a global crisis by straining medical resources worldwide. Objectives To develop and validate machine-learning models for prediction of mechanical ventilation (MV) for patients presenting to emergency room and for prediction of in-hospital mortality once a patient is admitted. Methods Two cohorts were used for the two different aims. 1980 COVID-19 patients were enrolled for the aim of prediction ofMV. 1036 patients’ data, including demographics, past smoking and drinking history, past medical history and vital signs at emergency room (ER), laboratory values, and treatments were collected for training and 674 patients were enrolled for validation using XGBoost algorithm. For the second aim to predict in-hospital mortality, 3491 hospitalized patients via ER were enrolled. CatBoost, a new gradient-boosting algorithm was applied for training and validation of the cohort. Results Older age, higher temperature, increased respiratory rate (RR) and a lower oxygen saturation (SpO2) from the first set of vital signs were associated with an increased risk of MV amongst the 1980 patients in the ER. The model had a high accuracy of 86.2% and a negative predictive value (NPV) of 87.8%. While, patients who required MV, had a higher RR, Body mass index (BMI) and longer length of stay in the hospital were the major features associated with in-hospital mortality. The second model had a high accuracy of 80% with NPV of 81.6%. Conclusion Machine learning models using XGBoost and catBoost algorithms can predict need for mechanical ventilation and mortality with a very high accuracy in COVID-19 patients.

scholarly journals Paediatric early warning systems for detecting and responding to clinical deterioration in children: a systematic review

BMJ Open ◽  
2017 ◽  
Vol 7 (3) ◽  
pp. e014497 ◽  
Author(s):  
Veronica Lambert ◽  
Anne Matthews ◽  
Rachel MacDonell ◽  
John Fitzsimons
Keyword(s):  
Systematic Review ◽  
Early Warning ◽  
Early Warning Systems ◽  
Clinical Deterioration ◽  
Warning Systems

scholarly journals Developing a Sociocultural Framework of Compliance: An Exploration of Factors Related to the Use of Early Warning Systems Among Acute Care Clinicians

2020 ◽  
Author(s):  
Tracy Flenady ◽  
Trudy Dwyer ◽  
Agnieszka Sobolewska ◽  
Danielle Le Lagadec ◽  
Justine Connor ◽  
...  
Keyword(s):  
Acute Care ◽  
Early Warning ◽  
Vital Signs ◽  
Warning System ◽  
Early Warning Systems ◽  
Public Hospitals ◽  
Sociocultural Factors ◽  
Warning Systems ◽  
Structured Interviews ◽  
Good Communication

Abstract Background: Early warning systems (EWS) are most effective when clinicians monitor patients’ vital signs and comply with the recommended escalation of care protocols once deterioration is recognised. Objectives: To explore sociocultural factors influencing acute care clinicians’ compliance with an early warning system commonly used in Queensland public hospitals in Australia. Methods: This interpretative qualitative study utilised inductive thematic analysis to analyse data collected from semi-structured interviews conducted with 30 acute care clinicians from Queensland, Australia.Results: This study identified that individuals and teams approached compliance with EWS in the context of 1) the use of EWS for patient monitoring; and 2) the use of EWS for the escalation of patient care. Individual and team compliance with monitoring and escalation processes is facilitated by intra and inter-professional factors such as acceptance and support, clear instruction, inter-disciplinary collaboration and good communication. Noncompliance with EWS can be attributed to intra and inter-professional hierarchy and poor communication. Conclusions: The overarching organisational context including the hospital’s embedded quality improvement and administrative protocols (training, resources and staffing) impact hospital-wide culture and influence clinicians’ and teams’ compliance or non-compliance with early warning system’s monitoring and escalation processes. Successful adoption of EWS relies on effective and meaningful interactions among multidisciplinary staff.

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