SWIFT: A Deep Learning Approach to Prediction of Hypoxemic Events in Critically-Ill Patients Using SpO2 Waveform Prediction (original) (raw)
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Background This study aimed to develop a deep-learning model and a risk-score system using clinical variables to predict intensive care unit (ICU) admission and in-hospital mortality in COVID-19 patients. Methods This retrospective study consisted of 5,766 persons-under-investigation for COVID-19 between 7 February 2020 and 4 May 2020. Demographics, chronic comorbidities, vital signs, symptoms and laboratory tests at admission were collected. A deep neural network model and a risk-score system were constructed to predict ICU admission and in-hospital mortality. Prediction performance used the receiver operating characteristic area under the curve (AUC). Results The top ICU predictors were procalcitonin, lactate dehydrogenase, C-reactive protein, ferritin and oxygen saturation. The top mortality predictors were age, lactate dehydrogenase, procalcitonin, cardiac troponin, C-reactive protein and oxygen saturation. Age and troponin were unique top predictors for mortality but not ICU ad...
Generalized Prediction of Shock in Intensive Care Units using Deep Learning
Shock is a major killer in the ICU and Deep learning based early predictions can potentially save lives. Generalization across age and geographical context is an unaddressed challenge. In this retrospective observational study, we built real-time shock prediction models generalized across age groups and continents. More than 1.5 million patient-hours of novel data from a pediatric ICU in New Delhi and 5 million patient-hours from the adult ICU MIMIC database were used to build models. We achieved model generalization through a novel fractal deep-learning approach and predicted shock up to 12 hours in advance. Our deep learning models showed a receiver operating curve (AUROC) drop from 78% (95%CI, 73-83) on MIMIC data to 66% (95%CI, 54-78) on New Delhi data, outperforming standard machine learning by nearly a 10% gap. Therefore, better representations and deep learning can partly address the generalizability-gap of ICU prediction models trained across geographies. Our data and algori...
Journal of Clinical Monitoring and Computing
The Hypotension Prediction Index (HPI) is a commercially available machine-learning algorithm that provides warnings for impending hypotension, based on real-time arterial waveform analysis. The HPI was developed with arterial waveform data of surgical and intensive care unit (ICU) patients, but has never been externally validated in the latter group. In this study, we evaluated diagnostic ability of the HPI with invasively collected arterial blood pressure data in 41 patients with COVID-19 admitted to the ICU for mechanical ventilation. Predictive ability was evaluated at HPI thresholds from 0 to 100, at incremental intervals of 5. After exceeding the studied threshold, the next 20 min were screened for positive (mean arterial pressure (MAP)
Deep Learning Methods to Predict Mortality in COVID-19 Patients: A Rapid Scoping Review
Studies in Health Technology and Informatics, 2021
The ongoing COVID-19 pandemic has become the most impactful pandemic of the past century. The SARS-CoV-2 virus has spread rapidly across the globe affecting and straining global health systems. More than 2 million people have died from COVID-19 (as of 30 January 2021). To lessen the pandemic’s impact, advanced methods such as Artificial Intelligence models are proposed to predict mortality, morbidity, disease severity, and other outcomes and sequelae. We performed a rapid scoping literature review to identify the deep learning techniques that have been applied to predict hospital mortality in COVID-19 patients. Our review findings provide insights on the important deep learning models, data types, and features that have been reported in the literature. These summary findings will help scientists build reliable and accurate models for better intervention strategies for predicting mortality in current and future pandemic situations.
PeerJ Computer Science, 2022
The global healthcare system is being overburdened by an increasing number of COVID-19 patients. Physicians are having difficulty allocating resources and focusing their attention on high-risk patients, partly due to the difficulty in identifying high-risk patients early. COVID-19 hospitalizations require specialized treatment capabilities and can cause a burden on healthcare resources. Estimating future hospitalization of COVID-19 patients is, therefore, crucial to saving lives. In this paper, an interpretable deep learning model is developed to predict intensive care unit (ICU) admission and mortality of COVID-19 patients. The study comprised of patients from the Stony Brook University Hospital, with patient information such as demographics, comorbidities, symptoms, vital signs, and laboratory tests recorded. The top three predictors of ICU admission were ferritin, diarrhoea, and alamine aminotransferase, and the top predictors for mortality were COPD, ferritin, and myalgia. The p...
BMC Medical Informatics and Decision Making
Background Mechanical Ventilation (MV) is a complex and central treatment process in the care of critically ill patients. It influences acid–base balance and can also cause prognostically relevant biotrauma by generating forces and liberating reactive oxygen species, negatively affecting outcomes. In this work we evaluate the use of a Recurrent Neural Network (RNN) modelling to predict outcomes of mechanically ventilated patients, using standard mechanical ventilation parameters. Methods We performed our analysis on VENTILA dataset, an observational, prospective, international, multi-centre study, performed to investigate the effect of baseline characteristics and management changes over time on the all-cause mortality rate in mechanically ventilated patients in ICU. Our cohort includes 12,596 adult patients older than 18, associated with 12,755 distinct admissions in ICUs across 37 countries and receiving invasive and non-invasive mechanical ventilation. We carry out four different...
Vital Signs Prediction for COVID-19 Patients in ICU
Sensors
This study introduces machine learning predictive models to predict the future values of the monitored vital signs of COVID-19 ICU patients. The main vital sign predictors include heart rate, respiration rate, and oxygen saturation. We investigated the performances of the developed predictive models by considering different approaches. The first predictive model was developed by considering the following vital signs: heart rate, blood pressure (systolic, diastolic and mean arterial, pulse pressure), respiration rate, and oxygen saturation. Similar to the first approach, the second model was developed using the same vital signs, but it was trained and tested based on a leave-one-subject-out approach. The third predictive model was developed by considering three vital signs: heart rate (HR), respiration rate (RR), and oxygen saturation (SpO2). The fourth model was a leave-one-subject-out model for the three vital signs. Finally, the fifth predictive model was developed based on the sa...
IRJET, 2022
Patients admitted to an Intensive Care Unit (ICU) have life-threatening health problems or are in poor health and require considerable care and surveillance in order to recover quickly. An early ICU mortality prediction is critical for identifying patients who are at a higher danger and for making better therapeutic decisions for the patient. Using Deep Learning-based approaches, an early Mortality Prediction can be used to support this analysis. The Medical Information Mart for Intensive Care (MIMIC-II), which is freely available, is being used for the evaluation. The F1 score, area under the receiver operating characteristic curve (AUC), and precision predictions show the model's ability. Two models, the recurrent neural network-long short term memory (RNN-LSTM) and the convolution neural network (CNN) are employed and evaluated in order to produce the best mortality prediction utilizing the SAPS-I score and related parameters.
Machine learning methods to predict mechanical ventilation and mortality in patients with COVID-19
PLOS ONE, 2021
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...
2019 Computing in Cardiology Conference (CinC)
The 20th PhysioNet/Computing in Cardiology Challenge 2019 utilises 40 hourly-collected physiological vital signs and laboratory test results from patients admitted to intensive care units (ICUs). The main aim is to challenge researchers to come up with novel solutions for early detection the clinical onset of sepsis, which is critical for early action using antibiotic treatment and therefore improving sepsis outcome. We used training date provided and develop machine learning classifiers to predict clinical sepsis 6-12 hours ahead of the clinical onset. Neural networks possess abilities to uncover insights from complex datasets. We have trained an ensemble classifier with a convolutional neural network (CNN) and a recurrent neural network (RNN) for early detection of sepsis. The classifiers were implemented in in Python using Keras with Tensorflow as back-end. Both networks were combined using bagging to achieve better performance. The database appeared imbalanced, and the class (positive) with small number of data entries was oversampled proportionally before training. 90 % of the augmented and oversampled data were used for training, with 10 % for testing. Accuracy of 92.7 % , AUROC of 0.964 and AUPRC of 0.383 were achieved in the testing set for detecting sepsis 12 hours before the clinical onset. Unity score from 5-fold cross-validation in released database was 0.786. The entry for the official phase of the Phys-ioNet/CinC 2019 competition received a normalized utility score of 0.288. Machine learning and neural networks approaches showed potential application for better prediction of sepsis, using real-world database with random missing data and imbalanced classes.