scholarly journals Development of a Clinical Decision Support System for Severity Risk Prediction and Triage of COVID-19 Patients at Hospital Admission: an International Multicenter Study

2020 ◽  
Author(s):  
Guangyao Wu ◽  
Pei Yang ◽  
Henry C. Woodruff ◽  
Xiangang Rao ◽  
Julien Guiot ◽  
...  
Keyword(s):  
Machine Learning ◽  
Critical Illness ◽  
High Risk ◽  
Hospital Admission ◽  
Risk Prediction ◽  
Retrospective Cohort ◽  
Interquartile Range ◽  
Learning Models ◽  
Medical Resources ◽  
Machine Learning Models

Key pointsQuestionHow do nomograms and machine-learning algorithms of severity risk prediction and triage of COVID-19 patients at hospital admission perform?FindingsThis model was prospectively validated on six test datasets comprising of 426 patients and yielded AUCs ranging from 0.816 to 0.976, accuracies ranging from 70.8% to 93.8%, sensitivities ranging from 83.7% to 100%, and specificities ranging from 41.0% to 95.7%. The cut-off probability values for low, medium, and high-risk groups were 0.072 and 0.244.MeaningThe findings of this study suggest that our models performs well for the diagnosis and prediction of progression to severe or critical illness of COVID-19 patients and could be used for triage of COVID-19 patients at hospital admission.IMPORTANCEThe outbreak of the coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality for severely and critically ill patients. However, the availability of validated nomograms and the machine-learning model to predict severity risk and triage of affected patients is limited.OBJECTIVETo develop and validate nomograms and machine-learning models for severity risk assessment and triage for COVID-19 patients at hospital admission.DESIGN, SETTING, AND PARTICIPANTSA retrospective cohort of 299 consecutively hospitalized COVID-19 patients at The Central Hospital of Wuhan, China, from December 23, 2019, to February 13, 2020, was used to train and validate the models. Six cohorts with 426 patients from eight centers in China, Italy, and Belgium, from February 20, 2020, to March 21, 2020, were used to prospectively validate the models.MAIN OUTCOME AND MEASURESThe main outcome was the onset of severe or critical illness during hospitalization. Model performances were quantified using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.RESULTSOf the 299 hospitalized COVID-19 patients in the retrospective cohort, the median age was 50 years ((interquartile range, 35.5-63.0; range, 20–94 years) and 137 (45.8%) were men. Of the 426 hospitalized COVID-19 patients in the prospective cohorts, the median age was 62.0 years ((interquartile range, 50.0-72.0; range, 19-94 years) and 236 (55.4%) were men. The model was prospectively validated on six cohorts yielding AUCs ranging from 0.816 to 0.976, with accuracies ranging from 70.8% to 93.8%, sensitivities ranging from 83.7% to 100%, and specificities ranging from 41.0% to 95.7%. The cut-off values of the low, medium, and high-risk probabilities were 0.072 and 0.244. The developed online calculators can be found at https://covid19risk.ai/.CONCLUSION AND RELEVANCEThe machine learning models, nomograms, and online calculators might be useful for the prediction of onset of severe and critical illness among COVID-19 patients and triage at hospital admission. Further prospective research and clinical feedback are necessary to evaluate the clinical usefulness of this model and to determine whether these models can help optimize medical resources and reduce mortality rates compared with current clinical practices.

Implementing clinical decision support for oncology advanced care planning: A systems engineering framework to optimize the usability and utility of a machine learning predictive model in clinical practice.

2021 ◽  
Vol 39 (28_suppl) ◽  
pp. 330-330
Author(s):  
Teja Ganta ◽  
Stephanie Lehrman ◽  
Rachel Pappalardo ◽  
Madalene Crow ◽  
Meagan Will ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Predictive Model ◽  
Systems Engineering ◽  
Care Planning ◽  
Learning Models ◽  
Predictive Tool ◽  
Risk Of Death ◽  
The Impact ◽  
Machine Learning Models

330 Background: Machine learning models are well-positioned to transform cancer care delivery by providing oncologists with more accurate or accessible information to augment clinical decisions. Many machine learning projects, however, focus on model accuracy without considering the impact of using the model in real-world settings and rarely carry forward to clinical implementation. We present a human-centered systems engineering approach to address clinical problems with workflow interventions utilizing machine learning algorithms. Methods: We aimed to develop a mortality predictive tool, using a Random Forest algorithm, to identify oncology patients at high risk of death within 30 days to move advance care planning (ACP) discussions earlier in the illness trajectory. First, a project sponsor defined the clinical need and requirements of an intervention. The data scientists developed the predictive algorithm using data available in the electronic health record (EHR). A multidisciplinary workgroup was assembled including oncology physicians, advanced practice providers, nurses, social workers, chaplain, clinical informaticists, and data scientists. Meeting bi-monthly, the group utilized human-centered design (HCD) methods to understand clinical workflows and identify points of intervention. The workgroup completed a workflow redesign workshop, a 90-minute facilitated group discussion, to integrate the model in a future state workflow. An EHR (Epic) analyst built the user interface to support the intervention per the group’s requirements. The workflow was piloted in thoracic oncology and bone marrow transplant with plans to scale to other cancer clinics. Results: Our predictive model performance on test data was acceptable (sensitivity 75%, specificity 75%, F-1 score 0.71, AUC 0.82). The workgroup identified a “quality of life coordinator” who: reviews an EHR report of patients scheduled in the upcoming 7 days who have a high risk of 30-day mortality; works with the oncology team to determine ACP clinical appropriateness; documents the need for ACP; identifies potential referrals to supportive oncology, social work, or chaplain; and coordinates the oncology appointment. The oncologist receives a reminder on the day of the patient’s scheduled visit. Conclusions: This workgroup is a viable approach that can be replicated at institutions to address clinical needs and realize the full potential of machine learning models in healthcare. The next steps for this project are to address end-user feedback from the pilot, expand the intervention to other cancer disease groups, and track clinical metrics.

Machine Learning-based Prediction Models for Diagnosis and Prognosis in Inflammatory Bowel Diseases: A Systematic Review

2021 ◽  
Author(s):  
Nghia H Nguyen ◽  
Dominic Picetti ◽  
Parambir S Dulai ◽  
Vipul Jairath ◽  
William J Sandborn ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Risk Prediction ◽  
Statistical Models ◽  
Prediction Models ◽  
Risk Of Bias ◽  
Learning Models ◽  
Bowel Diseases ◽  
Inflammatory Bowel ◽  
Machine Learning Models

Abstract Background and Aims There is increasing interest in machine learning-based prediction models in inflammatory bowel diseases (IBD). We synthesized and critically appraised studies comparing machine learning vs. traditional statistical models, using routinely available clinical data for risk prediction in IBD. Methods Through a systematic review till January 1, 2021, we identified cohort studies that derived and/or validated machine learning models, based on routinely collected clinical data in patients with IBD, to predict the risk of harboring or developing adverse clinical outcomes, and reported its predictive performance against a traditional statistical model for the same outcome. We appraised the risk of bias in these studies using the Prediction model Risk of Bias ASsessment (PROBAST) tool. Results We included 13 studies on machine learning-based prediction models in IBD encompassing themes of predicting treatment response to biologics and thiopurines, predicting longitudinal disease activity and complications and outcomes in patients with acute severe ulcerative colitis. The most common machine learnings models used were tree-based algorithms, which are classification approaches achieved through supervised learning. Machine learning models outperformed traditional statistical models in risk prediction. However, most models were at high risk of bias, and only one was externally validated. Conclusions Machine learning-based prediction models based on routinely collected data generally perform better than traditional statistical models in risk prediction in IBD, though frequently have high risk of bias. Future studies examining these approaches are warranted, with special focus on external validation and clinical applicability.

Effect of a Real-Time Risk Score on 30-day Readmission Reduction in Singapore

2021 ◽  
Vol 12 (02) ◽  
pp. 372-382
Author(s):  
Christine Xia Wu ◽  
Ernest Suresh ◽  
Francis Wei Loong Phng ◽  
Kai Pik Tai ◽  
Janthorn Pakdeethai ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Real Time ◽  
Risk Score ◽  
Patient Specific ◽  
Learning Models ◽  
Medicine Department ◽  
High Risk Patients ◽  
Risk Patients ◽  
Machine Learning Models

Abstract Objective To develop a risk score for the real-time prediction of readmissions for patients using patient specific information captured in electronic medical records (EMR) in Singapore to enable the prospective identification of high-risk patients for enrolment in timely interventions. Methods Machine-learning models were built to estimate the probability of a patient being readmitted within 30 days of discharge. EMR of 25,472 patients discharged from the medicine department at Ng Teng Fong General Hospital between January 2016 and December 2016 were extracted retrospectively for training and internal validation of the models. We developed and implemented a real-time 30-day readmission risk score generation in the EMR system, which enabled the flagging of high-risk patients to care providers in the hospital. Based on the daily high-risk patient list, the various interfaces and flow sheets in the EMR were configured according to the information needs of the various stakeholders such as the inpatient medical, nursing, case management, emergency department, and postdischarge care teams. Results Overall, the machine-learning models achieved good performance with area under the receiver operating characteristic ranging from 0.77 to 0.81. The models were used to proactively identify and attend to patients who are at risk of readmission before an actual readmission occurs. This approach successfully reduced the 30-day readmission rate for patients admitted to the medicine department from 11.7% in 2017 to 10.1% in 2019 (p < 0.01) after risk adjustment. Conclusion Machine-learning models can be deployed in the EMR system to provide real-time forecasts for a more comprehensive outlook in the aspects of decision-making and care provision.

Performance Metrics for the Comparative Analysis of Clinical Risk Prediction Models Employing Machine Learning

2021 ◽  
Author(s):  
Chenxi Huang ◽  
Shu-Xia Li ◽  
César Caraballo ◽  
Frederick A. Masoudi ◽  
John S. Rumsfeld ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Prediction ◽  
Health Care Professionals ◽  
Clinical Decision Making ◽  
Performance Metrics ◽  
Prediction Models ◽  
Learning Models ◽  
Risk Prediction Models ◽  
Clinical Risk ◽  
Machine Learning Models

Background: New methods such as machine learning techniques have been increasingly used to enhance the performance of risk predictions for clinical decision-making. However, commonly reported performance metrics may not be sufficient to capture the advantages of these newly proposed models for their adoption by health care professionals to improve care. Machine learning models often improve risk estimation for certain subpopulations that may be missed by these metrics. Methods and Results: This article addresses the limitations of commonly reported metrics for performance comparison and proposes additional metrics. Our discussions cover metrics related to overall performance, discrimination, calibration, resolution, reclassification, and model implementation. Models for predicting acute kidney injury after percutaneous coronary intervention are used to illustrate the use of these metrics. Conclusions: We demonstrate that commonly reported metrics may not have sufficient sensitivity to identify improvement of machine learning models and propose the use of a comprehensive list of performance metrics for reporting and comparing clinical risk prediction models.

scholarly journals Patient-Level Prediction of Cardio-Cerebrovascular Events in Hypertension Using Nationwide Claims Data (Preprint)

2018 ◽  
Author(s):  
Jaram Park ◽  
Jeong-Whun Kim ◽  
Borim Ryu ◽  
Eunyoung Heo ◽  
Se Young Jung ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
High Risk ◽  
Machine Learning Algorithms ◽  
Risk Level ◽  
Care Providers ◽  
Learning Models ◽  
Health Maintenance ◽  
External Test ◽  
Machine Learning Models

BACKGROUND Prevention and management of chronic diseases are the main goals of national health maintenance programs. Previously widely used screening tools, such as Health Risk Appraisal, are restricted in their achievement this goal due to their limitations, such as static characteristics, accessibility, and generalizability. Hypertension is one of the most important chronic diseases requiring management via the nationwide health maintenance program, and health care providers should inform patients about their risks of a complication caused by hypertension. OBJECTIVE Our goal was to develop and compare machine learning models predicting high-risk vascular diseases for hypertensive patients so that they can manage their blood pressure based on their risk level. METHODS We used a 12-year longitudinal dataset of the nationwide sample cohort, which contains the data of 514,866 patients and allows tracking of patients’ medical history across all health care providers in Korea (N=51,920). To ensure the generalizability of our models, we conducted an external validation using another national sample cohort dataset, comprising one million different patients, published by the National Health Insurance Service. From each dataset, we obtained the data of 74,535 and 59,738 patients with essential hypertension and developed machine learning models for predicting cardiovascular and cerebrovascular events. Six machine learning models were developed and compared for evaluating performances based on validation metrics. RESULTS Machine learning algorithms enabled us to detect high-risk patients based on their medical history. The long short-term memory-based algorithm outperformed in the within test (F1-score=.772, external test F1-score=.613), and the random forest-based algorithm of risk prediction showed better performance over other machine learning algorithms concerning generalization (within test F1-score=.757, external test F1-score=.705). Concerning the number of features, in the within test, the long short-term memory-based algorithms outperformed regardless of the number of features. However, in the external test, the random forest-based algorithm was the best, irrespective of the number of features it encountered. CONCLUSIONS We developed and compared machine learning models predicting high-risk vascular diseases in hypertensive patients so that they may manage their blood pressure based on their risk level. By relying on the prediction model, a government can predict high-risk patients at the nationwide level and establish health care policies in advance.

P1923Deep and machine learning models to improve risk prediction of cardiovascular disease using data extraction from electronic health records

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
I Korsakov ◽  
A Gusev ◽  
T Kuznetsova ◽  
D Gavrilov ◽  
R Novitskiy
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Cardiovascular Disease ◽  
Logistic Regression ◽  
Deep Learning ◽  
Risk Prediction ◽  
Learning Algorithm ◽  
Learning Models ◽  
Electronic Health ◽  
Machine Learning Models

Abstract Abstract Background Advances in precision medicine will require an increasingly individualized prognostic evaluation of patients in order to provide the patient with appropriate therapy. The traditional statistical methods of predictive modeling, such as SCORE, PROCAM, and Framingham, according to the European guidelines for the prevention of cardiovascular disease, not adapted for all patients and require significant human involvement in the selection of predictive variables, transformation and imputation of variables. In ROC-analysis for prediction of significant cardiovascular disease (CVD), the areas under the curve for Framingham: 0.62–0.72, for SCORE: 0.66–0.73 and for PROCAM: 0.60–0.69. To improve it, we apply for approaches to predict a CVD event rely on conventional risk factors by machine learning and deep learning models to 10-year CVD event prediction by using longitudinal electronic health record (EHR). Methods For machine learning, we applied logistic regression (LR) and recurrent neural networks with long short-term memory (LSTM) units as a deep learning algorithm. We extract from longitudinal EHR the following features: demographic, vital signs, diagnoses (ICD-10-cm: I21-I22.9: I61-I63.9) and medication. The problem in this step, that near 80 percent of clinical information in EHR is “unstructured” and contains errors and typos. Missing data are important for the correct training process using by deep learning & machine learning algorithm. The study cohort included patients between the ages of 21 to 75 with a dynamic observation window. In total, we got 31517 individuals in the dataset, but only 3652 individuals have all features or missing features values can be easy to impute. Among these 3652 individuals, 29.4% has a CVD, mean age 49.4 years, 68,2% female. Evaluation We randomly divided the dataset into a training and a test set with an 80/20 split. The LR was implemented with Python Scikit-Learn and the LSTM model was implemented with Keras using Tensorflow as the backend. Results We applied machine learning and deep learning models using the same features as traditional risk scale and longitudinal EHR features for CVD prediction, respectively. Machine learning model (LR) achieved an AUROC of 0.74–0.76 and deep learning (LSTM) 0.75–0.76. By using features from EHR logistic regression and deep learning models improved the AUROC to 0.78–0.79. Conclusion The machine learning models outperformed a traditional clinically-used predictive model for CVD risk prediction (i.e. SCORE, PROCAM, and Framingham equations). This approach was used to create a clinical decision support system (CDSS). It uses both traditional risk scales and models based on neural networks. Especially important is the fact that the system can calculate the risks of cardiovascular disease automatically and recalculate immediately after adding new information to the EHR. The results are delivered to the user's personal account.

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