scholarly journals Preventing non-attendance in outpatient appointments: predictive model development, validation, and clinical assessment

2021 ◽  
Author(s):  
Damià Valero-Bover ◽  
Pedro González ◽  
Gerard Carot-Sans ◽  
Isaac Cano ◽  
Pilar Saura ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Model Development ◽  
Phone Call ◽  
Attendance Rate ◽  
Machine Learning Model ◽  
Tree Models ◽  
Outpatient Appointments

Objective: To develop and validate an algorithm for predicting non-attendance to outpatient appointments. Results: We developed two decision tree models for dermatology and pneumology services (trained with 33,329 and 21,050 appointments, respectively). The prospective validation showed a specificity of 78.34% (95%CI 71.07, 84.51) and a balanced accuracy of 70.45% for dermatology; and 69.83% (95%CI 60.61, 78.00) - 65.53% for pneumology, respectively. When using the algorithm for identifying patients at high risk of non-attendance in the context of a phone-call reminder program, the non-attendance rate decreased 50.61% (P<.001) and 39.33% (P=.048) in the dermatology and pneumology services, respectively. Conclusions: A machine learning model can effectively identify patients at high risk of non-attendance based on information stored in electronic medical records. The use of this model to prioritize phone call reminders to patients at high risk of non-attendance significantly reduced the non-attendance rate.

scholarly journals Preventing non-attendance in outpatient appointments: predictive model development, validation, and clinical assessment

2021 ◽  
Author(s):  
Damià Valero-Bover ◽  
Pedro González ◽  
Gerard Carot-Sans ◽  
Isaac Cano ◽  
Pilar Saura ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Model Development ◽  
Phone Call ◽  
Attendance Rate ◽  
Machine Learning Model ◽  
Tree Models ◽  
Outpatient Appointments

Objective: To develop and validate an algorithm for predicting non-attendance to outpatient appointments. Results: We developed two decision tree models for dermatology and pneumology services (trained with 33,329 and 21,050 appointments, respectively). The prospective validation showed a specificity of 78.34% (95%CI 71.07, 84.51) and a balanced accuracy of 70.45% for dermatology; and 69.83% (95%CI 60.61, 78.00) - 65.53% for pneumology, respectively. When using the algorithm for identifying patients at high risk of non-attendance in the context of a phone-call reminder program, the non-attendance rate decreased 50.61% (P<.001) and 39.33% (P=.048) in the dermatology and pneumology services, respectively. Conclusions: A machine learning model can effectively identify patients at high risk of non-attendance based on information stored in electronic medical records. The use of this model to prioritize phone call reminders to patients at high risk of non-attendance significantly reduced the non-attendance rate.

scholarly journals Preventing Non-attendance in Outpatient Appointments: Predictive Model Development, Validation, and Clinical Assessment

2021 ◽  
Author(s):  
Damià Valero-Bover ◽  
Pedro González ◽  
Gerard Carot-Sans ◽  
Isaac Cano ◽  
Pilar Saura ◽  
...  
Keyword(s):  
High Risk ◽  
Model Development ◽  
Intervention Group ◽  
Resource Planning ◽  
Quality Of Healthcare ◽  
Phone Call ◽  
Outpatient Services ◽  
Healthcare Provision ◽  
Predictive Capacity ◽  
Outpatient Appointments

Abstract Background: Non-attendance to scheduled hospital outpatient appointments may compromise healthcare resource planning, which ultimately reduces the quality of healthcare provision by delaying assessments and increasing waiting lists. We developed a model for predicting non-attendance and assessed the effectiveness of an intervention for reducing non-attendance based on the model.Methods: Candidate models were built using retrospective data from appointments scheduled between January 1, 2015, and November 30, 2018, in the dermatology and pneumology outpatient services of the Hospital Municipal de Badalona (Spain). The predictive capacity of the selected model was then validated prospectively with appointments scheduled between January 7 and February 8, 2019. The effectiveness of selective phone call reminders to patients at high risk of non-attendance according to the model was assessed on all consecutive patients with at least one appointment scheduled between February 25 and April 19, 2019. Patients identified by the model as high risk of non-attendance were randomly assigned to either a control (no intervention) or intervention group, the last receiving phone call reminders one week before the appointment.Results: Models were trained and selected using 33,329 appointments in the dermatology service and 21,050 in the pneumology service. Average results for specificity and balanced accuracy for the prediction of non-attendance were 79.90% and 73.49% for dermatology, and 71.38% and 64.61% for pneumology outpatient services. The prospective validation showed a specificity of 78.34% (95%CI 71.07, 84.51) and balanced accuracy of 70.45% for dermatology; and 69.83% (95%CI 60.61, 78.00) for pneumology, respectively. The effectiveness of the intervention was assessed on 1,311 individuals identified as high risk of non-attendance according to the selected model. Overall, the intervention resulted in a significant reduction in the non-attendance rate to both the dermatology and pneumology services, with a decrease of 50.61% (p<0.001) and 39.33% (p=0.048), respectively.Conclusions: The risk of non-attendance can be adequately estimated using patient information stored in medical records. The patient stratification according to the non-attendance risk allows prioritizing interventions, such as phone call reminders, to effectively reduce non-attendance rates.

Development and validation of simplified machine learning algorithms to predict prognosis of hospitalized COVID-19 patients: a multi-center, retrospective study (Preprint)

2021 ◽  
Author(s):  
Fang He ◽  
John H Page ◽  
Kerry R Weinberg ◽  
Anirban Mishra
Keyword(s):  
Machine Learning ◽  
Respiratory Failure ◽  
High Risk ◽  
Learning Algorithms ◽  
Model Development ◽  
Machine Learning Algorithms ◽  
Validation Dataset ◽  
High Risk Patients ◽  
Risk Patients ◽  
Development And Validation

BACKGROUND The current COVID-19 pandemic is unprecedented; under resource-constrained setting, predictive algorithms can help to stratify disease severity, alerting physicians of high-risk patients, however there are few risk scores derived from a substantially large EHR dataset, using simplified predictors as input. OBJECTIVE To develop and validate simplified machine learning algorithms which predicts COVID-19 adverse outcomes, to evaluate the AUC (area under the receiver operating characteristic curve), sensitivity, specificity and calibration of the algorithms, to derive clinically meaningful thresholds. METHODS We conducted machine learning model development and validation via cohort study using multi-center, patient-level, longitudinal electronic health records (EHR) from Optum® COVID-19 database which provides anonymized, longitudinal EHR from across US. The models were developed based on clinical characteristics to predict 28-day in-hospital mortality, ICU admission, respiratory failure, mechanical ventilator usages at inpatient setting. Data from patients who were admitted prior to Sep 7, 2020, is randomly sampled into development, test and validation datasets; data collected from Sep 7, 2020 through Nov 15, 2020 was reserved as prospective validation dataset. RESULTS Of 3.7M patients in the analysis, a total of 585,867 patients were diagnosed or tested positive for SARS-CoV-2; and 50,703 adult patients were hospitalized with COVID-19 between Feb 1 and Nov 15, 2020. Among the study cohort (N=50,703), there were 6,204 deaths, 9,564 ICU admissions, 6,478 mechanically ventilated or EMCO patients and 25,169 patients developed ARDS or respiratory failure within 28 days since hospital admission. The algorithms demonstrated high accuracy (AUC = 0.89 (0.89 - 0.89) on validation dataset (N=10,752)), consistent prediction through the second wave of pandemic from September to November (AUC = 0.85 (0.85 - 0.86) on post-development validation (N= 14,863)), great clinical relevance and utility. Besides, a comprehensive 386 input covariates from baseline and at admission was included in the analysis; the end-to-end pipeline automates feature selection and model development process, producing 10 key predictors as input such as age, blood urea nitrogen, oxygen saturation, which are both commonly measured and concordant with recognized risk factors for COVID-19. CONCLUSIONS The systematic approach and rigorous validations demonstrate consistent model performance to predict even beyond the time period of data collection, with satisfactory discriminatory power and great clinical utility. Overall, the study offers an accurate, validated and reliable prediction model based on only ten clinical features as a prognostic tool to stratifying COVID-19 patients into intermediate, high and very high-risk groups. This simple predictive tool could be shared with a wider healthcare community, to enable service as an early warning system to alert physicians of possible high-risk patients, or as a resource triaging tool to optimize healthcare resources. CLINICALTRIAL N/A

scholarly journals Identification of newborns at risk for autism using electronic medical records and machine learning

2019 ◽  
Author(s):  
Rayees Rahman ◽  
Arad Kodesh ◽  
Stephen Z Levine ◽  
Sven Sandin ◽  
Abraham Reichenberg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Positive Predictive Value ◽  
Electronic Medical Records ◽  
Predictive Value ◽  
False Positive ◽  
Medical Records ◽  
False Positive Rate ◽  
Autism Spectrum ◽  
Positive Rate

AbstractImportanceCurrent approaches for early identification of individuals at high risk for autism spectrum disorder (ASD) in the general population are limited, where most ASD patients are not identified until after the age of 4. This is despite substantial evidence suggesting that early diagnosis and intervention improves developmental course and outcome.ObjectiveDevelop a machine learning (ML) method predicting the diagnosis of ASD in offspring in a general population sample, using parental electronic medical records (EMR) available before childbirthDesignPrognostic study of EMR data within a single Israeli health maintenance organization, for the parents of 1,397 ASD children (ICD-9/10), and 94,741 non-ASD children born between January 1st, 1997 through December 31st, 2008. The complete EMR record of the parents was used to develop various ML models to predict the risk of having a child with ASD.Main outcomes and measuresRoutinely available parental sociodemographic information, medical histories and prescribed medications data until offspring’s birth were used to generate features to train various machine learning algorithms, including multivariate logistic regression, artificial neural networks, and random forest. Prediction performance was evaluated with 10-fold cross validation, by computing C statistics, sensitivity, specificity, accuracy, false positive rate, and precision (positive predictive value, PPV).ResultsAll ML models tested had similar performance, achieving an average C statistics of 0.70, sensitivity of 28.63%, specificity of 98.62%, accuracy of 96.05%, false positive rate of 1.37%, and positive predictive value of 45.85% for predicting ASD in this dataset.Conclusion and relevanceML algorithms combined with EMR capture early life ASD risk. Such approaches may be able to enhance the ability for accurate and efficient early detection of ASD in large populations of children.Key pointsQuestionCan autism risk in children be predicted using the pre-birth electronic medical record (EMR) of the parents?FindingsIn this population-based study that included 1,397 children with autism spectrum disorder (ASD) and 94,741 non-ASD children, we developed a machine learning classifier for predicting the likelihood of childhood diagnosis of ASD with an average C statistic of 0.70, sensitivity of 28.63%, specificity of 98.62%, accuracy of 96.05%, false positive rate of 1.37%, and positive predictive value of 45.85%.MeaningThe results presented serve as a proof-of-principle of the potential utility of EMR for the identification of a large proportion of future children at a high-risk of ASD.

scholarly journals An Application of Machine Learning to Etiological Diagnosis of Secondary Hypertension: Retrospective Study Using Electronic Medical Records (Preprint)

2020 ◽  
Author(s):  
Xiaolin Diao ◽  
Yanni Huo ◽  
Zhanzheng Yan ◽  
Haibin Wang ◽  
Jing Yuan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Aortic Stenosis ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Thyroid Dysfunction ◽  
Outcome Prediction ◽  
Prediction Models ◽  
Secondary Hypertension ◽  
Validation Dataset ◽  
Composite Outcome

BACKGROUND Secondary hypertension is a kind of hypertension with definite etiology and may be cured. Patients with suspected secondary hypertension can benefit from detection and treatment in time and, conversely, will have higher risk of morbidity and mortality than patients with primary hypertension. OBJECTIVE The aim of this study was to develop and validate machine learning (ML) prediction models of common etiologies in patients with suspected secondary hypertension. METHODS The analyzed dataset was retrospectively extracted from electronic medical records (EMRs) of patients discharged from Fuwai hospital between January 1, 2016 and June 30, 2019. A total of 7532 unique patients were included and divided into two datasets by time: 6302 patients in 2016-2018 as training dataset for model building and 1230 patients in 2019 as validation dataset for further evaluation. Extreme Gradient Boosting (XGBoost) was adopted to develop five prediction models of four etiologies of secondary hypertension and occurrence of any of them, including renovascular hypertension (RVH), primary aldosteronism (PA), thyroid dysfunction and aortic stenosis. Both univariate logistic analysis and Gini impure method were used for feature selection, while grid search and 10-fold cross-validation were used to select the optimal hyperparameters for each model. RESULTS Validation of the composite outcome prediction model showed good performance with an area under the receiver-operating characteristic curve (AUC) of 0.924 in the validation dataset, while the four prediction models of RVH, PA, thyroid dysfunction and aortic stenosis achieved AUC of 0.938, 0.965, 0.959, 0.946, respectively, in the validation dataset. 79 clinical indicators were identified in all and finally used in our prediction models. The result of subgroup analysis on the composite outcome prediction model demonstrated high discrimination with AUCs all higher than 0.890 among all age groups of adults. CONCLUSIONS The ML prediction models in this study showed good performance in detecting four etiologies of patients with suspected secondary hypertension, thus they may potentially facilitate clinical diagnosis decision making of secondary hypertension in an intelligent way. CLINICALTRIAL

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