scholarly journals Machine Learning–Based Hospital Discharge Prediction for Patients With Cardiovascular Diseases: Development and Usability Study

10.2196/32662 ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. e32662
Author(s):  
Imjin Ahn ◽  
Hansle Gwon ◽  
Heejun Kang ◽  
Yunha Kim ◽  
Hyeram Seo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Cardiovascular Diseases ◽  
Predictive Model ◽  
Treatment Time ◽  
Optimal Treatment ◽  
Feature Reduction ◽  
Medical Services ◽  
Bed Management

Background Effective resource management in hospitals can improve the quality of medical services by reducing labor-intensive burdens on staff, decreasing inpatient waiting time, and securing the optimal treatment time. The use of hospital processes requires effective bed management; a stay in the hospital that is longer than the optimal treatment time hinders bed management. Therefore, predicting a patient’s hospitalization period may support the making of judicious decisions regarding bed management. Objective First, this study aims to develop a machine learning (ML)–based predictive model for predicting the discharge probability of inpatients with cardiovascular diseases (CVDs). Second, we aim to assess the outcome of the predictive model and explain the primary risk factors of inpatients for patient-specific care. Finally, we aim to evaluate whether our ML-based predictive model helps manage bed scheduling efficiently and detects long-term inpatients in advance to improve the use of hospital processes and enhance the quality of medical services. Methods We set up the cohort criteria and extracted the data from CardioNet, a manually curated database that specializes in CVDs. We processed the data to create a suitable data set by reindexing the date-index, integrating the present features with past features from the previous 3 years, and imputing missing values. Subsequently, we trained the ML-based predictive models and evaluated them to find an elaborate model. Finally, we predicted the discharge probability within 3 days and explained the outcomes of the model by identifying, quantifying, and visualizing its features. Results We experimented with 5 ML-based models using 5 cross-validations. Extreme gradient boosting, which was selected as the final model, accomplished an average area under the receiver operating characteristic curve score that was 0.865 higher than that of the other models (ie, logistic regression, random forest, support vector machine, and multilayer perceptron). Furthermore, we performed feature reduction, represented the feature importance, and assessed prediction outcomes. One of the outcomes, the individual explainer, provides a discharge score during hospitalization and a daily feature influence score to the medical team and patients. Finally, we visualized simulated bed management to use the outcomes. Conclusions In this study, we propose an individual explainer based on an ML-based predictive model, which provides the discharge probability and relative contributions of individual features. Our model can assist medical teams and patients in identifying individual and common risk factors in CVDs and can support hospital administrators in improving the management of hospital beds and other resources.

scholarly journals Machine Learning–Based Hospital Discharge Prediction for Patients With Cardiovascular Diseases: Development and Usability Study (Preprint)

2021 ◽  
Author(s):  
Imjin Ahn ◽  
Hansle Gwon ◽  
Heejun Kang ◽  
Yunha Kim ◽  
Hyeram Seo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Cardiovascular Diseases ◽  
Predictive Model ◽  
Treatment Time ◽  
Optimal Treatment ◽  
Feature Reduction ◽  
Medical Services ◽  
Bed Management

BACKGROUND Effective resource management in hospitals can improve the quality of medical services by reducing labor-intensive burdens on staff, decreasing inpatient waiting time, and securing the optimal treatment time. The use of hospital processes requires effective bed management; a stay in the hospital that is longer than the optimal treatment time hinders bed management. Therefore, predicting a patient’s hospitalization period may support the making of judicious decisions regarding bed management. OBJECTIVE First, this study aims to develop a machine learning (ML)–based predictive model for predicting the discharge probability of inpatients with cardiovascular diseases (CVDs). Second, we aim to assess the outcome of the predictive model and explain the primary risk factors of inpatients for patient-specific care. Finally, we aim to evaluate whether our ML-based predictive model helps manage bed scheduling efficiently and detects long-term inpatients in advance to improve the use of hospital processes and enhance the quality of medical services. METHODS We set up the cohort criteria and extracted the data from CardioNet, a manually curated database that specializes in CVDs. We processed the data to create a suitable data set by reindexing the date-index, integrating the present features with past features from the previous 3 years, and imputing missing values. Subsequently, we trained the ML-based predictive models and evaluated them to find an elaborate model. Finally, we predicted the discharge probability within 3 days and explained the outcomes of the model by identifying, quantifying, and visualizing its features. RESULTS We experimented with 5 ML-based models using 5 cross-validations. Extreme gradient boosting, which was selected as the final model, accomplished an average area under the receiver operating characteristic curve score that was 0.865 higher than that of the other models (ie, logistic regression, random forest, support vector machine, and multilayer perceptron). Furthermore, we performed feature reduction, represented the feature importance, and assessed prediction outcomes. One of the outcomes, the individual explainer, provides a discharge score during hospitalization and a daily feature influence score to the medical team and patients. Finally, we visualized simulated bed management to use the outcomes. CONCLUSIONS In this study, we propose an individual explainer based on an ML-based predictive model, which provides the discharge probability and relative contributions of individual features. Our model can assist medical teams and patients in identifying individual and common risk factors in CVDs and can support hospital administrators in improving the management of hospital beds and other resources. CLINICALTRIAL

scholarly journals Fatores de risco cardiovasculares e qualidade de vida em universitários

2018 ◽  
Vol 12 (10) ◽  
pp. 2743
Author(s):  
Ankilma Do Nascimento Andrade ◽  
Maria Enoi Gadelha Vale ◽  
Marta Ligia Vieira Melo ◽  
Ubiraídys De Andrade Isidório ◽  
Milena Nunes Alves de Sousa ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Risk Factors ◽  
Cardiovascular Diseases ◽  
University Students ◽  
Sleep Quality ◽  
Poor Sleep ◽  
Calidad De Vida ◽  
Significant Difference ◽  
Factores De Riesgo

RESUMO Objetivo: avaliar a associação dos fatores de risco para as doenças cardiovasculares e qualidade de vida em universitários que trabalham. Método: trata-se de um estudo quantitativo, transversal e analítico, com 40 discentes. Analisaram-se os dados no SPSS 21. Resultados: 55% da amostra possuem qualidade de sono ruim e que 15% distúrbio do sono. Quanto ao nível de atividade física, 65% dos que trabalham foram classificados com sedentários. Com relação aos domínios de “dor”, foi observada uma diferença estatisticamente significativa (p = 0,01) apontando que os universitários que trabalham apresentam mais dor. Conclusão: mesmo em uma população de adultos jovens, observou-se o estado de vulnerabilidade para o desenvolvimento de DCV, sendo preocupantes, entre os universitários que trabalham, o nível da qualidade de sono e o sedentarismo observados, que podem comprometer a saúde e a qualidade de vida dessa população. Descritores: Doenças Cardiovasculares; Estudantes; Fatores de Risco; Qualidade de vida; Doença Crônica; Sexo,ABSTRACT Objective: to evaluate the association of risk factors for cardiovascular diseases and quality of life among working university students. Method: this is a quantitative, transversal and analytical study with 40 students. Data were analyzed in SPSS 21. Results: 55% of the sample had poor sleep quality and 15% had sleep disturbance. Regarding the level of physical activity, 65% of those who work were classified as sedentary. Regarding the "pain" domains, a statistically significant difference (p = 0.01) was observed, indicating that the working university students presented more pain. Conclusion: Even in a population of young adults, the vulnerability to the development of CVD was observed, and the level of sleep quality and sedentary lifestyle observed among the working university students, which may compromise health and quality of life of this population. Descriptors: Cardiovascular Diseases; Students; Risk Factors; Quality of Life; Chronic Disease; Sex.RESUMEN Objetivo: evaluar la asociación de los factores de riesgo para las enfermedades cardiovasculares y la calidad de vida en los universitarios que trabajan. Método: se trata de un estudio cuantitativo, transversal y analítico, con 40 discentes. Se analizaron los datos en el SPSS 21. Resultados: el 55% de la muestra tiene una mala calidad de sueño y el 15% de los trastornos del sueño. En cuanto al nivel de actividad física, el 65% de los que trabajan fueron clasificados como sedentarios. Con respecto a los dominios de "dolor", se observó una diferencia estadísticamente significativa (p = 0,01) apuntando que los universitarios que trabajan presentan más dolor. Conclusión: incluso en una población de adultos jóvenes, se observó el estado de vulnerabilidad para el desarrollo de ECV, siendo preocupantes, entre los universitarios que trabajan, el nivel de la calidad de sueño y el sedentarismo observados, que pueden comprometer la salud y la calidad de vida de esa población. Descritores: Enfermedades Cardiovasculares; Estudiantes; Factores de Riesgo; Calidad de Vida; Doença Crónica; Sexo.

Cardiovascular diseases prediction by integrated risk factors assessment by means of machine learning

Vrach ◽  
2020 ◽  
Vol 31 (5) ◽  
pp. 41-46
Author(s):  
D. Gavrilov ◽  
L. Serova ◽  
I. Korsakov ◽  
A. Gusev ◽  
R. Novitsky ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Cardiovascular Diseases ◽  
Integrated Risk

scholarly journals Analysis of Risk Factors and Symptoms of Burnout Syndrome in Colombian School Teachers under Statutes 2277 and 1278 Using Machine Learning Interpretation

2020 ◽  
Vol 9 (3) ◽  
pp. 30
Author(s):  
Hugo F. Posada-Quintero ◽  
Paula N. Molano-Vergara ◽  
Ronald M. Parra-Hernández ◽  
Jorge I. Posada-Quintero
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Burnout Syndrome ◽  
Support Vector ◽  
Ministry Of Education ◽  
School Teachers ◽  
Overtime Work ◽  
Promotion System ◽  
Relevant Risk Factor

In 2002, the Colombian ministry of education released statute 1278, for teaching professionalization, superseding statute 2277 of 1977. Although statute 1278 was intended to increase the quality of the education service and teachers’ remuneration, there is evidence that the abundant evaluations and hindered promotion system introduced by statute 1278 resulted in an impairment of the quality of life of the teachers, and a higher incidence of burnout syndrome. We used two techniques for machine learning interpretability, SHapley Additive exPlanation summary plots and predictor importance, to interpret support vector machine and decision tree machine learning models, respectively, to better understand the differences on risk factors and symptoms of burnout syndrome in school teachers under statutes 2277 and 1278. We have surveyed 54 school teachers between August and October 2018, 17 under statute 2277, and 37 under statute 1278. Among the risk factors and symptoms of burnout syndrome considered in this study, we found that the satisfaction with earnt income was the most relevant risk factor, followed by the overtime work and the perceived severity of the sanctions on lower performance. The most relevant symptoms of burnout were fatigue at the end of the day, and frequent headaches. This methodology can be potentially used in other contexts and social groups, allowing institutional authorities and policy makers to allocate resources to specific issues affecting a particular group of workers.

scholarly journals A machine learning approach to predict early outcomes after pituitary adenoma surgery

2018 ◽  
Vol 45 (5) ◽  
pp. E8 ◽  
Author(s):  
Todd C. Hollon ◽  
Adish Parikh ◽  
Balaji Pandian ◽  
Jamaal Tarpeh ◽  
Daniel A. Orringer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Pituitary Adenoma ◽  
Predictive Model ◽  
Odds Ratio ◽  
Postoperative Outcomes ◽  
Learning Approach ◽  
Ratio Analysis ◽  
Machine Learning Approach ◽  
The Mean

OBJECTIVEPituitary adenomas occur in a heterogeneous patient population with diverse perioperative risk factors, endocrinopathies, and other tumor-related comorbidities. This heterogeneity makes predicting postoperative outcomes challenging when using traditional scoring systems. Modern machine learning algorithms can automatically identify the most predictive risk factors and learn complex risk-factor interactions using training data to build a robust predictive model that can generalize to new patient cohorts. The authors sought to build a predictive model using supervised machine learning to accurately predict early outcomes of pituitary adenoma surgery.METHODSA retrospective cohort of 400 consecutive pituitary adenoma patients was used. Patient variables/predictive features were limited to common patient characteristics to improve model implementation. Univariate and multivariate odds ratio analysis was performed to identify individual risk factors for common postoperative complications and to compare risk factors with model predictors. The study population was split into 300 training/validation patients and 100 testing patients to train and evaluate four machine learning models using binary classification accuracy for predicting early outcomes.RESULTSThe study included a total of 400 patients. The mean ± SD patient age was 53.9 ± 16.3 years, 59.8% of patients had nonfunctioning adenomas and 84.7% had macroadenomas, and the mean body mass index (BMI) was 32.6 ± 7.8 (58.0% obesity rate). Multivariate odds ratio analysis demonstrated that age < 40 years was associated with a 2.86 greater odds of postoperative diabetes insipidus and that nonobese patients (BMI < 30) were 2.2 times more likely to develop postoperative hyponatremia. Using broad criteria for a poor early postoperative outcome—major medical and early surgical complications, extended length of stay, emergency department admission, inpatient readmission, and death—31.0% of patients met criteria for a poor early outcome. After model training, a logistic regression model with elastic net (LR-EN) regularization best predicted early postoperative outcomes of pituitary adenoma surgery on the 100-patient testing set—sensitivity 68.0%, specificity 93.3%, overall accuracy 87.0%. The receiver operating characteristic and precision-recall curves for the LR-EN model had areas under the curve of 82.7 and 69.5, respectively. The most important predictive variables were lowest perioperative sodium, age, BMI, highest perioperative sodium, and Cushing’s disease.CONCLUSIONSEarly postoperative outcomes of pituitary adenoma surgery can be predicted with 87% accuracy using a machine learning approach. These results provide insight into how predictive modeling using machine learning can be used to improve the perioperative management of pituitary adenoma patients.

scholarly journals Structuring clinical text with AI: old vs. new natural language processing techniques evaluated on eight common cardiovascular diseases

2021 ◽  
Author(s):  
Xianghao Zhan ◽  
Marie Humbert-Droz ◽  
Pritam Mukherjee ◽  
Olivier Gevaert
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Cardiovascular Diseases ◽  
Free Text ◽  
Diagnostic Code ◽  
Word Embeddings ◽  
Clinical Notes ◽  
Diagnostic Codes ◽  
Mimic Iii

AbstractMining the structured data in electronic health records(EHRs) enables many clinical applications while the information in free-text clinical notes often remains untapped. Free-text notes are unstructured data harder to use in machine learning while structured diagnostic codes can be missing or even erroneous. To improve the quality of diagnostic codes, this work extracts structured diagnostic codes from the unstructured notes concerning cardiovascular diseases. Five old and new word embeddings were used to vectorize over 5 million progress notes from Stanford EHR and logistic regression was used to predict eight ICD-10 codes of common cardiovascular diseases. The models were interpreted by the important words in predictions and analyses of false positive cases. Trained on Stanford notes, the model transferability was tested in the prediction of corresponding ICD-9 codes of the MIMIC-III discharge summaries. The word embeddings and logistic regression showed good performance in the diagnostic code extraction with TF-IDF as the best word embedding model showing AU-ROC ranging from 0.9499 to 0.9915 and AUPRC ranging from 0.2956 to 0.8072. The models also showed transferability when tested on MIMIC-III data set with AUROC ranging from 0.7952 to 0.9790 and AUPRC ranging from 0.2353 to 0.8084. Model interpretability was showed by the important words with clinical meanings matching each disease. This study shows the feasibility to accurately extract structured diagnostic codes, impute missing codes and correct erroneous codes from free-text clinical notes with interpretable models for clinicians, which helps improve the data quality of diagnostic codes for information retrieval and downstream machine-learning applications.

scholarly journals Comparison of Routine Risk Factors for Atherosclerosis in Arab and Lor Patients

2022 ◽  
Vol In Press (In Press) ◽  
Author(s):  
Ahmadreza Assareh ◽  
Bijan Helli ◽  
Hoda Mombeini ◽  
Marzie Zilaiee ◽  
Mahshad Shokuhi Nasab ◽  
...  
Keyword(s):  
Risk Factors ◽  
Heart Disease ◽  
Cardiovascular Diseases ◽  
Data Collection ◽  
Analytical Study ◽  
Food Frequency ◽  
Significant Difference ◽  
Sugar Levels ◽  
And Control

Background: The awareness of the risk factors of atherosclerosis and attempts to correct and control them can effectively reduce the risk of complications. Objectives: This study was performed to evaluate the risk factors for routine atherosclerosis in patients with symptoms of heart disease in the Arab race, compared to those of Lor patients. Methods: This descriptive-analytical study was conducted on 200 patients with symptoms of heart disease. A food frequency questionnaire was used for data collection. Results: Out of 200 patients, 101 (51.5%) and 99 (48.5%) participants were Lor and Arab, respectively. Significant differences were observed between the two races for cholesterol and fasting blood sugar levels (P < 0.05). Additionally, no significant difference was observed between different quarters of following dietary patterns and lipid-glucose factors (P > 0.05). Conclusions: Risk factors for cardiovascular diseases, such as atherosclerosis, are multifactorial. Various factors can effectively affect the prevalence of this disease in each region, which necessitates the identification of risk factors to take steps to correct risk factors and improve the quality of patients’ life.

scholarly journals Machine learning models for prediction of co-occurrence of diabetes and cardiovascular diseases: a retrospective cohort study

2022 ◽  
Author(s):  
Ahmad Shaker Abdalrada ◽  
Jemal Abawajy ◽  
Tahsien Al-Quraishi ◽  
Sheikh Mohammed Shariful Islam
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
At Risk ◽  
Cardiovascular Diseases ◽  
Performance Metrics ◽  
Prediction Models ◽  
Screening Program ◽  
Multivariate Adaptive Regression Splines ◽  
Healthcare Burden ◽  
Common Risk Factors

Abstract Background Diabetic mellitus (DM) and cardiovascular diseases (CVD) cause significant healthcare burden globally and often co-exists. Current approaches often fail to identify many people with co-occurrence of DM and CVD, leading to delay in healthcare seeking, increased complications and morbidity. In this paper, we aimed to develop and evaluate a two-stage machine learning (ML) model to predict the co-occurrence of DM and CVD. Methods We used the diabetes complications screening research initiative (DiScRi) dataset containing >200 variables from >2000 participants. In the first stage, we used two ML models (logistic regression and Evimp functions) implemented in multivariate adaptive regression splines model to infer the significant common risk factors for DM and CVD and applied the correlation matrix to reduce redundancy. In the second stage, we used classification and regression algorithm to develop our model. We evaluated the prediction models using prediction accuracy, sensitivity and specificity as performance metrics. Results Common risk factors for DM and CVD co-occurrence was family history of the diseases, gender, deep breathing heart rate change, lying to standing blood pressure change, HbA1c, HDL and TC\HDL ratio. The predictive model showed that the participants with HbA1c >6.45 and TC\HDL ratio > 5.5 were at risk of developing both diseases (97.9% probability). In contrast, participants with HbA1c >6.45 and TC\HDL ratio ≤ 5.5 were more likely to have only DM (84.5% probability) and those with HbA1c ≤5.45 and HDL >1.45 were likely to be healthy (82.4%. probability). Further, participants with HbA1c ≤5.45 and HDL <1.45 were at risk of only CVD (100% probability). The predictive accuracy of the ML model to detect co-occurrence of DM and CVD is 94.09%, sensitivity 93.5%, and specificity 95.8%. Conclusions Our ML model can significantly predict with high accuracy the co-occurrence of DM and CVD in people attending a screening program. This might help in early detection of patients with DM and CVD who could benefit from preventive treatment and reduce future healthcare burden.

scholarly journals Relationship between Selected Risk Factors for Cardiovascular Diseases and the Quality of Life

2019 ◽  
Vol 07 (03) ◽  
pp. 106-119
Author(s):  
Rita Nkechi Ativie ◽  
Uzoma Emmanuella Onah
Keyword(s):  
Quality Of Life ◽  
Risk Factors ◽  
Cardiovascular Diseases
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