A Machine Learning Based Discharge Prediction of Cardiovascular Diseases Patients in Intensive Care Units
This paper targets a major challenge of how to effectively allocate medical resources in intensive care units (ICUs). We trained multiple regression models using the Medical Information Mart for Intensive Care III (MIMIC III) database recorded in the period between 2001 and 2012. The training and va...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Healthcare |
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| Online Access: | https://www.mdpi.com/2227-9032/10/6/966 |
| _version_ | 1797487069746954240 |
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| author | Kaouter Karboub Mohamed Tabaa |
| author_facet | Kaouter Karboub Mohamed Tabaa |
| author_sort | Kaouter Karboub |
| collection | DOAJ |
| description | This paper targets a major challenge of how to effectively allocate medical resources in intensive care units (ICUs). We trained multiple regression models using the Medical Information Mart for Intensive Care III (MIMIC III) database recorded in the period between 2001 and 2012. The training and validation dataset included pneumonia, sepsis, congestive heart failure, hypotension, chest pain, coronary artery disease, fever, respiratory failure, acute coronary syndrome, shortness of breath, seizure and transient ischemic attack, and aortic stenosis patients’ recorded data. Then we tested the models on the unseen data of patients diagnosed with coronary artery disease, congestive heart failure or acute coronary syndrome. We included the admission characteristics, clinical prescriptions, physiological measurements, and discharge characteristics of those patients. We assessed the models’ performance using mean residuals and running times as metrics. We ran multiple experiments to study the data partition’s impact on the learning phase. The total running time of our best-evaluated model is 123,450.9 mS. The best model gives an average accuracy of 98%, highlighting the location of discharge, initial diagnosis, location of admission, drug therapy, length of stay and internal transfers as the most influencing patterns to decide a patient’s readiness for discharge. |
| first_indexed | 2024-03-09T23:42:23Z |
| format | Article |
| id | doaj.art-f2baff1bdd05485994993c1470063740 |
| institution | Directory Open Access Journal |
| issn | 2227-9032 |
| language | English |
| last_indexed | 2024-03-09T23:42:23Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Healthcare |
| spelling | doaj.art-f2baff1bdd05485994993c14700637402023-11-23T16:50:45ZengMDPI AGHealthcare2227-90322022-05-0110696610.3390/healthcare10060966A Machine Learning Based Discharge Prediction of Cardiovascular Diseases Patients in Intensive Care UnitsKaouter Karboub0Mohamed Tabaa1FRDISI, Hassan II University Casablanca, Casablanca 20000, MoroccoLPRI, EMSI, Casablanca 23300, MoroccoThis paper targets a major challenge of how to effectively allocate medical resources in intensive care units (ICUs). We trained multiple regression models using the Medical Information Mart for Intensive Care III (MIMIC III) database recorded in the period between 2001 and 2012. The training and validation dataset included pneumonia, sepsis, congestive heart failure, hypotension, chest pain, coronary artery disease, fever, respiratory failure, acute coronary syndrome, shortness of breath, seizure and transient ischemic attack, and aortic stenosis patients’ recorded data. Then we tested the models on the unseen data of patients diagnosed with coronary artery disease, congestive heart failure or acute coronary syndrome. We included the admission characteristics, clinical prescriptions, physiological measurements, and discharge characteristics of those patients. We assessed the models’ performance using mean residuals and running times as metrics. We ran multiple experiments to study the data partition’s impact on the learning phase. The total running time of our best-evaluated model is 123,450.9 mS. The best model gives an average accuracy of 98%, highlighting the location of discharge, initial diagnosis, location of admission, drug therapy, length of stay and internal transfers as the most influencing patterns to decide a patient’s readiness for discharge.https://www.mdpi.com/2227-9032/10/6/966cardiovascular diseasesdischargeElectronic Health Recordsintensive care unitsmachine learning |
| spellingShingle | Kaouter Karboub Mohamed Tabaa A Machine Learning Based Discharge Prediction of Cardiovascular Diseases Patients in Intensive Care Units Healthcare cardiovascular diseases discharge Electronic Health Records intensive care units machine learning |
| title | A Machine Learning Based Discharge Prediction of Cardiovascular Diseases Patients in Intensive Care Units |
| title_full | A Machine Learning Based Discharge Prediction of Cardiovascular Diseases Patients in Intensive Care Units |
| title_fullStr | A Machine Learning Based Discharge Prediction of Cardiovascular Diseases Patients in Intensive Care Units |
| title_full_unstemmed | A Machine Learning Based Discharge Prediction of Cardiovascular Diseases Patients in Intensive Care Units |
| title_short | A Machine Learning Based Discharge Prediction of Cardiovascular Diseases Patients in Intensive Care Units |
| title_sort | machine learning based discharge prediction of cardiovascular diseases patients in intensive care units |
| topic | cardiovascular diseases discharge Electronic Health Records intensive care units machine learning |
| url | https://www.mdpi.com/2227-9032/10/6/966 |
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