Machine Learning and Medical Data: Predicting ICU Mortality and Re-admission Risks
Intensive care units (ICUs) are divisions where critically ill patients are treated by medical experts. The unmet and vital need for automated clinical decision-making mechanisms is critical to maneuvering the large influx of patients. This became more apparent after the COVID-19 pandemic. Existing...
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| Format: | Article |
| Language: | English |
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Çanakkale Onsekiz Mart University
2024-12-01
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| Series: | Journal of Advanced Research in Natural and Applied Sciences |
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| Online Access: | https://dergipark.org.tr/en/download/article-file/4148361 |
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| author | Runia Roy Ulya Bayram |
| author_facet | Runia Roy Ulya Bayram |
| author_sort | Runia Roy |
| collection | DOAJ |
| description | Intensive care units (ICUs) are divisions where critically ill patients are treated by medical experts. The unmet and vital need for automated clinical decision-making mechanisms is critical to maneuvering the large influx of patients. This became more apparent after the COVID-19 pandemic. Existing studies focus on determining the probability of patients dying in the ICUs and prioritizing patients in dire need. Only a few studies have calculated the patient's probability of returning to the ICUs after discharge. These studies reduce the problem into a binary task of predicting mortality or re-admission only. However, this is unrealistic since both outcomes are highly possible for each patient. In this interdisciplinary study, two main contributions are proposed for the automated clinical decision-making state-of-the-art: (1) using the real-life data collected from thousands of ICU patients by healthcare professionals, three possibilities (recovery, mortality, and returning to the intensive care unit within 30 days) are predicted for patients in intensive care instead of just one possibility. (2) A novel feature extraction approach is proposed by the biomedical expert in our team. Four machine learning algorithms are applied to the finalized feature set to understand the difference between the binary and the multi-class classification problems. Obtained results reach 78% success, proving the possibility of developing better clinical decision-making mechanisms for ICUs. |
| format | Article |
| id | doaj-art-a30fe01cff15475daab13f74be6d4d66 |
| institution | Kabale University |
| issn | 2757-5195 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Çanakkale Onsekiz Mart University |
| record_format | Article |
| series | Journal of Advanced Research in Natural and Applied Sciences |
| spelling | doaj-art-a30fe01cff15475daab13f74be6d4d662025-02-05T18:13:02ZengÇanakkale Onsekiz Mart UniversityJournal of Advanced Research in Natural and Applied Sciences2757-51952024-12-0110481983210.28979/jarnas.1533962453Machine Learning and Medical Data: Predicting ICU Mortality and Re-admission RisksRunia Roy0https://orcid.org/0000-0002-4013-7939Ulya Bayram1https://orcid.org/0000-0002-8150-4053Neural Stem Cell Instituteçanakkale onsekiz mart üniversitesiIntensive care units (ICUs) are divisions where critically ill patients are treated by medical experts. The unmet and vital need for automated clinical decision-making mechanisms is critical to maneuvering the large influx of patients. This became more apparent after the COVID-19 pandemic. Existing studies focus on determining the probability of patients dying in the ICUs and prioritizing patients in dire need. Only a few studies have calculated the patient's probability of returning to the ICUs after discharge. These studies reduce the problem into a binary task of predicting mortality or re-admission only. However, this is unrealistic since both outcomes are highly possible for each patient. In this interdisciplinary study, two main contributions are proposed for the automated clinical decision-making state-of-the-art: (1) using the real-life data collected from thousands of ICU patients by healthcare professionals, three possibilities (recovery, mortality, and returning to the intensive care unit within 30 days) are predicted for patients in intensive care instead of just one possibility. (2) A novel feature extraction approach is proposed by the biomedical expert in our team. Four machine learning algorithms are applied to the finalized feature set to understand the difference between the binary and the multi-class classification problems. Obtained results reach 78% success, proving the possibility of developing better clinical decision-making mechanisms for ICUs.https://dergipark.org.tr/en/download/article-file/4148361clinical decision makingmachine learningintensive care unitsmortality predictionre-admission prediction |
| spellingShingle | Runia Roy Ulya Bayram Machine Learning and Medical Data: Predicting ICU Mortality and Re-admission Risks Journal of Advanced Research in Natural and Applied Sciences clinical decision making machine learning intensive care units mortality prediction re-admission prediction |
| title | Machine Learning and Medical Data: Predicting ICU Mortality and Re-admission Risks |
| title_full | Machine Learning and Medical Data: Predicting ICU Mortality and Re-admission Risks |
| title_fullStr | Machine Learning and Medical Data: Predicting ICU Mortality and Re-admission Risks |
| title_full_unstemmed | Machine Learning and Medical Data: Predicting ICU Mortality and Re-admission Risks |
| title_short | Machine Learning and Medical Data: Predicting ICU Mortality and Re-admission Risks |
| title_sort | machine learning and medical data predicting icu mortality and re admission risks |
| topic | clinical decision making machine learning intensive care units mortality prediction re-admission prediction |
| url | https://dergipark.org.tr/en/download/article-file/4148361 |
| work_keys_str_mv | AT runiaroy machinelearningandmedicaldatapredictingicumortalityandreadmissionrisks AT ulyabayram machinelearningandmedicaldatapredictingicumortalityandreadmissionrisks |