Empowering decision support in healthcare with AI
Machine Learning has increasingly become an important tool for existing and new software systems in the healthcare industry [1]. Here, we showcase an End-to-End Critical Care Management System (CCMS) that focuses on providing practitioners a platform to predict the probability of death of the patie...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/144799 |
| _version_ | 1826122389030699008 |
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| author | Koh, Angie Kai Lin |
| author2 | Yu Han |
| author_facet | Yu Han Koh, Angie Kai Lin |
| author_sort | Koh, Angie Kai Lin |
| collection | NTU |
| description | Machine Learning has increasingly become an important tool for existing and new software systems in the healthcare industry [1]. Here, we showcase an End-to-End Critical Care Management System (CCMS) that focuses on providing practitioners a platform to predict the probability of death of the patient in ICU based on observed mortality rate from historical patient data using a novel federated learning boosting technique called LoAdaBoost. The prediction of each patient’s mortality rate is learned from the representation space of patient information and the prescriptions taken by them. During the process of training the model, LoAdaBoost would be incorporated into traditional federated learning technique to increase the overall model’s efficiency and performance. The critical care data are further split into IID and non-IID data distribution to investigate and observe the performance level and eventually prove that using the LoAdaBoost method could achieve higher performance with lower computational complexity than the classical FedAvg and later be chosen as the model to train CCMS data information. |
| first_indexed | 2024-10-01T05:47:42Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/144799 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:47:42Z |
| publishDate | 2020 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1447992020-11-25T02:15:03Z Empowering decision support in healthcare with AI Koh, Angie Kai Lin Yu Han School of Computer Science and Engineering han.yu@ntu.edu.sg Engineering::Computer science and engineering::Software::Software engineering Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Machine Learning has increasingly become an important tool for existing and new software systems in the healthcare industry [1]. Here, we showcase an End-to-End Critical Care Management System (CCMS) that focuses on providing practitioners a platform to predict the probability of death of the patient in ICU based on observed mortality rate from historical patient data using a novel federated learning boosting technique called LoAdaBoost. The prediction of each patient’s mortality rate is learned from the representation space of patient information and the prescriptions taken by them. During the process of training the model, LoAdaBoost would be incorporated into traditional federated learning technique to increase the overall model’s efficiency and performance. The critical care data are further split into IID and non-IID data distribution to investigate and observe the performance level and eventually prove that using the LoAdaBoost method could achieve higher performance with lower computational complexity than the classical FedAvg and later be chosen as the model to train CCMS data information. Bachelor of Engineering (Computer Science) 2020-11-25T02:15:03Z 2020-11-25T02:15:03Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/144799 en SCSE19-0669 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Software::Software engineering Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Koh, Angie Kai Lin Empowering decision support in healthcare with AI |
| title | Empowering decision support in healthcare with AI |
| title_full | Empowering decision support in healthcare with AI |
| title_fullStr | Empowering decision support in healthcare with AI |
| title_full_unstemmed | Empowering decision support in healthcare with AI |
| title_short | Empowering decision support in healthcare with AI |
| title_sort | empowering decision support in healthcare with ai |
| topic | Engineering::Computer science and engineering::Software::Software engineering Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence |
| url | https://hdl.handle.net/10356/144799 |
| work_keys_str_mv | AT kohangiekailin empoweringdecisionsupportinhealthcarewithai |