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Machine Learning Methods for Predicting Traumatic Injuries Outcomes.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine Learning Methods for Predicting Traumatic Injuries Outcomes./
作者:	Almaghrabi, Fatima Samir A.
面頁冊數:	1 online resource (139 pages)
附註:	Source: Dissertations Abstracts International, Volume: 83-01, Section: A.
Contained By:	Dissertations Abstracts International83-01A.
標題:	Comorbidity. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28431992click for full text (PQDT)
ISBN:	9798738604782

Machine Learning Methods for Predicting Traumatic Injuries Outcomes.
Almaghrabi, Fatima Samir A.

Machine Learning Methods for Predicting Traumatic Injuries Outcomes. - 1 online resource (139 pages)

Source: Dissertations Abstracts International, Volume: 83-01, Section: A.

Thesis (Ph.D.)--The University of Manchester (United Kingdom), 2019.

Includes bibliographical references

This doctoral thesis aims to investigate the application of different machine learning techniques to improve outcome prediction in the trauma field. The thesis covers three topics to fulfil that aim. Firstly, an interpretable machine learning (IML) method based on vital sign variables for predicting trauma patients' outcomes is developed. It contains different methods, including a maximum likelihood evidential reasoning framework, belief rule-based inference methodology based on evidential reasoning, and a non-linear optimisation for parameter tuning. Common ML techniques have been applied to find the most accurate model for trauma outcome prediction. Furthermore, to enhance the prediction of trauma outcome, the prediction accuracy of multiple models based on vital sign features has been evaluated, as vital signs features are commonly collected in trauma centres and units. Secondly, the evidential reasoning (ER) rule is introduced for feature selection to highlight the key features impacting the outcomes. The ER rule finds the optimal weight for each feature that maximises the prediction accuracy during model training. Other feature selection methods have also been implemented, such as random forest and Relief: F. Thirdly, the ER rule has been applied for ensemble learning and has the advantage of adjusting the weight for each classifier in the ensemble learning process. In this thesis, two sets of trauma data are acquired to implement the proposed techniques. The results show that the IML method improves prediction accuracy over other common ML techniques. Similarly, the ER rule achieves good prediction accuracy after ensemble learning. The results highlight the role of the proposed feature selection techniques in finding the key predictors of patients' outcomes.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798738604782Subjects--Topical Terms:

838466
Comorbidity.
Subjects--Index Terms:

Trauma patientsIndex Terms--Genre/Form:

542853
Electronic books.

Machine Learning Methods for Predicting Traumatic Injuries Outcomes.
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520 $a This doctoral thesis aims to investigate the application of different machine learning techniques to improve outcome prediction in the trauma field. The thesis covers three topics to fulfil that aim. Firstly, an interpretable machine learning (IML) method based on vital sign variables for predicting trauma patients' outcomes is developed. It contains different methods, including a maximum likelihood evidential reasoning framework, belief rule-based inference methodology based on evidential reasoning, and a non-linear optimisation for parameter tuning. Common ML techniques have been applied to find the most accurate model for trauma outcome prediction. Furthermore, to enhance the prediction of trauma outcome, the prediction accuracy of multiple models based on vital sign features has been evaluated, as vital signs features are commonly collected in trauma centres and units. Secondly, the evidential reasoning (ER) rule is introduced for feature selection to highlight the key features impacting the outcomes. The ER rule finds the optimal weight for each feature that maximises the prediction accuracy during model training. Other feature selection methods have also been implemented, such as random forest and Relief: F. Thirdly, the ER rule has been applied for ensemble learning and has the advantage of adjusting the weight for each classifier in the ensemble learning process. In this thesis, two sets of trauma data are acquired to implement the proposed techniques. The results show that the IML method improves prediction accuracy over other common ML techniques. Similarly, the ER rule achieves good prediction accuracy after ensemble learning. The results highlight the role of the proposed feature selection techniques in finding the key predictors of patients' outcomes.
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