Machine Learning in Healthcare and Business Analytics - Applications in Gait Analysis, Business Process Management, and Customer Service ; Machinelles Lernen für Gesundheitsweisen und Geschäftsanalytik - Anwendungen in der Ganganalyse, dem Prozessmanagement und dem Kundendienst
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Media type:
Doctoral Thesis;
Electronic Thesis;
E-Book
Title:
Machine Learning in Healthcare and Business Analytics - Applications in Gait Analysis, Business Process Management, and Customer Service ; Machinelles Lernen für Gesundheitsweisen und Geschäftsanalytik - Anwendungen in der Ganganalyse, dem Prozessmanagement und dem Kundendienst
Contributor:
Nguyen, An
[Author]
Published:
OPUS FAU - Online publication system of Friedrich-Alexander-Universität Erlangen-Nürnberg, 2023
Language:
English
Origination:
Footnote:
Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
Description:
Machine learning (ML) has advanced at impressive speed. The rapid progress can be explained by the increasing availability of data and access to low-cost and high-performance computational resources. These circumstances have led to the adaption of ML in almost all scientific domains. Two prominent application fields of ML are healthcare and business. The full potential of ML to drive impact in specific domains is an ongoing research endeavor. Therefore, this cumulative dissertation aims at utilizing ML to positively impact the fields of gait analysis in Parkinson’s Disease (PD), predictive business process monitoring (PBPM), and customer service. Each field is addressed by one contributed publication focusing on a specific objective. The first objective is to define gait clusters to extract distinct gait parameters and validate their clinical relevance. This objective is addressed in our publication [P1], where we contribute novel gait clustering methods and validate their clinical relevance. We developed unsupervised methods for defining and isolating distinct gait clusters based on data collected from PD patients performing a standardized gait test. According to our study, the detailed analysis of gait parameters in distinct gait clusters can provide clinically relevant information about gait and balance impairments in patients with PD. Our study demonstrated that the defined gait clusters were more accurate at distinguishing impaired from unimpaired gait and balance in patients with Parkinson’s disease than the baseline approach (analyzing all straight strides). The second objective is to propose a model to directly incorporate the inter-event dynamics and account for the event type imbalance to improve PBPM performance. We addressed this objective in the publication [P2] . Using a time-aware LSTM architecture, we demonstrated that modeling inter-event dynamics directly could enhance the performance of PBPM tasks. In addition, we showed that cost-sensitive learning could enhance performance in PBPM by ...