A hidden Markov modeling approach for admixture mapping based on case-control data

2004 ◽  
Vol 27 (3) ◽  
pp. 225-239 ◽  
Author(s):  
Chun Zhang ◽  
Kun Chen ◽  
Michael F. Seldin ◽  
Hongzhe Li
Keyword(s):  
Hidden Markov ◽  
Case Control ◽  
Admixture Mapping ◽  
Markov Modeling ◽  
Control Data ◽  
Modeling Approach ◽  
Hidden Markov Modeling

scholarly journals Ecosystem health monitoring in the Shanghai-Hangzhou Bay Metropolitan Area: A hidden Markov modeling approach

2019 ◽  
Vol 133 ◽  
pp. 105170 ◽  
Author(s):  
Rui Xiao ◽  
Xiaoyu Yu ◽  
Ruixing Shi ◽  
Zhonghao Zhang ◽  
Weixuan Yu ◽  
...  
Keyword(s):  
Metropolitan Area ◽  
Health Monitoring ◽  
Ecosystem Health ◽  
Hidden Markov ◽  
Hangzhou Bay ◽  
Markov Modeling ◽  
Modeling Approach ◽  
Hidden Markov Modeling

Deriving Effective Decision-Making Strategies of Prosthetists: Using Hidden Markov Modeling and Qualitative Analysis to Compare Experts and Novices

2021 ◽  
pp. 001872082110328
Author(s):  
Pratima Saravanan ◽  
Jessica Menold
Keyword(s):  
Decision Making ◽  
Qualitative Analysis ◽  
Clinical Decision Making ◽  
Hidden Markov ◽  
Activity Level ◽  
Markov Modeling ◽  
Support Tool ◽  
Case Complexity ◽  
Hidden Markov Modeling ◽  
Expert And Novice

Objective This research focuses on studying the clinical decision-making strategies of expert and novice prosthetists for different case complexities. Background With an increasing global amputee population, there is an urgent need for improved amputee care. However, current prosthetic prescription standards are based on subjective expertise, making the process challenging for novices, specifically during complex patient cases. Hence, there is a need for studying the decision-making strategies of prosthetists. Method An interactive web-based survey was developed with two case studies of varying complexities. Navigation between survey pages and time spent were recorded for 28 participants including experts ( n = 20) and novices ( n = 8). Using these data, decision-making strategies, or patterns of decisions, during prosthetic prescription were derived using hidden Markov modeling. A qualitative analysis of participants’ rationale regarding decisions was used to add a deep contextualized understanding of decision-making strategies derived from the quantitative analysis. Results Unique decision-making strategies were observed across expert and novice participants. Experts tended to focus on the personal details, activity level, and state of the residual limb prior to prescription, and this strategy was independent of case complexity. Novices tended to change strategies dependent upon case complexity, fixating on certain factors when case complexity was high. Conclusion The decision-making strategies of experts stayed the same across the two cases, whereas the novices exhibited mixed strategies. Application By modeling the decision-making strategies of experts and novices, this study builds a foundation for development of an automated decision-support tool for prosthetic prescription, advancing novice training, and amputee care.

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