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Bioethics and Precision Medicine
Published in Kirk A. Phillips, Dirk P. Yamamoto, LeeAnn Racz, Total Exposure Health, 2020
What is clear is that automated decision support will play an increasing role. Advances in genomics and human genetics have enabled a more detailed understanding of the impact of genetics in a disease and its treatment. In addition to a patient’s clinical signs and symptoms, physicians can now, or in near future, consider genetic data for their diagnosis and treatment decisions. This new information source, based on genome and gene expression analysis, makes clinical decision processes even more complex. Beyond that, behavioral and environmental aspects should also be considered in order to realize personalized medicine. Given these additional information sources, the need for support in decision-making is increasing (Denecke and Spreckelsen 2013, Castaneda et al. 2015).
Operability Testing of Command, Control, Communications, Computers, and Intelligence (C4I) Systems
Published in Samuel G. Charlton, Thomas G. O’Brien, Handbook of Human Factors Testing and Evaluation, 2019
Although software applications have relieved C4I operators of many computational and repetitive tasks, those tasks that remain in human hands are perhaps the most challenging and critical (Bainbridge, 1982; Meister, 1996). Typically, these functions are deemed too critical to place beyond human control or too complex to automate with current technology. Unfortunately, the result of this allocation of tasks is too often a vaguely defined set of human cognitive tasks for operators, upon whose quick and accurate performance may rest the ultimate success of the system’s mission (Lockhart, Strub, Hawley, & Tapia, 1993). Furthermore, as automation has become more intelligent, the growth in number of information sources and the resulting great quantity of information provided to the human authority requires automated decision support to ensure timely and accurate decisions (see Fig. 19.1).
Deep learning based cost estimation of circuit boards: a case study in the automotive industry
Published in International Journal of Production Research, 2022
Frank Bodendorf, Stefan Merbele, Jörg Franke
A survey of 3000 executives shows that 85 percent of them believe that predictive analytics provides a competitive advantage. However, this study also shows that only one in 20 companies are using these techniques (Ransbotham et al. 2017). This is also reflected in the business environment of our study. A key challenge for the management is to identify use cases of predictive analytics and in particular deep learning to add value in their department or the entire company. From an operational and strategic perspective, this paper motivates the feasibility of deep learning object recognition and cost estimation within the business analytics domain. This is illustrated by a case study in the automotive industry. The primary message of this paper is to demonstrate a smart linking of deep learning object recognition with cost estimation for decision support. As a direct implication, the proved concept proposed in this paper can be used to create an automated decision support system, which in turn can improve the quality of decisions in terms of both efficiency and effectiveness.