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Multistage sequential decision-making occurs in many real-world applications such as healthcare diagnosis and treatment. One concrete example is when the doctors need to decide to collect which kind of information from subjects so as to make the good medical decision cost-effectively. In this paper, an active learning-based method is developed to model the doctors' decision-making process that actively collects necessary information from each subject in a sequential manner. The effectiveness of the proposed model, especially its two-stage version, is validated on both simulation studies and a case study of common bile duct stone evaluation for pediatric patients. [ABSTRACT FROM AUTHOR]

Fourier-transform infrared spectroscopy (FTIR) is a widely adopted technique for characterizing the chemical composition in many physical and chemical analyses. However, FTIR spectra are subject to multiple sources of uncertainty, and thus the analysis of them relies on domain experts and can only lead to qualitative conclusions. This study aims to analyze the effect of a certain treatment on FTIR spectra subject to two commonly observed uncertainties, the offset shift and the multiplicative error. Due to these uncertainties, the pre-exposure FTIR spectra are modeled according to the physical understanding of the uncertainty—observed spectra can be viewed as translating and stretchering an underlying template signal, and the post-exposure FTIR spectra are modeled as the translated and stretchered template signal plus an extra functional treatment effect. To provide engineering interpretation, the treatment effect is modeled as the product of the pattern of modification and its corresponding magnitude. A two-step parameter estimation algorithm is developed to estimate the underlying template signal, the pattern of modification, and the magnitude of modification at various treatment strengths. The effectiveness of the proposed method is validated in a simulation study. Furtherly, in a real case study, the proposed method is used to investigate the effect of plasma exposure on the FTIR spectra. As a result, the proposed method effectively identifies the pattern of modification under uncertainties in the manufacturing environment, which matches the knowledge of the affected chemical components by the plasma treatment. And the recovered magnitude of modification provides guidance in selecting the control parameter of the plasma treatment. Note to Practitioners—FTIR spectrometer is often used to characterize the surface chemical composition of a material. Due to the large uncertainties associated with the nature of spectrometer and the measurement environment, the FTIR signals are usually examined visually by experienced engineers and technicians in industrial applications, which can be both time-consuming and inaccurate. To understand the effect of plasma exposure on the surface property of carbon fiber reinforced polymer (CFRP) material, the elimination of uncertainties associated with FTIR signals is investigated, and a systematic method is proposed to quantify the effect of surface treatments on FTIR signals. A two-step analytic procedure is proposed, which provides information on how the plasma exposure distorts the FTIR signals, and how the plasma distance relates to the magnitude of the distortion. The methodology in this article can be used to analyze the treatment effect on a variety of spectroscopic measurements that are subject to uncertainties such as offset and scaling errors, which expands the applications of in situ handheld spectrometer metrology in manufacturing industries. [ABSTRACT FROM AUTHOR]