A survey on online active learning

Online active learning is a paradigm in machine learning that aims to select the most informative data points to label from a data stream. The problem of minimizing the cost associated with collecting labeled observations has gained a lot of attention in recent years, particularly in real-world applications where data is only available in an unlabeled form. Annotating each observation can be time-consuming and costly, making it difficult to obtain large amounts of labeled data. To overcome this issue, many active learning strategies have been proposed in the last decades, aiming to select the most informative observations for labeling in order to improve the performance of machine learning models. These approaches can be broadly divided into two categories: static pool-based and stream-based active learning. Pool-based active learning involves selecting a subset of observations from a closed pool of unlabeled data, and it has been the focus of many surveys and literature reviews. However, the growing availability of data streams has led to an increase in the number of approaches that focus on online active learning, which involves continuously selecting and labeling observations as they arrive in a stream. This work aims to provide an overview of the most recently proposed approaches for selecting the most informative observations from data streams in the context of online active learning. We review the various techniques that have been proposed and discuss their strengths and limitations, as well as the challenges and opportunities that exist in this area of research. Our review aims to provide a comprehensive and up-to-date overview of the field and to highlight directions for future work

Online Active Learning for Soft Sensor Development using Semi-Supervised Autoencoders

Data-driven soft sensors are extensively used in industrial and chemical processes to predict hard-to-measure process variables whose real value is difficult to track during routine operations. The regression models used by these sensors often require a large number of labeled examples, yet obtaining the label information can be very expensive given the high time and cost required by quality inspections. In this context, active learning methods can be highly beneficial as they can suggest the most informative labels to query. However, most of the active learning strategies proposed for regression focus on the offline setting. In this work, we adapt some of these approaches to the stream-based scenario and show how they can be used to select the most informative data points. We also demonstrate how to use a semi-supervised architecture based on orthogonal autoencoders to learn salient features in a lower dimensional space. The Tennessee Eastman Process is used to compare the predictive performance of the proposed approaches.Comment: ICML 2022 Workshop on Adaptive Experimental Design and Active Learning in the Real Worl

Stream-based active learning with linear models

The proliferation of automated data collection schemes and the advances in sensorics are increasing the amount of data we are able to monitor in real-time. However, given the high annotation costs and the time required by quality inspections, data is often available in an unlabeled form. This is fostering the use of active learning for the development of soft sensors and predictive models. In production, instead of performing random inspections to obtain product information, labels are collected by evaluating the information content of the unlabeled data. Several query strategy frameworks for regression have been proposed in the literature but most of the focus has been dedicated to the static pool-based scenario. In this work, we propose a new strategy for the stream-based scenario, where instances are sequentially offered to the learner, which must instantaneously decide whether to perform the quality check to obtain the label or discard the instance. The approach is inspired by the optimal experimental design theory and the iterative aspect of the decision-making process is tackled by setting a threshold on the informativeness of the unlabeled data points. The proposed approach is evaluated using numerical simulations and the Tennessee Eastman Process simulator. The results confirm that selecting the examples suggested by the proposed algorithm allows for a faster reduction in the prediction error.Comment: Published in Knowledge-Based Systems (2022

Recurrent Hepatitis C in Liver Allografts: Prospective Assessment of Diagnostic Accuracy, Identification of Pitfalls, and Observations about Pathogenesis

Rationale and Design: The accuracy of a prospective histopathologic diagnosis of rejection and recurrent hepatitis C (HCV) was determined in 48 HCV RNA-positive liver allograft recipients enrolled in an "immunosuppression minimization protocol" between July 29, 2001 and January 24, 2003. Prospective entry of all pertinent treatment, laboratory, and histopathology results into an electronic data-base enabled a retrospective analysis of the accuracy of histopathologic diagnoses and the pathophysiologic relationship between recurrent HCV and rejection. Results: Time to first onset of acute rejection (AR) (mean, 107 days; median, 83 days; range, 7-329 days) overlapped with the time to first onset of recurrent HCV (mean, 115 days; median, 123 days; range, 22-315 days), making distinction between the two difficult. AR and chronic rejection (CR) with and without co-existent HCV showed overlapping but significantly different liver injury test profiles. One major and two minor errors occurred (positive predictive values for AR = 91%; recurrent HCV = 100%) ; all involved an overdiagnosis of AR in the context of recurrent HCV. Retrospective analysis of the mistakes showed that major errors can be avoided altogether and the impact of unavoidable minor errors can be minimized by strict adherence to specific histopathologic criteria, close clinicopathologic correlation including examination of HCV RNA levels, and a conservative approach to the use of additional immunosuppression. In addition, histopathologic diagnoses of moderate and severe AR and CR were associated with relatively low HCV RNA levels, whereas relatively high HCV RNA levels were associated with a histopathologic diagnosis of hepatitis alone, particularly the cholestatic variant of HCV. Conclusions: Liver allograft biopsy interpretation can rapidly and accurately distinguish between recurrent HCV and AR/CR. In addition, the histopathologic observations suggest that the immune mechanism responsible for HCV clearance overlap with those leading to significant rejection

Liver transplantation

Survival after liver transplantation has steadily improved, in part because of newer immunosuppression, which may offer decreased long-term side effects. Reduction of steroids early in the course of transplant continues to be a goal, with satisfactory results in terms of both risk of rejection and reduction of side effects. Dominating the literature and the press in 1999 was the controversy surrounding the way in which livers are allocated. Regulation by the federal government was proposed to change the way the United Network of Organ Sharing distributes and allocates livers. Prompted by the shortage of organs, living-donor liver transplantation has blossomed. Continued experience in pediatric patients has shown excellent survival rate and quality of life. In adults, further experience is being gained with respect to the use of right lobes for transplantation. Early data suggest that this is a potential alternative to cadaveric transplantation in adults, with acceptable risk to the donor. Despite advances made in improving the technical aspects of transplantation, recurrent disease remains a significant issue. Lamivudine appears to be a potent inhibitor of hepatitis B virus DNA replication after liver transplantation, although resistance remains a significant problem. Further review of transplantation for hepatitis C virus is encouraging, with excellent five-year survival rate. However, studies evaluating the evolution of fibrosis in these patients throw caution on those results, showing increased progression to cirrhosis over time. Further follow-up of these patients is needed to more accurately assess long-term impact of hepatitis C on morbidity and mortality rates after liver transplantation. © 2000 Lippincott Williams & Wilkins, Inc

DCs Pulsed with Novel HLA-A2-Restricted CTL Epitopes against Hepatitis C Virus Induced a Broadly Reactive Anti-HCV-Specific T Lymphocyte Response

OBJECTIVE: To determine the capacity of dendritic cells (DCs) loaded with single or multiple-peptide mixtures of novel hepatitis C virus (HCV) epitopes to stimulate HCV-specific cytotoxic T lymphocyte (CTL) effector functions. METHODS: A bioinformatics approach was used to predict HLA-A2-restricted HCV-specific CTL epitopes, and the predicted peptides identified from this screen were synthesized. Subsequent IFN-γ ELISPOT analysis detected the stimulating function of these peptides in peripheral blood mononuclear cells (PBMCs) from both chronic and self-limited HCV infected subjects (subjects exhibiting spontaneous HCV clearance). Mature DCs, derived in vitro from CD14(+) monocytes harvested from the study subjects by incubation with appropriate cytokine cocktails, were loaded with novel peptide or epitope peptide mixtures and co-cultured with autologous T lymphocytes. Granzyme B (GrB) and IFN-γ ELISPOT analysis was used to test for epitope-specific CTL responses. T-cell-derived cytokines contained in the co-cultured supernatant were detected by flow cytometry. RESULTS: We identified 7 novel HLA-A2-restricted HCV-specific CTL epitopes that increased the frequency of IFN-γ-producing T cells compared to other epitopes, as assayed by measuring spot forming cells (SFCs). Two epitopes had the strongest stimulating capability in the self-limited subjects, one found in the E2 and one in the NS2 region of HCV; five epitopes had a strong stimulating capacity in both chronic and self-limited HCV infection, but were stronger in the self-limited subjects. They were distributed in E2, NS2, NS3, NS4, and NS5 regions of HCV, respectively. We also found that mDCs loaded with novel peptide mixtures could significantly increase GrB and IFN-γ SFCs as compared to single peptides, especially in chronic HCV infection subjects. Additionally, we found that DCs pulsed with multiple epitope peptide mixtures induced a Th1-biased immune response. CONCLUSIONS: Seven novel and strongly stimulating HLA-A2-restricted HCV-specific CTL epitopes were identified. Furthermore, DCs loaded with multiple-epitope peptide mixtures induced epitope-specific CTLs responses

Consequences of Cold-Ischemia Time on Primary Nonfunction and Patient and Graft Survival in Liver Transplantation: A Meta-Analysis

Introduction: The ability to preserve organs prior to transplant is essential to the organ allocation process. Objective: The purpose of this study is to describe the functional relationship between cold-ischemia time (CIT) and primary nonfunction (PNF), patient and graft survival in liver transplant. Methods: To identify relevant articles Medline, EMBASE and the Cochrane database, including the non-English literature identified in these databases, was searched from 1966 to April 2008. Two independent reviewers screened and extracted the data. CIT was analyzed both as a continuous variable and stratified by clinically relevant intervals. Nondichotomous variables were weighted by sample size. Percent variables were weighted by the inverse of the binomial variance. Results: Twenty-six studies met criteria. Functionally, PNF%=-6.678281+0.9134701*CIT Mean+0.1250879*(CIT Mean-9.89535) 2 - 0.0067663*(CIT Mean-9.89535) 3, r2=.625, p<.0001. Mean patient survival: 93 % (1 month), 88 % (3 months), 83 % (6 months) and 83 % (12 months). Mean graft survival: 85.9 % (1 month), 80.5 % (3 months), 78.1 % (6 months) and 76.8 % (12 months). Maximum patient and graft survival occurred with CITs between 7.5-12.5 hrs at each survival interval. PNF was also significantly correlated with ICU time, % first time grafts and % immunologic mismatches. Conclusion: The results of this work imply that CIT may be the most important pre-transplant information needed in the decision to accept an organ. © 2008 Stahl et al