Noise Tolerance under Risk Minimization

In this paper we explore noise tolerant learning of classifiers. We formulate the problem as follows. We assume that there is an ${\bf unobservable}$ training set which is noise-free. The actual training set given to the learning algorithm is obtained from this ideal data set by corrupting the class label of each example. The probability that the class label of an example is corrupted is a function of the feature vector of the example. This would account for most kinds of noisy data one encounters in practice. We say that a learning method is noise tolerant if the classifiers learnt with the ideal noise-free data and with noisy data, both have the same classification accuracy on the noise-free data. In this paper we analyze the noise tolerance properties of risk minimization (under different loss functions), which is a generic method for learning classifiers. We show that risk minimization under 0-1 loss function has impressive noise tolerance properties and that under squared error loss is tolerant only to uniform noise; risk minimization under other loss functions is not noise tolerant. We conclude the paper with some discussion on implications of these theoretical results

Making Risk Minimization Tolerant to Label Noise

In many applications, the training data, from which one needs to learn a classifier, is corrupted with label noise. Many standard algorithms such as SVM perform poorly in presence of label noise. In this paper we investigate the robustness of risk minimization to label noise. We prove a sufficient condition on a loss function for the risk minimization under that loss to be tolerant to uniform label noise. We show that the $0-1$ loss, sigmoid loss, ramp loss and probit loss satisfy this condition though none of the standard convex loss functions satisfy it. We also prove that, by choosing a sufficiently large value of a parameter in the loss function, the sigmoid loss, ramp loss and probit loss can be made tolerant to non-uniform label noise also if we can assume the classes to be separable under noise-free data distribution. Through extensive empirical studies, we show that risk minimization under the $0-1$ loss, the sigmoid loss and the ramp loss has much better robustness to label noise when compared to the SVM algorithm

Complications of gynaecologic laparoscopy: an audit

Background: Minimal access surgery as a modality of treatment for various gynecologic conditions is rapidly gaining grounds in the recent years1. Approximately 30 years after its introduction; the use of laparoscopy in gynecology has evolved from diagnostic purposes into a more coordinated system for the repair or removal of diseased abdominal and pelvic organs. The rapid increase in the number of procedures being performed, the introduction of new equipment, and variability in the training of surgeons all contribute to the complication rate. The objective is to review complications associated with laparoscopic gynecological surgeries and identify associated risk factors.Methods: Hospital based descriptive observational study performed between January 2013 to December 2017 which included all gynecologic laparoscopies performed in present institute. Variables were recorded for patient characteristics, indication for surgery, length of hospital stay (in days), major and minor complications, conversions to laparotomy and postoperative complications. The laparoscopic procedures were divided into three subgroups: Diagnostic cases, tubal sterilization and Advanced operative laparoscopy.Results: Of all 3724 laparoscopies included, overall frequency of major was 1.96 %, and that of minor complications was 3.51%. Of 3724 laparoscopic procedures, 214 complications occurred (5.8% of all procedures) and one death occurred. The level of technical difficulty and existence of prior abdominal surgery were associated with a higher risk of major complications and conversions to laparotomy.Conclusions: Laparoscopic surgery has many advantages, but it is not without complications. Despite rapidly improving technical equipment’s and surgical skill; complication rates and preventable injuries demonstrate continuous pattern. Delayed recognition and intervention add to morbidity and mortality. Each laparoscopic surgeon should be aware of the potential complications, how they can be prevented and managed efficiently

Autologous stem cell transplantation vs bortezomib based chemotheraphy for the first‐line treatment of systemic light chain amyloidosis in the UK

OBJECTIVES: The benefit of autologous stem cell transplantation (ASCT) in the treatment of light chain (AL) amyloidosis requires re-evaluation in the modern era. This retrospective case-matched study compares ASCT to bortezomib for the treatment of patients with AL amyloidosis. METHODS: Newly diagnosed patients with AL amyloidosis treated with ASCT or bortezomib between 2001-2018 were identified. Patients were excluded if the time from diagnosis to treatment exceeded 12 months. Patients were matched on a 1:1 basis, using a propensity matched scoring approach. RESULTS: A total of 136 propensity-score matched patients were included (ASCT n= 68, bortezomib n=68). There was no significant difference in overall survival at two years (p=0.908, HR: 0.95, CI:0.41-2.20). For ASCT vs. bortezomib: overall haematological response rate at six months was 90.6% vs. 92.5%; organ response at 12 months: cardiac (70.0% vs. 54%, p>0.999), renal (74% vs.24%, p=0.463)) liver (21% vs. 22%, p=0.048); median progression free survival (50 vs. 42 months p=0.058, HR:0.61, CI:0.37-1.02) and time to next treatment (68 vs. 45 months, p=0.145, HR:0.61, CI:0.31-1.19). More patients required treatment in the bortezomib group compared to ASCT group at 24 months (41 vs. 23, Chi squared p=0.004) and 48 months (57 vs 41, Chi squared p= 0.004). CONCLUSIONS: This small retrospective study suggests that there is no clear survival advantage of ASCT over bortezomib therapy. A prospective randomised controlled trial evaluating ASCT in AL amyloidosis is critically needed

Small RNA Signatures of Acute Ischemic Stroke in L1CAM Positive Extracellular Vesicles

L1CAM-positive extracellular vesicles (L1EV) are an emerging biomarker that may better reflect ongoing neuronal damage than other blood-based biomarkers. The physiological roles and regulation of L1EVs and their small RNA cargoes following stroke is unknown. We sought to characterize L1EV small RNAs following stroke and assess L1EV RNA signatures for diagnosing stroke using weighted gene co-expression network analysis and random forest (RF) machine learning algorithms. Interestingly, small RNA sequencing of plasma L1EVs from patients with stroke and control patients (n = 28) identified micro(mi)RNAs known to be enriched in the brain. Weighted gene co-expression network analysis (WGCNA) revealed small RNA transcript modules correlated to diagnosis, initial NIH stroke scale, and age. L1EV RNA signatures associated with the diagnosis of AIS were derived from WGCNA and RF classification. These small RNA signatures demonstrated a high degree of accuracy in the diagnosis of AIS with an area under the curve (AUC) of the signatures ranging from 0.833 to 0.932. Further work is necessary to understand the role of small RNA L1EV cargoes in the response to brain injury, however, this study supports the utility of L1EV small RNA signatures as a biomarker of stroke