Active Learning for Auditory Hierarchy

Much audio content today is rendered as a static stereo mix: fundamentally a fixed single entity. Object-based audio envisages the delivery of sound content using a collection of individual sound ‘objects’ controlled by accompanying metadata. This offers potential for audio to be delivered in a dynamic manner providing enhanced audio for consumers. One example of such treatment is the concept of applying varying levels of data compression to sound objects thereby reducing the volume of data to be transmitted in limited bandwidth situations. This application motivates the ability to accurately classify objects in terms of their ‘hierarchy’. That is, whether or not an object is a foreground sound, which should be reproduced at full quality if possible, or a background sound, which can be heavily compressed without causing a deterioration in the listening experience. Lack of suitably labelled data is an acknowledged problem in the domain. Active Learning is a method that can greatly reduce the manual effort required to label a large corpus by identifying the most effective instances to train a model to high accuracy levels. This paper compares a number of Active Learning methods to investigate which is most effective in the context of a hierarchical labelling task on an audio dataset. Results show that the number of manual labels required can be reduced to 1.7% of the total dataset while still retaining high prediction accuracy

Identification of low-risk patients with acute symptomatic pulmonary embolism for outpatient therapy

Rationale: Patients with acute symptomatic pulmonary embolism (PE) deemed to be at low risk for early complications might be candidates for partial or complete outpatient treatment. Objectives: To develop and validate a clinical prediction rule that accurately identifies patients with PE and low risk of short-term complications and to compare its prognostic ability with two previously validated models (i.e., the Pulmonary Embolism Severity Index [PESI] and the Simplified PESI [sPESI]) Methods: Multivariable logistic regression of a large international cohort of patients with PE prospectively enrolled in the RIETE (Registro Informatizado de la Enfermedad TromboEmbólica) registry. Measurements and Main Results: All-cause mortality, recurrent PE, and major bleeding up to 10 days after PE diagnosis were determined. Of 18,707 eligible patients with acute symptomatic PE, 46 (0.25%) developed recurrent PE, 203 (1.09%) bled, and 471 (2.51%) died. Predictors included in the final model were chronic heart failure, recent immobilization, recent major bleeding, cancer, hypotension, tachycardia, hypoxemia, renal insufficiency, and abnormal platelet count. The area under receiver-operating characteristic curve was 0.77 (95% confidence interval [CI], 0.75-0.78) for the RIETE score, 0.72 (95% CI, 0.70-0.73) for PESI (P<0.05), and 0.71 (95% CI, 0.69-0.73) for sPESI (P<0.05). Our RIETE score outperformed the prognostic value of PESI in terms of net reclassification improvement (P<0.001), integrated discrimination improvement (P<0.001), and sPESI (net reclassification improvement, P<0.001; integrated discrimination improvement, P<0.001). Conclusions: We built a new score, based on widely available variables, that can be used to identify patients with PE at low risk of short-term complications, assisting in triage and potentially shortening duration of hospital stay

A machine-learning approach for automatic classification of volcanic seismicity at La Soufrière Volcano, Guadeloupe

The classification of seismo-volcanic signals is performed manually at La Soufrière Volcano, which is time consuming and can be biased by subjectivity of the operator. We propose here a machine-learning-based model for classification of these signals, to handle large datasets and provide objective and reproducible results. To describe the properties of the signals, we used 104 statistical, entropy, and shape descriptor features computed from the time waveform, the spectrum, and the cepstrum. First, we trained a random forest classifier with a dataset provided by the Observatoire Volcanologique et Sismologique de Guadeloupe that consisted of 845 labeled events that were recorded from 2013 to 2018: 542 volcano-tectonic (VT); 217 Nested; and 86 long period (LP). We obtained an overalll accuracy of 72%. We determined that the VT class includes a variety of signals that cover the VT, Nested and LP classes. After visual inspection of the waveforms and spectral characteristics of the data set, we introduced two new classes: Hybrid and Tornillo. A new random forest classifier was trained with this new information, and we obtained a much better overall accuracy of 82%. The model is very good for recognition of all event classes, except Hybrid events (67% accuracy, 70% precision). Hybrid events are often considered to be a mix of VT and LP events. This can be explained by the nature of this class and the physical processes that include both fracturing and resonating components with different modal frequencies. By analyzing the feature weights and by training a model with the most important features, we show that a subset of the 14 best features is sufficient to obtain a performance that is close to that of the model with the whole feature set. However, these best features are different from the 13 best features obtained for another volcano in Peru, with only one feature common to both sets of best features. Therefore, the model is not universal and it must be trained for each volcano, or it is too specific to the one station used here

Fondaparinux in the initial and long-term treatment of venous thromboembolism

Background: Even in the absence of evidence on its long-term efficacy and safety, a number of patients with venous thromboembolism (VTE) receive long-term therapy with fondaparinux alone in everyday practice. Methods: We used the Registro Informatizado de Enfermedad Tromboembólica (RIETE) registry to compare the rate of VTE recurrences and major bleeding at 10 and 90 days in patients with and without cancer. For long-term therapy, fondaparinux was compared with vitamin K antagonists (VKA) in patients without cancer and with low-molecular- weight heparin (LMWH) in those with cancer Results Of 47,378 patients recruited, 46,513 were initially treated with heparin, 865 with fondaparinux. Then, 263 patients (78 with cancer) were treated for at least 3 months with fondaparinux. After propensity-score matching, there were no differences between patients receiving initial therapy with heparin or fondaparinux. Among patients with cancer, there were no differences between fondaparinux and LMWH. Among patients without cancer, the long-term use of fondaparinux was associated with an increased risk of major bleeding (3.24 % vs. 0.95 %, p < 0.05). Conclusions: An unexpected high rate of major bleeding was observed in non-cancer patients treated with longterm fondaparinux. Our small sample does not allowto derive relevant conclusions on the use of fondaparinux in cancer patients

A prognostic score to identify low-risk outpatients with acute deep vein thrombosis in the lower limbs

BACKGROUND: No prior studies have identified which patients with deep vein thrombosis in the lower limbs are at a low risk for adverse events within the first week of therapy. METHODS: We used data from the Registro Informatizado de la Enfermedad TromboEmb\uf3lica (RIETE) to identify patients at low risk for the composite outcome of pulmonary embolism, major bleeding, or death within the first week. We built a prognostic score and compared it with the decision to treat patients at home. RESULTS: As of December 2013, 15,280 outpatients with deep vein thrombosis had been enrolled. Overall, 5164 patients (34%) were treated at home. Of these, 12 (0.23%) had pulmonary embolism, 8 (0.15%) bled, and 4 (0.08%) died. On multivariable analysis, chronic heart failure, recent immobility, recent bleeding, cancer, renal insufficiency, and abnormal platelet count independently predicted the risk for the composite outcome. Among 11,430 patients (75%) considered to be at low risk, 15 (0.13%) suffered pulmonary embolism, 22 (0.19%) bled, and 8 (0.07%) died. The C-statistic was 0.61 (95% confidence interval [CI], 0.57-0.65) for the decision to treat patients at home and 0.76 (95% CI, 0.72-0.79) for the score (P = .003). Net reclassification improvement was 41% (P < .001). Integrated discrimination improvement was 0.034 for the score and 0.015 for the clinical decision (P < .001). CONCLUSIONS: Using 6 easily available variables, we identified outpatients with deep vein thrombosis at low risk for adverse events within the first week. These data may help to safely treat more patients at home. This score, however, should be validated

Clinical outcome in patients with venous thromboembolism receiving concomitant anticoagulant and antiplatelet therapy

Introduction: Patients with arterial disease receiving antiplatelet agents may develop venous thromboembolism (VTE) and need anticoagulant therapy, although concomitant use of these drugsmay increase bleeding risk. We analyzed RIETE data and compared clinical outcomes depending on decision to discontinue or maintain antiplatelet therapy at VTE diagnosis. Methods: Consecutive patients with acute VTE were enrolled in RIETE. Only patients receiving antiplatelet therapy at baseline were included in this analysis. Primary outcomes were: rate of subsequent ischemic events, major bleeding or death during anticoagulation course. Results: 1178 patientswho received antiplatelet drugs at VTE diagnosis were included. Antiplatelet therapy was discontinued in 62% of patients. During anticoagulation course, patients also receiving antiplatelet therapy had higher rates of lower limb amputations (2.28 vs. 0.21 events per 100 patients-years; p < 0.01), any ischemic events (5.7 vs. 2.28 events per 100 patients-years; p < 0.05) or death (23.6 vs. 13.9 deaths per 100 patientsyears; p < 0.01). No differences in the rate of major bleeding or recurrent VTEwere revealed. In matched analysis, patients on antiplatelet therapy were found to have a significantly higher rate of limb amputations (odds ratio: 15.3; 95% CI: 1.02-229) and an increased number of composite outcomes including all-cause deaths, arterial and VTE events (odds ratio: 1.46; CI: 1.03-2.06), with no differences in major bleeding rate. Conclusion: Concomitant anticoagulant and antiplatelet therapy in patients with VTE and arterial disease is not associated with increased risk for bleeding, recurrent VTE or death. The worse outcome observed in patients who continued antiplatelet therapy requires further investigations

A prognostic score to identify low-risk outpatients with acute deep vein thrombosis in the upper extremity

Background: No studies have identified which patients with upper-extremity deep vein thrombosis (DVT) are at low risk for adverse events within the first week of therapy. Methods: We used data from Registro Informatizado de la Enfermedad TromboEmb\uf3lica to explore in patients with upper-extremity DVT a prognostic score that correctly identified patients with lower limb DVT at low risk for pulmonary embolism, major bleeding, or death within the first week. Results: As of December 2014, 1135 outpatients with upper-extremity DVT were recruited. Of these, 515 (45%) were treated at home. During the first week, three patients (0.26%) experienced pulmonary embolism, two (0.18%) had major bleeding, and four (0.35%) died. We assigned 1 point to patients with chronic heart failure, creatinine clearance levels 30-60 mL min -1 , recent bleeding, abnormal platelet count, recent immobility, or cancer without metastases; 2 points to those with metastatic cancer; and 3 points to those with creatinine clearance levels < 30 mL min -1 . Overall, 759 (67%) patients scored 64 1 point and were considered to be at low risk. The rate of the composite outcome within the first week was 0.26% (95% confidence interval [CI] 0.004-0.87) in patients at low risk and 1.86% (95% CI 0.81-3.68) in the remaining patients. C-statistics was 0.73 (95% CI 0.57-0.88). Net reclassification improvement was 22%, and integrated discrimination improvement was 0.0055. Conclusions: Using six easily available variables, we identified outpatients with upper-extremity DVT at low risk for adverse events within the first week. These data may help to safely treat more patients at home