When Personalization Harms: Reconsidering the Use of Group Attributes in Prediction

Machine learning models are often personalized with categorical attributes that are protected, sensitive, self-reported, or costly to acquire. In this work, we show models that are personalized with group attributes can reduce performance at a group level. We propose formal conditions to ensure the "fair use" of group attributes in prediction tasks by training one additional model -- i.e., collective preference guarantees to ensure that each group who provides personal data will receive a tailored gain in performance in return. We present sufficient conditions to ensure fair use in empirical risk minimization and characterize failure modes that lead to fair use violations due to standard practices in model development and deployment. We present a comprehensive empirical study of fair use in clinical prediction tasks. Our results demonstrate the prevalence of fair use violations in practice and illustrate simple interventions to mitigate their harm.Comment: ICML 2023 Ora

Diabetic Foot Due to Anaphylactic Shock: A Case Report

Introduction: Diabetic foot is a clinical disorder, which is commonly seen in patients with diabetes mellitus. It is also the major cause of below knee amputation in the world. There are many underlying causes such as neuropathic, ischemic, and infectious causes for diabetic foot. Local or systemic complications may develop after snake bite. Case Presentation: We reported a very rare case, involving a 78-year-old male admitted to the Emergency Department, who developed anaphylactic shock and diabetic foot after the snake bite. Conclusions: Reviewing the literature, this is the second reported case of snake bite associated with diabetic foot

Estimating the impact of unsafe water, sanitation and hygiene on the global burden of disease: evolving and alternative methods.

The 2010 global burden of disease (GBD) study represents the latest effort to estimate the global burden of disease and injuries and the associated risk factors. Like previous GBD studies, this latest iteration reflects a continuing evolution in methods, scope and evidence base. Since the first GBD Study in 1990, the burden of diarrhoeal disease and the burden attributable to inadequate water and sanitation have fallen dramatically. While this is consistent with trends in communicable disease and child mortality, the change in attributable risk is also due to new interpretations of the epidemiological evidence from studies of interventions to improve water quality. To provide context for a series of companion papers proposing alternative assumptions and methods concerning the disease burden and risks from inadequate water, sanitation and hygiene, we summarise evolving methods over previous GBD studies. We also describe an alternative approach using population intervention modelling. We conclude by emphasising the important role of GBD studies and the need to ensure that policy on interventions such as water and sanitation be grounded on methods that are transparent, peer-reviewed and widely accepted

Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretability

Post-hoc model-agnostic interpretation methods such as partial dependence plots can be employed to interpret complex machine learning models. While these interpretation methods can be applied regardless of model complexity, they can produce misleading and verbose results if the model is too complex, especially w.r.t. feature interactions. To quantify the complexity of arbitrary machine learning models, we propose model-agnostic complexity measures based on functional decomposition: number of features used, interaction strength and main effect complexity. We show that post-hoc interpretation of models that minimize the three measures is more reliable and compact. Furthermore, we demonstrate the application of these measures in a multi-objective optimization approach which simultaneously minimizes loss and complexity

Pre-existing invasive fungal infection is not a contraindication for allogeneic HSCT for patients with hematologic malignancies: a CIBMTR study.

Patients with prior invasive fungal infection (IFI) increasingly proceed to allogeneic hematopoietic cell transplantation (HSCT). However, little is known about the impact of prior IFI on survival. Patients with pre-transplant IFI (cases; n=825) were compared with controls (n=10247). A subset analysis assessed outcomes in leukemia patients pre- and post 2001. Cases were older with lower performance status (KPS), more advanced disease, higher likelihood of AML and having received cord blood, reduced intensity conditioning, mold-active fungal prophylaxis and more recently transplanted. Aspergillus spp. and Candida spp. were the most commonly identified pathogens. 68% of patients had primarily pulmonary involvement. Univariate and multivariable analysis demonstrated inferior PFS and overall survival (OS) for cases. At 2 years, cases had higher mortality and shorter PFS with significant increases in non-relapse mortality (NRM) but no difference in relapse. One year probability of post-HSCT IFI was 24% (cases) and 17% (control, P<0.001). The predominant cause of death was underlying malignancy; infectious death was higher in cases (13% vs 9%). In the subset analysis, patients transplanted before 2001 had increased NRM with inferior OS and PFS compared with later cases. Pre-transplant IFI is associated with lower PFS and OS after allogeneic HSCT but significant survivorship was observed. Consequently, pre-transplant IFI should not be a contraindication to allogeneic HSCT in otherwise suitable candidates. Documented pre-transplant IFI is associated with lower PFS and OS after allogeneic HSCT. However, mortality post transplant is more influenced by advanced disease status than previous IFI. Pre-transplant IFI does not appear to be a contraindication to allogeneic HSCT

A PRACTICAL REAL-TIME POWER QUALITY EVENT MONITORING APPLICATIONS USING DISCRETE WAVELET TRANSFORM AND ARTIFICIAL NEURAL NETWORK

Determining the events that affect Power Quality (PQ) disturbances is remarkable for consumers. The most important aspects in the assessment of PQ disturbances are real-time monitoring of PQ disturbances and their fast interpretation. In this study, Artificial Neural Networks (ANNs) was used as a classifier benefiting from estimated parameters in PQ disturbances based on Discrete Wavelet Transform (DWT) on the real-time environment for determining the disturbances in power systems. Voltage signals (sag, swell, interruption, transient, harmonic and normal) used in this study were recorded from real grids. DWT was used for featuring the extraction and calculation of the wavelet coefficients, and subsequently, calculated energy levels were used as an input to ANN. The results revealed analyzing the real data processed with DWT and ANN with 100% accuracy proved the superiority of this study. Based on the results of this study, identification of real-time PQ disturbances provided an important advantage for the firms and industry. Particularly, the reasons for the failures in the system related to PQ disturbances were simultaneously diagnosed, as well

Noises Cancelling Adaptive Methods in Control Telemetry Systems of Oil Electrical Submersible Pumps

The main ideas of this paper are that only some from more than 10 MATLAB Adaptive Methods library may be useful and can be recommended to filter out High-Noises in similar Control Telemetry Channels of Electric Power Components like ESP Systems: only four of applied have shown successfully good results in the early prediction of the ESP motor real insulation disruption (like Signerror, Sign-data and Sign-sign filters). The best among the ten analyzed adaptive filter algorithms was recognized to be, The Normalized LMS FIR filter algorithm — adaptfilt.nlms.Основная идея этой работы заключается в выборе наиболее эффективных адаптивных методов фильтрации сигналов, которые реализованы в MATLAB (из числа более десяти). Сигналы характеризуются высоким содержанием шумов, поскольку они передаются по каналам электропитания погружных электронасосов ПЭД. Исследования показали эффективность применения четырех библиотек адаптивных методов при решении задач прогнозирования состояния изоляции двигателя с целью предотвращения возможных разрушений. Наиболее эффективным адаптивным алгоритмом фильтрации для рассматриваемых задач является Normalized LMS FIR filter algorithm — adaptfilt.nlms