GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labels

Evaluating the performance of graph neural networks (GNNs) is an essential task for practical GNN model deployment and serving, as deployed GNNs face significant performance uncertainty when inferring on unseen and unlabeled test graphs, due to mismatched training-test graph distributions. In this paper, we study a new problem, GNN model evaluation, that aims to assess the performance of a specific GNN model trained on labeled and observed graphs, by precisely estimating its performance (e.g., node classification accuracy) on unseen graphs without labels. Concretely, we propose a two-stage GNN model evaluation framework, including (1) DiscGraph set construction and (2) GNNEvaluator training and inference. The DiscGraph set captures wide-range and diverse graph data distribution discrepancies through a discrepancy measurement function, which exploits the outputs of GNNs related to latent node embeddings and node class predictions. Under the effective training supervision from the DiscGraph set, GNNEvaluator learns to precisely estimate node classification accuracy of the to-be-evaluated GNN model and makes an accurate inference for evaluating GNN model performance. Extensive experiments on real-world unseen and unlabeled test graphs demonstrate the effectiveness of our proposed method for GNN model evaluation.Comment: Accepted by NeurIPS 202

Dynamic inter-treatment information sharing for heterogeneous treatment effects estimation

Existing heterogeneous treatment effects learners, also known as conditional average treatment effects (CATE) learners, lack a general mechanism for end-to-end inter-treatment information sharing, and data have to be split among potential outcome functions to train CATE learners which can lead to biased estimates with limited observational datasets. To address this issue, we propose a novel deep learning-based framework to train CATE learners that facilitates dynamic end-to-end information sharing among treatment groups. The framework is based on \textit{soft weight sharing} of \textit{hypernetworks}, which offers advantages such as parameter efficiency, faster training, and improved results. The proposed framework complements existing CATE learners and introduces a new class of uncertainty-aware CATE learners that we refer to as \textit{HyperCATE}. We develop HyperCATE versions of commonly used CATE learners and evaluate them on IHDP, ACIC-2016, and Twins benchmarks. Our experimental results show that the proposed framework improves the CATE estimation error via counterfactual inference, with increasing effectiveness for smaller datasets

Adversarial De-confounding in Individualised Treatment Effects Estimation

Observational studies have recently received significant attention from the machine learning community due to the increasingly available non-experimental observational data and the limitations of the experimental studies, such as considerable cost, impracticality, small and less representative sample sizes, etc. In observational studies, de-confounding is a fundamental problem of individualised treatment effects (ITE) estimation. This paper proposes disentangled representations with adversarial training to selectively balance the confounders in the binary treatment setting for the ITE estimation. The adversarial training of treatment policy selectively encourages treatment-agnostic balanced representations for the confounders and helps to estimate the ITE in the observational studies via counterfactual inference. Empirical results on synthetic and real-world datasets, with varying degrees of confounding, prove that our proposed approach improves the state-of-the-art methods in achieving lower error in the ITE estimation.Comment: accepted to AISTATS 202

Improving Diagnostics with Deep Forest Applied to Electronic Health Records

An electronic health record (EHR) is a vital high-dimensional part of medical concepts. Discovering implicit correlations in the information of this data set and the research and informative aspects can improve the treatment and management process. The challenge of concern is the data sources’ limitations in finding a stable model to relate medical concepts and use these existing connections. This paper presents Patient Forest, a novel end-to-end approach for learning patient representations from tree-structured data for readmission and mortality prediction tasks. By leveraging statistical features, the proposed model is able to provide an accurate and reliable classifier for predicting readmission and mortality. Experiments on MIMIC-III and eICU datasets demonstrate Patient Forest outperforms existing machine learning models, especially when the training data are limited. Additionally, a qualitative evaluation of Patient Forest is conducted by visualising the learnt representations in 2D space using the t-SNE, which further confirms the effectiveness of the proposed model in learning EHR representations

Modification on Direct Agglutination Antigen Preparation for Simplified Sero-Diagnosis of Human and Canine Visceral Leishmaniasis

Background: Visceral leishmaniasis is systematic serous parasitic disease with pub­lic health importance. Zoonotic form of visceral leishmaniasis is wide spread in Mediterranean basin and South America regions. Direct agglutination test (DAT) is an accurate, reliable and non-expensive serological test for the diagnosis of visceral leishmaniasis in human and canines but the antigen preparation involves some limita­tions. This study aimed to compare the conventional production of DAT anti­gen with our modified DAT antigen and then assessed on human and dog pooled sera. Methods: Conventional DAT antigen has been prepared at the School of Public Health, Tehran University of Medical Sciences and some modifications were car­ried out on it, which named as modified DAT antigen. Three positive and one nega­tive human and dog pooled serum were separately used for the comparison of modified DAT with conventional DAT antigen batches with one-month interval for a period of 9 months. Results: A good concordance was observed between modified DAT compared to conventional DAT antigens for the detection of visceral leishmaniasis on human (100%) and dog (94.4%) pooled sera, respectively. Conclusion: Since the modified DAT antigen could be reduced the preparation time from 3 days to several hours and a good degree of agreement was found between modified DAT and convention DAT antigen batches, it can be used as a simple and easy tool for screening and serodiagnosis of human and canine L. infan­tum infection

Major depressive disorder: biomarkers and biosensors

Depressive disorders belong to highly heterogeneous psychiatric diseases. Loss of in interest in previously enjoyed activities and a depressed mood are the main characteristics of major depressive disorder (MDD). Moreover, due to significant heterogeneity in clinical presentation and lack of applicable biomarkers, diagnosis and treatment remains challenging. Identification of relevant biomarkers would allow for improved disease classification and more personalized treatment strategies. Herein, we review the current state of these biomarkers and then discuss diagnostic techniques of aimed to specifically target these analytes using state of the art biosensor technology.publishe

Cutaneous and post kala-azar dermal leishmaniasis caused by Leishmania infantum in endemic areas of visceral leishmaniasis, northwestern Iran 2002-2011 : a case series

Visceral leishmaniasis (VL) is endemic in Northwest and southern Iran. Reports of cutaneous leishmaniasis (CL) in Northwest areas are rare, and its etiological agents are unknown. In the current study, we report six CL and two post kala-azar dermal leishmaniasis (PKDL) cases caused by Leishmania infantum from endemic areas of VL in the Northwest. Smears were made from skin lesions of 30 suspected patients in 2002-2011, and CL was determined by microscopy or culture. Leishmania spp. were identified by nested-PCR assay. The disease was confirmed in 20 out of 30 (66%) suspected patients by parasitological examinations. L. infantum was identified in eight and Leishmania major in 12 CL cases by nested-PCR. Cutaneous leishmaniasis patients infected with L. major had the history of travel to CL endemic areas. L. infantum antibodies were detected by direct agglutination test (DAT) at titers of 1:3200 in two cases with history of VL. Results of this study indicated that L. infantum is a causative agent of CL as well as PKDL in the VL endemic areas