Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairs

This work addresses the problem of automated covariate selection under limited prior knowledge. Given an exposure-outcome pair {X,Y} and a variable set Z of unknown causal structure, the Local Discovery by Partitioning (LDP) algorithm partitions Z into subsets defined by their relation to {X,Y}. We enumerate eight exhaustive and mutually exclusive partitions of any arbitrary Z and leverage this taxonomy to differentiate confounders from other variable types. LDP is motivated by valid adjustment set identification, but avoids the pretreatment assumption commonly made by automated covariate selection methods. We provide theoretical guarantees that LDP returns a valid adjustment set for any Z that meets sufficient graphical conditions. Under stronger conditions, we prove that partition labels are asymptotically correct. Total independence tests is worst-case quadratic in |Z|, with sub-quadratic runtimes observed empirically. We numerically validate our theoretical guarantees on synthetic and semi-synthetic graphs. Adjustment sets from LDP yield less biased and more precise average treatment effect estimates than baselines, with LDP outperforming on confounder recall, test count, and runtime for valid adjustment set discovery

Data heterogeneity in federated learning with Electronic Health Records: Case studies of risk prediction for acute kidney injury and sepsis diseases in critical care

With the wider availability of healthcare data such as Electronic Health Records (EHR), more and more data-driven based approaches have been proposed to improve the quality-of-care delivery. Predictive modeling, which aims at building computational models for predicting clinical risk, is a popular research topic in healthcare analytics. However, concerns about privacy of healthcare data may hinder the development of effective predictive models that are generalizable because this often requires rich diverse data from multiple clinical institutions. Recently, federated learning (FL) has demonstrated promise in addressing this concern. However, data heterogeneity from different local participating sites may affect prediction performance of federated models. Due to acute kidney injury (AKI) and sepsis’ high prevalence among patients admitted to intensive care units (ICU), the early prediction of these conditions based on AI is an important topic in critical care medicine. In this study, we take AKI and sepsis onset risk prediction in ICU as two examples to explore the impact of data heterogeneity in the FL framework as well as compare performances across frameworks. We built predictive models based on local, pooled, and FL frameworks using EHR data across multiple hospitals. The local framework only used data from each site itself. The pooled framework combined data from all sites. In the FL framework, each local site did not have access to other sites’ data. A model was updated locally, and its parameters were shared to a central aggregator, which was used to update the federated model’s parameters and then subsequently, shared with each site. We found models built within a FL framework outperformed local counterparts. Then, we analyzed variable importance discrepancies across sites and frameworks. Finally, we explored potential sources of the heterogeneity within the EHR data. The different distributions of demographic profiles, medication use, and site information contributed to data heterogeneity. Author summary The availability of a large amount of healthcare data such as Electronic Health Records (EHR) and advances of artificial intelligence (AI) techniques provides opportunities to build predictive models for disease risk prediction. Due to the sensitive nature of healthcare data, it is challenging to collect the data together from different hospitals and train a unified model on the combined data. Recent federated learning (FL) demonstrates promise in addressing the fragmented healthcare data sources with privacy-preservation. However, data heterogeneity in the FL framework may influence prediction performance. Exploring the heterogeneity of data sources would contribute to building accurate disease risk prediction models in FL. In this study, we take acute kidney injury (AKI) and sepsis prediction in intensive care units (ICU) as two examples to explore the effects of data heterogeneity in the FL framework for disease risk prediction using EHR data across multiple hospital sites. In particular, multiple predictive models were built based on local, pooled, and FL frameworks. The local framework only used data from each site itself. The pooled framework combined data from all sites. In the FL framework, each local site did not have access to other sites’ data. We found models built within a FL framework outperformed local counterparts. Then, we analyzed variable importance discrepancies across sites and frameworks. Finally, we explored potential sources of the heterogeneity within EHR data. The different distributions of demographic profiles, medication use, site information such as the type of ICU at admission contributed to data heterogeneity

SpottingNet: Learning the Similarity of Word Images with Convolutional Neural Network for Word Spotting in Handwritten Historical Documents

International audienceWord spotting is a content-based retrieval process that obtains a ranked list of word image candidates similar to the query word in digital document images. In this paper, we propose a similarity score fusion method integrated with hybrid deep-learning classification and regression models to enhance performance for Query-by-Example (QBE) word spotting. Based on the convolutional neural networkend-to-end framework, the presented models enable conjointly learning of the representative word image descriptors and evaluation of the similarity measure between word descriptors directly from the word image, which are the two crucial factors in this task. In addition, we present a sample generation method using location jitter to balance similar and dissimilar image pairs and enlarge the dataset. Experiments are conducted on the classical George Washington (GW) dataset without involving any recognition methods or prior word category information. Our experiments show that the proposed model yields state-of-the-art mean average precision (mAP) of 80.03%, significantly outperforming previous results

Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sources

Machine learning (ML) in healthcare presents numerous opportunities for enhancing patient care, population health, and healthcare providers' workflows. However, the real-world clinical and cost benefits remain limited due to challenges in data privacy, heterogeneous data sources, and the inability to fully leverage multiple data modalities. In this perspective paper, we introduce "patchwork learning" (PL), a novel paradigm that addresses these limitations by integrating information from disparate datasets composed of different data modalities (e.g., clinical free-text, medical images, omics) and distributed across separate and secure sites. PL allows the simultaneous utilization of complementary data sources while preserving data privacy, enabling the development of more holistic and generalizable ML models. We present the concept of patchwork learning and its current implementations in healthcare, exploring the potential opportunities and applicable data sources for addressing various healthcare challenges. PL leverages bridging modalities or overlapping feature spaces across sites to facilitate information sharing and impute missing data, thereby addressing related prediction tasks. We discuss the challenges associated with PL, many of which are shared by federated and multimodal learning, and provide recommendations for future research in this field. By offering a more comprehensive approach to healthcare data integration, patchwork learning has the potential to revolutionize the clinical applicability of ML models. This paradigm promises to strike a balance between personalization and generalizability, ultimately enhancing patient experiences, improving population health, and optimizing healthcare providers' workflows

The effect of co-existing nitrogen on hydrogen permeation through thin Pd composite membranes

This study reports the poisoning effect of co-existing nitrogen on hydrogen permeation through Pd composite membranes, consisting of thin Pd layers supported on alpha-Al2O3 hollow fibers. Hydrogen permeation of the composite membranes was measured at temperatures of 623-773 K for pure gas permeation in sequence of hydrogen, nitrogen and hydrogen and for mixture gas separation of equimolar H-2/N-2. The composite membranes were defect-free and gave high hydrogen permeance of 31.2m(3)/m(2) h bar at 773 K. However, when the H-2 activated composite membranes were exposed to nitrogen for a certain time, the followed hydrogen permeance decreased in comparison with the original value. The degree of decrease increased with decreasing temperature and with exposure time. Furthermore, when the composite membranes were exposed to the mixture feed of equimolar H2/N2 with certain total flow rate at temperatures of 673-723 K, the hydrogen flux on the permeate side kept stable only for several 100 min then decreased gradually to some extent. And significant reduction could be obtained during the followed pure hydrogen permeance test in comparison with the original value for the fresh membranes. Fortunately, no additional defects were formed on the Pd layers during these processes and the deactivations of the composite membranes were reversible. A certain-time hydrogen treatment at 773 K was sufficient to regenerate the deactivated membranes. The blocking of the active sites on Pd surface for hydrogen diffusion by the formed nitrogen-containing species (NHx, x = 0-2) was responsible for the deactivation of the membranes in the suggested deactivation mechanism. (c) 2006 Elsevier B.V. All rights reserved

Additional file 1 of Revisiting an old relationship: the causal associations of the ApoB/ApoA1 ratio with cardiometabolic diseases and relative risk factors—a mendelian randomization analysis

Supplementary Material