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Combining Experimental and Observational Data for Identification and Estimation of Long-Term Causal Effects

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arXiv 2022年

作者： Ghassami, AmirEmad Yang, Alan Richardson, David Shpitser, Ilya Tchetgen, Eric Tchetgen Department of Computer Science Johns Hopkins University United States Department of Electrical Engineering Stanford University United States Department of Environmental and Occupational Health Program in Public Health University of California Irvine United States Department of Statistics and Data Science The Wharton School University of Pennsylvania United States

We consider the task of identifying and estimating the causal effect of a treatment variable on a long-term outcome variable using data from an observational domain and an experimental domain. The observational domain is subject to unobserved confounding. Furthermore, subjects in the experiment are only followed for a short period of time;hence, long-term effects of treatment are unobserved but short-term effects will be observed. Therefore, data from neither domain alone suffices for causal inference about the effect of the treatment on the long-term outcome, and must be pooled in a principled way, instead. Athey et al. (2020) proposed a method for systematically combining such data for identifying the downstream causal effect in view. Their approach is based on the assumptions of internal and external validity of the experimental data, and an extra novel assumption called latent unconfoundedness. In this paper, we first review their proposed approach, and then we propose three alternative approaches for data fusion for the purpose of identifying and estimating average treatment effect as well as the effect of treatment on the treated. Our first approach is based on assuming equi-confounding bias for the short-term and long-term outcomes. Our second approach is based on a relaxed version of the equi-confounding bias assumption, where we assume the existence of an observed confounder such that the short-term and long-term potential outcome variables have the same partial additive association with that confounder. Our third approach is based on the proximal causal inference framework, in which we assume the existence of an extra variable in the system which is a proxy of the latent confounder of the treatment-outcome relation. We propose influence function-based estimation strategies for each of our data fusion frameworks and study the robustness properties of the proposed estimators. © 2022, CC BY.

关键词： Data fusion

Smart city governance through big data: Transformation towards sustainability

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Smart city governance through big data: Transformation towar...

2021 International Conference of Women in Data Science at Taif University, WiDSTaif 2021

作者： Sarker, Md Nazirul Islam Kamruzzaman, M.M. Huq, Md Enamul Zaman, Roksana Hossain, Babul Khurshid, Sumaira School of Political Science and Public Administration Neijiang Normal University Neijiang614112 China Department of Computer and Information Science Jouf University Sakaka Al-Jouf Saudi Arabia State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China School of Electrical Engineering and Information Southwest Petroleum University Chengdu China Research Center for Environment and Society Hohai University Nanjing China School of Education and Science Neijiang Normal University Neijiang China

ISBN: (纸本)9781665449489

Smart city governance (SCG) is a new paradigm of urban policy, planning, and management that able to solve emerging challenges of urban areas along with ensuring urban sustainability. Data-driven technology provides an opportunity to transform the conventional practices into smart practices at all levels of urban actors. This study assesses the suitability of big data approaches for ensuring SCG. A systematic desk review has been conducted under the framework of PRISMA. This study reveals a difference among the opinions of scholars on SCG, such as, a group of scholars emphasizes on smart characteristics of technology, people, and their integration, while other group focuses on the digital transformation of urban management practices of key actors like public, private and community. This study argues that SCG is a combined function of public, private, and community that adapted advanced technology like big data in their practices for transformation from conventional one for ensuring urban sustainability. This study offers a number of indicators for the concerned authorities to understand the SCG and take priority-based decisions from systemic data and information. © 2021 IEEE.

关键词： Smart city

A Survey on the Scope of Cloud Computing

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SSRN

SSRN 2021年

作者： Agarwal, Parul Sharma, Dilip Kumar Varun, V.L. Venkatesh, Pruthvi Raj Kanchibhotla, Chaitanya Ventayen, Randy Joy Magno Mohiddin, Md. Khaja Department of Computer science and engineering Jamia Hamdard Delhi India GLA University SJB Institute of Technology Bengaluru India Dr. Ambedkar Institute of Technology Bangalore India Warangal India Public Relations Publication and Information Office Pangasinan State University Pangasinan Philippines Department of Electrical and Computer Engineering

Distributed computing is an accelerated innovation that has resulted in major transformations and opportunities in India in different fields. This is an inevitable approximation of how to change the knowledge system and management that is increasingly interested in the use of distributed computing in the field of training. This study seeks to survey an applied distributed model of computation between the academic network and the society for a composite effort. This paper presents distributed electronic computing, with points: its functions, its organizational structure, its goals and its problems. This document represents an intelligent evaluation of the work of distributed computer processing in the field of advice for working companies. The requisite evaluation was finished with the key partners of the specific preparation systems, which are performed for informative use. The course was conducted through the use of distributed computing and the right subjective approach in the exam classroom. Approximately 8 evaluation tasks were perceived following a thorough examination of open writing and addressed the significance of cloud in the school environment and the ease of significant usage. The examination considers and evaluates that the main accomplices in planning should be used. The comprehensive research indicates that familiarization with board preparation is practical in order to make the desirable circumstances clearer. © 2021, The Authors. All rights reserved.

关键词： Surveys

Visualizing environmental justice issues in urban areas with a community-based approach

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arXiv 2021年

作者： Flax-Hatch, Joel Srabanti, Sanjana Miranda, Fabio Sambanis, Apostolis Cailas, Michael D. Environmental and Occupational Health Sciences School of Public Health University of Illinois Chicago United States Department of Computer Science College of Engineering University of Illinois Chicago United States Health Policy and Administration School of Public Health University of Illinois Chicago United States

According to environmental justice, environmental degradation and benefits should not be disproportionately shared between communities. Identifying disparities in the spatial distribution of environmental degradation is therefore a prerequisite for validating the state of environmental justice in a geographic region. Under ideal circumstances, environmental risk assessment is a preferred metric, but only when exposure levels have been quantified reliably after estimating the risk. In this study, we adopt a proximity burden metric caused by adjacent hazardous sources, allowing us to evaluate the environmental burden distribution and vulnerability to pollution sources. In close collaboration with a predominantly Latinx community in Chicago, we highlight the usefulness of our approach through a case study that shows how certain community areas in the city are likely to bear a disproportionate burden of environmental pollution caused by industrial roads. Copyright © 2021, The Authors. All rights reserved.

关键词： Risk assessment

Relationship among state reopening policies, health outcomes and economic recovery through first wave of the COVID-19 pandemic in the U.S

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arXiv 2021年

作者： Ligo, Alexandre K. Mahoney, Emerson Cegan, Jeffrey Trump, Benjamin D. Jin, Andrew S. Kitsak, Maksim Keenan, Jesse Linkov, Igor US Army Corps of Engineers Engineer Research and Development Center ConcordMA United States University of Virginia Department of Engineering Systems and Environment CharlottesvilleVA United States Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft Netherlands Carnegie Mellon University Department of Engineering and Public Policy PittsburghPA United States Tulane University School of Architecture New OrleansLA United States

State governments in the U.S. have been facing difficult decisions involving tradeoffs between economic and health-related outcomes during the COVID-19 pandemic. Despite evidence of the effectiveness of government-mandated restrictions mitigating the spread of contagion, these orders are stigmatized due to undesirable economic consequences. This tradeoff resulted in state governments employing mandates at widely different ways. We compare the different policies states implemented during periods of restriction ("lockdown") and reopening with indicators of COVID-19 spread and consumer card spending at each state during the first "wave" of the pandemic in the U.S. between March and August 2020. We find that while some states enacted reopening decisions when the incidence rate of COVID-19 was minimal or sustained in its relative decline, other states relaxed socioeconomic restrictions near their highest incidence and prevalence rates experienced so far. Nevertheless, all states experienced similar trends in consumer card spending recovery, which was strongly correlated with reopening policies following the lockdowns and relatively independent from COVID-19 incidence rates at the time. Our findings suggest that consumer card spending patterns can be attributed to government mandates rather than COVID-19 incidence in the states. We estimate the recovery in states that reopened in late April was more than the recovery in states that did not reopen in the same period – 15% for consumer card spending and 18% for spending by high income households. This result highlights the important role of state policies in minimizing health impacts while promoting economic recovery and helps planning effective interventions in subsequent waves and immunization efforts. © 2021, CC BY.

关键词： SARS

Multimodal Deep Learning for Low-Resource Settings: A Vector Embedding Alignment Approach for Healthcare Applications

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arXiv 2024年

作者： Restrepo, David Wu, Chenwei Cajas, Sebastían Andrés Nakayama, Luis Filipe Celi, Leo Anthony López, Diego M. Laboratory for Computational Physiology Massachusetts Institute of Technology CambridgeMA United States Departamento de Telemática Universidad del Cauca Cauca Popayán Colombia Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States John A. Paulson School of Engineering and Applied Sciences Harvard University BostonMA United States School of Computer Science University College Dublin Belfield Dublin Ireland Department of Ophthalmology São Paulo Federal University São Paulo São Paulo Brazil Department of Biostatistics Harvard TH Chan School of Public Health BostonMA United States Department of Medicine Beth Israel Deaconess Medical Center BostonMA United States

Objective: Large-scale multi-modal deep learning models and datasets have revolutionized various domains such as healthcare, underscoring the critical role of computational power. However, in resource-constrained regions like Low and Middle-Income Countries (LMICs), GPU and data access is limited, leaving many dependent solely on CPUs. To address this, we advocate leveraging vector embeddings for flexible and efficient computational methodologies, aiming to democratize multimodal deep learning across diverse contexts. Background and Significance: Our paper investigates the computational efficiency and effectiveness of leveraging vector embeddings, extracted from singlemodal foundation models and multi-modal Vision-Language Models (VLM), for multimodal deep learning in low-resource environments, particularly in healthcare applications. Additionally, we propose an easy but effective inference-time method to enhance performance by further aligning image-text embeddings. Materials and Methods: By comparing these approaches with traditional multimodal deep learning methods, we assess their impact on computational efficiency and model performance using accuracy, F1-score, inference time, training time, and memory usage across 3 medical modalities such as BRSET (ophthalmology), HAM10000 (dermatology), and SatelliteBench (public health). Results: Our findings indicate that embeddings reduce computational demands without compromising the model's performance, and show that our embedding alignment method improves the performance of the models in medical tasks. Discussion: This research contributes to sustainable AI practices by optimizing computational resources in resource-constrained environments. It highlights the potential of embedding-based approaches for efficient multimodal learning. Conclusion: Vector embeddings democratize multimodal deep learning in LMICs, especially in healthcare. Our study showcases their effectiveness, enhancing AI adaptability in varied use cases

关键词： Embeddings

Retrieval-Augmented and Knowledge-Grounded Language Models for Faithful Clinical Medicine

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arXiv 2022年

作者： Liu, Fenglin Yang, Bang You, Chenyu Wu, Xian Ge, Shen Liu, Zhangdaihong Sun, Xu Yang, Yang Clifton, David A. Department of Engineering Science University of Oxford United Kingdom School of ECE Peking University China Department of Electrical Engineering Yale University United States Tencent JARVIS Lab China Oxford-Suzhou Centre for Advanced Research China MOE Key Lab of Computational Linguistics School of Computer Science Peking University China School of Public Health Shanghai Jiao Tong University School of Medicine China

Language models (LMs), including large language models (such as ChatGPT), have the potential to assist clinicians in generating various clinical notes. However, LMs are prone to produce "hallucinations", i.e., generated content that is not aligned with facts and knowledge. In this paper, we propose the Re3Writer method with retrieval-augmented generation and knowledge-grounded reasoning to enable LMs to generate faithful clinical texts. We demonstrate the effectiveness of our method in generating patient discharge instructions. It requires the LMs not to only understand the patients’ long clinical documents, i.e., the health records during hospitalization, but also to generate critical instructional information provided both to carers and to the patient at the time of discharge. The proposed Re3Writer imitates the working patterns of physicians to first retrieve related working experience from historical instructions written by physicians, then reason related medical knowledge. Finally, it refines the retrieved working experience and reasoned medical knowledge to extract useful information, which is used to generate the discharge instructions for previously-unseen patients. Our experiments show that, using our method, the performance of five representative LMs can be substantially boosted across all metrics. Meanwhile, we show results from human evaluations to measure the effectiveness in terms of fluency, faithfulness, and comprehensiveness. © 2022, CC BY-NC-SA.

关键词： Electronic health record

Fisher-Pitman permutation tests based on nonparametric Poisson mixtures with application to single cell genomics

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arXiv 2021年

作者： Miao, Zhen Kong, Weihao Vinayak, Ramya Korlakai Sun, Wei Han, Fang Department of Statistics University of Washington Seattle United States Google Inc Department of Electrical and Computer Engineering University of Wisconsin-Madison Public Health Science Division Fred Hutchinson Cancer Research Center

This paper investigates the theoretical and empirical performance of Fisher-Pitman-type permutation tests for assessing the equality of unknown Poisson mixture distributions. Building on nonparametric maximum likelihood estimators (NPMLEs) of the mixing distribution, these tests are theoretically shown to be able to adapt to complicated unspecified structures of count data and also consistent against their corresponding ANOVA-type alternatives;the latter is a result in parallel to classic claims made by Robinson (Robinson, 1973). The studied methods are then applied to a single-cell RNA-seq data obtained from different cell types from brain samples of autism subjects and healthy controls;empirically, they unveil genes that are differentially expressed between autism and control subjects yet are missed using common tests. For justifying their use, rate optimality of NPMLEs is also established in settings similar to nonparametric Gaussian (Wu and Yang, 2020a) and binomial mixtures (Tian et al., 2017;Vinayak et al., 2019). Copyright © 2021, The Authors. All rights reserved.

关键词： Genes

RCMNet: A deep learning model assists CAR-T therapy for leukemia

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arXiv 2022年

作者： Zhang, Ruitao Han, Xueying Gul, Ijaz Zhai, Shiyao Liu, Ying Zhang, Yongbing Dong, Yuhan Ma, Lan Yu, Dongmei Zhou, Jin Qin, Peiwu Institute of Biomedical and Health Engineering Tsinghua Shenzhen International Graduate School China Precision Medicine and Public Health Tsinghua-Berkeley Shenzhen Institute United States The First Affiliated Hospital of Harbin Medical University China Food Inspection & Quarantine Center Shenzhen Custom China Department of Computer Science Harbin Insitute of Technology Shenzhen China School of Mc chanical Electrical & Information Engineering Shandong University China

Acute leukemia is a type of blood cancer with a high mortality rate. Current therapeutic methods include bone marrow transplantation, supportive therapy, and chemotherapy. Although a satisfactory remission of the disease can be achieved, the risk of recurrence is still high. Therefore, novel treatments are demanding. Chimeric antigen receptor-T (CAR-T) therapy has emerged as a promising approach to treat and cure acute leukemia. To harness the therapeutic potential of CAR-T cell therapy for blood diseases, reliable cell morphological identification is crucial. Nevertheless, the identification of CAR-T cells is a big challenge posed by their phenotypic similarity with other blood cells. To address this substantial clinical challenge, herein we first construct a CAR-T dataset with 500 original microscopy images after staining. Following that, we create a novel integrated model called RCMNet (ResNet18 with CBAM and MHSA) that combines the convolutional neural network (CNN) and Transformer. The model shows 99.63% top-1 accuracy on the public dataset. Compared with previous reports, our model obtains satisfactory results for image classification. Although testing on the CAR-T cells dataset, a decent performance is observed, which is attributed to the limited size of the dataset. Transfer learning is adapted for RCMNet and a maximum of 83.36% accuracy has been achieved, which is higher than other SOTA models. The study evaluates the effectiveness of RCMNet on a big public dataset and translates it to a clinical dataset for diagnostic applications. Copyright © 2022, The Authors. All rights reserved.

关键词： T-cells