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  1. Book ; Online: Causal Data Integration

    Youngmann, Brit / Cafarella, Michael / Salimi, Babak / Zeng, Anna

    2023  

    Abstract: Causal inference is fundamental to empirical scientific discoveries in natural and social sciences; however, in the process of conducting causal inference, data management problems can lead to false discoveries. Two such problems are (i) not having all ... ...

    Abstract Causal inference is fundamental to empirical scientific discoveries in natural and social sciences; however, in the process of conducting causal inference, data management problems can lead to false discoveries. Two such problems are (i) not having all attributes required for analysis, and (ii) misidentifying which attributes are to be included in the analysis. Analysts often only have access to partial data, and they critically rely on (often unavailable or incomplete) domain knowledge to identify attributes to include for analysis, which is often given in the form of a causal DAG. We argue that data management techniques can surmount both of these challenges. In this work, we introduce the Causal Data Integration (CDI) problem, in which unobserved attributes are mined from external sources and a corresponding causal DAG is automatically built. We identify key challenges and research opportunities in designing a CDI system, and present a system architecture for solving the CDI problem. Our preliminary experimental results demonstrate that solving CDI is achievable and pave the way for future research.
    Keywords Computer Science - Databases
    Publishing date 2023-05-15
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article: Alvopem

    Seifi, Sharareh / Salimi, Babak / Monfared, Zahra Esfahani / Sabahi, Cyrus / Kafi, Hamidreza / Khosravi, Adnan

    Journal of pharmaceutical policy and practice

    2023  Volume 16, Issue 1, Page(s) 16

    Abstract: Background: Lung cancer is the leading cause of cancer deaths worldwide in both men and women, and non-small cell lung cancer (NSCLC) accounts for the majority (~ 85%) of lung cancers. This post-marketing surveillance (PMS) study aimed to evaluate the ... ...

    Abstract Background: Lung cancer is the leading cause of cancer deaths worldwide in both men and women, and non-small cell lung cancer (NSCLC) accounts for the majority (~ 85%) of lung cancers. This post-marketing surveillance (PMS) study aimed to evaluate the safety of Pemetrexed (Alvopem
    Methods: The present study is an observational, single-center, open-label, and post-authorization study. All eligible non-squamous NSCLC and malignant pleural mesothelioma (MPM) patients who received pemetrexed based on the physicians' decision, were enrolled.
    Results: A total of 199 patients with non-squamous NSCLC [186 patients (93.47%) or MPM (12 patients (6.03%)] were enrolled from March 2016 to February 2020. The most common reported adverse event (AE) was anemia (89.39%), followed by neutropenia (28.79%) and leukopenia (24.75%). The most important grade 3 AEs were anemia and neutropenia, with the incidence rate of 3.54% and 7.58%, respectively. No grade 4 AEs were reported. Moreover, the results of our study showed negative statistically significant correlations between patients' age and mean neutrophil count (r = - 0.17; P = 0.0156) and hemoglobin (r = - 0.16; P = 0.0201) in all six visits.
    Conclusions: The results of this open-label, observational PMS showed that Pemetrexed (Alvopem
    Trial registration: The trial was registered at ClinicalTrials.gov (NCT04843007) in April 13th, 2021.
    Language English
    Publishing date 2023-01-25
    Publishing country England
    Document type Journal Article
    ZDB-ID 2734772-2
    ISSN 2052-3211
    ISSN 2052-3211
    DOI 10.1186/s40545-023-00524-5
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Book ; Online: Consistent Range Approximation for Fair Predictive Modeling

    Zhu, Jiongli / Galhotra, Sainyam / Sabri, Nazanin / Salimi, Babak

    2022  

    Abstract: This paper proposes a novel framework for certifying the fairness of predictive models trained on biased data. It draws from query answering for incomplete and inconsistent databases to formulate the problem of consistent range approximation (CRA) of ... ...

    Abstract This paper proposes a novel framework for certifying the fairness of predictive models trained on biased data. It draws from query answering for incomplete and inconsistent databases to formulate the problem of consistent range approximation (CRA) of fairness queries for a predictive model on a target population. The framework employs background knowledge of the data collection process and biased data, working with or without limited statistics about the target population, to compute a range of answers for fairness queries. Using CRA, the framework builds predictive models that are certifiably fair on the target population, regardless of the availability of external data during training. The framework's efficacy is demonstrated through evaluations on real data, showing substantial improvement over existing state-of-the-art methods.
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Databases ; Statistics - Machine Learning
    Subject code 006
    Publishing date 2022-12-21
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: HypeR

    Galhotra, Sainyam / Gilad, Amir / Roy, Sudeepa / Salimi, Babak

    Hypothetical Reasoning With What-If and How-To Queries Using a Probabilistic Causal Approach

    2022  

    Abstract: What-if (provisioning for an update to a database) and how-to (how to modify the database to achieve a goal) analyses provide insights to users who wish to examine hypothetical scenarios without making actual changes to a database and thereby help plan ... ...

    Abstract What-if (provisioning for an update to a database) and how-to (how to modify the database to achieve a goal) analyses provide insights to users who wish to examine hypothetical scenarios without making actual changes to a database and thereby help plan strategies in their fields. Typically, such analyses are done by testing the effect of an update in the existing database on a specific view created by a query of interest. In real-world scenarios, however, an update to a particular part of the database may affect tuples and attributes in a completely different part due to implicit semantic dependencies. To allow for hypothetical reasoning while accommodating such dependencies, we develop HypeR, a framework that supports what-if and how-to queries accounting for probabilistic dependencies among attributes captured by a probabilistic causal model. We extend the SQL syntax to include the necessary operators for expressing these hypothetical queries, define their semantics, devise efficient algorithms and optimizations to compute their results using concepts from causality and probabilistic databases, and evaluate the effectiveness of our approach experimentally.

    Comment: Full version of the SIGMOD 2022 paper with the same title
    Keywords Computer Science - Databases
    Subject code 005
    Publishing date 2022-03-28
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: Combining Counterfactuals With Shapley Values To Explain Image Models

    Lahiri, Aditya / Alipour, Kamran / Adeli, Ehsan / Salimi, Babak

    2022  

    Abstract: With the widespread use of sophisticated machine learning models in sensitive applications, understanding their decision-making has become an essential task. Models trained on tabular data have witnessed significant progress in explanations of their ... ...

    Abstract With the widespread use of sophisticated machine learning models in sensitive applications, understanding their decision-making has become an essential task. Models trained on tabular data have witnessed significant progress in explanations of their underlying decision making processes by virtue of having a small number of discrete features. However, applying these methods to high-dimensional inputs such as images is not a trivial task. Images are composed of pixels at an atomic level and do not carry any interpretability by themselves. In this work, we seek to use annotated high-level interpretable features of images to provide explanations. We leverage the Shapley value framework from Game Theory, which has garnered wide acceptance in general XAI problems. By developing a pipeline to generate counterfactuals and subsequently using it to estimate Shapley values, we obtain contrastive and interpretable explanations with strong axiomatic guarantees.
    Keywords Computer Science - Machine Learning
    Subject code 006
    Publishing date 2022-06-14
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article: Circulating Levels of Monocytic Myeloid-Derived Suppressor Cells (M-MDSC) and CXCL-8 in Non-Small Cell Lung Cancer (NSCLC).

    Zadian, Seyed Sajjad / Adcock, Ian M / Salimi, Babak / Mortaz, Esmaeil

    Tanaffos

    2021  Volume 20, Issue 1, Page(s) 15–21

    Abstract: Background: Myeloid-derived suppressor cells (MDSC) are categorized as granulocytic (G-MDSCs) and monocytic (M-MDSCs) and their expansion play a role in cancer progression. Recruitment to the cancer site depends upon the presence of a chemoattractant. ... ...

    Abstract Background: Myeloid-derived suppressor cells (MDSC) are categorized as granulocytic (G-MDSCs) and monocytic (M-MDSCs) and their expansion play a role in cancer progression. Recruitment to the cancer site depends upon the presence of a chemoattractant. We aimed to investigate the presence of MDSC subtypes and of interleukin-8 (CXCL-8) in the peripheral blood in lung cancer subtypes including non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) patients.
    Materials and methods: Peripheral blood samples of 26 NSCLC patients, 18 SCLC patients, and 8 healthy control donors (HDs) were harvested and the surface expression of CD14, CD15, CD11b, and HLA-DR on MDSCs was measured using flow cytometry. The level of serum CXCL8 was measured by the ELISA method.
    Results: The frequency of circulating M-MDSCs was significantly higher in patients with NSCLC than in SCLC and HDs. In contrast, there was no statistical difference concerning the frequency of circulating G-MDSCs between the three groups. The concentration of CXCL-8 was significantly higher in the NSCLC and SCLC patients than in HD control with no significant difference between NSCLC and SCLC groups. There was no correlation between serum CXCL8 and G-MDSC levels.
    Conclusion: Our data confirm a higher frequency of circulating M-MDSCs, but not G-MDSCs, in the blood of those suffering from NSCLC but not for SCLC cases. Measuring MDSC subtypes and serum chemotactic factors may have implications for the differential diagnosis of NSCLC.
    Language English
    Publishing date 2021-07-27
    Publishing country Iran
    Document type Journal Article
    ZDB-ID 2233372-1
    ISSN 1735-0344
    ISSN 1735-0344
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: FAPI Uptake in Gallbladder Is It Normal Biodistribution?

    Behnam-Manesh, Hossein / Doroudinia, Abtin / Bayat, Mohadeseh / Bakhshayesh Karam, Mehrdad / Salimi, Babak / Nejabat, Marzieh / Mehrian, Payam

    Clinical nuclear medicine

    2023  Volume 49, Issue 1, Page(s) e40–e41

    Abstract: Abstract: Radiolabeled fibroblast activation protein inhibitors (FAPIs) have been extensively used in different types of cancers, although not yet FDA approved. Normal patterns of FAPI biodistribution have been investigated, and it is known that FAPI is ...

    Abstract Abstract: Radiolabeled fibroblast activation protein inhibitors (FAPIs) have been extensively used in different types of cancers, although not yet FDA approved. Normal patterns of FAPI biodistribution have been investigated, and it is known that FAPI is expressed in nonmalignant pathophysiological lesions, characterized by tissue remodeling such as atherosclerosis, arthritis, and scar/fibrotic tissues. In this interesting image, we are presenting the accumulation of 68 Ga-FAPI in the gallbladder. This finding could be related to a normal distribution of the radiotracer as a physiologic finding. This is a potentially important finding as FAPI may be used as theragnostic agent in the future.
    MeSH term(s) Humans ; Gallbladder/diagnostic imaging ; Tissue Distribution ; Biological Transport ; Arthritis ; Gallium Radioisotopes ; Positron Emission Tomography Computed Tomography
    Chemical Substances Gallium Radioisotopes
    Language English
    Publishing date 2023-11-16
    Publishing country United States
    Document type Journal Article
    ZDB-ID 197628-x
    ISSN 1536-0229 ; 0363-9762
    ISSN (online) 1536-0229
    ISSN 0363-9762
    DOI 10.1097/RLU.0000000000004955
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Book ; Online: Interpretable Data-Based Explanations for Fairness Debugging

    Pradhan, Romila / Zhu, Jiongli / Glavic, Boris / Salimi, Babak

    2021  

    Abstract: A wide variety of fairness metrics and eXplainable Artificial Intelligence (XAI) approaches have been proposed in the literature to identify bias in machine learning models that are used in critical real-life contexts. However, merely reporting on a ... ...

    Abstract A wide variety of fairness metrics and eXplainable Artificial Intelligence (XAI) approaches have been proposed in the literature to identify bias in machine learning models that are used in critical real-life contexts. However, merely reporting on a model's bias, or generating explanations using existing XAI techniques is insufficient to locate and eventually mitigate sources of bias. We introduce Gopher, a system that produces compact, interpretable and causal explanations for bias or unexpected model behavior by identifying coherent subsets of the training data that are root-causes for this behavior. Specifically, we introduce the concept of causal responsibility that quantifies the extent to which intervening on training data by removing or updating subsets of it can resolve the bias. Building on this concept, we develop an efficient approach for generating the top-k patterns that explain model bias that utilizes techniques from the machine learning (ML) community to approximate causal responsibility and uses pruning rules to manage the large search space for patterns. Our experimental evaluation demonstrates the effectiveness of Gopher in generating interpretable explanations for identifying and debugging sources of bias.

    Comment: Proceedings of the 2022 ACM SIGMOD International Conference on Management of Data (SIGMOD). 2022
    Keywords Computer Science - Machine Learning ; Computer Science - Databases
    Subject code 006
    Publishing date 2021-12-17
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Book ; Online: Explaining Image Classifiers Using Contrastive Counterfactuals in Generative Latent Spaces

    Alipour, Kamran / Lahiri, Aditya / Adeli, Ehsan / Salimi, Babak / Pazzani, Michael

    2022  

    Abstract: Despite their high accuracies, modern complex image classifiers cannot be trusted for sensitive tasks due to their unknown decision-making process and potential biases. Counterfactual explanations are very effective in providing transparency for these ... ...

    Abstract Despite their high accuracies, modern complex image classifiers cannot be trusted for sensitive tasks due to their unknown decision-making process and potential biases. Counterfactual explanations are very effective in providing transparency for these black-box algorithms. Nevertheless, generating counterfactuals that can have a consistent impact on classifier outputs and yet expose interpretable feature changes is a very challenging task. We introduce a novel method to generate causal and yet interpretable counterfactual explanations for image classifiers using pretrained generative models without any re-training or conditioning. The generative models in this technique are not bound to be trained on the same data as the target classifier. We use this framework to obtain contrastive and causal sufficiency and necessity scores as global explanations for black-box classifiers. On the task of face attribute classification, we show how different attributes influence the classifier output by providing both causal and contrastive feature attributions, and the corresponding counterfactual images.
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Artificial Intelligence
    Subject code 006
    Publishing date 2022-06-10
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Book ; Online: Detecting Treatment Effect Modifiers in Social Networks

    Gilad, Amir / Parikh, Harsh / Roy, Sudeepa / Salimi, Babak

    2021  

    Abstract: We study treatment effect modifiers for causal analysis in a social network, where neighbors' characteristics or network structure may affect the outcome of a unit, and the goal is to identify sub-populations with varying treatment effects using such ... ...

    Abstract We study treatment effect modifiers for causal analysis in a social network, where neighbors' characteristics or network structure may affect the outcome of a unit, and the goal is to identify sub-populations with varying treatment effects using such network properties. We propose a novel framework for this purpose that facilitates data-driven decision making by testing hypotheses about complex effect modifiers in terms of network features or network patterns (e.g., characteristics of neighbors of a unit or belonging to a triangle), and by identifying sub-populations for which a treatment is likely to be effective or harmful. We describe a hypothesis testing approach that accounts for a unit's covariates, their neighbors' covariates, and patterns in the social network, and devise an algorithm incorporating ideas from causal inference, hypothesis testing, and graph theory to verify a hypothesized effect modifier. In addition, we develop a novel algorithm for the discovery of network patterns that are potential effect modifiers. We perform extensive experimental evaluations with a real development economics dataset about the treatment effect of belonging to a financial support network called self-help groups on risk tolerance, and also with a synthetic dataset with known ground truths simulating a vaccine efficacy trial, to evaluate our framework and algorithms.
    Keywords Computer Science - Social and Information Networks
    Subject code 006
    Publishing date 2021-05-21
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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