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Challenges in the Differential Classification of Individual Diagnoses from Co-Occurring Autism and ADHD Using Survey data

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arXiv 2024年

作者： Jaiswal, Aditi Wall, Dennis P. Washington, Peter Information and Computer Sciences Department University of Hawaii at Manoa Honolulu United States Departments of Pediatrics & Biomedical Data Science Stanford University Stanford United States

Autism and Attention-Deficit Hyperactivity Disorder (ADHD) are two of the most commonly observed neurodevelopmental conditions in childhood. Providing a specific computational assessment to distinguish between the two can prove difficult and time intensive. Given the high prevalence of their co-occurrence, there is a need for scalable and accessible methods for distinguishing the co-occurrence of autism and ADHD from individual diagnoses. The first step is to identify a core set of features that can serve as the basis for behavioral feature extraction. We trained machine learning models on data from the National Survey of Children's Health to identify behaviors to target as features in automated clinical decision support systems. A model trained on the binary task of distinguishing either developmental delay (autism or ADHD) vs. neither achieved sensitivity >92% and specificity >94%, while a model trained on the 4-way classification task of autism vs. ADHD vs. both vs. none demonstrated >65% sensitivity and >66% specificity. While the performance of the binary model was respectable, the relatively low performance in the differential classification of autism and ADHD highlights the challenges that persist in achieving specificity within clinical decision support tools for developmental delays. Nevertheless, this study demonstrates the potential of applying behavioral questionnaires not traditionally used for clinical purposes towards supporting digital screening assessments for pediatric developmental delays. © 2024, CC BY.

关键词： Diseases

Learning-Augmented Algorithms with Explicit Predictors

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arXiv 2024年

作者： Eliáš, Marek Kaplan, Haim Mansour, Yishay Moran, Shay Bocconi University Milan Italy Tel Aviv University Google Research Israel Departments of Mathematics Computer Science and Data and Decision Sciences Technion Google Research Israel

Recent advances in algorithmic design show how to utilize predictions obtained by machine learning models from past and present data. These approaches have demonstrated an enhancement in performance when the predictions are accurate, while also ensuring robustness by providing worst-case guarantees when predictions fail. In this paper we focus on online problems;prior research in this context was focused on a paradigm where the predictor is pre-trained on past data and then used as a black box (to get the predictions it was trained for). In contrast, in this work, we unpack the predictor and integrate the learning problem it gives rise for within the algorithmic challenge. In particular we allow the predictor to learn as it receives larger parts of the input, with the ultimate goal of designing online learning algorithms specifically tailored for the algorithmic task at hand. Adopting this perspective, we focus on a a number of fundamental problems, including caching and scheduling, which have been well-studied in the black-box setting. For each of the problems we consider, we introduce new algorithms that take advantage of explicit learning algorithms which we carefully design towards optimizing the overall performance. We demonstrate the potential of our approach by deriving performance bounds which improve over those established in previous work. © 2024, CC BY.

关键词： Forecasting

Probably Approximately Precision and Recall Learning

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arXiv 2024年

作者： Cohen, Lee Mansour, Yishay Moran, Shay Shao, Han Stanford United States Tel Aviv University Google Research Departments of Mathematics Computer Science and Data and Decision Sciences Technion and Google Research Harvard United States

Precision and Recall are foundational metrics in machine learning applications where both accurate predictions and comprehensive coverage are essential, such as in recommender systems and multi-label learning. In these tasks, balancing precision (the proportion of relevant items among those predicted) and recall (the proportion of relevant items successfully predicted) is crucial. A key challenge is that one-sided feedback—where only positive examples are observed during training—is inherent in many practical problems. For instance, in recommender systems like YouTube, training data only consists of songs or videos that a user has actively selected, while unselected items remain unseen. Despite this lack of negative feedback in training, it becomes crucial at test time;for example, in a recommender system, it’s essential to avoid recommending items the user would likely dislike. We introduce a Probably Approximately Correct (PAC) learning framework where each hypothesis is represented by a graph, with edges indicating positive interactions, such as between users and items. This framework subsumes the classical binary and multi-class PAC learning models as well as multi-label learning with partial feedback, where only a single random correct label per example is observed, rather than all correct labels. Our work uncovers a rich statistical and algorithmic landscape, with nuanced boundaries on what can and cannot be learned. Notably, classical methods like Empirical Risk Minimization fail in this setting, even for simple hypothesis classes with only two hypotheses. To address these challenges, we develop novel algorithms that learn exclusively from positive data, effectively minimizing both precision and recall losses. Specifically, in the realizable setting, we design algorithms that achieve optimal sample complexity guarantees. In the agnostic case, we show that it is impossible to achieve additive error guarantees (i.e., additive regret)—as is standard in PAC learn

关键词： Contrastive Learning

Content Moderation and the Formation of Online Communities: A Theoretical Framework

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arXiv 2023年

作者： Dwork, Cynthia Hays, Chris Kleinberg, Jon Raghavan, Manish Department of Computer Science Harvard University United States Institute for Data Systems and Society Massachusetts Institute of Technology United States Departments of Computer Science and Information Science Cornell University United States Sloan School of Management Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States

We study the impact of content moderation policies in online communities. In our theoretical model, a platform chooses a content moderation policy and individuals choose whether or not to participate in the community according to the fraction of user content that aligns with their preferences. The effects of content moderation, at first blush, might seem obvious: it restricts speech on a platform. However, when user participation decisions are taken into account, its effects can be more subtle - and counter-intuitive. For example, our model can straightforwardly demonstrate how moderation policies may increase participation and diversify content available on the platform. In our analysis, we explore a rich set of interconnected phenomena related to content moderation in online communities. We first characterize the effectiveness of a natural class of moderation policies for creating and sustaining communities. Building on this, we explore how resource-limited or ideological platforms might set policies, how communities are affected by differing levels of personalization, and competition between platforms. Our model provides a vocabulary and mathematically tractable framework for analyzing platform decisions about content moderation. Copyright © 2023, The Authors. All rights reserved.

关键词： Online systems

Invertibility of Discrete-Time Linear Systems with Sparse Inputs

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arXiv 2024年

作者： Poe, Kyle Mallada, Enrique Vidal, René Applied Mathematics and Computational Science Group University of Pennsylvania PA19104 United States Department of Electrical and Computer Engineering Johns Hopkins University MD21218 United States Departments of Electrical and Systems Engineering Radiology Computer and Information Science Statistics and Data Science University of Pennsylvania PA19104 United States

One of the fundamental problems of interest for discrete-time linear systems is whether its input sequence may be recovered given its output sequence, a.k.a. the left inversion problem. Many conditions on the state space geometry, dynamics, and spectral structure of a system have been used to characterize the well-posedness of this problem, without assumptions on the inputs. However, certain structural assumptions, such as input sparsity, have been shown to translate to practical gains in the performance of inversion algorithms, surpassing classical guarantees. Establishing necessary and sufficient conditions for left invertibility of systems with sparse inputs is therefore a crucial step toward understanding the performance limits of system inversion under structured input assumptions. In this work, we provide the first necessary and sufficient characterizations of left invertibility for linear systems with sparse inputs, echoing classic characterizations for standard linear systems. The key insight in deriving these results is in establishing the existence of two novel geometric invariants unique to the sparse-input setting, the weakly unobservable and strongly reachable subspace arrangements. By means of a concrete example, we demonstrate the utility of these characterizations. We conclude by discussing extensions and applications of this framework to several related problems in sparse control. © 2024, CC BY.

关键词： Linear systems

Estimating Higher-Order Mixed Memberships via the 2,∞ Tensor Perturbation Bound

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arXiv 2022年

作者： Agterberg, Joshua Zhang, Anru R. Departments of Electrical and Systems Engineering and Statistics and Data Science University of Pennsylvania United States Departments of Biostatistics & Bioinformatics Computer Science Mathematics and Statistical Science Duke University United States

Higher-order multiway data is ubiquitous in machine learning and statistics and often exhibits community-like structures, where each component (node) along each different mode has a community membership associated with it. In this paper we propose the tensor mixed-membership blockmodel, a generalization of the tensor blockmodel positing that memberships need not be discrete, but instead are convex combinations of latent communities. We establish the identifiability of our model and propose a computationally efficient estimation procedure based on the higher-order orthogonal iteration algorithm (HOOI) for tensor SVD composed with a simplex corner-finding algorithm. We then demonstrate the consistency of our estimation procedure by providing a per-node error bound, which showcases the effect of higher-order structures on estimation accuracy. To prove our consistency result, we develop the 2,∞ tensor perturbation bound for HOOI under independent, heteroskedastic, subgaussian noise that may be of independent interest. Our analysis uses a novel leave-one-out construction for the iterates, and our bounds depend only on spectral properties of the underlying low-rank tensor under nearly optimal signal-to-noise ratio conditions such that tensor SVD is computationally feasible. Finally, we apply our methodology to real and simulated data, demonstrating some effects not identifiable from the model with discrete community memberships. © 2022, CC BY.

关键词： Tensors

Algebraic and Statistical Properties of the Ordinary Least Squares Interpolator

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arXiv 2023年

作者： Shen, Dennis Song, Dogyoon Ding, Peng Sekhon, Jasjeet S. Department of Data Sciences & Operations University of Southern California United States Department of Electrical Engineering and Computer Science University of Michigan United States Department of Statistics University of California Berkeley United States Departments of Statistics & Data Science and Political Science Yale University United States

Deep learning research has uncovered the phenomenon of benign overfitting for overparameterized statistical models, which has drawn significant theoretical interest in recent years. Given its simplicity and practicality, the ordinary least squares (OLS) interpolator has become essential to gain foundational insights into this phenomenon. While properties of OLS are well established in classical, underparameterized settings, its behavior in high-dimensional, overparameterized regimes is less explored (unlike for ridge or lasso regression) though significant progress has been made of late. We contribute to this growing literature by providing fundamental algebraic and statistical results for the minimum 2-norm OLS interpolator. In particular, we provide algebraic equivalents of (i) the leave-k-out residual formula, (ii) Cochran’s formula, and (iii) the Frisch-Waugh-Lovell theorem in the overparameterized regime. These results aid in understanding the OLS interpolator’s ability to generalize and have substantive implications for causal inference. Under the Gauss-Markov model, we present statistical results such as an extension of the Gauss-Markov theorem and an analysis of variance estimation under homoskedastic errors for the overparameterized regime. To substantiate our theoretical contributions, we conduct simulations that further explore the stochastic properties of the OLS interpolator. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

Developing a Research Center for Artificial Intelligence in Medicine

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Mayo Clinic Proceedings: Digital Health

Mayo Clinic Proceedings: Digital Health 2024年第4期2卷 677-686页

作者： Langlotz, Curtis P. Kim, Johanna Shah, Nigam Lungren, Matthew P. Larson, David B. Datta, Somalee Li, Fei Fei Montine, Thomas J. Harrington, Robert A. Gold, Garry E. Departments of Radiology Medicine and Biomedical Data Science Center for Artificial Intelligence in Medicine and Imaging Stanford University Stanford CA United States Center for Artificial Intelligence in Medicine and Imaging Stanford University Stanford CA United States Departments of Medicine and Biomedical Data Science Center for Artificial Intelligence in Medicine and Imaging Stanford Health Care Stanford University Stanford CA United States Department of Radiology Stanford University Stanford CA United States Department of Computer Science Institute for Human Centered Artificial Intelligence Stanford University Stanford CA United States Department of Pathology Stanford University Stanford CA United States Office of the Dean Weill Cornell Medicine New York NY United States Microsoft Redmond WA United States Research Information Technology Stanford University School of Medicine Stanford CA United States Department of Psychiatry and Behavioral Sciences Stanford University School of Medicine Stanford CA United States

Artificial intelligence (AI) and machine learning (ML) are driving innovation in biosciences and are already affecting key elements of medical scholarship and clinical care. Many schools of medicine are capitalizing on the promise of these new technologies by establishing academic units to catalyze and grow research and innovation in AI/ML. At Stanford University, we have developed a successful model for an AI/ML research center with support from academic leaders, clinical departments, extramural grants, and industry partners. The Center for Artificial Intelligence in Medicine and Imaging uses the following 4 key tactics to support AI/ML research: project-based learning opportunities that build interdisciplinary collaboration;internal grant programs that catalyze extramural funding;infrastructure that facilitates the rapid creation of large multimodal AI-ready clinical data sets;and educational and open data programs that engage the broader research community. The center is based on the premise that foundational and applied research are not in tension but instead are complementary. Solving important biomedical problems with AI/ML requires high-quality foundational team science that incorporates the knowledge and expertise of clinicians, clinician scientists, computer scientists, and data scientists. As AI/ML becomes an essential component of research and clinical care, multidisciplinary centers of excellence in AI/ML will become a key part of the scholarly portfolio of academic medical centers and will provide a foundation for the responsible, ethical, and fair implementation of AI/ML systems. © 2024 The Authors

关键词： AI artificial intelligence AIMI Center Stanford Center for Artificial Intelligence in Medicine and Imaging CT computed tomography EHR electronic health record FDA Food and Drug Administration HAI Stanford Institute for Human Centered Artificial Intelligence ML machine learning STARR Stanford Research Repository

Conformal Frequency Estimation using Discrete Sketched data with Coverage for Distinct Queries

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arXiv 2022年

作者： Sesia, Matteo Favaro, Stefano Dobriban, Edgar Departments of Data Sciences and Operations Computer Science University of Southern California Los AngelesCA United States Department of Economics and Statistics University of Torino Collegio Carlo Alberto Torino Italy Departments of Statistics and Data Science Computer and Information Science University of Pennsylvania PhiladelphiaPA United States

This paper develops conformal inference methods to construct a confidence interval for the frequency of a queried object in a very large discrete data set, based on a sketch with a lower memory footprint. This approach requires no knowledge of the data distribution and can be combined with any sketching algorithm, including but not limited to the renowned count-min sketch, the count-sketch, and variations thereof. After explaining how to achieve marginal coverage for exchangeable random queries, we extend our solution to provide stronger inferences that can account for the discreteness of the data and for heterogeneous query frequencies, increasing also robustness to possible distribution shifts. These results are facilitated by a novel conformal calibration technique that guarantees valid coverage for a large fraction of distinct random queries. Finally, we show our methods have improved empirical performance compared to existing frequentist and Bayesian alternatives in simulations as well as in examples of text and SARS-CoV-2 DNA data. © 2022, CC BY-NC-ND.

关键词： Frequency estimation