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  1. Article ; Online: Explainable

    Bertsimas, Dimitris / Margonis, Georgios Antonios

    Translational cancer research

    2023  Volume 12, Issue 2, Page(s) 217–220

    Language English
    Publishing date 2023-01-19
    Publishing country China
    Document type Editorial ; Comment
    ZDB-ID 2901601-0
    ISSN 2219-6803 ; 2218-676X
    ISSN (online) 2219-6803
    ISSN 2218-676X
    DOI 10.21037/tcr-22-2427
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Personalized Breast Cancer Screening.

    Bertsimas, Dimitris / Ma, Yu / Nohadani, Omid

    JCO clinical cancer informatics

    2023  Volume 7, Page(s) e2300026

    Abstract: Purpose: Abundant literature and clinical trials indicate that routine cancer screenings decrease patient mortality for several common cancers. However, current national cancer screening guidelines heavily rely on patient age as the predominant factor ... ...

    Abstract Purpose: Abundant literature and clinical trials indicate that routine cancer screenings decrease patient mortality for several common cancers. However, current national cancer screening guidelines heavily rely on patient age as the predominant factor in deciding cancer screening timing, neglecting other important medical characteristics of individual patients. This approach either delays screening or prescribes excessive screenings. Another disadvantage of the current approach is its inability to combine information across hospital systems because of the lack of a coherent records system.
    Methods: We propose to use claims data and medical insurance transactions that use consistent and pre-established sets of codes for diagnosis, procedures, and medications to develop a clinical support tool to supply supplemental insights and precautions for physicians to make more informed decisions. Furthermore, we propose a novel machine learning framework to recommend personalized, data-driven, and dynamic screening decisions.
    Results: We apply this new method to the study of breast cancer mammograms using claims data from 378,840 female patients to demonstrate that across different risk populations, personalized screening reduces the average delay in a cancer diagnosis by 2-3 months with statistical significance, with even stronger benefits for individual patients up to 10 months.
    Conclusion: Incorporating personal medical characteristics using claims data and novel machine learning methodologies into breast cancer screening improves screening delay by more dynamically considering changing patient risks. Future incorporation of the proposed methodology in health care settings could be provided as a potential support tool for clinicians.
    MeSH term(s) Humans ; Female ; Breast Neoplasms/diagnosis ; Breast Neoplasms/prevention & control ; Early Detection of Cancer ; Mammography ; Physicians
    Language English
    Publishing date 2023-10-16
    Publishing country United States
    Document type Journal Article
    ISSN 2473-4276
    ISSN (online) 2473-4276
    DOI 10.1200/CCI.23.00026
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Book ; Online: Global Flood Prediction

    Zeng, Cynthia / Bertsimas, Dimitris

    a Multimodal Machine Learning Approach

    2023  

    Abstract: Flooding is one of the most destructive and costly natural disasters, and climate changes would further increase risks globally. This work presents a novel multimodal machine learning approach for multi-year global flood risk prediction, combining ... ...

    Abstract Flooding is one of the most destructive and costly natural disasters, and climate changes would further increase risks globally. This work presents a novel multimodal machine learning approach for multi-year global flood risk prediction, combining geographical information and historical natural disaster dataset. Our multimodal framework employs state-of-the-art processing techniques to extract embeddings from each data modality, including text-based geographical data and tabular-based time-series data. Experiments demonstrate that a multimodal approach, that is combining text and statistical data, outperforms a single-modality approach. Our most advanced architecture, employing embeddings extracted using transfer learning upon DistilBert model, achieves 75\%-77\% ROCAUC score in predicting the next 1-5 year flooding event in historically flooded locations. This work demonstrates the potentials of using machine learning for long-term planning in natural disaster management.

    Comment: 6 pages
    Keywords Computer Science - Machine Learning ; Computer Science - Computers and Society
    Subject code 006
    Publishing date 2023-01-29
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: Multistage Stochastic Optimization via Kernels

    Bertsimas, Dimitris / Carballo, Kimberly Villalobos

    2023  

    Abstract: We develop a non-parametric, data-driven, tractable approach for solving multistage stochastic optimization problems in which decisions do not affect the uncertainty. The proposed framework represents the decision variables as elements of a reproducing ... ...

    Abstract We develop a non-parametric, data-driven, tractable approach for solving multistage stochastic optimization problems in which decisions do not affect the uncertainty. The proposed framework represents the decision variables as elements of a reproducing kernel Hilbert space and performs functional stochastic gradient descent to minimize the empirical regularized loss. By incorporating sparsification techniques based on function subspace projections we are able to overcome the computational complexity that standard kernel methods introduce as the data size increases. We prove that the proposed approach is asymptotically optimal for multistage stochastic optimization with side information. Across various computational experiments on stochastic inventory management problems, {our method performs well in multidimensional settings} and remains tractable when the data size is large. Lastly, by computing lower bounds for the optimal loss of the inventory control problem, we show that the proposed method produces decision rules with near-optimal average performance.
    Keywords Mathematics - Optimization and Control ; Computer Science - Machine Learning
    Subject code 510 ; 006
    Publishing date 2023-03-11
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: Robust Regression over Averaged Uncertainty

    Bertsimas, Dimitris / Ma, Yu

    2023  

    Abstract: We propose a new formulation of robust regression by integrating all realizations of the uncertainty set and taking an averaged approach to obtain the optimal solution for the ordinary least-squared regression problem. We show that this formulation ... ...

    Abstract We propose a new formulation of robust regression by integrating all realizations of the uncertainty set and taking an averaged approach to obtain the optimal solution for the ordinary least-squared regression problem. We show that this formulation surprisingly recovers ridge regression and establishes the missing link between robust optimization and the mean squared error approaches for existing regression problems. We first prove the equivalence for four uncertainty sets: ellipsoidal, box, diamond, and budget, and provide closed-form formulations of the penalty term as a function of the sample size, feature size, as well as perturbation protection strength. We then show in synthetic datasets with different levels of perturbations, a consistent improvement of the averaged formulation over the existing worst-case formulation in out-of-sample performance. Importantly, as the perturbation level increases, the improvement increases, confirming our method's advantage in high-noise environments. We report similar improvements in the out-of-sample datasets in real-world regression problems obtained from UCI datasets.
    Keywords Computer Science - Machine Learning ; Mathematics - Optimization and Control
    Subject code 519
    Publishing date 2023-11-12
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Book ; Online: Improving Stability in Decision Tree Models

    Bertsimas, Dimitris / Digalakis Jr, Vassilis

    2023  

    Abstract: Owing to their inherently interpretable structure, decision trees are commonly used in applications where interpretability is essential. Recent work has focused on improving various aspects of decision trees, including their predictive power and ... ...

    Abstract Owing to their inherently interpretable structure, decision trees are commonly used in applications where interpretability is essential. Recent work has focused on improving various aspects of decision trees, including their predictive power and robustness; however, their instability, albeit well-documented, has been addressed to a lesser extent. In this paper, we take a step towards the stabilization of decision tree models through the lens of real-world health care applications due to the relevance of stability and interpretability in this space. We introduce a new distance metric for decision trees and use it to determine a tree's level of stability. We propose a novel methodology to train stable decision trees and investigate the existence of trade-offs that are inherent to decision tree models - including between stability, predictive power, and interpretability. We demonstrate the value of the proposed methodology through an extensive quantitative and qualitative analysis of six case studies from real-world health care applications, and we show that, on average, with a small 4.6% decrease in predictive power, we gain a significant 38% improvement in the model's stability.
    Keywords Statistics - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Machine Learning ; Mathematics - Optimization and Control
    Subject code 006
    Publishing date 2023-05-26
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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  7. Book ; Online: Ensemble Modeling for Time Series Forecasting

    Bertsimas, Dimitris / Boussioux, Leonard

    an Adaptive Robust Optimization Approach

    2023  

    Abstract: Accurate time series forecasting is critical for a wide range of problems with temporal data. Ensemble modeling is a well-established technique for leveraging multiple predictive models to increase accuracy and robustness, as the performance of a single ... ...

    Abstract Accurate time series forecasting is critical for a wide range of problems with temporal data. Ensemble modeling is a well-established technique for leveraging multiple predictive models to increase accuracy and robustness, as the performance of a single predictor can be highly variable due to shifts in the underlying data distribution. This paper proposes a new methodology for building robust ensembles of time series forecasting models. Our approach utilizes Adaptive Robust Optimization (ARO) to construct a linear regression ensemble in which the models' weights can adapt over time. We demonstrate the effectiveness of our method through a series of synthetic experiments and real-world applications, including air pollution management, energy consumption forecasting, and tropical cyclone intensity forecasting. Our results show that our adaptive ensembles outperform the best ensemble member in hindsight by 16-26% in root mean square error and 14-28% in conditional value at risk and improve over competitive ensemble techniques.
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Mathematics - Optimization and Control
    Subject code 519
    Publishing date 2023-04-09
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Book ; Online: A Machine Learning Approach to Two-Stage Adaptive Robust Optimization

    Bertsimas, Dimitris / Kim, Cheol Woo

    2023  

    Abstract: We propose an approach based on machine learning to solve two-stage linear adaptive robust optimization (ARO) problems with binary here-and-now variables and polyhedral uncertainty sets. We encode the optimal here-and-now decisions, the worst-case ... ...

    Abstract We propose an approach based on machine learning to solve two-stage linear adaptive robust optimization (ARO) problems with binary here-and-now variables and polyhedral uncertainty sets. We encode the optimal here-and-now decisions, the worst-case scenarios associated with the optimal here-and-now decisions, and the optimal wait-and-see decisions into what we denote as the strategy. We solve multiple similar ARO instances in advance using the column and constraint generation algorithm and extract the optimal strategies to generate a training set. We train a machine learning model that predicts high-quality strategies for the here-and-now decisions, the worst-case scenarios associated with the optimal here-and-now decisions, and the wait-and-see decisions. We also introduce an algorithm to reduce the number of different target classes the machine learning algorithm needs to be trained on. We apply the proposed approach to the facility location, the multi-item inventory control and the unit commitment problems. Our approach solves ARO problems drastically faster than the state-of-the-art algorithms with high accuracy.
    Keywords Computer Science - Machine Learning ; Mathematics - Optimization and Control
    Subject code 006
    Publishing date 2023-07-23
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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  9. Book ; Online: Global Optimization

    Bertsimas, Dimitris / Margaritis, Georgios

    A Machine Learning Approach

    2023  

    Abstract: ... such limitations, Bertsimas and Ozturk (2023) proposed OCTHaGOn as a way of solving black-box global optimization ...

    Abstract Many approaches for addressing Global Optimization problems typically rely on relaxations of nonlinear constraints over specific mathematical primitives. This is restricting in applications with constraints that are black-box, implicit or consist of more general primitives. Trying to address such limitations, Bertsimas and Ozturk (2023) proposed OCTHaGOn as a way of solving black-box global optimization problems by approximating the nonlinear constraints using hyperplane-based Decision-Trees and then using those trees to construct a unified mixed integer optimization (MIO) approximation of the original problem. We provide extensions to this approach, by (i) approximating the original problem using other MIO-representable ML models besides Decision Trees, such as Gradient Boosted Trees, Multi Layer Perceptrons and Suport Vector Machines, (ii) proposing adaptive sampling procedures for more accurate machine learning-based constraint approximations, (iii) utilizing robust optimization to account for the uncertainty of the sample-dependent training of the ML models, and (iv) leveraging a family of relaxations to address the infeasibilities of the final MIO approximation. We then test the enhanced framework in 81 Global Optimization instances. We show improvements in solution feasibility and optimality in the majority of instances. We also compare against BARON, showing improved optimality gaps or solution times in 11 instances.

    Comment: Submitted to the Journal of Global Optimization. 35 pages
    Keywords Mathematics - Optimization and Control ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2023-11-03
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Book ; Online: Compressed Sensing

    Bertsimas, Dimitris / Johnson, Nicholas

    A Discrete Optimization Approach

    2023  

    Abstract: We study the Compressed Sensing (CS) problem, which is the problem of finding the most sparse vector that satisfies a set of linear measurements up to some numerical tolerance. CS is a central problem in Statistics, Operations Research and Machine ... ...

    Abstract We study the Compressed Sensing (CS) problem, which is the problem of finding the most sparse vector that satisfies a set of linear measurements up to some numerical tolerance. CS is a central problem in Statistics, Operations Research and Machine Learning which arises in applications such as signal processing, data compression and image reconstruction. We introduce an $\ell_2$ regularized formulation of CS which we reformulate as a mixed integer second order cone program. We derive a second order cone relaxation of this problem and show that under mild conditions on the regularization parameter, the resulting relaxation is equivalent to the well studied basis pursuit denoising problem. We present a semidefinite relaxation that strengthens the second order cone relaxation and develop a custom branch-and-bound algorithm that leverages our second order cone relaxation to solve instances of CS to certifiable optimality. Our numerical results show that our approach produces solutions that are on average $6.22\%$ more sparse than solutions returned by state of the art benchmark methods on synthetic data in minutes. On real world ECG data, for a given $\ell_2$ reconstruction error our approach produces solutions that are on average $9.95\%$ more sparse than benchmark methods, while for a given sparsity level our approach produces solutions that have on average $10.77\%$ lower reconstruction error than benchmark methods in minutes.
    Keywords Electrical Engineering and Systems Science - Signal Processing ; Computer Science - Machine Learning ; Statistics - Machine Learning
    Subject code 004
    Publishing date 2023-06-04
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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