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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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Book ; Online: Hurricane Forecasting

Boussioux, Léonard / Zeng, Cynthia / Guénais, Théo / Bertsimas, Dimitris

A Novel Multimodal Machine Learning Framework

2020

Abstract: This paper describes a novel machine learning (ML) framework for tropical cyclone intensity and track forecasting, combining multiple ML techniques and utilizing diverse data sources. Our multimodal framework, called Hurricast, efficiently combines ... ...

Abstract	This paper describes a novel machine learning (ML) framework for tropical cyclone intensity and track forecasting, combining multiple ML techniques and utilizing diverse data sources. Our multimodal framework, called Hurricast, efficiently combines spatial-temporal data with statistical data by extracting features with deep-learning encoder-decoder architectures and predicting with gradient-boosted trees. We evaluate our models in the North Atlantic and Eastern Pacific basins on 2016-2019 for 24-hour lead time track and intensity forecasts and show they achieve comparable mean absolute error and skill to current operational forecast models while computing in seconds. Furthermore, the inclusion of Hurricast into an operational forecast consensus model could improve over the National Hurricane Center's official forecast, thus highlighting the complementary properties with existing approaches. In summary, our work demonstrates that utilizing machine learning techniques to combine different data sources can lead to new opportunities in tropical cyclone forecasting. Comment: Published by the AMS' Weather and Forecasting journal; Spotlight talk at NeurIPS 2021, Tackling Climate Change with AI https://journals.ametsoc.org/view/journals/wefo/37/6/WAF-D-21-0091.1.xml
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Physics - Atmospheric and Oceanic Physics
Subject code	006
Publishing date	2020-11-11
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Automated Segmentation of Sacral Chordoma and Surrounding Muscles Using Deep Learning Ensemble.

Boussioux, Leonard / Ma, Yu / Thomas, Nancy Knight / Bertsimas, Dimitris / Shusharina, Nadya / Pursley, Jennifer / Chen, Yen-Lin / DeLaney, Thomas F / Qian, Jack / Bortfeld, Thomas

International journal of radiation oncology, biology, physics

2023 Volume 117, Issue 3, Page(s) 738–749

Abstract: Purpose: The manual segmentation of organ structures in radiation oncology treatment planning is a time-consuming and highly skilled task, particularly when treating rare tumors like sacral chordomas. This study evaluates the performance of automated ... ...

Abstract	Purpose: The manual segmentation of organ structures in radiation oncology treatment planning is a time-consuming and highly skilled task, particularly when treating rare tumors like sacral chordomas. This study evaluates the performance of automated deep learning (DL) models in accurately segmenting the gross tumor volume (GTV) and surrounding muscle structures of sacral chordomas. Methods and materials: An expert radiation oncologist contoured 5 muscle structures (gluteus maximus, gluteus medius, gluteus minimus, paraspinal, piriformis) and sacral chordoma GTV on computed tomography images from 48 patients. We trained 6 DL auto-segmentation models based on 3-dimensional U-Net and residual 3-dimensional U-Net architectures. We then implemented an average and an optimally weighted average ensemble to improve prediction performance. We evaluated algorithms with the average and standard deviation of the volumetric Dice similarity coefficient, surface Dice similarity coefficient with 2- and 3-mm thresholds, and average symmetric surface distance. One independent expert radiation oncologist assessed the clinical viability of the DL contours and determined the necessary amount of editing before they could be used in clinical practice. Results: Quantitatively, the ensembles performed the best across all structures. The optimal ensemble (volumetric Dice similarity coefficient, average symmetric surface distance) was (85.5 ± 6.4, 2.6 ± 0.8; GTV), (94.4 ± 1.5, 1.0 ± 0.4; gluteus maximus), (92.6 ± 0.9, 0.9 ± 0.1; gluteus medius), (85.0 ± 2.7, 1.1 ± 0.3; gluteus minimus), (92.1 ± 1.5, 0.8 ± 0.2; paraspinal), and (78.3 ± 5.7, 1.5 ± 0.6; piriformis). The qualitative evaluation suggested that the best model could reduce the total muscle and tumor delineation time to a 19-minute average. Conclusions: Our methodology produces expert-level muscle and sacral chordoma tumor segmentation using DL and ensemble modeling. It can substantially augment the streamlining and accuracy of treatment planning and represents a critical step toward automated delineation of the clinical target volume in sarcoma and other disease sites.
MeSH term(s)	Humans ; Deep Learning ; Chordoma/diagnostic imaging ; Chordoma/radiotherapy ; Tomography, X-Ray Computed/methods ; Algorithms ; Muscles ; Image Processing, Computer-Assisted/methods
Language	English
Publishing date	2023-07-13
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	197614-x
ISSN	1879-355X ; 0360-3016
ISSN (online)	1879-355X
ISSN	0360-3016
DOI	10.1016/j.ijrobp.2023.03.078
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Book ; Online: Holistic Deep Learning

Bertsimas, Dimitris / Carballo, Kimberly Villalobos / Boussioux, Léonard / Li, Michael Lingzhi / Paskov, Alex / Paskov, Ivan

2021

Abstract: This paper presents a novel holistic deep learning framework that simultaneously addresses the challenges of vulnerability to input perturbations, overparametrization, and performance instability from different train-validation splits. The proposed ... ...

Abstract	This paper presents a novel holistic deep learning framework that simultaneously addresses the challenges of vulnerability to input perturbations, overparametrization, and performance instability from different train-validation splits. The proposed framework holistically improves accuracy, robustness, sparsity, and stability over standard deep learning models, as demonstrated by extensive experiments on both tabular and image data sets. The results are further validated by ablation experiments and SHAP value analysis, which reveal the interactions and trade-offs between the different evaluation metrics. To support practitioners applying our framework, we provide a prescriptive approach that offers recommendations for selecting an appropriate training loss function based on their specific objectives. All the code to reproduce the results can be found at https://github.com/kimvc7/HDL. Comment: Submitted to Machine Learning
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
Subject code	006
Publishing date	2021-10-29
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Integrated multimodal artificial intelligence framework for healthcare applications.

Soenksen, Luis R / Ma, Yu / Zeng, Cynthia / Boussioux, Leonard / Villalobos Carballo, Kimberly / Na, Liangyuan / Wiberg, Holly M / Li, Michael L / Fuentes, Ignacio / Bertsimas, Dimitris

NPJ digital medicine

2022 Volume 5, Issue 1, Page(s) 149

Abstract: Artificial intelligence (AI) systems hold great promise to improve healthcare over the next decades. Specifically, AI systems leveraging multiple data sources and input modalities are poised to become a viable method to deliver more accurate results and ... ...

Abstract	Artificial intelligence (AI) systems hold great promise to improve healthcare over the next decades. Specifically, AI systems leveraging multiple data sources and input modalities are poised to become a viable method to deliver more accurate results and deployable pipelines across a wide range of applications. In this work, we propose and evaluate a unified Holistic AI in Medicine (HAIM) framework to facilitate the generation and testing of AI systems that leverage multimodal inputs. Our approach uses generalizable data pre-processing and machine learning modeling stages that can be readily adapted for research and deployment in healthcare environments. We evaluate our HAIM framework by training and characterizing 14,324 independent models based on HAIM-MIMIC-MM, a multimodal clinical database (N = 34,537 samples) containing 7279 unique hospitalizations and 6485 patients, spanning all possible input combinations of 4 data modalities (i.e., tabular, time-series, text, and images), 11 unique data sources and 12 predictive tasks. We show that this framework can consistently and robustly produce models that outperform similar single-source approaches across various healthcare demonstrations (by 6-33%), including 10 distinct chest pathology diagnoses, along with length-of-stay and 48 h mortality predictions. We also quantify the contribution of each modality and data source using Shapley values, which demonstrates the heterogeneity in data modality importance and the necessity of multimodal inputs across different healthcare-relevant tasks. The generalizable properties and flexibility of our Holistic AI in Medicine (HAIM) framework could offer a promising pathway for future multimodal predictive systems in clinical and operational healthcare settings.
Language	English
Publishing date	2022-09-20
Publishing country	England
Document type	Journal Article
ISSN	2398-6352
ISSN (online)	2398-6352
DOI	10.1038/s41746-022-00689-4
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: TabText

Carballo, Kimberly Villalobos / Na, Liangyuan / Ma, Yu / Boussioux, Léonard / Zeng, Cynthia / Soenksen, Luis R. / Bertsimas, Dimitris

A Flexible and Contextual Approach to Tabular Data Representation

2022

Abstract: Tabular data is essential for applying machine learning tasks across various industries. However, traditional data processing methods do not fully utilize all the information available in the tables, ignoring important contextual information such as ... ...

Abstract	Tabular data is essential for applying machine learning tasks across various industries. However, traditional data processing methods do not fully utilize all the information available in the tables, ignoring important contextual information such as column header descriptions. In addition, pre-processing data into a tabular format can remain a labor-intensive bottleneck in model development. This work introduces TabText, a processing and feature extraction framework that extracts contextual information from tabular data structures. TabText addresses processing difficulties by converting the content into language and utilizing pre-trained large language models (LLMs). We evaluate our framework on nine healthcare prediction tasks ranging from patient discharge, ICU admission, and mortality. We show that 1) applying our TabText framework enables the generation of high-performing and simple machine learning baseline models with minimal data pre-processing, and 2) augmenting pre-processed tabular data with TabText representations improves the average and worst-case AUC performance of standard machine learning models by as much as 6%.
Keywords	Computer Science - Machine Learning
Subject code	006
Publishing date	2022-06-21
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: oIRL

Venuto, David / Chakravorty, Jhelum / Boussioux, Leonard / Wang, Junhao / McCracken, Gavin / Precup, Doina

Robust Adversarial Inverse Reinforcement Learning with Temporally Extended Actions

2020

Abstract: Explicit engineering of reward functions for given environments has been a major hindrance to reinforcement learning methods. While Inverse Reinforcement Learning (IRL) is a solution to recover reward functions from demonstrations only, these learned ... ...

Abstract	Explicit engineering of reward functions for given environments has been a major hindrance to reinforcement learning methods. While Inverse Reinforcement Learning (IRL) is a solution to recover reward functions from demonstrations only, these learned rewards are generally heavily \textit{entangled} with the dynamics of the environment and therefore not portable or \emph{robust} to changing environments. Modern adversarial methods have yielded some success in reducing reward entanglement in the IRL setting. In this work, we leverage one such method, Adversarial Inverse Reinforcement Learning (AIRL), to propose an algorithm that learns hierarchical disentangled rewards with a policy over options. We show that this method has the ability to learn \emph{generalizable} policies and reward functions in complex transfer learning tasks, while yielding results in continuous control benchmarks that are comparable to those of the state-of-the-art methods.
Keywords	Computer Science - Machine Learning ; Statistics - Machine Learning
Subject code	629
Publishing date	2020-02-20
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: InsectUp

Boussioux, Léonard / Giro-Larraz, Tomás / Guille-Escuret, Charles / Cherti, Mehdi / Kégl, Balázs

Crowdsourcing Insect Observations to Assess Demographic Shifts and Improve Classification

2019

Abstract: Insects play such a crucial role in ecosystems that a shift in demography of just a few species can have devastating consequences at environmental, social and economic levels. Despite this, evaluation of insect demography is strongly limited by the ... ...

Abstract	Insects play such a crucial role in ecosystems that a shift in demography of just a few species can have devastating consequences at environmental, social and economic levels. Despite this, evaluation of insect demography is strongly limited by the difficulty of collecting census data at sufficient scale. We propose a method to gather and leverage observations from bystanders, hikers, and entomology enthusiasts in order to provide researchers with data that could significantly help anticipate and identify environmental threats. Finally, we show that there is indeed interest on both sides for such collaboration. Comment: Appearing at the International Conference on Machine Learning, AI for Social Good Workshop, Long Beach, United States, 2019 Appearing at the International Conference on Computer Vision, AI for Wildlife Conservation Workshop, Seoul, South Korea, 2019 5 pages, 6 figures
Keywords	Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Artificial Intelligence ; Computer Science - Machine Learning ; Statistics - Machine Learning
Publishing date	2019-05-29
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Integrated multimodal artificial intelligence framework for healthcare applications

Soenksen, Luis R. / Ma, Yu / Zeng, Cynthia / Boussioux, Leonard D. J. / Carballo, Kimberly Villalobos / Na, Liangyuan / Wiberg, Holly M. / Li, Michael L. / Fuentes, Ignacio / Bertsimas, Dimitris

2022

Abstract	Artificial intelligence (AI) systems hold great promise to improve healthcare over the next decades. Specifically, AI systems leveraging multiple data sources and input modalities are poised to become a viable method to deliver more accurate results and deployable pipelines across a wide range of applications. In this work, we propose and evaluate a unified Holistic AI in Medicine (HAIM) framework to facilitate the generation and testing of AI systems that leverage multimodal inputs. Our approach uses generalizable data pre-processing and machine learning modeling stages that can be readily adapted for research and deployment in healthcare environments. We evaluate our HAIM framework by training and characterizing 14,324 independent models based on MIMIC-IV-MM, a multimodal clinical database (N=34,537 samples) containing 7,279 unique hospitalizations and 6,485 patients, spanning all possible input combinations of 4 data modalities (i.e., tabular, time-series, text and images), 11 unique data sources and 12 predictive tasks. We show that this framework can consistently and robustly produce models that outperform similar single-source approaches across various healthcare demonstrations (by 6-33%), including 10 distinct chest pathology diagnoses, along with length-of-stay and 48-hour mortality predictions. We also quantify the contribution of each modality and data source using Shapley values, which demonstrates the heterogeneity in data type importance and the necessity of multimodal inputs across different healthcare-relevant tasks. The generalizable properties and flexibility of our Holistic AI in Medicine (HAIM) framework could offer a promising pathway for future multimodal predictive systems in clinical and operational healthcare settings.
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Computers and Society
Subject code	006
Publishing date	2022-02-25
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: From predictions to prescriptions: A data-driven response to COVID-19.

Health care management science

2021 Volume 24, Issue 2, Page(s) 253–272

Abstract: The COVID-19 pandemic has created unprecedented challenges worldwide. Strained healthcare providers make difficult decisions on patient triage, treatment and care management on a daily basis. Policy makers have imposed social distancing measures to slow ... ...

Abstract	The COVID-19 pandemic has created unprecedented challenges worldwide. Strained healthcare providers make difficult decisions on patient triage, treatment and care management on a daily basis. Policy makers have imposed social distancing measures to slow the disease, at a steep economic price. We design analytical tools to support these decisions and combat the pandemic. Specifically, we propose a comprehensive data-driven approach to understand the clinical characteristics of COVID-19, predict its mortality, forecast its evolution, and ultimately alleviate its impact. By leveraging cohort-level clinical data, patient-level hospital data, and census-level epidemiological data, we develop an integrated four-step approach, combining descriptive, predictive and prescriptive analytics. First, we aggregate hundreds of clinical studies into the most comprehensive database on COVID-19 to paint a new macroscopic picture of the disease. Second, we build personalized calculators to predict the risk of infection and mortality as a function of demographics, symptoms, comorbidities, and lab values. Third, we develop a novel epidemiological model to project the pandemic's spread and inform social distancing policies. Fourth, we propose an optimization model to re-allocate ventilators and alleviate shortages. Our results have been used at the clinical level by several hospitals to triage patients, guide care management, plan ICU capacity, and re-distribute ventilators. At the policy level, they are currently supporting safe back-to-work policies at a major institution and vaccine trial location planning at Janssen Pharmaceuticals, and have been integrated into the US Center for Disease Control's pandemic forecast.
MeSH term(s)	Aged ; COVID-19/drug therapy ; COVID-19/mortality ; COVID-19/physiopathology ; Databases, Factual ; Female ; Forecasting ; Humans ; Intensive Care Units ; Machine Learning ; Male ; Middle Aged ; Models, Statistical ; Pandemics ; Policy Making ; Prognosis ; Risk Assessment/statistics & numerical data ; SARS-CoV-2 ; Ventilators, Mechanical/supply & distribution
Language	English
Publishing date	2021-02-15
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	1469148-6
ISSN	1572-9389 ; 1386-9620
ISSN (online)	1572-9389
ISSN	1386-9620
DOI	10.1007/s10729-020-09542-0
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