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arXiv 2024年

作者： Restrepo, David Wu, Chenwei Vásquez-Venegas, Constanza Matos, João Gallifant, Jack Celi, Leo Anthony Bitterman, Danielle S. Nakayama, Luis Filipe Laboratory for Computational Physiology Massachusetts Institute of Technology CambridgeMA United States Departamento de Telematica Universidad del Cauca Cauca Popayán Colombia Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States Scientific Image Analysis Lab Faculty of Medicine Universidad de Chile RM Santiago Chile Department of Computer Science Faculty of Engineering Universidad de Concepción Biobio Concepción Chile Department of Biostatistics Harvard TH Chan School of Public Health BostonMA United States Department of Medicine Beth Israel Deaconess Medical Center BostonMA United States Artificial Intelligence in Medicine Program Mass General Brigham Harvard Medical School BostonMA United States Department of Ophthalmology São Paulo Federal University São Paulo São Paulo Brazil

The deployment of large language models (LLMs) in healthcare has demonstrated substantial potential for enhancing clinical decision-making, administrative efficiency, and patient outcomes. However, the underrepresentation of diverse groups in the development and application of these models can perpetuate biases, leading to inequitable healthcare delivery. This paper presents a comprehensive scientometric analysis of LLM research for healthcare, including data from January 1, 2021, to July 1, 2024. By analyzing metadata from PubMed and Dimensions, including author affiliations, countries, and funding sources, we assess the diversity of contributors to LLM research. Our findings highlight significant gender and geographic disparities, with a predominance of male authors and contributions primarily from high-income countries (HICs). We introduce a novel journal diversity index based on Gini diversity to measure the inclusiveness of scientific publications. Our results underscore the necessity for greater representation in order to ensure the equitable application of LLMs in healthcare. We propose actionable strategies to enhance diversity and inclusivity in artificial intelligence research, with the ultimate goal of fostering a more inclusive and equitable future in healthcare innovation. © 2024, CC BY.

关键词： Decision making

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

arXiv

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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

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Promoting Rural Entrepreneurship through Technology: A Case Study using Productivity Enhancing Technology Experience Kits (PETE-Kits)

arXiv

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arXiv 2023年

作者： Rutherford, Matthew W. Whitacre, Brian E. Captain, Levi Ekin, Sabit Angle, Julie Hensley, Tom O'Hara, John F. The School of Electrical and Computer Engineering Oklahoma State University StillwaterOK74078 United States The Spears School of Business Oklahoma State University StillwaterOK74078 United States The Department of Agricultural Economics Oklahoma State University StillwaterOK74078 United States The Department of Engineering Technology & Industrial Distribution Texas A&M University College StationTX77843 United States The School of Teaching Learning and Educational Sciences Oklahoma State University StillwaterOK74078 United States Frederick Public High School FrederickOK73542 United States

Contribution: Case study of a rural-focused educational program with two components: 1) introducing high school students and teachers to Smart and Connected Technologies (SCTs) that can be used to solve local problems;2) engaging the local community in supporting local technology-driven entrepreneurship. Background: Rural communities typically lag behind in terms of participation in the digital economy, and use of technology in general. Yet they often have the most to gain, due to high rates of self-employment and lower private-sector job opportunities. Research Questions: Can a broadly-scoped rural technology education program lead to improvements in 1) student and teacher SCT awareness, 2) SCT skills, 3) aspirations for future SCT use directed toward entrepreneurship and overall community wellbeing? Methodology: Our multidisciplinary team used a mixed-methods approach to engage a rural high school robotics team as well as the local community. Over the course of one year, students took part in hands-on-training with SCTs ("PETE-Kits" and associated curriculum) and brainstormed entrepreneurial projects via ideation events. Community members were involved at the beginning and end of the project, including judging a "shark-tank" style event where student business ideas using SCT were presented. Findings: Results from student pre/post activity assessments suggest that the program was effective at increasing comfort with technology and combining technical skills with entrepreneurial opportunities. Post surveys from community members, including teachers, demonstrated clear support for the program and an appreciation of how SCTs/digital skills could benefit the local economy and wellbeing. Copyright © 2023, The Authors. All rights reserved.

关键词： Students

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Time variation of high-risk groups for liver function deteriorations within fluctuating long-term liver function after hepatic radiotherapy in patients with hepatocellular carcinoma

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European Journal of Medical Research 2024年第1期29卷 104-104页

作者： Tsai, Yu-Lun Yu, Pei-Chieh Nien, Hsin-Hua Lu, Tzu-Pin Institute of Epidemiology and Preventive Medicine College of Public Health National Taiwan University Taipei Taiwan Department of Radiation Oncology Cathay General Hospital Taipei Taiwan School of Medicine China Medical University Taichung Taiwan School of Medicine Fu Jen Catholic University New Taipei City Taiwan Institute of Biomedical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Bioinformatics and Biostatistics Core Center of Genomic and Precision Medicine National Taiwan University Taipei Taiwan Institute of Health Data Analytics and Statistics College of Public Health National Taiwan University Taipei Taiwan

PURPOSE: The purpose of this study is to find essential risk factors associated with liver function (LF) deteriorations within fluctuating long-term LF and their time-varying effects in patients with hepatocellular carcinoma (HCC) receiving hepatic radiotherapy and to identify high-risk groups for adverse LF deteriorations and their changes over time in facilitating the prevention of hepatic decompensation and the improvement of survival. MATERIALS AND METHODS: A total of 133 HCC patients treated by hepatic radiotherapy were enrolled. A study design was conducted to convert posttreatment long-term LF with fluctuating levels over time to recurrent LF events using defined upgrades in a grading scale. The hazard ratios (HR) of pretreatment biochemical, demographic, clinical, and dosimetric factors in developing posttreatment LF events were estimated using the Cox model. Methodologies of the counting process approach, robust variance estimation, goodness-of-fit testing based on the Schoenfeld residuals, and time-dependent covariates in survival analysis were employed to handle the correlation within subjects and evaluate the time-varying effects during long-term follow-up. RESULTS: Baseline LF score before radiotherapy and gender were significant factors. Initial HR in developing LF events was 1.17 (95% CI 1.11-1.23;P < 0.001) for each increase of baseline LF score and kept almost constant over time (HR, 1.00;95% CI 1.00-1.01;P = 0.065). However, no difference was observed regarding initial hazards for gender (HR, 1.00;95% CI 0.64-1.56;P = 0.994), but the hazard for women got higher monthly over time compared with men (HR, 1.04;95% CI 1.01-1.07;P = 0.006). CONCLUSIONS: High-risk groups for adverse LF deteriorations after hepatic radiotherapy may change over time. Patients with poor baseline LF are vulnerable from the beginning. Women require prevention strategies and careful monitoring for deteriorations at a later stage. © 2024. The Author(s).

关键词： Hepatic radiotherapy Hepatocellular carcinoma High-risk group Long-term liver function Time variation

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A multimodal framework for extraction and fusion of satellite images and public health data

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Scientific Data 2024年第1期11卷 634页

作者： Moukheiber, Dana Restrepo, David Cajas, Sebastián Andrés Montoya, María Patricia Arbeláez Celi, Leo Anthony Kuo, Kuan-Ting López, Diego M. Moukheiber, Lama Moukheiber, Mira Moukheiber, Sulaiman Osorio-Valencia, Juan Sebastian Purkayastha, Saptarshi Paddo, Atika Rahman Wu, Chenwei Kuo, Po-Chih Laboratory for Computational Physiology Massachusetts Institute of Technology Cambridge MA United States Departamento de Telemática Universidad del Cauca Cauca Popayán Colombia John A. Paulson School of Engineering and Applied Sciences Harvard University Boston MA United States School of Computer Science University College Dublin Dublin Ireland Grupo de Epidemiología Facultad Nacional de Salud Pública Universidad de Antioquia Medellín Colombia Department of Biostatistics Harvard TH Chan School of Public Health Boston MA United States Department of Medicine Beth Israel Deaconess Medical Center Boston MA United States Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Computer Science Worcester Polytechnic Institute Worcester MA United States ScienteLab Cundinamarca Bogota Colombia Department of BioHealth Informatics Indiana University Luddy School of Informatics Computing and Engineering Indianapolis IN United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI United States

In low- and middle-income countries, the substantial costs associated with traditional data collection pose an obstacle to facilitating decision-making in the field of public health. Satellite imagery offers a potential solution, but the image extraction and analysis can be costly and requires specialized expertise. We introduce SatelliteBench, a scalable framework for satellite image extraction and vector embeddings generation. We also propose a novel multimodal fusion pipeline that utilizes a series of satellite imagery and metadata. The framework was evaluated generating a dataset with a collection of 12,636 images and embeddings accompanied by comprehensive metadata, from 81 municipalities in Colombia between 2016 and 2018. The dataset was then evaluated in 3 tasks: including dengue case prediction, poverty assessment, and access to education. The performance showcases the versatility and practicality of SatelliteBench, offering a reproducible, accessible and open tool to enhance decision-making in public health. © The Author(s) 2024.

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Smart city governance through big data: Transformation towards sustainability

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

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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

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DRStageNet: Deep Learning for Diabetic Retinopathy Staging from Fundus Images

arXiv

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arXiv 2023年

作者： Men, Yevgeniy Fhima, Jonathan Celi, Leo Anthony Ribeiro, Lucas Zago Nakayama, Luis Filipe Behar, Joachim A. The Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering The Faculty of Biomedical Engineering Technion Israel Institute of Technology Haifa3200003 Israel The Department of Applied Mathematics The Biomedical Engineering Faculty Technion Israel Institute of Technology Haifa3200003 Israel The Laboratory for Computational Physiology Massachusetts Institute of Technology CambridgeMA02139 United States The Division of Pulmonary Critical Care and Sleep Medicine Beth Israel Deaconess Medical Center BostonMA02215 United States The Department of Biostatistics Harvard T.H. Chan School of Public Health BostonMA02115 United States The Ophthalmology department São Paulo Federal University São Paulo Brazil The Institute for Medical Engineering and Science Massachusetts Institute of Technology CambridgeMA United States The Department of Ophthalmology São Paulo Federal University São Paulo Brazil The Faculty of Biomedical Engineering Technion Israel Institute of Technology Haifa3200003 Israel

Diabetic retinopathy (DR) is a prevalent complication of diabetes associated with a significant risk of vision loss. Timely identification is critical to curb vision impairment. Algorithms for DR staging from digital fundus images (DFIs) have been recently proposed. However, models often fail to generalize due to distribution shifts between the source domain on which the model was trained and the target domain where it is deployed. A common and particularly challenging shift is often encountered when the source- and target-domain supports do not fully overlap. In this research, we introduce DRStageNet, a deep learning model designed to mitigate this challenge. We used seven publicly available datasets, comprising a total of 93,534 DFIs that cover a variety of patient demographics, ethnicities, geographic origins and comorbidities. We fine-tune DINOv2, a pretrained model of self-supervised vision transformer, and implement a multi-source domain fine-tuning strategy to enhance generalization performance. We benchmark and demonstrate the superiority of our method to two state-of-the-art benchmarks, including a recently published foundation model. We adapted the gradrollout method to our regression task in order to provide high-resolution explainability heatmaps. The error analysis showed that 59% of the main errors had incorrect reference labels. DRStageNet is accessible at URL [upon acceptance of the manuscript]. © 2023, CC BY.

关键词： Error analysis

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Designing Survey-Based Mobile Interfaces for Rural Patients With Cancer Using Apple's ResearchKit and CareKit: Usability Study

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JMIR Formative Research 2024年 8卷 e57801页

作者： Donawa, Alyssa Powell, Christian Wang, Rong Chih, Ming-Yuan Patel, Reema Zinner, Ralph Aronoff-Spencer, Eliah Baker, Corey E. Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA United States Department of Computer Science University of Kentucky Lexington KY United States Department of Human and Organizational Development Vanderbilt University Nashville TN United States Department of Health and Clinical Sciences University of Kentucky Lexington KY United States Division of Medical Oncology Markey Cancer Center University of Kentucky Lexington KY United States Division of Infectious Diseases & Global Public Health School of Medicine University of California San Diego San Diego CA United States

Background: Despite the increased accessibility and availability of technology in recent years, equality and access to health-related technology remain limited to some demographics. In particular, patients who are older or from rural communities represent a large segment of people who are currently underusing mobile health (mHealth) solutions. System usability continues to hinder mHealth adoption among users with nontraditional digital literacy. Objective: This study aims to investigate if state-of-the-art mobile app interfaces from open-source libraries provide sufficient usability for rural patients with cancer, with minimal design changes and forgoing the co-design process. Methods: We developed Assuage (Network Reconnaissance Lab) as a research platform for any mHealth study. We conducted a pilot study using Assuage to assess the usability of 4 mobile user interfaces (UIs) based on open-source libraries from Apple's ResearchKit and CareKit. These UIs varied in complexity for reporting distress symptoms. Patients with cancer were recruited at the Markey Cancer Center, and all research procedures were conducted in person. Participants completed the distress assessment using a randomly selected UI in Assuage with little to no assistance. Data were collected on participant age, location, mobile app use, and familiarity with mHealth apps. Participants rated usability with the System Usability Scale (SUS), and usability issues were documented and compared. A one-way ANOVA was used to compare the effect of the UIs on the SUS scores. Results: We recruited 30 current or postsurgery patients with cancer for this pilot study. Most participants were aged >50 years (24/30, 80%), from rural areas (25/30, 83%), had up to a high school education (19/30, 63%), and were unfamiliar with mHealth apps (21/30, 70%). General mobile app use was split, with 43% (14/30) of the patients not regularly using mobile apps. The mean SUS score across the UIs was 75.8 (SD 22.2), with UI 3 and UI

关键词： apps CareKit digital divide digital literacy distress ehealth adoption ehealth literacy HealthKit mHealth mobile health patients with cancer rural health usability usability testing

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Clinician-in-The-loop decision making: Reinforcement learning with near-optimal set-valued policies 37

Clinician-in-The-loop decision making: Reinforcement learnin...

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37th International Conference on Machine Learning, ICML 2020

作者： Tang, Shengpu Modi, Aditya Sjoding, Michael W. Wiens, Jenna Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor United States Department of Internal Medicine Michigan Medicine University of Michigan Ann Arbor United States Institute for Healthcare Policy and Innovation University of Michigan Ann Arbor United States

ISBN: (纸本)9781713821120

Standard reinforcement learning (RL) aims to find an optimal policy that identifies the best action for each state. However, in healthcare settings, many actions may be near-equivalent with respect to the reward (e.g., survival). We consider an alternative objective learning set-valued policies to capture near-equivalent actions that lead to similar cumulative rewards. We propose a modelfree algorithm based on temporal difference learning and a near-greedy heuristic for action selection. We analyze the theoretical properties of the proposed algorithm, providing optimality guarantees and demonstrate our approach on simulated environments and a real clinical task. Empirically, the proposed algorithm exhibits good convergence properties and discovers meaningful near-equivalent actions. Our work provides theoretical, as well as practical, foundations for clinician/human-in-The-loop decision making, in which humans (e.g., clinicians, patients) can incorporate additional knowledge (e.g., side effects, patient preference) when selecting among nearequivalent actions. © 2020 by the Authors.

关键词： Reinforcement learning

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

arXiv

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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

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