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Enhanced Labeling Technique for Reddit Text and Fine-Tuned Longformer Models for Classifying Depression Severity in English and Luganda

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arXiv 2024年

作者： Kimera, Richard Rim, Daniela N. Kirabira, Joseph Udomah, Ubong Godwin Choi, Heeyoul Department of Advanced Convergence Handong Global University Pohang Korea Republic of School of Computer Science and Electrical Engineering Handong Global University Pohang Korea Republic of Department of Psychiatry Busitema University Faculty of Health Sciences Mbale Uganda Department of Psychiatry University of Uyo Teaching Hospital Uyo Akwa Ibom Nigeria

Depression is a global burden and one of the most challenging mental health conditions to control. Using the BDI questionnaire, experts can detect its severity early, administer appropriate medication to patients, and impede its progression. Owing to the fear of potential stigmatization, many patients turn to social media platforms such as Reddit for advice and assistance at various stages of their journey. This research extracts text from Reddit to facilitate the diagnostic process, employs a proposed labelling approach to categorize the text, and subsequently fine-tunes the Longformer model. The model's performance is compared against the baseline models, including Naive Bayes, Random Forest, Support Vector Machines, and Gradient Boosting. Our findings reveal that the Longformer model outperforms the baseline models in both English (48%) and Luganda (45%) languages on a custom-made dataset. © 2024, CC BY.

关键词： Support vector machines

Bringing the Algorithms to the Data - Secure Distributed Medical Analytics using the Personal Health Train (PHT-meDIC)

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arXiv 2022年

作者： de Arruda Botelho Herr, Marius Graf, Michael Placzek, Peter König, Florian Bötte, Felix Stickel, Tyra Hieber, David Zimmermann, Lukas Slupina, Michael Mohr, Christopher Biergans, Stephanie Akgün, Mete Pfeifer, Nico Kohlbacher, Oliver Institute for Translational Bioinformatics University Hospital Tübingen Tübingen Germany Institute for Bioinformatics and Medical Informatics University of Tübingen Tübingen Germany Methods in Medical Informatics Department of Computer Science University of Tübingen Germany Applied Bioinformatics Department of Computer Science University of Tübingen Germany Medical Data Integration Center University Hospital Tübingen Tübingen Germany Medical Data Privacy and Privacy-Preserving ML on Healthcare Data Department of Computer Science University of Tübingen Germany

The need for data privacy and security – enforced through increasingly strict data protection regulations – renders the use of healthcare data for machine learning difficult. In particular, the transfer of data between different hospitals is often not permissible and thus cross-site pooling of data not an option. The Personal Health Train (PHT) paradigm proposed within the GO-FAIR initiative implements an’algorithm to the data’ paradigm that ensures that distributed data can be accessed for analysis without transferring any sensitive data. We present PHT-meDIC, a productively deployed open-source implementation of the PHT concept. Containerization allows us to easily deploy even complex data analysis pipelines (e.g, genomics, image analysis) across multiple sites in a secure and scalable manner. We discuss the underlying technological concepts, security models, and governance processes. The implementation has been successfully applied to distributed analyses of large-scale data, including applications of deep neural networks to medical image data. © 2022, CC BY.

关键词： Bioinformatics

Note on the Margolus-Levitin quantum speed limit for arbitrary fidelity

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arXiv 2023年

作者： Andrzejewski, Krzysztof Bolonek-Lason, Katarzyna Kosiński, Piotr Department of Computer Science Faculty of Physics and Applied Informatics University of Lodz 149/153 Pomorska St. Lodz90-236 Poland Department of Statistical Methods Faculty of Economics and Sociology University of Lodz 41/43 Rewolucji 1905 St. Lodz90-214 Poland

For vanishing fidelity between initial and final states two important quantum speed limits, the Mandelstam-Tamm limit (involving energy dispersion) and Margolus-Levitin one (involving excitation energy expectation value) have been derived. While the generalization of the former limit to the case of arbitrary fidelity is straightforward, the relevant generalization of the latter, given in the seminal paper by Giovanetti et al (Phys. Rev. A67 (2003), 052109) was based on the conjectured equality of lower and upper bounds on the right hand side of generalized Margolus-Levitin inequality, verified numerically up to seven digits. Only recently there appear two proofs of the conjecture. We provide below a very elementary new proof, based on the simplest tools from differential calculus. Thus the generalized Margolus-Levitin speed limit can be derived much in the spirit of the original one valid for vanishing fidelity. © 2023, CC BY.

关键词： Differentiation (calculus)

Automated Ultrasound Video Enhancement

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Automated Ultrasound Video Enhancement

2021 International Conference on Communication and Information Technology, ICICT 2021

作者： Jabbar, Shaima I. Day, Charles R. Aladi, Abathar Q. Chadwick, Edward K. Frurat Al Awsat Technical University Babylon technical Institute Babylon Iraq Keele University Computer Science Department United Kingdom Babylon Health Directory Mirjan Teaching Hospital Babylon Iraq University of Aberdeen School of Engineering United Kingdom

ISBN: (纸本)9781665439145

The video ultrasound imaging is one of the techniques that is in demand at the present time. The reason of using ultrasound video is to provide a clearer impression of the organ's details or any area in the body to a specialist. Therefore, automated improving of ultrasound video increase a chance of getting a correct diagnosis and it is possible to add as an option in the ultrasound machine (improving the ultrasound video during recording). In this paper, we introduced and applied a novel technique, which depends on the principle of fuzzy inference system, through designing an effective fuzzy rule to treat image regions of low contrast based on the analysis of the image properties. Proposed technique was performed on muscklusketal ultrasound video and musculoskeletal panoramic ultrasound image. The proposed technique is Fuzzy Inference Ultrasound Video (FIUV), a superior result was obtained when it applied on panoramic images compared to ultrasound video. The performance of the FIUV technique with approximately 18% greater than recent technique in the case of panoramic ultrasound images and 4% less than recent technique when was applied on ultrasound video. Therefore, the results showed that the response of the panoramic images to this proposed technique was more than the videography. However, as future work it is possible to modify this method and improve its performance on video imaging. © 2021 IEEE.

关键词： Fuzzy inference

Uncertainty-aware similarity measures - properties and construction method 11

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Uncertainty-aware similarity measures - properties and const...

11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019

作者： Żywica, Patryk Stachowiak, Anna Department of Imprecise Information Processing Methods Faculty of Mathematics and Computer Science Adam Mickiewicz University in Poznań Umultowska 87 Poznań61-614 Poland

ISBN: (纸本)9789462527706

A concept of uncertainty-aware similarity measure is being defined and discussed. The aim of the paper is to support the opinion that both definition and construction of such measure should take into account the epistemic nature of compared incomplete (uncertain) information. This approach, however, generates new challenges resulted from the computational complexity of the problem. We define a set of properties to be satisfied by uncertainty-aware similarity measure and we propose a new technique of constructing such measures for Interval-Valued Fuzzy Sets. Copyright © 2019, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://***/licenses/by-nc/4.0/).

关键词： Fuzzy sets

Evaluating and Improving Tool-Augmented Computation-Intensive Math Reasoning

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arXiv 2023年

作者： Zhang, Beichen Zhou, Kun Wei, Xilin Zhao, Wayne Xin Sha, Jing Wang, Shijin Wen, Ji-Rong Gaoling School of Artificial Intelligence Renmin University of China China School of Information Renmin University of China China Beijing Key Laboratory of Big Data Management and Analysis Methods China College of Computer Science Sichuan University China iFLYTEK Research China iFLYTEK AI Research China

Chain-of-thought prompting (CoT) and tool augmentation have been validated in recent work as effective practices for improving large language models (LLMs) to perform step-by-step reasoning on complex math-related tasks. However, most existing math reasoning datasets may be not able to fully evaluate and analyze the ability of LLMs in manipulating tools and performing reasoning, as they may only require very few invocations of tools or miss annotations for evaluating intermediate reasoning steps. To address the issue, we construct CARP, a new Chinese dataset consisting of 4,886 computation-intensive algebra problems with formulated annotations on intermediate steps. In CARP, we test four LLMs with CoT prompting, and find that they are all prone to make mistakes at the early steps of the solution, leading to wrong answers. Based on this finding, we propose a new approach that can deliberate the reasoning steps with tool interfaces, namely DELI. In DELI, we first initialize a step-by-step solution based on retrieved exemplars, then iterate two deliberation procedures that check and refine the intermediate steps of the generated solution, from the perspectives of tool manipulation and natural language reasoning, until obtaining converged solutions or reaching the maximum turn. Experimental results on CARP and six other datasets show that the proposed DELI mostly outperforms competitive baselines, and can further boost the performance of existing CoT methods. Our data and code are available in https://***/RUCAIBox/CARP. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

Learning to Answer Questions in Dynamic Audio-Visual Scenarios

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arXiv 2022年

作者： Li, Guangyao Wei, Yake Tian, Yapeng Xu, Chenliang Wen, Ji-Rong Hu, Di Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Department of Computer Science University of Rochester Rochester United States

In this paper, we focus on the Audio-Visual Question Answering (AVQA) task, which aims to answer questions regarding different visual objects, sounds, and their associations in videos. The problem requires comprehensive multimodal understanding and spatio-temporal reasoning over audio-visual scenes. To benchmark this task and facilitate our study, we introduce a large-scale MUSIC-AVQA dataset, which contains more than 45K question-answer pairs covering 33 different question templates spanning over different modalities and question types. We develop several baselines and introduce a spatio-temporal grounded audio-visual network for the AVQA problem. Our results demonstrate that AVQA benefits from multisensory perception and our model outperforms recent A-, V-, and AVQA approaches. We believe that our built dataset has the potential to serve as testbed for evaluating and promoting progress in audio-visual scene understanding and spatio-temporal reasoning. Code and dataset: http://***/MUSIC-AVQA/ Copyright © 2022, The Authors. All rights reserved.

关键词： Large dataset

Confounder Balancing for Instrumental Variable Regression with Latent Variable

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arXiv 2022年

作者： Wu, Anpeng Kuang, Kun Xiong, Ruoxuan Li, Bo Wu, Fei Department of Computer Science and Technology Zhejiang University Hangzhou310007 China Department of Quantitative Theory and Methods Emory University Atlanta30322 United States School of Economics and Management Tsinghua University Beijing100084 China

This paper studies the confounding effects from the unmeasured confounders and the imbalance of observed confounders in IV regression and aims at unbiased causal effect estimation. Recently, nonlinear IV estimators were proposed to allow for nonlinear model in both stages. However, the observed confounders may be imbalanced in stage 2, which could still lead to biased treatment effect estimation in certain cases. To this end, we propose a Confounder Balanced IV Regression (CB-IV) algorithm to jointly remove the bias from the unmeasured confounders and the imbalance of observed confounders. Theoretically, by redefining and solving an inverse problem for potential outcome function, we show that our CB-IV algorithm can unbiasedly estimate treatment effects and achieve lower variance. The IV methods have a major disadvantage in that little prior or theory is currently available to pre-define a valid IV in real-world scenarios. Thus, we study two more challenging settings without pre-defined valid IVs: (1) indistinguishable IVs implicitly present in observations, i.e., mixed-variable challenge, and (2) latent IVs don’t appear in observations, i.e., latent-variable challenge. To address these two challenges, we extend our CB-IV by a latent-variable module, namely CB-IV-L algorithm. Extensive experiments demonstrate that our CB-IV(-L) outperforms the existing approaches. Copyright © 2022, The Authors. All rights reserved.

关键词： Inverse problems

A Classification Benchmark for Artificial Intelligence Detection of Laryngeal Cancer from Patient Speech

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arXiv 2024年

作者： Paterson, Mary Moor, James Cutillo, Luisa School of Computer Science University of Leeds LeedsLS2 9JT United Kingdom Leeds Teaching Hospitals NHS Trust LeedsLS1 3EX United Kingdom School of Mathematics University of Leeds LeedsLS2 9JT United Kingdom

Cases of laryngeal cancer are predicted to rise significantly in the coming years. Current diagnostic pathways cause many patients to be incorrectly referred to urgent suspected cancer pathways, putting undue stress on both patients and the medical system. Artificial intelligence offers a promising solution by enabling non-invasive detection of laryngeal cancer from patient speech, which could help prioritise referrals more effectively and reduce inappropriate referrals of non-cancer patients. To realise this potential, open science is crucial. A major barrier in this field is the lack of open-source datasets and reproducible benchmarks, forcing researchers to start from scratch. Our work addresses this challenge by introducing a benchmark suite comprising 36 models trained and evaluated on open-source datasets. These models are accessible in a public repository, providing a foundation for future research. They evaluate three different algorithms and three audio feature sets, offering a comprehensive benchmarking framework. We propose standardised metrics and evaluation methodologies to ensure consistent and comparable results across future studies. The presented models include both audio-only inputs and multimodal inputs that incorporate demographic and symptom data, enabling their application to datasets with diverse patient information. By providing these benchmarks, future researchers can evaluate their datasets, refine the models, and use them as a foundation for more advanced approaches. This work aims to provide a baseline for establishing reproducible benchmarks, enabling researchers to compare new methods against these standards and ultimately advancing the development of AI tools for detecting laryngeal cancer. © 2024, CC BY.

关键词： Lung cancer