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Unlocking the Secrets Behind Advanced Artificial Intelligence Language Models in Deidentifying Chinese-English Mixed Clinical Text: Development and Validation Study

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Journal of Medical Internet Research 2024年第1期26卷 e48443页

作者： Lee, You-Qian Chen, Ching-Tai Chen, Chien-Chang Lee, Chung-Hong Chen, Peitsz Wu, Chi-Shin Dai, Hong-Jie Dialogue System Technical Department Asustek Computer Inc Taipei Taiwan Intelligent System Laboratory Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Bioinformatics and Medical Engineering Asia University Taichung Taiwan Center for Precision Health Research Asia University Taichung Taiwan Electromagnetic Sensing Control and AI Computing System Laboratory Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Knowledge Discovery and Data Mining Lab Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Chemical Engineering Feng Chia University Taichung Taiwan National Center for Geriatrics and Welfare Research National Health Research Institutes Zhunan Taiwan National Institute of Cancer Research National Health Research Institutes Tainan Taiwan School of Post-Baccalaureate Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Center for Big Data Research Kaohsiung Medical University Kaohsiung Taiwan

Background: The widespread use of electronic health records in the clinical and biomedical fields makes the removal of protected health information (PHI) essential to maintain privacy. However, a significant portion of information is recorded in unstructured textual forms, posing a challenge for deidentification. In multilingual countries, medical records could be written in a mixture of more than one language, referred to as code mixing. Most current clinical natural language processing techniques are designed for monolingual text, and there is a need to address the deidentification of code-mixed text. Objective: The aim of this study was to investigate the effectiveness and underlying mechanism of fine-tuned pretrained language models (PLMs) in identifying PHI in the code-mixed context. Additionally, we aimed to evaluate the potential of prompting large language models (LLMs) for recognizing PHI in a zero-shot manner. Methods: We compiled the first clinical code-mixed deidentification data set consisting of text written in Chinese and English. We explored the effectiveness of fine-tuned PLMs for recognizing PHI in code-mixed content, with a focus on whether PLMs exploit naming regularity and mention coverage to achieve superior performance, by probing the developed models’ outputs to examine their decision-making process. Furthermore, we investigated the potential of prompt-based in-context learning of LLMs for recognizing PHI in code-mixed text. Results: The developed methods were evaluated on a code-mixed deidentification corpus of 1700 discharge summaries. We observed that different PHI types had preferences in their occurrences within the different types of language-mixed sentences, and PLMs could effectively recognize PHI by exploiting the learned name regularity. However, the models may exhibit suboptimal results when regularity is weak or mentions contain unknown words that the representations cannot generate well. We also found that the availability of cod

关键词： ChatGPT code mixing deidentification electronic health record large language model pretrained language model

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Prediction of Electronic parameters of Carbon Nanotube random network Field-effect Transistors under liquid gated conditions using a machine learning approach

Prediction of Electronic parameters of Carbon Nanotube rando...

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2022 IEEE International Conference on Nanoelectronics, Nanophotonics, Nanomaterials, Nanobioscience and Nanotechnology, 5NANO 2022

作者： Murugathas, Thanihaichelvan Veena, R. Plank, Natalie O.V. Keerththinaathan, Pirunthan Petra, I. Mohandas, Prabu University of Jaffna Faculty of Science Department of Physics Jaffna40000 Sri Lanka Universiti Brunei Darussalam Faculty of Integrated Technologies BE1410 Brunei Victoria University of Wellington School of Chemical and Physical Sciences Wellington6021 New Zealand University of Jaffna Faculty of Technology Department of Electrical Technology Kilinochchi40000 Sri Lanka National Institute of Technology Calicut Intelligent Computing Lab Department of Computer Science and Engineering Kerala India

ISBN: (纸本)9781665437288

In this work, we employed a machine learning approach to predict the performance parameters of CNT bundle network field-effect transistors (FETs). The CNT bundle network FETs are complex system not only due to the random nature of the network but also due to the conductivity and work functions of bundles with both metallic and semiconducting tubes. It is difficult to model a whole network of CNT bundles with unknown conductivity and work function. In a recent work we examined 119 devices for investigate the role of CNT junctions in the electrical conduction and gating of CNT network FETs under liquid gated conditions. In this work we used the experimental data to train and predict the performance using the Support vector machine (SVM) technique. The CNT bundle density of the film measured by atomic force microscope images was used as the input parameter and the on-current, off-current and threshold voltage were the target. We were able to estimate the on current with an accuracy better than 90 percentage. However OFF current and threshold voltage prediction has an accuracy about 82 and 77 percent respectively. The 90-percentage accuracy for the on-current prediction can be attributed to the strong dependency of on-current with the CNT bundle density. However, the off-current and threshold voltage are depending on other parameters namely the presence of metallic CNTs within the bundles and the overall variations in the composition of network. Based on the experimental results, despite the unknown variables, we managed to predict the electronic parameters of a CNT network FETs with using the SVM techniques with significantly high accuracy. © 2022 IEEE.

关键词： Threshold voltage

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Screening cognitive impairment in patients with atrial fibrillation: A deep learning model based on retinal fundus photographs

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Heart Rhythm O2 2025年第5期6卷 678.0-686.0页

作者： Wang, Zhen Li, Mingxiao Xia, Peng Jiang, Chao Shen, Ting Ma, Jiaming Bai, Yu Zhang, Suhui Lai, Yiwei Li, Sitong Xu, Hui Xu, Yang Ma, Tong Ju, Lie He, Liu Dong, Li Sang, Caihua Long, Deyong Chen, Yuzhong Du, Xin Ge, Zongyuan Dong, Jianzeng Wei, Wen-Bin Ma, Changsheng Department of Cardiology Beijing Anzhen Hospital Capital Medical University Beijing China Engineering Research Center of Medical Devices for Cardiovascular Diseases Ministry of Education Capital Medical University Beijing China National Clinical Research Center for Cardiovascular Diseases Beijing China Beijing Airdoc Technology Co. Ltd. Beijing China Department of Electrical and Computer Systems Engineering Faculty of Engineering Monash University Clayton Australia Yale School of Medicine New Haven Connecticut United States School of Clinical Medicine Peking Union Medical College and Chinese Academy of Medical Science Beijing China Beijing Tongren Eye Center Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment Beijing Ophthalmology & Visual Sciences Key Lab Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology Beijing Tongren Hospital Capital Medical University Beijing China Monash e-Research Center Monash University Clayton Australia

Background: Patients with atrial fibrillation (AF) have a higher risk of cognitive impairment (CI). However, complexity of CI diagnosis and lack of simple screening approaches limited early screening and intervention of CI in AF patients. Objective: Our study aimed to develop deep learning models based on fundus photographs for easy screening of CI in AF patients. Methods: From May 2021 to April 2023, patients who completed fundus examination and cognitive function evaluation in the Chinese Atrial Fibrillation Registry Study were included. The training and validation sets were randomly split at an 8:2 ratio. Participants from the Beijing Eye Study served as the external validation set. Different deep learning models were trained, and their CI detection ability was validated. Results: A total of 899 patients in the Chinese Atrial Fibrillation Registry Study were included. In the validation set, the vision-ensemble model based on fundus images alone had an area under the receiver-operating characteristic curve (AUROC) of 0.855 (95% confidence interval 0.816–0.894) for CI screening. The multimodal model (AUROC 0.861, 95% confidence interval 0.823–0.898), based on fundus photographs and 4 clinical variables, performed comparably to the vision-ensemble model. The AUROC of the vision-ensemble model for CI screening achieved 0.773 (95% confidence interval 0.709–0.837) in the external test set. In the saliency map, the vision-ensemble model focused on areas around retinal vessels and the optic disc. Conclusion: A vision-ensemble model based on fundus images might be practical for preliminary screening of CI in AF patients. © 2025 Heart Rhythm Society

关键词： Artificial intelligence Atrial fibrillation Cognitive impairment Deep learning Fundus photography

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A Lightweight Blockchain and Fog-enabled Secure Remote Patient Monitoring System

arXiv

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arXiv 2023年

作者： Cheikhrouhou, Omar Mershad, Khaleel Jamil, Faisal Mahmud, Redowan Koubaa, Anis Moosavi, Sanaz Rahimi Ces Laboratory University of Sfax Tunisia Computer Science and Mathematics Department Lebanese American University Beirut Lebanon Department of Computer Engineering Jeju National University Korea Republic of School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Australia Robotics and Internet of Things Lab Prince Sultan University Riyadh Saudi Arabia United States Isima University of Monastir Mahdia Tunisia

IoT has enabled the rapid growth of smart remote healthcare applications. These IoT-based remote healthcare applications deliver fast and preventive medical services to patients at risk or with chronic diseases. However, ensuring data security and patient privacy while exchanging sensitive medical data among medical IoT devices is still a significant concern in remote healthcare applications. Altered or corrupted medical data may cause wrong treatment and create grave health issues for patients. Moreover, current remote medical applications' efficiency and response time need to be addressed and improved. Considering the need for secure and efficient patient care, this paper proposes a lightweight Blockchain-based and Fogenabled remote patient monitoring system that provides a high level of security and efficient response time. Simulation results and security analysis show that the proposed lightweight blockchain architecture fits the resource-constrained IoT devices well and is secure against attacks. Moreover, the augmentation of Fog computing improved the responsiveness of the remote patient monitoring system by 40%. © 2023, CC BY-NC-ND.

关键词： Fog computing

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A Single Channel-Based Neonatal Sleep-Wake Classification using Hjorth Parameters and Improved Gradient Boosting

arXiv

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arXiv 2024年

作者： Arslan, Muhammad Mubeen, Muhammad Abbasi, Saadullah Farooq Khan, Muhammad Shahbaz Boulila, Wadii Ahmad, Jawad Department of Computer Science Loyola University ChicagoIL60660 United States Department of Computer Science University of People PasadenaCA91101 United States Department of Electronic Electrical and Systems Engineering University of Birmingham BirminghamB15 2TT United Kingdom School of Computing Engineering and the Built Environment Edinburgh Napier University EdinburghEH105DT United Kingdom RIOTU Lab Prince Sultan University Riyadh12435 Saudi Arabia

Sleep plays a crucial role in neonatal development. Monitoring the sleep patterns in neonates in a Neonatal Intensive Care Unit (NICU) is imperative for understanding the maturation process. While polysomnography (PSG) is considered the best practice for sleep classification, its expense and reliance on human annotation pose challenges. Existing research often relies on multichannel EEG signals;however, concerns arise regarding the vulnerability of neonates and the potential impact on their sleep quality. This paper introduces a novel approach to neonatal sleep stage classification using a single-channel gradient boosting algorithm with Hjorth features. The gradient boosting parameters are fine-tuned using random search cross-validation (randomsearchCV), achieving an accuracy of 82.35% for neonatal sleep-wake classification. Validation is conducted through 5-fold cross-validation. The proposed algorithm not only enhances existing neonatal sleep algorithms but also opens avenues for broader applications. © 2024, CC BY.

关键词： Neonatal monitoring

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Multi-Level Speaker Representation for Target Speaker Extraction

arXiv

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arXiv 2024年

作者： Zhang, Ke Li, Junjie Wang, Shuai Wei, Yangjie Wang, Yi Wang, Yannan Li, Haizhou Key Laboratory of Intelligent Computing in Medical Image Northeastern University China SDS SRIBD The Chinese University of Hong Kong Shenzhen China The Hong Kong Polytechnic University Hong Kong Tencent Ethereal Audio Lab Tencent Shenzhen China University of Bremen Germany Department of Electrical and Computer Engineering National University of Singapore Singapore

Target speaker extraction (TSE) relies on a reference cue of the target to extract the target speech from a speech mixture. While a speaker embedding is commonly used as the reference cue, such embedding pre-trained with a large number of speakers may suffer from confusion of speaker identity. In this work, we propose a multi-level speaker representation approach, from raw features to neural embeddings, to serve as the speaker reference cue. We generate a spectral-level representation from the enrollment magnitude spectrogram as a raw, low-level feature, which significantly improves the model’s generalization capability. Additionally, we propose a contextual embedding feature based on cross-attention mechanisms that integrate frame-level embeddings from a pre-trained speaker encoder. By incorporating speaker features across multiple levels, we significantly enhance the performance of the TSE model. Our approach achieves a 2.74 dB improvement and a 4.94% increase in extraction accuracy on Libri2mix test set over the baseline. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

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Accurate and Efficient Event-based Semantic Segmentation Using Adaptive Spiking Encoder-Decoder Network

arXiv

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arXiv 2023年

作者： Zhang, Rui Leng, Luziwei Che, Kaiwei Zhang, Hu Cheng, Jie Guo, Qinghai Liao, Jianxing Cheng, Ran Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China ACSLab Huawei Technologies Co. Ltd China Advanced Computing and Storage Lab Huawei Technologies Co. Ltd. Shenzhen518055 China

Spiking neural networks (SNNs), known for their low-power, event-driven computation and intrinsic temporal dynamics, are emerging as promising solutions for processing dynamic, asynchronous signals from event-based sensors. Despite their potential, SNNs face challenges in training and architectural design, resulting in limited performance in challenging eventbased dense prediction tasks compared to artificial neural networks (ANNs). In this work, we develop an efficient spiking encoder-decoder network (SpikingEDN) for large-scale eventbased semantic segmentation tasks. To enhance the learning efficiency from dynamic event streams, we harness the adaptive threshold which improves network accuracy, sparsity and robustness in streaming inference. Moreover, we develop a dualpath Spiking Spatially-Adaptive Modulation module, which is specifically tailored to enhance the representation of sparse events and multi-modal inputs, thereby considerably improving network performance. Our SpikingEDN attains a mean intersection over union (MIoU) of 72.57% on the DDD17 dataset and 58.32% on the larger DSEC-Semantic dataset, showing competitive results to the state-of-the-art ANNs while requiring substantially fewer computational resources. Our results shed light on the untapped potential of SNNs in event-based vision applications. The source code will be made publicly available. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Optimizing UAV Stability and Control with FPGA-Based PID Control System Design

Optimizing UAV Stability and Control with FPGA-Based PID Con...

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International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Hocine Khati Arezki Fekik Ahmad Taher Azar Mohamed Amine Nehmar Saim Ahmed Nashwa Ahmad Kamal Chakib Ben Njima Hand Talem Rabah Mellah Faculty of Electrical and Computing Engineering University Mouloud Mammeri of Tizi-Ouzou Algeria Department of Electrical Engineering University Akli Mohand Oulhadj-Bouria Algeria CNRS LS2N UMR 6004 Nantes Université École Centrale Nantes Nantes France College of Computer & Information Sciences Prince Sultan University Riyadh Saudi Arabia Automated Systems & Soft Computing Lab (ASSCL) Prince Sultan University Riyadh Saudi Arabia Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Faculty of Engineering Cairo University Giza Egypt University of Sousse Tunisia

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

In this paper, a study on the Field Programmable Gate Array (FPGA)-based Proportional-Integral-Derivative (PID) control of an Unmanned Aerial Vehicle (UAV) is presented. The goal is to implement four PID controllers in FIL (FPGA In-the-Loop) execution mode using Simulink. These controllers are designed to manage rotation angles and altitude, aiming to stabilize the quadrotor. The controllers were developed within the MATlab Simulink environment, utilizing blocks supported by HDL Coder. PID parameters were fine-tuned through the PID Tuner feature. The control strategy was subsequently implemented on a Zybo FPGA board, employing the HDL Workflow Advisor to assess the controllers' behavior in FIL mode. The obtained results underscore the effectiveness and performance of the adopted control design.

关键词： PI control Tuners Software packages Autonomous aerial vehicles PD control Hardware design languages Field programmable gate arrays

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Hierarchical Mutual Information Analysis: Towards Multi-View Clustering in the Wild

Hierarchical Mutual Information Analysis: Towards Multi-View...

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International Joint Conference on Neural Networks (IJCNN)

作者： Jiatai Wang Zhiwei Xu Xuewen Yang Xin Wang Li Tao Haihe Lab of ITAI Tianjin China College of Data Science and Application Inner Mongolia University of Technology Huhhot China Institute of Computing Technology Chinese Academy of Sciences Beijing China InnoPeak Technology Inc Palo Alto California USA Department of Electrical and Computer Engineering Stony Brook University New York U.S.A.

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Multi-view clustering (MVC) can explore common semantics from multiple views and has been extensively used to support management with unsupervised training data. However, the issue of spatio-temporal asynchronism often leads to multi-view data being missing or unaligned in the real world. This limit poses significant challenges in learning consistent representations. This paper proposes a deep MVC framework where data recovery and alignment are fused hierarchically from an information-theoretic perspective, maximizing the mutual information among different views and ensuring the consistency of their latent spaces. To address the issue of missing views, we use dual prediction for instance-level alignment. While leveraging contrastive reconstruction enhances the mutual information of features within the same class for class-level alignment. This could be the first attempt to view recovery and alignment can be solved simultaneously in a unified theoretical framework. Extensive experiments show that our method outperforms baseline methods even in the cases of missing and unaligned views.

关键词： Semantics Noise Neural networks Decision making Training data Feature extraction Entropy

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HyperScore: A unified measure to model hypertension progression using multi-modality measurements and semi-supervised learning

HyperScore: A unified measure to model hypertension progress...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Mohanad Alkhodari Winok Lapidaire Zhaohan Xiong Turkay Kart Yasser Iturria-Medina Leontios Hadjileontiadis Ahsan Khandoker Adam J. Lewandowski Abhirup Banerjee Paul Leeson Radcliffe Department of Medicine Cardiovascular Clinical Research Facility University of Oxford Oxford UK Department of Biomedical Engineering Healthcare Engineering Innovation Center Khalifa University Abu Dhabi UAE Department of Computing Biomedical Image Analysis Group Imperial College London London UK Neuroinformatics for Personalized Medicine Lab McGill University Montreal Canada Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Department of Engineering Science Institute of Biomedical Engineering University of Oxford Oxford UK

Hypertension is a serious medical condition that affects over a billion people worldwide. The proper management of disease progression requires an extended knowledge of the overall functional and structural changes in the whole body in response to the hypertension. Here, we propose HyperScore, an integrative and unified measure of hypertension progression relative to multi-organ and multi-modality clinical measurements and based on a semi-supervised machine learning (ML) approach. We developed the measure based on a large participating cohort from the UK Biobank database (n=27,099) with over 500 imaging and clinical variables from multiple modalities. The semi-supervised approach was developed based on the contrastive trajectory inference mechanism to provide a score that reflects the proximity of a participant to the disease state (range: 0–1). Modelling revealed that majority of hypertensive participants had scores above 0.25, whereas normotensives had scores below this threshold. The sensitivity and specificity were above 89%, with an area under the receiver operating characteristics of 96.4%. The modelling showed a stable performance when evaluating hidden testing sets on a 10-fold cross-validation scheme with nearly 0.1 error. There was a strong association (r 2 >0.6) between HyperScore and organs’ phenotypic patterns, especially for variables such as white matter hyperintensity and body mass index. This study is the first to potentiate ML-based modelling of hypertension progression from a multi-organ perspective, which could significantly aid in clinical decision making to save lives.

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