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Towards Instruction-Tuned Verification for Improving Biomedical Information Extraction with Large Language Models

Towards Instruction-Tuned Verification for Improving Biomedi...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Jin Yuan, Ruifan Tian, Yu Li, Jingsong Nanjing University of Information Science and Technology School of Artificial Intelligence Nanjing China Zhejiang University College of Biomedical Engineering & Instrumental Science Hangzhou China Research Center for Data Hub and Security Zhejiang Lab Hangzhou China

ISBN: (纸本)9798350386226

Biomedical information extraction, including tasks like Named Entity Recognition (NER) and Relationship Extraction (RE), plays a critical role in analyzing unstructured biomedical data, facilitating more accurate downstream applications such as clinical decision support and personalized medicine. Although Large Language Models (LLMs) excel in various domains, they often struggle with issues like hallucinations and domain-specific inaccuracies, limiting their effectiveness in biomedical applications. To tackle these challenges, we propose a novel framework that incorporates an external dual-stage verifier to enhance biomedical information extraction. This verifier is prompted to first identify and complete missing entities and relations, and then filter out incorrect extractions, thereby enhancing both precision and recall in information extraction. Furthermore, we proposed an approach for constructing the instruction-tuning dataset using both positive and negative demonstrations, which improves the verifier's correction capabilities through Parameter-Efficient Fine-Tuning (PEFT). Our extensive experiments on three public benchmark datasets cover both biomedical NER and RE tasks, comparing four LLMs ranging from 7 to 175 billion parameters to validate the effectiveness and scalability of our approach. The results demonstrate that by integrating an external verifier specifically finetuned for biomedical tasks, we achieve substantial improvements in precision, recall, and F1 scores for NER and RE tasks. Our method consistently outperforms In-Context Learning (ICL) approaches in F1 scores, achieving increases of up to 20% in some cases. Furthermore, ablation studies demonstrate the effectiveness of both stages in the verification process. The proposed framework provides valuable insights for accurate biomedical information extraction using LLMs, highlighting its potential applications in real-world clinical settings. © 2024 IEEE.

关键词： Personalized medicine

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An API Field Design Method Based on Pre-trained Model 14

An API Field Design Method Based on Pre-trained Model

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14th International Conference on Software technology and engineering, ICSTE 2024

作者： Wang, Hai Lou, Yihua Yang, Yi Xu, Ranbin Yang, Jingbo Liao, Xingchuang Center of Civil Aviation Big Data TravelSky Technology Limited Beijing Engineering Research Beijing China School of Computer Science and Engineering Beihang University Beijing China School of Artificial Intelligence Beihang University Beijing China

ISBN: (纸本)9798350378955

Application programming interface (API) plays an important role in modern software development, a well designed API can provide tremendous convenience for developers. However, designing an excellent API is not a trivial task, each field in the API should avoid any ambiguity. Besides, in some special cases, for instance, a Chinese developer might not accurately translate the meaning of a Chinese API field into its English counterpart. Therefore, there exists a tremendous requirement in designing high quality API field. In this paper, a fine-tuning pre-trained model based method is proposed for API field design task. Firstly, we consider the design of API fields as a translation task, converting the fields from another language into English. Secondly, we fine-tune a pre-trained model for API field design task. In the end, we curate two datasets to illustrate our method and the experiment results demonstrate that our method has a better performance in designing API field. © 2024 IEEE.

关键词： Application programs

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Utilizing Machine Learning Techniques for Forecasting Agricultural Commodity Prices 2

Utilizing Machine Learning Techniques for Forecasting Agricu...

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2nd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Benjamin, Minu Inba Shanthini Watson Gowtham, D. Reddy, S. Anand Kumar Krishna, D. Durga Radha Vel Tech Rangarajan Dr.Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr.Sagunthala R and D Institute of Science and Technology Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350327533

Crop prices in India are vulnerable to environmental factors such as rainfall and temperature which can cause supply variations and price shifts. Government policies are intended to keep prices stable during severe weather occurrences. An extensive crop price prediction approach utilizing diverse machine learning techniques and encompassing features such as temperature extremes, humidity, precipitation, and probabilities is introduced in this research work. Employing algorithms including linear regression, decision trees, random forests, K-Nearest Neighbors (KNN), XGBoost, and Bayesian regression, the research conducts comprehensive evaluation and comparative analysis to identify optimal techniques for crop price prediction, thereby yielding valuable insights for agricultural applications. Amid the intricacies of contemporary agricultural markets, where numerous factors influence prices, machine learning emerges as a potent tool capable of capturing intricate influences. By exploring a range of machine learning algorithms, complex relationships within data are unveiled, establishing a basis for evidence-based methodology selection. The research results heighten forecasting precision and empower stakeholders with actionable insights, facilitating informed decisions, risk mitigation, and capitalization on opportunities. This signifies a substantial stride towards the convergence of machine learning and agriculture, fostering enhanced predictive accuracy in crop price forecasting. © 2024 IEEE.

关键词： Bayesian regression Decision tree Ensemble methods exploratory data analysis hybrid models K-Nearest Neighbors linear regression Machine learning Random Forest XGBoost

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Revolutionizing Cardiovascular Attack Prediction: A Comprehensive Machine Learning Approach for Accurate and Timely Detection 4

Revolutionizing Cardiovascular Attack Prediction: A Comprehe...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Durai, S. Jaganathan, D. Prabhu, V. Harini, R. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Dept. of CSE Chennai India Madanapalle Institute of Technology & Science Department of Artificial Intelligence Andhra Pradesh India R.M.K. Engineering College Department of Computer Science and Engineering Thiruvallur Kavaraipettai India vel Tech Multi Tech Dr.Rangarajan Dr. Sakunthala Engineering College Artificial Intelligence & Data Science Chennai India

ISBN: (纸本)9798350385793

Cardiac attacks are a leading cause of mortality worldwide, underscoring the critical need for timely and accurate healthcare detection. Predicting cardiovascular attacks is a formidable challenge in clinical data analysis. Machine Learning (ML) proves to be a valuable tool for analyzing vast healthcare data. This innovative approach focuses on employing ML techniques to identify significant features, thereby improving the accuracy of cardiovascular attack prediction. A variety of characteristics and well-known classifiers, including Naïve Bayes (NB), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Logistic Regression (LR), are used to create the final prediction model. Through collaborative efforts, these algorithms pave the way for binary classification, effectively distinguishing between true and false outcomes. By analyzing their accuracies, this approach enables the possibilities for successful predictions. © 2024 IEEE.

关键词： Adversarial machine learning

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Cardiac Arrhythmia - Prediction and Classification using Support Vector Machine

Cardiac Arrhythmia - Prediction and Classification using Sup...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Navya, R. Bv, Santhosh Krishna Rawat, Anurag Satheeshkumar, S. New Horizon College of Engineering Department of CSE India Himalayan Institute of Medical Science Department of Cardiology India Bannari Amman Institute of Technology Artificial Intelligence and Data Science India

ISBN: (纸本)9798350315127

Untreated, arrhythmia is a potentially life-threatening condition that can lead to severe illness in patients. Early detection of heart arrhythmias can save lives. Based on ECG recordings. New advances in various types of equipment and machine learning models provide an opportunity to use computer-aided diagnosis (CAD) to make ECG classification procedures applicable in a real clinical setting. This allows classification of electrocardiographic arrhythmias between patients. The objective of this paper is to design and evaluate an innovative cross-patient approach for analyzing extensive electrocardiogram datasets, with the goal of enhancing the identification of rare arrhythmia We study four supervised machine learning models: support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF), and a suite of three methods. We will test the effectiveness of these strategies in classifying: normal beaters (NOR), left leg blockers (LBBB), right leg blockers (RBBB), failure of atrial complexes (PAC) and premature ventricular complexes (PVC), next. segmenting and normalizing the data, obtaining patients' actual ECG data from MIT-DB, and measuring four parameters: precision, accuracy, recall, and f1 score. The results of the experiment show that, without the use of complex data preprocessing or engineering methods, the SVM classifier outperforms other methods using our patient interviews in all cases. An achieved Calculation value accuracy is of 0.84;is still very important. ECG is important in using the arrhythmia classification model. This approach is more accurate in clinical settings. where multiple ECG signals are collected from different patients. © 2023 IEEE.

关键词： Electrocardiograms

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Machine Learning-based Biomedical Antenna for Brain Tumor Detection

Machine Learning-based Biomedical Antenna for Brain Tumor De...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Hasan, Nafiul Aktar, Mousumi Rana, Md. Masud Moni, Mohammad Ali Rajshahi University of Engineering and Technology Department of Electrical and Electronic Engineering Rajshahi Bangladesh School of Health and Rehabilitation Sciences The University of Queensland Artificial Intelligence & Data Science St Lucia Australia

ISBN: (纸本)9798350316001

Automated identification and categorization of brain anomalies like tumors is crucial to understanding and monitoring patient outcomes. In this study, we applied machine learning models to improve the effectiveness of a step-constant tapered slot antenna (STSA) for diagnosing gliomas in the brain. We have performed simulation with the CST Microwave Studio 2015 to a dataset consisting of astrocytoma (tumor radius 5 mm, skin-tumor distance 24 mm) and matching healthy controls. Phantom, an artificial human head, has four antennas placed at right angles to it. Later on, several machine learning models including the Support Vector Machine (SVM), K Nearest Neighbor (KNN), Random Forest Classifier (RFC), and artificial Neural Network (ANN) were applied to detect and diagnose brain cancers. There was a rigorous investigation of the scattering (S), admittance (Y), and impedance (Z) parameters and we observed the best result (99.60% accuracy) from the application of ANN and the Z parameter. The proposed technology could be applied for brain tumor detection accurately and clinical applications for a more efficient healthcare system based on biomedical antennas. © 2023 IEEE.

关键词： Support vector machines

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Early Diabetes prediction with optimal feature selection using ML based Prediction Framework 4

Early Diabetes prediction with optimal feature selection usi...

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4th International Conference on Signal Processing and Communication, ICSPC 2023

作者： Ashisha, G.R. Mary, X. Anitha Ashif, H. Mohamed Karthikeyan, I. Roshan, J. Karunya Institute of Technology and Sciences Electronics and Instrumentation Engineering Coimbatore India Karunya Institute of Technology and Sciences Robotics Engineering Coimbatore India Karunya Institute of Technology and Sciences Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350300772

The notable developments in healthcare sciences and biotechnology have led to a significant increase in information technology, for instance, clinical data and high efficiency genetic information, produced from intensive Electronic Medical data. The use of data mining and machine learning techniques are essential in the biomedical sciences to distinctly transform all accessible health records into useful knowledge. Early detection of diseases like diabetes is crucial as the growing number of people with diabetes is rising rapidly. According to an International Diabetes Federation report, the number of diabetic cases globally is expected to reach 642 million by 2040. This growing disease requires a lot of attention. Machine learning has quickly advanced, and many facets of medical health have benefited from its use. Due to their capacity for prediction, machine learning algorithms are presently significant in the healthcare industry. The major goal of this work is to design a model that can more accurately predict patients' diabetes. The research work analyzed the hospital clinical examination data obtained from Sree Guru Hospital, Kanyakumari district, TamilNadu. To improve the accuracy of the system, imbalanced data in the real time dataset were eliminated using oversampling technique. Extra Trees classifier model, and Random Forest (RF) was used to predict diabetes. The result of this work shows that prediction of diabetes with the Extra Trees classifier had the highest accuracy of 98%. Finally, this work effectively determines the pervasiveness and detection of diabetes. © 2023 IEEE.

关键词： Forecasting

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Yolo5-Based UAV Surveillance for Tiny Object Detection on Airport Runways

Yolo5-Based UAV Surveillance for Tiny Object Detection on Ai...

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2023 International Conference on data science, Agents and artificial intelligence, ICDSAAI 2023

作者： Sri Hari, R.V. Ambalam, Rishywanth Ruban Kumar, B. Ibrahim, M. Ponnusamy, R. Chennai Institute of Technology Department of Artificial Intelligence and Data Science Tamilnadu Chennai India Chennai Institute of Technology Department of Mechanical Engineering Tamilnadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350348910

Foreign Object Debris (FOD) present on runways poses a threat to various aircraft components, including the frame, windshield, tires, and engine, while also posing a risk of injury to personnel in the surrounding area. The spectrum of foreign objects includes small rocks, pebbles, nuts, bolts, metal pieces, papers, and objects of millimetric sizes. Currently, a substantial number of air personnel engage in manual runway inspections to identify FOD, a process that demands significant manpower. Conducting visual inspections becomes challenging. An automated object detection framework has been developed to enhance the efficiency of the detection process and minimize dependence on human resources. Utilizing YOLOv5, this study explores Unmanned Aerial Vehicle (UAV) surveillance for precise detection of small objects on airport runways. The focus is on enhancing efficiency and accuracy in monitoring runway spaces prone to Foreign Object Debris (FOD). The integration of YOLOv5 technology offers a robust solution for proactive detection and maintenance of runway safety. © 2023 IEEE.

关键词： Object detection

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An Enhanced Power Management and Prediction for Smart Grid Using Machine Learning

An Enhanced Power Management and Prediction for Smart Grid U...

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International Conference on data science, Computation and Security, IDSCS 2023

作者： Kumar, Shilpa Mohan Nagaraj, Sharmila Veerabhadraswamy, Pushpalatha Nanjundaswamy, Mahendra Hanumanapura Srikantaswamy, Mallikarjunaswamy Chandratta, Kiran Yarehalli Department of Information Science and Engineering Bangalore Institute of Technology Karnataka Bengaluru India Electrical and Electronics Engineering JSS Science and Technology University Karnataka Mysore India Artificial Intelligence and Data Science Global Academy of Technology Karnataka Bengaluru India Electronics and Communication Engineering JSS Academy of Technical Education Karnataka Bengaluru India Information Science Engineering Global Academy of Technology Karnataka Bengaluru India

ISBN: (纸本)9789819709748

In recent years, managing power generation and utilisation to stay on track has played a major role. Apart from this, analysis of energy bill generation and smoothing the peak demand curve to an optimal level are very difficult with the existing system. More power loss has occurred due to the fault detection in the smart grid;it has created manipulation data and obstacles for the training of the data. Hence, to overcome this, the proposed machine learning-based power management and prediction system (ML-PMPS) for the smart grid has been operated, which is essential for providing an uninterrupted power supply to consumers. As per simulation analysis, the proposed method performs better as compared to the conventional methods such as Multilayer Perceptron (MLP) and Naive Bayesian (NB) with respect to accuracy, prediction rate, and power management. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Internet of things

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Emergency vs Non-Emergency Vehicle Classification: Enhancing Intelligent Traffic Management Systems

Emergency vs Non-Emergency Vehicle Classification: Enhancing...

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2023 IEEE International Conference on Network, Multimedia and Information technology, NMITCON 2023

作者： Madan, S. Shetty, Kishan Kodipalli, Ashwini Rao, Trupthi Kumaraswamy, S. Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India Global Academy of Technology Dept of Artificial Intelligence and Data Science Karnataka Bengaluru India Global Academy of Technology Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350300826

Due to its dense population, India frequently experiences traffic congestion, which puts lives in danger by trapping vehicles like ambulances, police cars, and fire trucks which run on road for emergency purpose. It becomes crucial to give these vehicles priority and allow for their easy passage. However, it becomes challenging or even impossible for traffic police to effectively handle such circumstances. Therefore, there is a requirement for an automated system that can locate emergency vehicles in high-traffic areas, alert the controller, or drive itself to direct other vehicles to make room. This study suggests an automated method for identifying emergency vehicles from CCTV footage that makes use of deep convolutional neural networks (CNN). The objective is to effectively identify and classify emergency vehicles in real-time, leveraging the power of advanced object detection techniques. With an accuracy of 91.73% and a loss of 0.2120, the suggested technique outperformed existing optimizers in accurately recognizing and classifying emergency vehicles. © 2023 IEEE.

关键词： Object detection

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