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Reliability assessment of models for risk stratification: A knowledge-based approach

Reliability assessment of models for risk stratification: A ...

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Zhan Zhao Paulo Gil João Loureiro Lorena Petrella Jorge Henriques Dep. of Informatics Engr. CISUC University of Coimbra Coimbra Portugal CISUC University of Coimbra Portugal Dep. of Electrical and Computer Engr. CTS NOVA School of Sci. and Technology Portugal

ISBN: (数字)9798331530631

ISBN: (纸本)9798331530648

The present work proposes a framework aiming to develop reliability metrics for model predictions, integrating a priori clinical knowledge with data and emphasising feature relevance. The approach combines clinical knowledge, represented as rules, with selected features, such as risk factors, where their significance is captured through assigned weights. Validation of the proposed methodology is conducted using the Global Registry of Acute Coronary Events (GRACE) risk model, applied to a dataset from the Centro Hospitalar e Universitário de Coimbra, focusing on risk assessment. The results demonstrate that these reliability metrics are closely linked to the performance of individual GRACE predictions, enabling the identification of cases or groups where the GRACE model yields reliable or unreliable predictions. This framework provides a valuable supplementary metric to existing global risk models.

关键词： Measurement Knowledge based systems Focusing Predictive models Data models Reliability Risk management Data mining Forecasting Context modeling

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Improved Distance (Sensitivity) Oracles with Subquadratic Space

Improved Distance (Sensitivity) Oracles with Subquadratic Sp...

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Annual IEEE Symposium on Foundations of computer Science

作者： Davide Bilò Shiri Chechik Keerti Choudhary Sarel Cohen Tobias Friedrich Martin Schirneck Dep. of Inf. Eng. CS and Mathematics University of L'Aquila Department of Computer Science Tel Aviv University Dep. of CS and Engineering Indian Institute of Technology Delhi School of Computer Science The Academic College of Tel Aviv-Yaffo Hasso Plattner Institute University of Potsdam Faculty of Computer Science University of Vienna

ISBN: (数字)9798331516741

ISBN: (纸本)9798331516758

A distance oracle (DO) for a graph $G$ is a data structure that, when queried with vertices $s,t$ , returns an estimate $\widehat{d}(s,t)$ of their distance in $G$ . The oracle has stretch $(\alpha, \beta)$ if the estimate satisfies $d(s,t)\leqslant \widehat{d}(s,t)\leqslant \alpha\cdot d(s,t)+\beta$ . An $f-\mathbf{edge}$ fault-tolerant distance sensitivity oracle $(f-\mathbf{DSO})$ additionally receives a set $F$ of up to $f$ edges and estimates the distance in $G-F$ . Our first contribution is the design of new distance oracles with subquadratic space for undirected graphs. We show that introducing a small additive stretch $\beta > 0$ allows one to make the multiplicative stretch $\alpha$ arbitrarily small. This sidesteps a known lower bound of $\alpha\geqslant 3$ (for $\beta=0$ and subquadratic space) [Thorup & Zwick, JACM 2005]. We present a DO for graphs with edge weights in $[0, W]$ that, for any positive integer $\ell$ and any $c\in(0,\ell/2]$ , has stretch $(1+\frac{1}{\ell},2W)$ , space $\widetilde{O}(n^{2-\frac{c}{\ell}})$ , and query time $O(n^{c})$ , generalizing results by Agarwal and Godfrey [SODA 2013] to arbitrarily dense graphs. Our second contribution is a framework that turns an $(\alpha,\beta)- \mathbf{stretch}$ DO for unweighted graphs into an $(\alpha(1+\varepsilon),\beta)-\mathbf{stretch}. f-\mathbf{DSO}$ with sensitivity $f=o(\log(n)/\log\log n)$ retaining sub-quadratic space. This generalizes a result by Bilò, Chechik, Choudhary, Cohen, Friedrich, Krogmann, and Schirneck [TheoretiCS 2024]. Combining the framework with our new DO gives an $f-\mathbf{DSO}$ that, for any $\gamma\in(0, (\ell+1)/2]$ , has stretch $((1+\frac{1}{\ell})(1+\varepsilon), 2)$ , space $n^{2-\frac{\gamma}{(t+1)(f+1)}+o(1)}/\varepsilon^{f+2}$ , and query time $\widetilde{O}(n^{\gamma}/\varepsilon^{2})$ . This is the first $f-\mathbf{DSO}$ with subquadratic space, n

关键词： computer science Fault tolerance Sensitivity Additives Fault tolerant systems Data structures

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Brain Tumor Classification System Using Decision Tree Algorithm

Brain Tumor Classification System Using Decision Tree Algori...

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Cybernetics and Innovations (ICCI), International Conference on

作者： Sattarpoom Thaiparnit Thanakrit Janchidfah Adil Farooq Dep. Of Information System and Computer Business Faculty of Business Administration Rajamangala University of Technology Suvarnabhumi Suphan Buri Thailand dep. Of Digital Media Technology Muban Chombueng Rajabhat University Ratchaburi Thailand Department of Electronic Engineering Maynooth University Ireland

ISBN: (数字)9798331533267

ISBN: (纸本)9798331533274

This research aims to study, design, and develop a brain tumor classification system using artificial intelligence, specifically decision tree algorithms. The system's primary objective is to assist medical personnel in making decisions, as distinguishing between two types of brain tumors-benign and malignant (cancerous)-can be challenging, especially for less experienced medical staff. Due to the similarities between these two types of tumors, it may be difficult for medical practitioners with limited expertise to differentiate between them in a timely manner. The proposed brain tumor classification system is designed to address this challenge. The research team conducted the study over approximately seven months, starting from the second semester of the 2023 academic year through the first semester of the 2024 academic year. Experimental results revealed that among the tree-based algorithms, Random Forest achieved the highest accuracy, followed by M5P. The best average accuracy from the experiments was obtained using a 70% training and 30% testing split, yielding an accuracy of 96.83%. Therefore, the research team selected the 70% training and 30% testing split for model development, as it provided the highest accuracy compared to the 60% training and 40% testing split, and the 80% training and 20% testing split, which showed lower accuracy. The results demonstrate that the 70% training and 30% testing approach is optimal for the brain tumor classification system, ensuring high accuracy in classifying brain tumors.

关键词： Training Technological innovation Accuracy Machine learning algorithms Brain tumors Brain modeling Classification algorithms Decision trees Testing Cancer

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Knowledge-based reliability assessment of models with application to risk stratification

Knowledge-based reliability assessment of models with applic...

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Mediterranean Electrotechnical Conference (MELECON)

作者： Z. Zhao P. Gil J. Loureiro L. Petrella J. Henriques Dep. of Informatics Engr. University of Coimbra Coimbra Portugal CISUC University of Coimbra Portugal Dep. of Electrical and Computer Engr. CTS NOVA School of Sci. and Technology Portugal Dep. of Informatics Engr. CISUC University of Coimbra Coimbra Portugal

ISBN: (数字)9798350387025

ISBN: (纸本)9798350387032

This paper proposes a framework for developing reliability metrics for model predictions based on a priori clinical knowledge. The approach relies on rule-based knowledge along with selected features under the form of risk factors. Moreover, personalization of predictions plays a crucial role for grouping individuals into specific groups or profiles based on their intrinsic characteristics. Appropriate rules according to the individuals’ profile are derived and used within the framework. The proposed approach is validated considering the Global Registry of Acute Coronary Events risk model and applied to a dataset from the Centro Hospitalar da Universidade de Coimbra, with focus on risk assessment. Results obtained using the proposed approach indicate that by using those metrics to assess reliability it is possible to identify particular cases in which GRACE presents almost predictable outcomes.

关键词： Measurement Accuracy Knowledge based systems Predictive models Feature extraction Reliability Risk management

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Biological age estimation from ECG using Artificial Intelligence approaches

Biological age estimation from ECG using Artificial Intellig...

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Biomedical Engineering Conference (JIBEC), Jordanian International

作者： Jana Sleiman Mohamad Daher Mohamad Khalil Ahmad Diab Abdallah Kassem Computer and Communications Engineering Lebanese University Tripoli Lebanon Dep. of Computer Science/Dep. of Computer Science & Information Technology Faculty of Science/Faculty of Applied Sciences & Technology Tripoli Lebanon Electrical Engineering Lebanese University Tripoli Lebanon Biomedical Engineering Lebanese International University Beirut Lebanon ECCE Dept. Faculty Of Engineering Notre Dame University Zouk Mosbeh Lebanon

ISBN: (数字)9798331539542

ISBN: (纸本)9798331539559

This paper explores the application of Artificial Intelligence (AI) techniques in healthcare, specifically focusing on electrocardiogram (ECG) data analysis and biological age estimation. The study begins with an overview of ECG as a diagnostic tool and discusses the integration of Deep Learning (DL) models, particularly Convolutional Neural Networks (CNNs), in healthcare for various applications. Previous studies reviewed in this context consistently identify CNNs as the superior model for ECG analysis and prediction tasks. The primary research investigates the use of Machine Learning (ML) algorithms to estimate biological age from ECG data, employing metrics such as Mean Absolute Error (MAE) and variance to evaluate model performance. Subsequently, DL models are implemented and compared, demonstrating superior accuracy in estimating the biological age of new patients. While both ML and DL approaches were initially explored, this paper primarily focuses on deep learning methodologies due to their demonstrated superiority in healthcare applications. The findings highlight the efficacy of deep learning over traditional ML methods, underscoring its potential for enhancing diagnostic accuracy and personalized patient care. For instance, the most accurate deep learning model, the Convolutional Neural Network (CNN), achieved a Mean Absolute Error (MAE) of 1.49 and an R 2 score of 0.53, whereas the best-performing traditional ML model, Random Forest, achieved an MAE of 1.54 and an R 2 score of 0.55. These results clearly indicate the advantage of deep learning models in capturing complex patterns in ECG data, leading to more precise and reliable estimations of biological age.

关键词： Deep learning Maximum likelihood estimation Accuracy Data analysis Biological system modeling Medical services Electrocardiography Data models Convolutional neural networks Random forests

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A Novel Health Care System for Improving Dermatological Diagnosis: A Model for Skin Lesion Classification

A Novel Health Care System for Improving Dermatological Diag...

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Conference on Human System Interactions, HSI

作者： Atif Mehmood Ahed Abugabah Farah Shahid Dep. of Computer Science and Technology Zhejiang Normal University Jinhua China College of Technological Innovation Zayed University Abu Dhabi UAE

ISBN: (数字)9798350362916

ISBN: (纸本)9798350362923

Melanoma, nevus, and basal cell carcinoma (BCC) lesions are three types of skin cancer that pose a substantial threat to people's health all over the world. Early identification makes a significant contribution to the improvement of patient outcomes and the reduction of mortality rates. Within the scope of this research article, we investigate various deep-learning approaches for categorizing three lesions. The primary objective is to strengthen dermatological diagnosis. Furthermore, we overcome the issue of less annotated data directly affecting the model performance. We proposed the multi-output convolutional neural network (MO-CNN) model with two transfer learning approaches, VGG-19 with frozen layers and AlexN et with fine-tuning. The performance of our suggested MO-CNN model is compared to that of state-of-the-art methods like CNN, Transfer learning, and DSCC. According to the findings, the MO-CNN model achieves high accuracy, precision, recall, and Fl-Score levels. The model's accuracy reaches 97.81%, the precision of the model reaches 99.19%, the recall of the model reaches 97.04 %, and the Fl-Score reaches 97.01 %. The results of this study shed light on the potential of deep learning models, in particular the D-CNN architecture, to provide accurate and efficient classification of skin cancer. The findings of this study contribute to the development of enhanced diagnostic tools for melanoma and nevus lesions, which will ultimately result in improvements to patient treatment and prognosis in the field of dermatology.

关键词： Deep learning Accuracy Transfer learning Dermatology Medical treatment Melanoma Skin

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A Low-Cost Buoy System with Artificial Intelligence (AI) for Offshore Aquaculture

A Low-Cost Buoy System with Artificial Intelligence (AI) for...

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2021 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2021

作者： Lu, Hoang-Yang Cheng, Chih-Yung Cheng, Shyi-Chyi Lo, Wen-Chen Cheng, Yu-Hao Nan, Fan-Hua Chang, Shun-Hsyun National Taiwan Ocean University Taiwan National Kaohsiung University of Science. and Technology Taiwan Dep. of Electrical Eng. Dep. of Computer Sci. and Eng. Dep. of Aquaculture Dep. of Microelectronics Eng Taiwan

ISBN: (纸本)9781665419512

Since ocean resources are rapidly dep.eted in the recent decade, the complementary role of aquaculture has become more important than before. Further, determining the quality and success of aquaculture, the key factor is the quality of water. Hence, monitoring the quality of water is a vital work for aquaculture. To this end, in this paper we propose a low-cost and easy-to-build artificial intelligence (AI) buoy system. The proposed buoy system autonomously measures the relevant water quality data and then instantly forwards them via wireless channel to the shore server. In particular, the data not only provides aquaculture staffs with real-time information about the current water quality, but also assists server-side programs to implement AI learning and training to provide prediction for the short term changes of water quality. In addition, to achieve the goal of low-cost design, simple electronic devices and server-side AI programs are combined in the proposed buoy system to facilitate the measurement of water velocity. © 2021 IEEE.

关键词： Aquaculture

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Development of a Test Automation Framework based on a Comparison of different Approaches for Test Automation in the Embedded Systems Area

Development of a Test Automation Framework based on a Compar...

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Industrial Cyber-Physical Systems (ICPS)

作者： Stefan Arthofer Stefan Wilker Thilo Sauter TU Wien / Siemens AGÖsterreich Vienna Austria Institute of Computer Technology TU Wien Vienna Austria Dep. for Integrated Sensor Systems Institute of Computer Technology TU Wien University for Continuing Education Krems

ISBN: (数字)9798350363012

ISBN: (纸本)9798350363029

Embedded systems have become an integral part of our everyday lives. Devices are increasingly connected, especially in the Internet of Things (IoT) environment. The interfaces between the devices require high-quality software and hardware to ensure reliable communication between them. This vital interconnection also leads to systems becoming increasingly complex, and therefore, the complexity of the tests increases. Therefore, such tests can usually only be done by means of automation, as manual testing is very time-consuming and cost-intensive. A System Under Test (SUT) has different requirements dep.nding on the application area and the functionality, which is why different test automation approaches exist. This work reviews possible approaches for test automation of embedded devices and compares them with each other based on different criteria. Based on this comparison and in cooperation with Siemens Smart Infrastructure EA, a test automation system called Kubrotest was designed. To verify the test automation in a real usecase, Kubrotest is used to test a Siemens SICAM 8 series device.

关键词： Automation Embedded systems Reviews Manuals Cyber-physical systems Software Hardware

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Effective speech emotion recognition using deep learning approaches for Algerian dialect

Effective speech emotion recognition using deep learning app...

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2021 International Conference of Women in Data Science at Taif University, WiDSTaif 2021

作者： Yahia Cherif, Raoudha Moussaoui, Abdelouahab Frahta, Nabila Berrimi, Mohamed Dep. of Computer Science University of Ferhat Abbes Setif I Setif Algeria Dep. of Technology University of Ferhat Abbes Setif I Setif Algeria

ISBN: (纸本)9781665449489

Speech Emotion Recognition is an active research problem in the field of deep learning. Where the goal is to detect different emotions within audio speech using deep artificial neural networks. In this work, we focus on speech emotion recognition in the Algerian dialect. The paper introduces a new large Algerian speech emotion dataset collected from different Algerian TV shows. The audio signals were manually annotated as happy, angry, neutral, or sad. After the data collection, we applied several classification methods such as machine learning-based models, convolutional neural networks (CNNs), Long Short Term Memory (LSTM) networks, and Bidirectional LSTM (BLSTM). After applying the proposed models on our dataset, the best classification accuracy is 93.34% achieved by the LSTM-CNN model. © 2021 IEEE.

关键词： Long short-term memory

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Energy Access Assessment Report Focusing on Energy Poverty and Energy Demand for Rwanda

Energy Access Assessment Report Focusing on Energy Poverty a...

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Sciences and Techniques of Automatic Control and computer Engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Adimchinobi Asiegbu Ali Almaktoof Abdurazag Khalat Dep. of Electrical Electronics and Computer Engineering Cape Peninsula University of Technology Cape Town South Africa Dep. of Electrical and Electronics Engineering Sabratha University Sabratha Libya

This technical report is a researched document that examines energy access in Rwanda, focusing on energy poverty assessment and energy demand assessment. The key findings and observations can help mitigate the energy access problems and improve access to modern energy in Rwanda. Studies have shown that sub-Saharan African countries have poor access or no access to modern energy, Rwanda being part of sub-Saharan Africa also has Poor access to modern and clean energy. To carefully study these energy access problems and proffer solutions to increase energy access in Rwanda based on realities in today’s Africa, (where most households fall below poverty lines, with social issues, low Purchasing Power Parity (PPP) and Gross Domestic Power (GDP)), an assessment of these energy problems is done based on Energy Poverty Assessment (EPA) and Energy Demand Assessment (EDA) Composite Multidimensional Indicators (CMDI), respectively. Based on the findings of this research, non-poor energy households increased their energy consumption by 29.26% to 0.68 billion kWh. The clean cooking gas usage in Rwanda is very low at 2.1%, The 2016 estimated electricity consumption is 0.5273 billion kWh, growing by 57.41% to 0.83 billion kWh for energy-poor households in 2021, compared to 52.6% predicted by International Energy Agency (IEA). Finally, there is a direct relationship between energy access and economic development including quality of life, standard of living, per capita income, urbanization, modern infrastructure, etc. Thus, the key findings and observations in this research report will improve economic and infrastructural development in Rwanda, if implemented.

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