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Case study - Feature engineering inspired by domain experts on real world medical data

Intelligence-Based Medicine 2023年 8卷 100110-100110页

作者： Björneld, Olof Carlsson, Martin Löwe, Welf eHealth Institute Department of Medicine and Optometry Linnaeus University Kalmar S-391 82 Sweden Data Intensive Sciences and Applications (DISA-IDP) Department of Computer Science and Media Technology (CM) Faculty of Technology Linnaeus University Kalmar S-391 82 Sweden Business Intelligence IT Division Region Kalmar County Kalmar S-392 32 Sweden

To perform data mining projects for knowledge discovery based on health data produced in a daily health care stored in electronic health records (EHR) can be time consuming. This study exemplifies that the involvement of a data scientist improves classification performances. We have performed a case study that comprises two real world medical research projects, comparing feature engineering and knowledge discovery based on classification performance. Project (P1) comprised 82,742 patients with the research question “Can we predict patient falls by use of EHR data” and the second project (P2) included 23,396 patients with the focus on “Negative side effects of antiepileptic drug consumption on bone structure”. The results concluded three salient results. (i) It is valuable for medical researchers to involve a data scientist when medical research based on real world medical data is performed. The findings were justified with an analysis of classification metrics when iteratively engineered features were used. The features were generated from domain experts and computer scientists in collaboration with medical researchers. We gave this process the name domain knowledge-driven feature engineering (KDFE). To evaluate the classification performance the metric area under the receiver operating characteristic curve (AUROC) was used. (ii) Domain experts are benefited in quantitative terms by KDFE. When KDFE was compared to baseline, the average classification performance measured by AUROC for the engineered features rose for P1 from 0.62 to 0.82 and for P2 from 0.61 to 0.89 (p-values << 0.001). (iii) The engineered features were represented in a systematic structure, which is the foundation of a theoretical model for automated KDFE (aKDFE). To our knowledge, this is the first study that proves that via quantitative measures KDFE adds value to real-world. However, the method is not limited to the medical domain. Other areas with similar data properties should also benefit fro

关键词： Domain knowledge Electronic health record (EHR) Feature engineering Knowledge discovery in databases (KDD) Medical registry research Quantitative measures

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Security analysis of Rhee et al.’s public encryption with keyword search schemes: A review

Journal of Network Intelligence

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Journal of Network Intelligence 2018年第1期3卷 16-25页

作者： Wu, Tsu-Yang Chen, Chien-Ming Wang, King-Hang Pan, Jeng-Shyang Zheng, Weimin Chu, Shu-Chuan Roddick, John F. Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No33 Xuefunan Road University Town Fuzhou350118 China National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology No33 Xuefunan Road University Town Fuzhou350118 China School of Computer Science and Technology Harbin Institute of Technology Shenzhen HIT Campus of University Town of Shenzhen Shenzhen518055 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong College of Science and Engineering Flinders University Sturt Rd Bedford Park SA5042 Australia

Public key encryption with keyword search (PEKS) provides an efficient way to search encrypted files. Recently, Rhee et al. contributed their knowledge to propose several literatures in this research area. In this paper, we first review their three famous schemes and then summarize the security weaknesses of the three schemes. Finally, we discuss the security problems about Rhee et al. like scheme and remain an open problem. © 2018, Taiwan Ubiquitous Information. All rights reserved.

关键词： Search engines

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A Brief Review of Explainable Artificial Intelligence in Healthcare

SSRN

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SSRN 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalo, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Swinburne University of Technology HawthornVIC3122 Australia Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, The Authors. All rights reserved.

关键词： Decision making

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Statistically-informed deep learning for gravitational wave parameter estimation

arXiv

引用

arXiv 2019年

作者： Shen, Hongyu Huerta, E.A. O'Shea, Eamonn Kumar, Prayush Zhao, Zhizhen Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Coordinated Science Laboratory University of Illinois at Urbana-Champaign UrbanaIL61801 United States Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Computer Science University of Chicago ChicagoIL60637 United States Cornell Center for Astrophysics and Planetary Science Cornell University IthacaNY14853 United States International Centre for Theoretical Sciences Tata Institute of Fundamental Research Bangalore560089 India National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States

We introduce deep learning models to estimate the masses of the binary components of black hole mergers, (m1,m2), and three astrophysical properties of the post-merger compact remnant, namely, the final spin, af, and the frequency and damping time of the ringdown oscillations of the fundamental = m = 2 bar mode, (ωR,ωI). Our neural networks combine a modified WaveNet architecture with contrastive learning and normalizing flow. We validate these models against a Gaussian conjugate prior family whose posterior distribution is described by a closed analytical expression. Upon confirming that our models produce statistically consistent results, we used them to estimate the astrophysical parameters (m1,m2,af,ωR,ωI) of five binary black holes: GW150914, GW170104, GW170814, GW190521 and GW190630. We use PyCBC Inference to directly compare traditional Bayesian methodologies for parameter estimation with our deep learning based posterior distributions. Our results show that our neural network models predict posterior distributions that encode physical correlations, and that our data-driven median results and 90% confidence intervals are similar to those produced with gravitational wave Bayesian analyses. This methodology requires a single V100 NVIDIA GPU to produce median values and posterior distributions within two milliseconds for each event. This neural network, and a tutorial for its use, are available at the data and Learning Hub for *** Codes 68T01, 68T35, 83C35, 83C57 Copyright © 2019, The Authors. All rights reserved.

关键词： Astrophysics

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LEADER FORMATION WITH MEAN-FIELD BIRTH AND DEATH MODELS

arXiv

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arXiv 2018年

作者： Albi, Giacomo Bongini, Mattia Rossi, Francesco Solombrino, Francesco Department of Computer Science University of Verona Str. Le Grazie 15 VeronaIT-37134 Italy Big Data and Marketing Analytics CRIF Via M. Fantin 3 BolognaIT-40131 Italy Department of Mathematics "Tullio Levi-Civita" University of Padova Via Trieste 63 PadovaIT-35121 Italy Department of Mathematics and Applications "R. Caccioppoli" University of Naples "Federico II" Via Cintia Monte S. Angelo NaplesIT-80126 Italy

We provide a mean-field description for a leader-follower dynamics with mass transfer among the two populations. This model allows the transition from followers to leaders and vice versa, with scalar-valued transition rates depending nonlinearly on the global state of the system at each time. We first prove the existence and uniqueness of solutions for the leader-follower dynamics, under suitable assumptions. We then establish, for an appropriate choice of the initial datum, the equivalence of the system with a PDE-ODE system, that consists of a continuity equation over the state space and an ODE for the transition from leader to follower or vice versa. We further introduce a stochastic process approximating the PDE, together with a jump process that models the switch between the two populations. Using a propagation of chaos argument, we show that the particle system generated by these two processes converges in probability to a solution of the PDE-ODE system. Finally, several numerical simulations of social interactions dynamics modeled by our system are *** Codes 35Q82, 35Q91, 70F45, 65M08, 82C22, 82B21 Copyright © 2018, The Authors. All rights reserved.

关键词： Markov processes

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APACE: AlphaFold2 and advanced computing as a service for accelerated discovery in biophysics

arXiv

引用

arXiv 2023年

作者： Park, Hyun Patel, Parth Haas, Roland Huerta, E.A. Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Theoretical and Computational Biophysics Group Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Biophysics and Quantitative Biology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Computer Science University of Illinois at Urbana-Champaign Illinois Urbana61801 United States National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Computer Science The University of Chicago ChicagoIL60637 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States

The prediction of protein 3D structure from amino acid sequence is a computational grand challenge in biophysics, and plays a key role in robust protein structure prediction algorithms, from drug discovery to genome interpretation. The advent of AI models, such as AlphaFold, is revolutionizing applications that depend on robust protein structure prediction algorithms. To maximize the impact, and ease the usability, of these novel AI tools we introduce APACE, AlphaFold2 and advanced computing as a service, a novel computational framework that effectively handles this AI model and its TB-size database to conduct accelerated protein structure prediction analyses in modern supercomputing environments. We deployed APACE in the Delta and Polaris supercomputers, and quantified its performance for accurate protein structure predictions using four exemplar proteins: 6AWO, 6OAN, 7MEZ, and 6D6U. Using up to 300 ensembles, distributed across 200 NVIDIA A100 GPUs, we found that APACE is up to two orders of magnitude faster than off-the-self AlphaFold2 implementations, reducing time-to-solution from weeks to minutes. This computational approach may be readily linked with robotics laboratories to automate and accelerate scientific discovery. Copyright © 2023, The Authors. All rights reserved.

关键词： Supercomputers

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Predicting blood glucose level using salivary glucose and other associated factors: A machine learning model selection and evaluation study

引用

Informatics in Medicine Unlocked 2024年 48卷

作者： Chopra, Aditi Rao, Rohini R. Kamath, Shobha U. Akhila Arun, Sanjana Shettigar, Laasya Department of Periodontology Manipal College of Dental Sciences Manipal Academy of Higher Education Manipal Manipal Pin: 576 104 Karnataka India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal Manipal Pin: 576 104 Karnataka India Department of Biochemistry Kasturba Medical College and Hospital Manipal Academy of Higher Education Manipal Manipal Pin: 576 104 Karnataka India

Introduction: There is a need for designing non-invasive methods to predict blood glucose levels to ensure timely diagnosis of Diabetes Mellitus. Needle anxiety and bleeding disorders preclude many from undertaking blood tests. Objectives: The primary objective of this study was to assess if biomarkers like saliva can be used to estimate blood glucose levels. The second objective was to develop and evaluate Machine Learning (ML) models to predict blood glucose levels based on salivary glucose and associated features. An insight into the patient's features, which was important for predicting blood glucose levels, was also required. Methods: A cross-sectional study was conducted, and blood and saliva samples, along with patient-related data, were collected from healthy and diabetic patients. ML techniques were applied to the data to develop a tool for predicting blood glucose levels using patient features. The prediction intervals were computed, clinical accuracy was assessed, and important features for the prediction were identified. Results: The Random Forest Regressor Model, with features identified using the wrapper method, was selected as the best, with an average RMSE of 43.28. The prediction intervals were computed for point estimate, MAE = 23.821, and coverage was 100 percent, the clinical accuracy was compared with that of glucometers and continuous monitoring systems. All predicted values are in Zones A and B of the Clarke error grid, and the bias was 6.41. The most important feature for predicting blood glucose level is salivary glucose level, followed by known risk factors like Family History, BMI, etc. The study found that salivary glucose levels are insufficient to classify blood glucose levels as high or normal. Conclusion: The study concluded that salivary glucose with associated patient features could be a potential non-invasive biomarker for predicting blood glucose levels in patients. The developed ML model could be deployed in a device that inputs

关键词： Blood glucose level Confirmatory prediction Diabetes mellitus Feature selection Machine learning model Non-invasive method Random forest regressor Salivary glucose

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Unextendible and Strongly Uncompletable Product Bases

arXiv

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arXiv 2024年

作者： Zhen, Xiao-Fan Zuo, Hui-Juan Shi, Fei Fei, Shao-Ming School of Mathematical Sciences Hebei Normal University Shijiazhuang050024 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Hebei Key Laboratory of Computational Mathematics and Applications Shijiazhuang050024 China Hebei International Joint Research Center for Mathematics and Interdisciplinary Science Shijiazhuang050024 China Department of Computer Science School of Computing and Data Science University of Hong Kong Hong Kong999077 Hong Kong School of Mathematical Sciences Capital Normal University Beijing10048 China

In 2003, DiVincenzo et al. put forward the question that whether there exists an unextendible product basis (UPB) which is an uncompletable product basis (UCPB) in every bipartition [DiVincenzo et al. Commun. Math. Phys. 238, 379-410(2003)]. Recently, Shi et al. presented a UPB in tripartite systems that is also a strongly uncompletable product basis (SUCPB) in every bipartition [Shi et al. New J. Phys. 24, 113-025 (2022)]. However, whether there exist UPBs that are SUCPBs in only one or two bipartitions remains unknown. We provide a sufficient condition for the existence of SUCPBs based on a quasi U-tile structure. We analyze all possible cases about the relationship between UPBs and SUCPBs in tripartite systems. In particular, we construct a UPB with smaller size d3 − 3d2 + 1 in Cd Cd Cd, which is an SUCPB in every bipartition and has a smaller cardinality than the existing one. Copyright © 2024, The Authors. All rights reserved.

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Deep Learning Algorithms for Business Management: Ethical Considerations

Deep Learning Algorithms for Business Management: Ethical Co...

引用

Communication, computer sciences and Engineering (IC3SE), International Conference on

作者： Shiney Chib B Santhosh Kumar V Asha Navdeep Singh Melanie Lourens Dyuti Banerjee Management Datta Meghe Institute of Management Studies Nagpur Maharashtra Institute of Aeronautical Engineering Dundigal Hyderabad Department of Computer Applications New Horizon College of Engineering Bangalore Lovely Professional University Phagwara Deputy Dean Faculty of Management Sciences Durban University of Technology South Africa Artificial intelligence & Data Science (AI&DS) Koneru Lakshmaiah Education Foundation Green Fields Vaddeswaram Guntur District Andhra Pradesh

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

By applying the right method to extract both structured and unstructured data, data science may add value. Accelerated deep learning advancement has led to substantial changes in data science operations. Deep learning is a sophisticated machine learning algorithmic method. It differs significantly from other machine learning procedures in that it uses a straightforward method to provide data to the system. To tackle difficult issues and make wise business decisions, deep learning leverages cutting-edge analytics. As smart manufacturing initiatives expand, deep leaning is seen as a potential avenue to boost efficiency and sustainability even further. In this study, we look into how DL models can enhance the application of business intelligence by organisational management. The results show that DL models may be trained on diverse data sources to identify linkages and patterns in the data. These models can assist decision-makers in making well-informed decisions based on accurate and reliable data.

关键词： Deep learning Accuracy Computational modeling Soft sensors Decision making data science Prediction algorithms

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Evaluating Large Language Models on Spatial Tasks: A Multi-Task Benchmarking Study

arXiv

引用

arXiv 2024年

作者： Xu, Liuchang Zhao, Shuo Lin, Qingming Chen, Luyao Luo, Qianqian Wu, Sensen Ye, Xinyue Feng, Hailin Du, Zhenhong School of Mathematics and Computer Science Zhejiang Agriculture and Forestry University Hangzhou311300 China School of Earth Sciences Zhejiang University Hangzhou310058 China Department of Landscape Architecture and Urban Planning Center for Geospatial Sciences Applications and Technology Texas A&M University College StationTX77843 United States Financial Big Data Research Institute Sunyard Technology Co. Ltd Hangzhou310053 China

The advent of large language models such as ChatGPT, Gemini, and others has underscored the importance of evaluating their diverse capabilities, ranging from natural language understanding to code generation. However, their performance on spatial tasks has not been comprehensively assessed. This study addresses this gap by introducing a novel multi-task spatial evaluation dataset, designed to systematically explore and compare the performance of several advanced models on spatial tasks. The dataset encompasses twelve distinct task types, including spatial understanding and simple route planning, each with verified, accurate answers. We evaluated multiple models, including OpenAI’s gpt-3.5-turbo, gpt-4-turbo, gpt-4o, ZhipuAI’s glm-4, Anthropic’s claude-3-sonnet-20240229, and MoonShot’s moonshot-v1-8k, through a two-phase testing approach. Initially, we conducted zero-shot testing, followed by categorizing the dataset by difficulty and performing prompt tuning tests. Results indicate that gpt-4o achieved the highest overall accuracy in the first phase, with an average of 71.3%. Although moonshotv1-8k slightly underperformed overall, it surpassed gpt-4o in place name recognition tasks. The study also highlights the impact of prompt strategies on model performance in specific tasks. For example, the Chain-of-Thought (CoT) strategy increased gpt-4o’s accuracy in simple route planning from 12.4% to 87.5%, while a one-shot strategy enhanced moonshot-v1-8k’s accuracy in mapping tasks from 10.1% to 76.3%. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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