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Use of natural language processing in electronic medical records to identify pregnant women with suicidal behavior: towards a solution to the complex classification problem

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EUROPEAN JOURNAL OF EPIDEMIOLOGY 2019年第2期34卷 153-162页

作者： Zhong, Qiu-Yue Mittal, Leena P. Nathan, Margo D. Brown, Kara M. Gonzalez, Deborah Knudson Cai, Tianrun Finan, Sean Gelaye, Bizu Avillach, Paul Smoller, Jordan W. Karlson, Elizabeth W. Cai, Tianxi Williams, Michelle A. Harvard TH Chan Sch Publ Hlth Dept Epidemiol 677 Huntington Ave Boston MA 02115 USA Brigham & Womens Hosp Div Womens Mental Hlth Dept Psychiat 75 Francis St Boston MA 02115 USA Univ S Florida Morsani Coll Med Dept Psychiat & Behav Neurosci Tampa FL USA Brigham & Womens Hosp Dept Med Div Rheumatol Immunol & Allergy 75 Francis St Boston MA 02115 USA Boston Childrens Hosp Childrens Hosp Informat Program Boston MA USA Harvard Med Sch Dept Biomed Informat Boston MA USA Massachusetts Gen Hosp Psychiat & Neurodev Genet Unit Boston MA 02114 USA Harvard TH Chan Sch Publ Hlth Dept Biostat Boston MA USA

We developed algorithms to identify pregnant women with suicidal behavior using information extracted from clinical notes by natural language processing (NLP) in electronic medical records. Using both codified data and NLP applied to unstructured clinical notes, we first screened pregnant women in Partners HealthCare for suicidal behavior. Psychiatrists manually reviewed clinical charts to identify relevant features for suicidal behavior and to obtain gold-standard labels. Using the adaptive elastic net, we developed algorithms to classify suicidal behavior. We then validated algorithms in an independent validation dataset. From 275,843 women with codes related to pregnancy or delivery, 9331 women screened positive for suicidal behavior by either codified data (N=196) or NLP (N=9,145). Using expert-curated features, our algorithm achieved an area under the curve of 0.83. By setting a positive predictive value comparable to that of diagnostic codes related to suicidal behavior (0.71), we obtained a sensitivity of 0.34, specificity of 0.96, and negative predictive value of 0.83. The algorithm identified 1423 pregnant women with suicidal behavior among 9331 women screened positive. Mining unstructured clinical notes using NLP resulted in a 11-fold increase in the number of pregnant women identified with suicidal behavior, as compared to solely reliance on diagnostic codes.

关键词： Natural language processing Suicidal behavior Pregnant women Electronic medical Records classification algorithm

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A Review of classification algorithms for Data Mining

A Review of Classification Algorithms for Data Mining

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2019 2nd International Conference on Mechanical,Electronic and Engineering Technology(MEET 2019)

作者： Li Mindong Chen Qingwei Huang Panling Zhou Jun Gong Weike Shandong University

classification algorithm is one of the important algorithms in data mining. Common classification algorithms such as decision tree, Bayesian network, support vector machine, association rules based classification algorithm and K-nearest neighbor algorithm have been widely used. This paper introduces the classical classification algorithm, compares the advantages and disadvantages of each algorithm and the latest research progress of each algorithm.

关键词： data mining classification algorithm review

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Prediction Based on the Solution of the Set of classification Problems of Supervised Learning and Degrees of Membership

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PATTERN RECOGNITION AND IMAGE ANALYSIS 2020年第1期30卷 63-69页

作者： Lukanin, A. A. Ryazanov, V. V. Kiselyova, N. N. Natl Res Univ Moscow Inst Phys & Technol Moscow 115184 Russia Russian Acad Sci Fed Res Ctr Comp Sci & Control Moscow 119333 Russia Russian Acad Sci Baikov Inst Met & Mat Sci Moscow 119991 Russia

It is proposed to use the degrees of membership of objects to each class in the process of recognition in the linear corrector model to solve the problem of restoring dependences from precedent samples. Two models of the algorithm for calculating estimates are used as classifiers. The work of the proposed model is compared with the original method and with the well-known data analysis methods. The dependence of the work of the linear corrector on its parameters is studied.

关键词： regression dependence feature classification algorithm linear corrector algorithm for calculating estimates degree of membership

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An extensive review on crop/weed classification models

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WEB INTELLIGENCE 2023年第4期21卷 473-488页

作者： Panda, Bikramaditya Mishra, Manoj Kumar Mishra, Bhabani Shankar Prasad Tiwari, Abhinandan Kumar KIIT Univ KIIT Rd Bhubaneswar Odisha India KIIT Univ Sch Comp Engn KIIT Rd Bhubaneswar Odisha India

Crop and weed identification remains a challenge for unmanned weed control. Due to the small range between the chopping tine and the important crop location, weed identification against the annual crops must be extremely exact. This study endeavor included a literature evaluation, which included the most important 50 research publications in IEEE, Science Direct, and Springer journals. From 2012 until 2022, all of these papers are gathered. In fact, the diagnosis steps include: preprocessing, feature extraction, and crop/weed classification. This research analyzes the 50 research articles in several aspects, such as the dataset used for evaluations, different strategies used for pre-processing, feature extraction, and classification to get a clear picture of them. Furthermore, each work's high performance in accuracy, sensitivity, and precision is demonstrated. Furthermore, the present hurdles in crop and weed identification are described, which serve as a benchmark for upcoming researchers.

关键词： Crop/weed identification pre-processing feature extraction classification algorithm

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A clustering method of asset cybersecurity classification

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IFAC-PapersOnLine 2019年第13期52卷 928-933页

作者： Promyslov, Vitaly G. Semenkov, Kirill V. Shumov, Alexander S. V.A. Trapeznikov Institute of Control Sciences Russian Academy of Science 65 Profsoyuznaya Moscow117997 Russia

A formal method for asset classifying by cyber security is provided. The problem is solved in the frame of a clustering problem. The data structure and content are specified. A classification algorithm for systems with arbitrary number of subsystems is provided. © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Formal methods Hierarchical clustering Hierarchical systems Security of data Arbitrary number classification algorithm Clustering Clustering methods Clustering problems Cyber security

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Water quality prediction and classification based on principal component regression and gradient boosting classifier approach

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第8期34卷 4773-4781页

作者： Khan, Md. Saikat Islam Islam, Nazrul Uddin, Jia Islam, Sifatul Nasir, Mostofa Kamal Dept Comp Sci & Engn Santosh 1902 Tangail Bangladesh Dept Informat & Commun & Technol Santosh 1902 Tangail Bangladesh Woosong Univ Endicott Coll Dept Technol Studies Daejeon South Korea Mawlana Bhashani Sci & Technol Univ Santosh 1902 Tangail Bangladesh Mawlana Bhashani Sci & Technol Univ Dept Informat & Commun & Technol Santosh 1902 Tangail Bangladesh

Estimating water quality has been one of the significant challenges faced by the world in recent decades. This paper presents a water quality prediction model utilizing the principal component regression tech-nique. Firstly, the water quality index (WQI) is calculated using the weighted arithmetic index method. Secondly, the principal component analysis (PCA) is applied to the dataset, and the most dominant WQI parameters have been extracted. Thirdly, to predict the WQI, different regression algorithms are used to the PCA output. Finally, the Gradient Boosting Classifier is utilized to classify the water quality status. The proposed system is experimentally evaluated on a Gulshan Lake-related dataset. The results demonstrate 95% prediction accuracy for the principal component regression method and 100% classification accuracy for the Gradient Boosting Classifier method, which show credible performance compared with the state -of-art models. (c) 2021 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Water quality index Principal component regression classification algorithm Boxplot analysis

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Antenna classification Using Gaussian Mixture Models (GMM) and Machine Learning

IEEE OPEN JOURNAL OF ANTENNAS AND PROPAGATION

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IEEE OPEN JOURNAL OF ANTENNAS AND PROPAGATION 2020年第1期1卷 320-328页

作者： Ma, Yihan Hao, Yang Queen Mary Univ London Elect Engn Dept London E1 4NS England

Radio frequency fingerprinting (RFF) is the concept arising from classification of wireless emitters due to their unique radio frequency features. RFF has been further extended to applications including both RF devices classification and wireless signal identification. In this paper, we adopt Gaussian Mixture Models (GMM) technique as feature extraction approach and firstly apply it to extract RFF of antennas. 9 classical antennas with 3 different load conditions (open, short, match) were studied in our experiment. Moreover, we also made a theoretical analysis about the reason scattered signal has the unique features. Specifically, we adopt the Random Noise Radar (RNR) technique to obtain reflected RF signals of antenna under test (AUT) and apply the GMM technique to fit RF signals and then extract the RFF of AUT. A support vector machine (SVM) is proposed to recognize the RFF at different signal-to-noise ratio (SNR) environment. Compared with the conventional feature extraction approaches, for example, from variance, skewness and kurtosis (VSK) values, our method demonstrates better performance on large datasets with classification accuracy above 88% using a SVM classifier. Moreover, the accuracy remains higher than 75% even when the Signal to Noise Ratio (SNR) is equal to 0dB, indicating that the proposed approach has the strong capability of noise immunity.

关键词： Antenna Gaussian mixture models radar signal processing classification algorithm machine learning

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Fault classification in Power System with Inverter-Interfaced Renewable Energy Resources Using Machine Learning

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JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS 2024年第6期35卷 1019-1038页

作者： Krishnamurthy, Padmasri Thangavel, S. Dhanalakshmi, R. Khushi, S. N. Natl Inst Technol Elect Engn Dept Pondicherry India Indian Inst Informat Technol Tiruchirappalli Dept CSE Tiruchirappalli India ABB Global Ind & Serv Pvt Ltd Bangalore India

Fault classification is crucial in fault mitigation to maintain selectivity in tripping only the faulted phase or zone in power system networks. However, inverter-interfaced renewable energy sources' unique fault current profile poses challenges to classifiers designed for conventional systems, which are inadequate in the presence of renewable energy resources such as inverter-interfaced photovoltaic (PV) or wind turbine systems in the grid. The inverters have internal protection schemes that trip during unbalanced conditions;however, in grids with high penetration of renewable energy, the inverter must ride through the fault and let relays protect the system. Moreover, the different control strategies for inverters can make the fault current small enough to be unreliable to use as a parameter in fault classifications. This study proposes a reliable fault classification method that can accurately identify faults in power systems with high penetration of renewable energy sources. This paper discusses a machine learning (ML)-based classifier using phase current and voltage magnitude to classify faults. The performance of the proposed classifier is validated against different fault scenarios in power systems like the IEEE 9-bus system. The classifier discussed in this paper achieved a satisfactory accuracy of 99.78% with voltage measurements for test conditions within three-quarters of a cycle. The classifier can be used for any three-phase system to provide correct faulted phase information to other protection components. The same methodology is extended to identify evolving faults, achieving an accuracy of 99.6% in determining the evolving fault type. Thus, the proposed ML-based classifier provides a reliable and accurate method for fault classification in power systems with high penetration of renewable energy sources.

关键词： Change detection classification algorithm Electrical fault detection Machine learning Evolving faults Inverters Photovoltaic (PV) system Wind turbine system

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Design, Construction and Analysis of Model Dataset for Indian Road Network and Performing classification to Estimate Accuracy of Different Classifier with Its Comparison Summary Evaluation 1

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6th International Conference on Swarm, Evolutionary and Memetic Computing (SEMCCO)

作者： Gothane, Suwarna Sarode, M. V. Raju, K. Srujan CMR Tech Campus Hyderabad Andhra Prades India Jagadambha Coll Engn & Technol Yavatmal India

ISBN: (数字)9783319489599

ISBN: (纸本)9783319489599;9783319489582

Road network consist of various problems. Pothole, crack and patches are the common problems of road network. Various manual and automated solutions have been proposed by the expertise in the previous work. To overcome the problem we have came here with a novel solution approach to identify road quality. Identification of maintenance severity level and providing repair solution is done using WEKA tool 3.7. This paper presents comparison summary of classification approach and estimated which algorithm gives efficient accuracy for classification. In this paper we have obtained highest accuracy of classification 98.84 % by Support Vector Machine (SMO Function).

关键词： Pothole Patches Road Cracks classification algorithm Accuracy

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Arguments around weighting the first year in degree classification algorithms

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Perspectives: Policy and Practice in Higher Education 2019年第1期23卷 24-27页

作者： O’Neill, Michael Chemistry Department University of Hull Hull United Kingdom

The UK’s HE system is mired in public debate around ‘grade inflation’, and there is substantial pressure to address the perceived devaluation of degrees through blunt policy measures such as modifying classification algorithms. Policy-makers should be aware of the impact of their actions upon students’ learning;this article frames some of the neglected pedagogical arguments in concise syllogistic forms, focusing particularly on the issues around weighting the first year of a traditional UK bachelor’s degree classification algorithm. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.

关键词： classification algorithm first year transition into HE

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