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Applicability of Machine Learning Methods to Multi-label Medical Text Classification [chapter]

Iuliia Lenivtceva, Evgenia Slasten, Mariya Kashina, Georgy Kopanitsa
2020 Lecture Notes in Computer Science  
In this work we investigate the applicability of several machine learning models and classifier chains (CC) to medical unstructured text classification.  ...  Structuring medical text using international standards allows to improve interoperability and quality of predictive modelling. Medical text classification task facilitates information extraction.  ...  This work is financially supported by National Center for Cognitive Research of ITMO University.  ... 
doi:10.1007/978-3-030-50423-6_38 fatcat:3rujvlcuzzgdfd5xnna55lveui

A Novel Online Real-time Classifier for Multi-label Data Streams [article]

Rajasekar Venkatesan, Meng Joo Er, Shiqian Wu, Mahardhika Pratama
2016 arXiv   pre-print
Multi-label classification is one of the actively researched machine learning paradigm that has gained much attention in the recent years due to its rapidly increasing real world applications.  ...  In contrast to traditional binary and multi-class classification, multi-label classification involves association of each of the input samples with a set of target labels simultaneously.  ...  ACKNOWLEDGEMENT The authors would like to acknowledge the funding support from the Ministry of Education, Singapore (Tier 1 AcRF, RG30/14), Rajasekar Venkatesan is supported by NTU Research Student Scholarship  ... 
arXiv:1608.08905v1 fatcat:yhonmykzxnharj2redook3e4g4

Applications of Multi-Label Classification

2020 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
The absence of labels and the bad quality of data is a prevailing challenge in numerous data mining and machine learning problems.  ...  Multi-label classification is a challenging research problem that emerges in several applications such as multi-object recognition, text categorization, music categorization and image classification.  ...  INTRODUCTION Multi-label classification is a primary but challenging problem in machine learning.  ... 
doi:10.35940/ijitee.d1008.0394s220 fatcat:4bdfhdhgkrgrhlris7nemahg3m

Multi-label classification method based on extreme learning machines

Rajasekar Venkatesan, Meng Joo Er
2014 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV)  
The comparative results shows that the proposed Extreme Learning Machine based multi-label classification technique is a better alternative than the existing state of the art methods for multi-label problems  ...  In this paper, an Extreme Learning Machine (ELM) based technique for Multi-label classification problems is proposed and discussed.  ...  ACKNOWLEDGMENT The first author would like to thank Nanyang Technological University, Singapore for the NTU Research Student Scholarship.  ... 
doi:10.1109/icarcv.2014.7064375 dblp:conf/icarcv/VenkatesanE14 fatcat:ahplndu6s5dhxdtzw5dotbddpy

Text Data Analysis for Advertisement Recommendation System Using Multi-label Classification of Machine Learning

Rushikesh Chandrakant Konapure, Dr. L.M.R.J. Lobo
2020 Zenodo  
In this proposed system the title and description of video will be taken as input to classify the video using a natural language processing text classification method.  ...  The proposed system would extract features from videos like title, description, and hashtags based on these extracted features we intend producing classification labels with the use of multi-label classification  ...  multi-label classification of machine learning.  ... 
doi:10.5281/zenodo.3600112 fatcat:4zjk42oiufhzndek4rzqia37ru

A Weight Based Labeled Classifier Using Machine Learning Technique for Classification of Medical Data

Mohammed Zaheer Ahmed, Chitraivel Mahesh
2021 Revue d'intelligence artificielle : Revue des Sciences et Technologies de l'Information  
The proposed model is compared to traditional methods and the results suggest that the proposed model is better suited to the proper classification of medical data.  ...  The proposed work uses a Weight Based Labeled Classifier using a Machine Learning (WbLCML) model designed to improve diagnostic efficiency, accuracy and reliability.  ...  This contributed to the development of a new classification in machine learning, the Multi-label Category.  ... 
doi:10.18280/ria.350104 fatcat:ki6lxkbn6jgwbac4moiqtgiu5e

A High Speed Multi-label Classifier Based on Extreme Learning Machines [chapter]

Meng Joo Er, Rajasekar Venkatesan, Ning Wang
2016 Proceedings in Adaptation, Learning and Optimization  
The proposed work extends the extreme learning machine technique to adapt to the multi-label problems.  ...  The proposed high speed multi-label classifier is applied to six benchmark datasets comprising of different application areas such as multi-media, text and biology.  ...  Acknowledgements This work is supported by the National Natural Science Foundation of P. R. China  ... 
doi:10.1007/978-3-319-28373-9_37 fatcat:rzi3ae2lsje23iki2dgtc5yyl4

An Ensemble Based Classification Approach For Medical Images

B.Chitradevi, N.Thinaharan
2017 Zenodo  
Now-a-days more researchers are applying the ensemble learning algorithm for classification to obtain high accuracy in an effectual manner.  ...  The main discovery of the ensemble classifier, constructed by ensemble machine algorithms is to perform much better accuracy than the single classifiers.  ...  Finally, in the multi-label case, multi-label classification and the strongly related problem of multi-output classification are variants of the classification problem where multiple target labels must  ... 
doi:10.5281/zenodo.891658 fatcat:cintk4ngdzah7eymf4azuj7zqm

Multi-Label Learning from Medical Plain Text with Convolutional Residual Models [article]

Xinyuan Zhang, Ricardo Henao, Zhe Gan, Yitong Li, Lawrence Carin
2018 arXiv   pre-print
Predicting diagnoses from Electronic Health Records (EHRs) is an important medical application of multi-label learning.  ...  We propose a convolutional residual model for multi-label classification from doctor notes in EHR data. A given patient may have multiple diagnoses, and therefore multi-label learning is required.  ...  One important application of multi-label learning in the medical domain is to predict diagnoses given features from the EHR.  ... 
arXiv:1801.05062v2 fatcat:6k3jebjoebamjkxhjzbbf2wh5e

Automated Machine Learning for Healthcare and Clinical Notes Analysis

Akram Mustafa, Mostafa Rahimi Azghadi
2021 Computers  
To accelerate embedding ML in more applications and incorporating it in real-world scenarios, automated machine learning (AutoML) is emerging.  ...  Machine learning (ML) has been slowly entering every aspect of our lives and its positive impact has been astonishing.  ...  Acknowledgments: M.R.A. acknowledges the support of a JCU Rising Star ECR fellowship. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/computers10020024 fatcat:sojvfgq255f3zeccsunzwu4ve4

The Utility of General Domain Transfer Learning for Medical Language Tasks [article]

Daniel Ranti, Katie Hanss, Shan Zhao, Varun Arvind, Joseph Titano, Anthony Costa, Eric Oermann
2020 arXiv   pre-print
Model performance was benchmarked to a logistic regression using bag-of-words vectorization and a long short-term memory (LSTM) multi-label multi-class classification model, and compared to the published  ...  literature in medical text classification.  ...  Natural Language Processing, Text Preprocessing, Transformer Frameworks NLP is an application of machine learning and deep learning that is specifically geared towards machine interpretations of textual  ... 
arXiv:2002.06670v1 fatcat:etbat5fpbvcm5dxnumlwh2le6e

Augmented Machine Learning Ensemble Extension Model for Social Media Health Trends Predictions

2019 International journal of recent technology and engineering  
The model is to use temporal datasets to deduce multi-label classification of health-related topics.  ...  Ensemble Learning wherein an array of various Machine Learning techniques can be employed to achieve better classification or clustering results.  ...  Ensemble methods for multi-label classification Ensemble methods have been shown to be an effective tool for solving multi-label classification tasks.  ... 
doi:10.35940/ijrte.b1091.0782s719 fatcat:f57k7bqqojg4vimz7vqrvjmxnu

A novel online multi-label classifier for high-speed streaming data applications

Rajasekar Venkatesan, Meng Joo Er, Mihika Dave, Mahardhika Pratama, Shiqian Wu
2016 Evolving Systems  
The proposed work exploits the high-speed nature of the extreme learning machines to achieve real-time multi-label classification of streaming data.  ...  In this paper, a high-speed online neural network classifier based on extreme learning machines for multi-label classification is proposed.  ...  It is also to be highlighted that the proposed method is the first extreme learning machine based real-time online multi-label classifier.  ... 
doi:10.1007/s12530-016-9162-8 fatcat:jcj5hohcs5fmncd2pth56xyavu

Automatic ICD-10 Classification of Diseases from Dutch Discharge Letters

Ayoub Bagheri, Arjan Sammani, Peter Van Der Heijden, Folkert Asselbergs, Daniel Oberski
2020 Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies  
The international classification of diseases (ICD) is a widely used tool to describe patient diagnoses.  ...  We find that stateof-the-art methods outperform the baseline for the single-label version of the task only.  ...  Baseline: Support Vector Machines using Bag-of-Words We use a one-vs-all, multi-label binary SVM classifier as the baseline learning method for ICD-10 classification. Baghdadi et al.  ... 
doi:10.5220/0009372602810289 dblp:conf/biostec/BagheriSHAO20 fatcat:ananifbbqrf63fsu4eoo5f2viy

A Novel Progressive Multi-label Classifier for Classincremental Data [article]

Mihika Dave, Sahil Tapiawala, Meng Joo Er, Rajasekar Venkatesan
2016 arXiv   pre-print
In this paper, a progressive learning algorithm for multi-label classification to learn new labels while retaining the knowledge of previous labels is designed.  ...  Based on the Extreme Learning Machine framework, a novel universal classifier with plug and play capabilities for progressive multi-label classification is developed.  ...  ACKNOWLEDGEMENT The authors would like to acknowledge the funding support from the Ministry of Education, Singapore (Tier 1 AcRF, RG30/14).  ... 
arXiv:1609.07215v1 fatcat:feczl5iflba3jcjkizn4uuxzfi
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