scholarly journals Classification of Current Procedural Terminology Codes from Electronic Health Record Data Using Machine Learning

2020 ◽  
Vol 132 (4) ◽  
pp. 738-749 ◽  
Author(s):  
Michael L. Burns ◽  
Michael R. Mathis ◽  
John Vandervest ◽  
Xinyu Tan ◽  
Bo Lu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Quality Improvement ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Support Vector ◽  
Current Procedural Terminology ◽  
Test Dataset ◽  
Quality Improvement Research

Abstract Background Accurate anesthesiology procedure code data are essential to quality improvement, research, and reimbursement tasks within anesthesiology practices. Advanced data science techniques, including machine learning and natural language processing, offer opportunities to develop classification tools for Current Procedural Terminology codes across anesthesia procedures. Methods Models were created using a Train/Test dataset including 1,164,343 procedures from 16 academic and private hospitals. Five supervised machine learning models were created to classify anesthesiology Current Procedural Terminology codes, with accuracy defined as first choice classification matching the institutional-assigned code existing in the perioperative database. The two best performing models were further refined and tested on a Holdout dataset from a single institution distinct from Train/Test. A tunable confidence parameter was created to identify cases for which models were highly accurate, with the goal of at least 95% accuracy, above the reported 2018 Centers for Medicare and Medicaid Services (Baltimore, Maryland) fee-for-service accuracy. Actual submitted claim data from billing specialists were used as a reference standard. Results Support vector machine and neural network label-embedding attentive models were the best performing models, respectively, demonstrating overall accuracies of 87.9% and 84.2% (single best code), and 96.8% and 94.0% (within top three). Classification accuracy was 96.4% in 47.0% of cases using support vector machine and 94.4% in 62.2% of cases using label-embedding attentive model within the Train/Test dataset. In the Holdout dataset, respective classification accuracies were 93.1% in 58.0% of cases and 95.0% among 62.0%. The most important feature in model training was procedure text. Conclusions Through application of machine learning and natural language processing techniques, highly accurate real-time models were created for anesthesiology Current Procedural Terminology code classification. The increased processing speed and a priori targeted accuracy of this classification approach may provide performance optimization and cost reduction for quality improvement, research, and reimbursement tasks reliant on anesthesiology procedure codes. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New

scholarly journals Natural Language Processing and Machine Learning Classifier used for Detecting the Author of the Sentence

2019 ◽  
Vol 8 (3) ◽  
pp. 936-939 ◽  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Support Vector ◽  
Learning Classifier ◽  
Number Of Classes

Detecting the author of the sentence in a collective document can be done by choosing a suitable set of features and implementing using Natural Language Processing in Machine Learning. Training our machine is the basic idea to identify the author name of a specific sentence. This can be done by using 8 different NLP steps like applying stemming algorithm, finding stop-list words, preprocessing the data, and then applying it to a machine learning classifier-Support vector machine (SVM) which classify the dataset into a number of classes specifying the author of the sentence and defines the name of author for each and every sentence with an accuracy of 82%.This paper helps the readers who are interested in knowing the names of the authors who have written some specific words

The Applications of Support Vector Machine in Natural Language Processing

2013 ◽  
Vol 427-429 ◽  
pp. 2572-2575
Author(s):  
Xiao Hua Li ◽  
Shu Xian Liu
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Basic Knowledge ◽  
Support Vector

This article provides a brief introduction to Natural Language Processing and basic knowledge of Machine Learning and Support Vector Machine at first, and then, gives a more detailed introduction about how to use SVM models in several major directions about NLP, and at the end, a brief summary about the application of SVM in Natural Language Processing is given.

scholarly journals Sentiment analysis of tweets through Altmetrics: A machine learning approach

2020 ◽  
pp. 016555152093091
Author(s):  
Saeed-Ul Hassan ◽  
Aneela Saleem ◽  
Saira Hanif Soroya ◽  
Iqra Safder ◽  
Sehrish Iqbal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Naive Bayes ◽  
The Other ◽  
Support Vector

The purpose of the study is to (a) contribute to annotating an Altmetrics dataset across five disciplines, (b) undertake sentiment analysis using various machine learning and natural language processing–based algorithms, (c) identify the best-performing model and (d) provide a Python library for sentiment analysis of an Altmetrics dataset. First, the researchers gave a set of guidelines to two human annotators familiar with the task of related tweet annotation of scientific literature. They duly labelled the sentiments, achieving an inter-annotator agreement (IAA) of 0.80 (Cohen’s Kappa). Then, the same experiments were run on two versions of the dataset: one with tweets in English and the other with tweets in 23 languages, including English. Using 6388 tweets about 300 papers indexed in Web of Science, the effectiveness of employed machine learning and natural language processing models was measured by comparing with well-known sentiment analysis models, that is, SentiStrength and Sentiment140, as the baseline. It was proved that Support Vector Machine with uni-gram outperformed all the other classifiers and baseline methods employed, with an accuracy of over 85%, followed by Logistic Regression at 83% accuracy and Naïve Bayes at 80%. The precision, recall and F1 scores for Support Vector Machine, Logistic Regression and Naïve Bayes were (0.89, 0.86, 0.86), (0.86, 0.83, 0.80) and (0.85, 0.81, 0.76), respectively.

scholarly journals Klasifikasi Laporan Keluhan Pelayanan Publik Berdasarkan Instansi Menggunakan Metode LDA-SVM

2021 ◽  
Vol 8 (6) ◽  
pp. 1265
Author(s):  
Muhammad Alkaff ◽  
Andreyan Rizky Baskara ◽  
Irham Maulani
Keyword(s):  
Support Vector Machine ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Latent Dirichlet Allocation ◽  
Confusion Matrix ◽  
Support Vector ◽  
Topic Distribution ◽  
The Government ◽  
Dirichlet Allocation

<p>Sebuah sistem layanan untuk menyampaikan aspirasi dan keluhan masyarakat terhadap layanan pemerintah Indonesia, bernama Lapor! Pemerintah sudah lama memanfaatkan sistem tersebut untuk menjawab permasalahan masyarakat Indonesia terkait permasalahan birokrasi. Namun, peningkatan volume laporan dan pemilahan laporan yang dilakukan oleh operator dengan membaca setiap keluhan yang masuk melalui sistem menyebabkan sering terjadi kesalahan dimana operator meneruskan laporan tersebut ke instansi yang salah. Oleh karena itu, diperlukan suatu solusi yang dapat menentukan konteks laporan secara otomatis dengan menggunakan teknik Natural Language Processing. Penelitian ini bertujuan untuk membangun klasifikasi laporan secara otomatis berdasarkan topik laporan yang ditujukan kepada instansi yang berwenang dengan menggabungkan metode Latent Dirichlet Allocation (LDA) dan Support Vector Machine (SVM). Proses pemodelan topik untuk setiap laporan dilakukan dengan menggunakan metode LDA. Metode ini mengekstrak laporan untuk menemukan pola tertentu dalam dokumen yang akan menghasilkan keluaran dalam nilai distribusi topik. Selanjutnya, proses klasifikasi untuk menentukan laporan agensi tujuan dilakukan dengan menggunakan SVM berdasarkan nilai topik yang diekstraksi dengan metode LDA. Performa model LDA-SVM diukur dengan menggunakan confusion matrix dengan menghitung nilai akurasi, presisi, recall, dan F1 Score. Hasil pengujian menggunakan teknik split train-test dengan skor 70:30 menunjukkan bahwa model menghasilkan kinerja yang baik dengan akurasi 79,85%, presisi 79,98%, recall 72,37%, dan Skor F1 74,67%.</p><p> </p><p><em><strong>Abstract</strong></em></p><p><em>A service system to convey aspirations and complaints from the public against Indonesia's government services, named Lapor! The Government has used the Government for a long time to answer the problems of the Indonesian people related to bureaucratic problems. However, the increasing volume of reports and the sorting of reports carried out by operators by reading every complaint that comes through the system cause frequent errors where operators forward the reports to the wrong agencies. Therefore, we need a solution that can automatically determine the report's context using Natural Language Processing techniques. This study aims to build automatic report classifications based on report topics addressed to authorized agencies by combining Latent Dirichlet Allocation (LDA) and Support Vector Machine (SVM). The topic-modeling process for each report was carried out using the LDA method. This method extracts reports to find specific patterns in documents that will produce output in topic distribution values. Furthermore, the classification process to determine the report's destination agency carried out using the SVM based on the value of the topics extracted by the LDA method. The LDA-SVM model's performance is measured using a confusion matrix by calculating the value of accuracy, precision, recall, and F1 Score. The test results using the train-test split technique with a 70:30 show that the model produces good performance with 79.85% accuracy, 79.98% precision, 72.37% recall, and 74.67% F1 Score</em></p><p><em><strong><br /></strong></em></p>

scholarly journals Multi - Class Document Classification: Effective and Systematized Method to Categorize Documents

2020 ◽  
pp. 118-123 ◽  
Author(s):  
Kaushika Pal ◽  
Biraj V. Patel
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
English Language ◽  
Nearest Neighbor ◽  
Research Work ◽  
Support Vector ◽  
Indian Languages ◽  
K Nearest Neighbor

A large section of World Wide Web is full of Documents, content; Data, Big data, unformatted data, formatted data, unstructured and unorganized data and we need information infrastructure, which is useful and easily accessible as an when required. This research work is combining approach of Natural Language Processing and Machine Learning for content-based classification of documents. Natural Language Processing is used which will divide the problem of understanding entire document at once into smaller chucks and give us only with useful tokens responsible for Feature Extraction, which is machine learning technique to create Feature Set which helps to train classifier to predict label for new document and place it at appropriate location. Machine Learning subset of Artificial Intelligence is enriched with sophisticated algorithms like Support Vector Machine, K – Nearest Neighbor, Naïve Bayes, which works well with many Indian Languages and Foreign Language content’s for classification. This Model is successful in classifying documents with more than 70% of accuracy for major Indian Languages and more than 80% accuracy for English Language.

scholarly journals Automatic classification of written descriptions by healthy adults: An overview of the application of natural language processing and machine learning techniques to clinical discourse analysis

2014 ◽  
Vol 8 (3) ◽  
pp. 227-235 ◽  
Author(s):  
Cíntia Matsuda Toledo ◽  
Andre Cunha ◽  
Carolina Scarton ◽  
Sandra Aluísio
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Binary Classification ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Brain Injured ◽  
Written Descriptions

Discourse production is an important aspect in the evaluation of brain-injured individuals. We believe that studies comparing the performance of brain-injured subjects with that of healthy controls must use groups with compatible education. A pioneering application of machine learning methods using Brazilian Portuguese for clinical purposes is described, highlighting education as an important variable in the Brazilian scenario.OBJECTIVE: The aims were to describe how to: (i) develop machine learning classifiers using features generated by natural language processing tools to distinguish descriptions produced by healthy individuals into classes based on their years of education; and (ii) automatically identify the features that best distinguish the groups.METHODS: The approach proposed here extracts linguistic features automatically from the written descriptions with the aid of two Natural Language Processing tools: Coh-Metrix-Port and AIC. It also includes nine task-specific features (three new ones, two extracted manually, besides description time; type of scene described - simple or complex; presentation order - which type of picture was described first; and age). In this study, the descriptions by 144 of the subjects studied in Toledo18 were used, which included 200 healthy Brazilians of both genders.RESULTS AND CONCLUSION:A Support Vector Machine (SVM) with a radial basis function (RBF) kernel is the most recommended approach for the binary classification of our data, classifying three of the four initial classes. CfsSubsetEval (CFS) is a strong candidate to replace manual feature selection methods.

scholarly journals Automated identification of patients with syncope in the textual health record – a feasibility study using machine learning and natural language processing

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
P Brekke ◽  
I Pilan ◽  
H Husby ◽  
T Gundersen ◽  
F.A Dahl ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
University Hospital ◽  
Support Vector ◽  
Funding Source ◽  
Learning Approaches ◽  
Patient Identification ◽  
Linear Classifiers

Abstract Background Syncope is a commonly occurring presenting symptom in emergency departments. While the majority of episodes are benign, syncope is associated with worse prognosis in hypertrophic cardiomyopathy, arrhythmia syndromes, heart failure, aortic stenosis and coronary heart disease. Flagging documented syncope in these patients may be crucial to management decisions. Previous studies show that the International Classification of Diseases (ICD) codes for syncope have a sensitivity of around 0.63, leading to a large number of false negatives if patient identification is based on administrative codes. Thus, in order to provide data-driven, clinical decision support, and to improve identification of patient cohorts for research, better tools are needed. A recent study manually annotated more than 30.000 patient records in order to develop a natural language processing (NLP) tool, which achieved a sensitivity of 92.2%. Since access to medical records and annotation resources is limited, we aimed to investigate whether an unsupervised machine learning and NLP approach with no manual input could achieve similar performance. Methods Our data was admission notes for adult patients admitted between 2005 and 2016 at a large university hospital in Norway. 500 records from patients with, and 500 without a “R55 Syncope” ICD code at discharge were drawn at random. R55 code was considered “ground truth”. Headers containing information about tentative diagnoses were removed from the notes, when present, using regular expressions. The dataset was divided into 70%/15%/15% subsets for training, validation and testing. Baseline identification was calculated by a simple lexical matching using the term “synkope”. We evaluated two linear classifiers, a Support Vector Machine (SVM) and a Linear Regression (LR) model, with a term frequency–inverse document frequency vectorizer, using a bag-of-words approach. In addition, we evaluated a simple convolutional neural network (CNN) consisting of a convolutional layer concatenating filter sizes of 3–5, max pooling and a dropout of 0.5 with randomly initialised word embeddings of 300 dimensions. Results Even a baseline regular expression model achieved a sensitivity of 78% and a specificity of 91% when classifying admission notes as belonging to the syncope class or not. The SVM model and the LR model achieved a sensitivity of 91% and 89%, respectively, and a specificity of 89% and 91%. The CNN model had a sensitivity of 95% and a specificity of 84%. Conclusion With a limited non-English dataset, common NLP and machine learning approaches were able to achieve approximately 90–95% sensitivity for the identification of admission notes related to syncope. Linear classifiers outperformed a CNN model in terms of specificity, as expected in this small dataset. The study demonstrates the feasibility of training document classifiers based on diagnostic codes in order to detect important clinical events. ROC curves for SVM and LR models Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): The Research Council of Norway

scholarly journals ANALISIS SENTIMEN KOMENTAR FACEBOOK BERBASIS LEXICON DAN SUPPORT VECTOR MACHINE

SAINTEKBU ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 40-44
Author(s):  
Iin Kurniasari
Keyword(s):  
Support Vector Machine ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Support Vector

Facebook adalah salah satu media sosial yang sering digunakan. Terutama pada pandemi co-19 saat ini. Banyak sekali sentimen publik yang beredar, terutama di Facebook dalam bentuk komentar atas informasi yang ada tentang covid-19 yang menantang untuk dianalisis untuk beberapa tujuan. Teknik NLP (Natural Language Processing) yang terdiri dari casefolding, tokenizing, filtering dan stemming dapat digunakan dalam kasus ini. Studi ini berfokus pada pengembangan analisis sentimen di Facebook menggunakan Lexicon dan Support Vector Machine. Data Lexicon yang diperoleh memiliki akurasi lebih rendah daripada menggunakan Support Vector Machine.

scholarly journals PhageAI - Bacteriophage Life Cycle Recognition with Machine Learning and Natural Language Processing

2020 ◽  
Author(s):  
Piotr Tynecki ◽  
Arkadiusz Guziński ◽  
Joanna Kazimierczak ◽  
Michał Jadczuk ◽  
Jarosław Dastych ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Life Cycle ◽  
Natural Language ◽  
Language Processing ◽  
3D Visualization ◽  
Life Cycles ◽  
Nucleotide Sequences ◽  
Support Vector ◽  
Model Classification

AbstractBackgroundAs antibiotic resistance is becoming a major problem nowadays in a treatment of infections, bacteriophages (also known as phages) seem to be an alternative. However, to be used in a therapy, their life cycle should be strictly lytic. With the growing popularity of Next Generation Sequencing (NGS) technology, it is possible to gain such information from the genome sequence. A number of tools are available which help to define phage life cycle. However, there is still no unanimous way to deal with this problem, especially in the absence of well-defined open reading frames. To overcome this limitation, a new tool is definitely needed.ResultsWe developed a novel tool, called PhageAI, that allows to access more than 10 000 publicly available bacteriophages and differentiate between their major types of life cycles: lytic and lysogenic. The tool included life cycle classifier which achieved 98.90% accuracy on a validation set and 97.18% average accuracy on a test set. We adopted nucleotide sequences embedding based on the Word2Vec with Ship-gram model and linear Support Vector Machine with 10-fold cross-validation for supervised classification. PhageAI is free of charge and it is available at https://phage.ai/. PhageAI is a REST web service and available as Python package.ConclusionsMachine learning and Natural Language Processing allows to extract information from bacteriophages nucleotide sequences for lifecycle prediction tasks. The PhageAI tool classifies phages into either virulent or temperate with a higher accuracy than any existing methods and shares interactive 3D visualization to help interpreting model classification results.

scholarly journals Framework for Infectious Disease Analysis: A comprehensive and integrative multi-modeling approach to disease prediction and management

2017 ◽  
Vol 25 (4) ◽  
pp. 1170-1187 ◽  
Author(s):  
Madhav Erraguntla ◽  
Josef Zapletal ◽  
Mark Lawley
Keyword(s):  
Infectious Disease ◽  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Support Vector ◽  
Human Populations ◽  
Illustrative Case ◽  
The Impact ◽  
Disease Analysis

The impact of infectious disease on human populations is a function of many factors including environmental conditions, vector dynamics, transmission mechanics, social and cultural behaviors, and public policy. A comprehensive framework for disease management must fully connect the complete disease lifecycle, including emergence from reservoir populations, zoonotic vector transmission, and impact on human societies. The Framework for Infectious Disease Analysis is a software environment and conceptual architecture for data integration, situational awareness, visualization, prediction, and intervention assessment. Framework for Infectious Disease Analysis automatically collects biosurveillance data using natural language processing, integrates structured and unstructured data from multiple sources, applies advanced machine learning, and uses multi-modeling for analyzing disease dynamics and testing interventions in complex, heterogeneous populations. In the illustrative case studies, natural language processing from social media, news feeds, and websites was used for information extraction, biosurveillance, and situation awareness. Classification machine learning algorithms (support vector machines, random forests, and boosting) were used for disease predictions.

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