scholarly journals Development of a Structured Query Language and Natural Language Processing Algorithm to Identify Lung Nodules in a Cancer Centre

2021 ◽  
Vol 8 ◽  
Author(s):  
Benjamin Hunter ◽  
Sara Reis ◽  
Des Campbell ◽  
Sheila Matharu ◽  
Prashanthi Ratnakumar ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Query Language ◽  
Lung Nodule ◽  
Service Evaluation ◽  
Support Vector ◽  
Lung Nodules ◽  
Cancer Centre ◽  
Structured Query Language

Importance: The stratification of indeterminate lung nodules is a growing problem, but the burden of lung nodules on healthcare services is not well-described. Manual service evaluation and research cohort curation can be time-consuming and potentially improved by automation.Objective: To automate lung nodule identification in a tertiary cancer centre.Methods: This retrospective cohort study used Electronic Healthcare Records to identify CT reports generated between 31st October 2011 and 24th July 2020. A structured query language/natural language processing tool was developed to classify reports according to lung nodule status. Performance was externally validated. Sentences were used to train machine-learning classifiers to predict concerning nodule features in 2,000 patients.Results: 14,586 patients with lung nodules were identified. The cancer types most commonly associated with lung nodules were lung (39%), neuro-endocrine (38%), skin (35%), colorectal (33%) and sarcoma (33%). Lung nodule patients had a greater proportion of metastatic diagnoses (45 vs. 23%, p < 0.001), a higher mean post-baseline scan number (6.56 vs. 1.93, p < 0.001), and a shorter mean scan interval (4.1 vs. 5.9 months, p < 0.001) than those without nodules. Inter-observer agreement for sentence classification was 0.94 internally and 0.98 externally. Sensitivity and specificity for nodule identification were 93 and 99% internally, and 100 and 100% at external validation, respectively. A linear-support vector machine model predicted concerning sentence features with 94% accuracy.Conclusion: We have developed and validated an accurate tool for automated lung nodule identification that is valuable for service evaluation and research data acquisition.

scholarly journals EventEpi–A Natural Language Processing Framework for Event-Based Surveillance

2019 ◽  
Author(s):  
Auss Abbood ◽  
Alexander Ullrich ◽  
Rüdiger Busche ◽  
Stéphane Ghozzi
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Web Application ◽  
Fine Tuning ◽  
Entity Recognition ◽  
World Health ◽  
Support Vector ◽  
Event Based ◽  
Processing Framework

AbstractAccording to the World Health Organization (WHO), around 60% of all outbreaks are detected using informal sources. In many public health institutes, including the WHO and the Robert Koch Institute (RKI), dedicated groups of epidemiologists sift through numerous articles and newsletters to detect relevant events. This media screening is one important part of event-based surveillance (EBS). Reading the articles, discussing their relevance, and putting key information into a database is a time-consuming process. To support EBS, but also to gain insights into what makes an article and the event it describes relevant, we developed a natural-language-processing framework for automated information extraction and relevance scoring. First, we scraped relevant sources for EBS as done at RKI (WHO Disease Outbreak News and ProMED) and automatically extracted the articles’ key data: disease, country, date, and confirmed-case count. For this, we performed named entity recognition in two steps: EpiTator, an open-source epidemiological annotation tool, suggested many different possibilities for each. We trained a naive Bayes classifier to find the single most likely one using RKI’s EBS database as labels. Then, for relevance scoring, we defined two classes to which any article might belong: The article is relevant if it is in the EBS database and irrelevant otherwise. We compared the performance of different classifiers, using document and word embeddings. Two of the tested algorithms stood out: The multilayer perceptron performed best overall, with a precision of 0.19, recall of 0.50, specificity of 0.89, F1 of 0.28, and the highest tested index balanced accuracy of 0.46. The support-vector machine, on the other hand, had the highest recall (0.88) which can be of higher interest for epidemiologists. Finally, we integrated these functionalities into a web application called EventEpi where relevant sources are automatically analyzed and put into a database. The user can also provide any URL or text, that will be analyzed in the same way and added to the database. Each of these steps could be improved, in particular with larger labeled datasets and fine-tuning of the learning algorithms. The overall framework, however, works already well and can be used in production, promising improvements in EBS. The source code is publicly available at https://github.com/aauss/EventEpi.

scholarly journals Natural language processing for cognitive therapy: Extracting schemas from thought records

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0257832
Author(s):  
Franziska Burger ◽  
Mark A. Neerincx ◽  
Willem-Paul Brinkman
Keyword(s):  
Mental Health ◽  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Recurrent Neural Networks ◽  
Support Vector ◽  
Free Text ◽  
Cognitive Approach ◽  
Text Input

The cognitive approach to psychotherapy aims to change patients’ maladaptive schemas, that is, overly negative views on themselves, the world, or the future. To obtain awareness of these views, they record their thought processes in situations that caused pathogenic emotional responses. The schemas underlying such thought records have, thus far, been largely manually identified. Using recent advances in natural language processing, we take this one step further by automatically extracting schemas from thought records. To this end, we asked 320 healthy participants on Amazon Mechanical Turk to each complete five thought records consisting of several utterances reflecting cognitive processes. Agreement between two raters on manually scoring the utterances with respect to how much they reflect each schema was substantial (Cohen’s κ = 0.79). Natural language processing software pretrained on all English Wikipedia articles from 2014 (GLoVE embeddings) was used to represent words and utterances, which were then mapped to schemas using k-nearest neighbors algorithms, support vector machines, and recurrent neural networks. For the more frequently occurring schemas, all algorithms were able to leverage linguistic patterns. For example, the scores assigned to the Competence schema by the algorithms correlated with the manually assigned scores with Spearman correlations ranging between 0.64 and 0.76. For six of the nine schemas, a set of recurrent neural networks trained separately for each of the schemas outperformed the other algorithms. We present our results here as a benchmark solution, since we conducted this research to explore the possibility of automatically processing qualitative mental health data and did not aim to achieve optimal performance with any of the explored models. The dataset of 1600 thought records comprising 5747 utterances is published together with this article for researchers and machine learning enthusiasts to improve upon our outcomes. Based on our promising results, we see further opportunities for using free-text input and subsequent natural language processing in other common therapeutic tools, such as ecological momentary assessments, automated case conceptualizations, and, more generally, as an alternative to mental health scales.

scholarly journals Structuring Natural Language to Query Language: A Review

2020 ◽  
Vol 10 (6) ◽  
pp. 6521-6525
Author(s):  
B. Nethravathi ◽  
G. Amitha ◽  
A. Saruka ◽  
T. P. Bharath ◽  
S. Suyagya
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Database Management ◽  
Query Language ◽  
Native Language ◽  
Database Management System ◽  
Novice User ◽  
Complex Queries ◽  
Sql Query

SQL (Structured Query Language) is a structured language for specialized purposes used to communicate with the data stored in a database management system. It uses dynamic and sophisticated query commands for processing and controlling data in a database, which can become an obstacle for users with no previous experience. In order to address this constraint, we have analyzed the existing models in Natural Language Processing, which convert a native-language query into an SQL query. Thus, any novice user can use the SQL program and eliminate the need to generate any complex queries. This work is a detailed survey of the existing literature.

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 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

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.

Abstract P225: Improving Prehospital Stroke Identification Using Natural Language Processing of Paramedic Reports

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Anoop Mayampurath ◽  
Zahra Parnianpour ◽  
William J Meurer ◽  
Jungwha Lee ◽  
Bruce Ankenman ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Predictive Model ◽  
Language Processing ◽  
Prehospital Setting ◽  
Screening Tools ◽  
Superior Performance ◽  
Support Vector ◽  
Stroke Registry ◽  
C Statistic

Introduction: Early identification of stroke by emergency medical services (EMS) providers in the prehospital setting is associated with increased treatment rates, improved functional outcomes, and reduced mortality. We hypothesize that a predictive model utilizing machine learning and natural language processing (NLP) techniques can be developed to analyze EMS run reports to identify stroke patients accurately. Methods: We analyzed EMS data from the Chicago Fire Department matched with inpatient data on confirmed and suspected strokes from 17 Chicago hospitals in the Get With The Guidelines-Stroke (GWTG-Stroke) registry from 11/28/2018 to 5/31/2019. Using features derived from paramedic notes, we developed a support vector machine classifier to predict the following categories: any stroke, AIS-LVO, severe stroke (NIHSS>5), and CSC-eligible stroke (AIS-LVO or ICH/SAH). Individuals were randomly assigned into model derivation (70%) and validation cohorts (30%). C-statistics were used to evaluate discrimination of the classifier for stroke categories. Results: A total of 965 patients were included for analysis. In a validation cohort of 289 patients, the text-based model predicted stroke better than models trained using the Cincinnati Prehospital Stroke Scale (CPSS, c-statistic: 0.73 vs. 0.67, P=0.165) and the 3-Item Stroke Scale (3I-SS, c-statistic: 0.73 vs. 0.53, P <0.001) scores. The text-based model also demonstrated improved performance over the CPSS and 3I-SS models in discriminating patients with other stroke categories (Table 1). Conclusion: We derived a predictive model using clinical text from paramedic reports that has superior performance to existing prehospital clinical screening tools to identify stroke in the prehospital setting. Future studies can evaluate the implementation of an NLP-based decision tool to assist in prehospital stroke evaluation and destination decision-making.

A COMPARATIVE STUDY OF STATISTICAL AND NATURAL LANGUAGE PROCESSING TECHNIQUES FOR SENTIMENT ANALYSIS

2015 ◽  
Vol 77 (18) ◽  
Author(s):  
Wai-Howe Khong ◽  
Lay-Ki Soon ◽  
Hui-Ngo Goh
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Word Sense Disambiguation ◽  
Statistical Technique ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Word Sense ◽  
Pos Tagging

Sentiment analysis has emerged as one of the most powerful tools in business intelligence. With the aim of proposing an effective sentiment analysis technique, we have performed experiments on analyzing the sentiments of 3,424 tweets using both statistical and natural language processing (NLP) techniques as part of our background study.  For statistical technique, machine learning algorithms such as Support Vector Machines (SVMs), decision trees and Naïve Bayes have been explored. The results show that SVM consistently outperformed the rest in both classifications. As for sentiment analysis using NLP techniques, we used two different tagging methods for part-of-speech (POS) tagging.  Subsequently, the output is used for word sense disambiguation (WSD) using WordNet, followed by sentiment identification using SentiWordNet.  Our experimental results indicate that adjectives and adverbs are sufficient to infer the sentiment of tweets compared to other combinations. Comparatively, the statistical approach records higher accuracy than the NLP approach by approximately 17%.

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