Natural Language Processing Processing Research Articles

Identifying Elective Induction of Labor Among a Diverse Pregnant Population from Electronic Health Records within a Large Integrated Healthcare System.

Distinguishing between medically indicated induction of labor (iIOL) and elective induction of labor (eIOL) is a daunting process for researchers. We aimed to develop a Natural Language Processing (NLP) algorithm to identify eIOLs from electronic health records (EHR) within a large integrated healthcare system. We used structured and unstructured data from Kaiser Permanente Southern California's EHR of patients who were <35 years old and had singleton deliveries between 37 and 40 gestational weeks. Induction of labor (IOL) pregnancies were identified if there was evidence of an IOL diagnosis code, procedure code, or documentation in a delivery flowsheet or progress note. A comprehensive NLP algorithm was developed and refined through an iterative process of chart reviews and adjudications, where IOL-associated reasons (medically indicated versus elective induction) were reviewed. The final algorithm was applied to discern the indications of IOLs performed during the study period. A total of 332,163 eligible pregnancies were identified between 01/01/2008-12/31/2022. Of these eligible pregnancies, 68,541 (20.6%) were IOL, of which 6,824 (10.0%) were eIOL. Validation of the NLP process against 300 randomly selected pregnancies (100 eIOL, iIOL, and non-IOL cases each) yielded a positive predictive value of 83.0% and 88.0% for eIOL and iIOL, respectively. The rates of eIOL among the maternal age groups ranged between 9.6% -10.3%, except for the < 20 years group (12.2%). Non-Hispanic White individuals had the highest rate of eIOL (13.2%), while non-Hispanic Asian/Pacific Islanders had the lowest rate of eIOL (7.8%). The rate of eIOL increased from 1.0% in the 37 weeks gestational age group to 20.6% the 40 weeks gestational age group. Findings suggest that the developed NLP algorithm effectively identifies eIOL. It can be utilized to support eIOL-related pharmaco-epidemiological studies, filling in knowledge gaps and providing content more relevant to researchers.

Medication Recommendation System Using AI

Abstract: In an era marked by the convergence of personal and technical facets of life, the need for a versatile and intelligent companion becomes increasingly apparent. This project introduces the development of a novel Chatbot, aptly named the "Personal and Technical Companion," designed to seamlessly integrate into users' lives by offering personalized support and technical assistance. The Chatbot employs state-of-the-art Natural Language Processing (NLP) techniques to engage users in natural language conversations, providing a unique blend of personal and technical guidance. The system architecture encompasses a user-friendly interface, a robust NLP engine for intent recognition, a dynamic backend server handling logic and personalization, and integration with external services to enrich the user experience. Simultaneously, it serves as a technical companion by offering code assistance, troubleshooting guidance, and recommending relevant learning resources. Implementation leverages cutting-edge technologies, including Python for backend logic, Flask for the server, TensorFlow for NLP processing, and HTML/CSS for the user interface.

Bridging information gaps in menopause status classification through natural language processing.

To use natural language processing (NLP) of clinical notes to augment existing structured electronic health record (EHR) data for classification of a patient's menopausal status. A rule-based NLP system was designed to capture evidence of a patient's menopause status including dates of a patient's last menstrual period, reproductive surgeries, and postmenopause diagnosis as well as their use of birth control and menstrual interruptions. NLP-derived output was used in combination with structured EHR data to classify a patient's menopausal status. NLP processing and patient classification were performed on a cohort of 307512 female Veterans receiving healthcare at the US Department of Veterans Affairs (VA). NLP was validated at 99.6% precision. Including the NLP-derived data into a menopause phenotype increased the number of patients with data relevant to their menopausal status by 118%. Using structured codes alone, 81173 (27.0%) are able to be classified as postmenopausal or premenopausal. However, with the inclusion of NLP, this number increased 167804 (54.6%) patients. The premenopausal category grew by 532.7% with the inclusion of NLP data. By employing NLP, it became possible to identify documented data elements that predate VA care, originate outside VA networks, or have no corresponding structured field in the VA EHR that would be otherwise inaccessible for further analysis. NLP can be used to identify concepts relevant to a patient's menopausal status in clinical notes. Adding NLP-derived data to an algorithm classifying a patient's menopausal status significantly increases the number of patients classified using EHR data, ultimately enabling more detailed assessments of the impact of menopause on health outcomes.

&lt;b&gt;Systematic Review of Semantic Analysis Methods&lt;/b&gt;

NLP (natural language processing) is a very broad subcategory of computer science that focuses on human language processing in computers. Each different NLP processing technique focuses on different parts of linguistics, with semantics being the main focus of this manuscript. This manuscript aims to utilize a systematic review of several published papers ranging from 1998 to the current day to review and summarize critical analysis regarding the three main models that this paper focuses on: Latent Semantic Analysis (LSA), Explicit Semantic Analysis (ESA), and Neural Network based models. This manuscript compares these models against each other, weighs their advantages and disadvantages, and provides their uses. The selected studies in this review were analyzed and examined to ensure that they meet the quality and standards of the proposed research methodology. The results show that Neural network-based solutions are the most popular Semantic Analysis model in Academia (doubling the number of results of ESA and LSA combined), and they are usually the best in most tasks. However, there are specific scenarios and circumstances in which relying on the older LSA and ESA models could be beneficial.

Churn Identification and Prediction from a Large-Scale Telecommunication Dataset Using NLP

The identification of customer churn is a major issue for large telecom businesses. In order to manage the data of current customers as well as acquire and manage new customers, every day, a substantial volume of data gets generated. Therefore, it's crucial to identify the causes of client churn so that the appropriate steps can be taken to lower it. Numerous researchers have already discussed their efforts to combine static and dynamic approaches in order to reduce churn in big data sets, but these systems still have many issues when it comes to actually identifying churn. In this paper, we suggested two methods, the first of which is churn identification and using Natural Language Processing (NLP) methods and machine learning techniques, we make predictions based on a vast telecommunication data set. The NLP process involves data pre-processing, normalization, feature extraction, and feature selection. For feature extraction, we employ unique techniques like TF-IDF, Stanford NLP, and occurrence correlation methods, have been suggested. Throughout the lesson, a machine learning classification algorithm is used for training and testing. Finally, the system employs a variety of cross validation techniques and training and evaluating Machine learning algorithms. The experimental analysis shows the system's efficacy and accuracy.

Consultative engagement of stakeholders toward a roadmap for African language technologies

There has been a rise in natural language processing (NLP) communities across the African continent (Masakhane, AfricaNLP workshops). With this momentum noted, and given the existing power asymmetries that plague the African continent, there is an urgent need to ensure that these technologies move toward shared goals between organizations and stakeholders, not only to improve the representation of African languages in cutting-edge NLP research but also to ensure that NLP research enables technological advances toward human dignity, well-being, and equity for those who speak African languages. This study investigates the motivations, focus, and challenges faced by various stakeholders who are at the core of the NLP process. We perform structured stakeholder identification to identify core stakeholders in the NLP process. Interviews with representatives of these stakeholder groups are performed and are collated into relevant themes. Finally, a set of recommendations are proposed for use by policy and artificial intelligence (AI) researchers.

AI 기반 챗봇 한국어 텍스트의 자연어 분석 및 한국어 교육 활용 모색

The recent advancement of Artificial Intelligence(AI) technology has reached a new phase, thanks to the convergence of data and computing. In particular, AI-based chatbot systems are constructing interactive structures for conversation with humans by utilizing various technologies such as Natural Language Processing(NLP), Machine Learning, Deep Learning, and rule-based systems. This paper verifies the potential of the representative modern AI-based chatbots, ChatGPT and New-Bing, in the field of Korean education. To this end, responses were extracted in text form using the two models, and the preprocessing steps of Korean text were conducted to analyze the NLP process. Subsequently, text similarity was calculated using FastText, and a process was undergone to verify the reliability of similarity values between extracted high-frequency words. The ultimate goal is to describe the applications of AI-based chatbots in the field of Korean education and to provide examples of their correct and efficient use. Considering the future advancement of AI technology and various directions for its application, it is anticipated that the yet-to-be-developed Korean-specific chatbots will not only offer text conversation but also extend to voice recognition features, and thus be applicable to many aspects of Korean education.

P052 Hydroxychloroquine retinopathy incidence in a UK specialist centre: a machine learning/natural language processing pilot study

Abstract Background/Aims Hydroxychloroquine (HCQ) is an antimalarial drug commonly used in autoimmune conditions such as systemic lupus erythematous (SLE) and rheumatoid arthritis (RA). In SLE, it has been shown to significantly improve survival and morbidity, but is known to be rarely associated with sight-threatening retinopathy. Data from the US suggested HCQ retinopathy was significantly more common than previously thought (up to 7%), causing the Royal College of Ophthalmology to recommend increased screening in 2018. Natural language processing (NLP) is a machine learning technique which purports to be able to identify patients with a specific diagnosis or taking specific mediations from unstructured data such as clinic letters. This study aimed to understand whether NLP could be used to (i) identify patients taking HCQ and (ii) determine rates of HCQ retinopathy from clinic letters in a UK tertiary Rheumatology centre. Methods The study was set across the Kellgren Centre for Rheumatology (regional tertiary lupus centre) and the Manchester Eye Hospital. Analysis was performed using CogStack/MedCat, an open source self-supervised NLP framework which extracts SNOMED coded items from unstructured electronic health data such as clinic letters. All clinical documents 2012-2022 were identified for patients attending clinics coded as HCQ retinopathy screening. These were processed through Cogstack/MedCat in two stages. Firstly, it identified all patients exposed to HCQ according to their clinic letters. Subsequently, MedCat searched all clinical documents for those patients for references to HCQ retinopathy, HCQ retinopathy screening or retinopathy. Diabetic retinopathy was used to sense-check for other forms of retinopathy. Finally, manual validation of the HCQ retinopathy positive cases was performed. Results From an original 202,704 documents, HCQ exposure was identified in 3904 unique patients. These patients had 74,377 associated clinical documents, from which the NLP process indicated 2815 referenced HCQ retinopathy or retinopathy alone (including screening). From clinic codes 1299 individual patients (33.3% of the HCQ exposed population) were confirmed to have undergone screening; 1094 (84.2%) were female, mean age 54.8 years at the time of screening. Of those screened, NLP highlighted 568 references to HCQ retinopathy from 507 patients, within which 10 (0.77%) were confirmed to have HCQ retinopathy by manual validation. Conclusion The study has demonstrated that NLP is a promising technique to quickly identify subsets of patients (e.g. those taking HCQ) from large numbers of outpatient clinic letters. Further training of the algorithm may improve its ability to identify rare but important clinical outcomes. In a tertiary SLE centre, roughly one third of patients on HCQ were screened for HCQ retinopathy and 0.77% confirmed HCQ retinopathy. This is much lower than data from the previous studies in the US, but is in keeping with more recent publications in UK data, reflected in recent amendments to screening guidelines. Disclosure Y. Mushtaq: None. G. Tilston: None. K.L. Hyrich: None. A. Barton: None. N. Peek: None. B. Parker: None. D. Griffiths: None. J.H. Humphreys: None.

Evaluation of Sentiment Analysis of Text Using Rule-Based and Automatic Approach

The technique of determining whether a text is good, negative or neutral is known as sentiment analysis (SA).Sentiment Analysis can be identified by many names like Textual Analysis, Opinion Mining. Sentiment Analysis is a branch of Natural Language Processing (NLP) that focuses on the expression of subjective views and feelings about a topic gathered from multiple sources. Sentiment Analysis is a collection of methods for detecting and extracting opinions and uses them for the benefit of business operation. It is a classification algorithm aimed at finding opinions and decision-making point of view. Sentiment Analysis is performed in many ways, Automatic classification approach involves Nave Bayes (NB), Support Vector Machine (SVM), and Linear Regression is examples of supervised machine learning methods (LR). The data is explored using unsupervised machine learning. Recurrent Neural Network (RNN) derivatives are also used for classification. Rule-based approach involves various NLP process for classification.

Word Embedding Methods for Word Representation in Deep Learning for Natural Language Processing

Natural Language Processing (NLP) deals with analysing, understanding and generating languages likes human. One of the challenges of NLP is training computers to understand the way of learning and using a language as human. Every training session consists of several types of sentences with different context and linguistic structures. Meaning of a sentence depends on actual meaning of main words with their correct positions. Same word can be used as a noun or adjective or others based on their position. In NLP, Word Embedding is a powerful method which is trained on large collection of texts and encoded general semantic and syntactic information of words. Choosing a right word embedding generates more efficient result than others. Most of the papers used pretrained word embedding vector in deep learning for NLP processing. But, the major issue of pretrained word embedding vector is that it can’t use for all types of NLP processing. In this paper, a local word embedding vector formation process have been proposed and shown a comparison between pretrained and local word embedding vectors for Bengali language. The Keras framework is used in Python for local word embedding implementation and analysis section of this paper shows proposed model produced 87.84% accuracy result which is better than fastText pretrained word embedding vectors accuracy 86.75%. Using this proposed method NLP researchers of Bengali language can easily build the specific word embedding vectors for word representation in Natural Language Processing.

Natural Language Processing for Virtual Reference Analysis

Objective – Chat transcript analysis can illuminate user needs by identifying common question topics, but traditional hand coding methods for topic analysis are time-consuming and poorly suited to large datasets. The research team explored the viability of automatic and natural language processing (NLP) strategies to perform rapid topic analysis on a large dataset of transcripts from a consortial chat service. Methods – The research team developed a toolchain for data processing and analysis, which incorporated targeted searching for query terms using regular expressions and natural language processing using the Python spaCy library for automatic topic analysis. Processed data was exported to Tableau for visualization. Results were compared to hand-coded data to test the accuracy of conclusions. Results – The processed data provided insights about the volume of chats originating from each participating library, the proportion of chats answered by operator groups for each library, and the percentage of chats answered by different staff types. The data also captured the top referring URLs for the service, course codes and file extensions mentioned, and query hits. Natural language processing revealed that the most common topics were related to citation, subscription databases, and finding full-text articles, which aligns with common question types identified in hand-coded transcripts. Conclusion – Compared to hand coding, automatic and NLP processing approaches have benefits in terms of the volume of data that can be analyzed and the time frame required for analysis, but they come with a trade-off in accuracy, such as false hits. Therefore, computational approaches should be used to supplement traditional hand coding methods. As NLP becomes more accurate, approaches such as these may widen avenues of insight into virtual reference and patron needs.

Predicting the impact of the third wave of COVID-19 in India using hybrid statistical machine learning models: A time series forecasting and sentiment analysis approach

BackgroundSince January 2020, India has faced two waves of COVID-19; preparation for the upcoming waves is the primary challenge for public health sectors and governments. Therefore, it is important to forecast future cumulative confirmed cases to plan and implement control measures effectively. MethodsThis study proposed a hybrid autoregressive integrated moving average (ARIMA) and Prophet model to predict daily confirmed and cumulative confirmed cases. The built-in auto.arima function was first used to select the optimal hyperparameter values of the ARIMA model. Then, the modified ARIMA model was used to find the best fit between the test and forecast data to find the best model parameter combinations. Articles, blog posts, and news stories from virologists, scientists, and health experts related to the third wave of COVID-19 were gathered using the Python web scraping package Beautiful Soup. Their opinions (sentiments) toward the potential third wave were analyzed using natural language processing (NLP) libraries. ResultsA spike in daily confirmed and cumulative confirmed cases was predicted in India in the next 180 days based on past time series data. The results were validated using various analytical tools and evaluation metrics, producing a root mean square error (RMSE) of 0.14 and a mean absolute percentage error (MAPE) of 0.06. The NLP processing results revealed negative sentiments in most articles and blogs, with few exceptions. ConclusionThe findings of this study suggest that there will be more active cases in the upcoming days. The proposed models can forecast future daily confirmed and cumulative confirmed cases. This study will help the country and states plan appropriate public health measures for the upcoming waves of COVID-19.

A comprehensive review on Arabic word sense disambiguation for natural language processing applications

AbstractIn communication, textual data are a vital attribute. In all languages, ambiguous or polysemous words' meaning changes depending on the context in which they are used. The ability to determine the ambiguous word's correct meaning is a Know‐distill challenging task in natural language processing (NLP). Word sense disambiguation (WSD) is an NLP process to analyze and determine the correct meaning of polysemous words in a text. WSD is a computational linguistics task that automatically identifies the polysemous word's set of senses. Based on the context some word comes into view, WSD recognizes and tags the word to its correct priori known meaning. Semitic languages like Arabic have even more significant challenges than other languages since Arabic lacks diacritics, standardization, and a massive shortage of available resources. Recently, many approaches and techniques have been suggested to solve word ambiguity dilemmas in many different ways and several languages. In this review paper, an extensive survey of research works is presented, seeking to solve Arabic word sense disambiguation with the existing AWSD datasets.This article is categorized under: Algorithmic Development &gt; Text Mining Technologies &gt; Machine Learning

P–782 A natural language processing approach of global survey results on what the embryologist thinks and faces

Abstract Study question What are the major problems faced by embryologists at 1) Clinic level, 2) Professional level, 3) Personal level, and 4) What are their career goals? Summary answer Embryologists, essential professionals of Fertility Centres, are less satisfied in many quantifiable aspects, but they love their profession and have many aspirational goals. What is known already IVF success depends in part on embryologists’ skills. The need to recognize clinical embryology as a specialty and clinical embryologists’ educational level, responsibilities, and workload have been addressed by a few national societies. However, data are lacking from the embryologists’ viewpoint at a global level about their profession. Qualitative data-analysis methods provide thick, rich descriptions of subjects’ thoughts, feelings, and lived experiences but can be time-consuming, labor-intensive, and prone to bias. Study design, size, duration A questionnaire was prepared using SurveyMonkey online software (SurveyMonkey, Inc., USA) and distributed to IVF lab professionals through embryology societies, online social media, and email databases. The questionnaire consisted of open-ended questions focused on identifying problems faced by embryologists at the clinic, in the profession, and in a personal level, as well as questions about their career outlook. The survey was active from May 2016 until February 2017. From 73 countries, 720 responses were obtained. Participants/materials, setting, methods Using natural language processing (NLP), the top 15 most frequently used keywords were identified and correlated with each other. Stronger correlation (≥0.5) between semantically similar words expressing a strong signal from each answer, and their usage was further analyzed for positive versus negative sentiment. By normalizing the frequency of positive/negative samples for each keyword as a percentage, “sentiment wheels” were produced, identifying the key concepts that respondents answered and quantifying how they felt about them. Main results and the role of chance The responses received were from 80% private, 17% public and 3% other ART settings distributed all over the world. From the embryologists’ viewpoints reported and after the NLP processing it was shown that the common topics related to strong negative sentiments were: embryologists’ remuneration (0.6) at the Clinic level; certification (0.7), recognition (0.5), respect (0.5), learn (0.5) and experience (0.5) at the Professional level; and remuneration (0.7), emotional (0.5) dealing (0.5) at the Personal level. Renumeration was reported and strongly related to embryologists’ viewpoint at both the clinic and personal level in combination with the need for certification, recognition and ongoing development at the Professional level. Moreover, the NLP processing demonstrated that the common topics on career goal analysis related to strong positive sentiments were: teaching (0.7), education (0.7), and continuation (0.5) all three topics are compatible with a professional orientation open to ongoing development and practice advancement. The NLP and the manual data analysis project an image of the typical embryologist as a knowledge seeking professional who is deeply dedicated to the job but feels the need for professional development and suffers some lack of recognition and feels in some cases not fairly treated as an employee. Limitations, reasons for caution The data obtained is limited. Only one natural language processing model was used to analyze the results. Different analysts using other methods may have different results. For these reasons, the results should be interpreted with caution. Wider implications of the findings: It is important to focus on the lab as an organization and not just a service for the patients in treatment at the moment. The NLP results ultimately obtained may help streamline professional satisfaction efforts, and guide future quality management strategies Trial registration number Not applicable

An Annotated Corpus of Crime-Related Portuguese Documents for NLP and Machine Learning Processing

Criminal investigations collect and analyze the facts related to a crime, from which the investigators can deduce evidence to be used in court. It is a multidisciplinary and applied science, which includes interviews, interrogations, evidence collection, preservation of the chain of custody, and other methods and techniques of investigation. These techniques produce both digital and paper documents that have to be carefully analyzed to identify correlations and interactions among suspects, places, license plates, and other entities that are mentioned in the investigation. The computerized processing of these documents is a helping hand to the criminal investigation, as it allows the automatic identification of entities and their relations, being some of which difficult to identify manually. There exists a wide set of dedicated tools, but they have a major limitation: they are unable to process criminal reports in the Portuguese language, as an annotated corpus for that purpose does not exist. This paper presents an annotated corpus, composed of a collection of anonymized crime-related documents, which were extracted from official and open sources. The dataset was produced as the result of an exploratory initiative to collect crime-related data from websites and conditioned-access police reports. The dataset was evaluated and a mean precision of 0.808, recall of 0.722, and F1-score of 0.733 were obtained with the classification of the annotated named-entities present in the crime-related documents. This corpus can be employed to benchmark Machine Learning (ML) and Natural Language Processing (NLP) methods and tools to detect and correlate entities in the documents. Some examples are sentence detection, named-entity recognition, and identification of terms related to the criminal domain.

Establishing a carotid artery stenosis disease cohort for comparative effectiveness research using natural language processing

ObjectiveInvestigation of asymptomatic carotid stenosis treatment is hindered by the lack of a contemporary population-based disease cohort. We describe the use of natural language processing (NLP) to identify stenosis in patients undergoing carotid imaging. MethodsAdult patients with carotid imaging between 2008 and 2012 in a large integrated health care system were identified and followed through 2017. An NLP process was developed to characterize carotid stenosis according to the Society of Radiologists in Ultrasound (for ultrasounds) and North American Symptomatic Carotid Endarterectomy Trial (NASCET) (for axial imaging) guidelines. The resulting algorithm assessed text descriptors to categorize normal/non-hemodynamically significant stenosis, moderate or severe stenosis as well as occlusion in both carotid ultrasound (US) and axial imaging (computed tomography and magnetic resonance angiography [CTA/MRA]). For US reports, internal carotid artery systolic and diastolic velocities and velocity ratios were assessed and matched for laterality to supplement accuracy. To validate the NLP algorithm, positive predictive value (PPV or precision) and sensitivity (recall) were calculated from simple random samples from the population of all imaging studies. Lastly, all non-normal studies were manually reviewed for confirmation for prevalence estimates and disease cohort assembly. ResultsA total of 95,896 qualifying index studies (76,276 US and 19,620 CTA/MRA) were identified among 94,822 patients including 1059 patients who underwent multiple studies on the same day. For studies of normal/non-hemodynamically significant stenosis arteries, the NLP algorithm showed excellent performance with a PPV of 99% for US and 96.5% for CTA/MRA. PPV/sensitivity to identify a non-normal artery with correct laterality in the CTA/MRA and US samples were 76.9% (95% confidence interval [CI], 74.1%-79.5%)/93.1% (95% CI, 91.1%-94.8%) and 74.7% (95% CI, 69.3%-79.5%)/94% (95% CI, 90.2%-96.7%), respectively. Regarding cohort assembly, 15,522 patients were identified with diseased carotid artery, including 2674 exhibiting equal bilateral disease. This resulted in a laterality-specific cohort with 12,828 moderate, 5283 severe, and 1895 occluded arteries and 326 diseased arteries with unknown stenosis. During follow-up, 30.1% of these patients underwent 61,107 additional studies. ConclusionsUse of NLP to detect carotid stenosis or occlusion can result in accurate exclusion of normal/non-hemodynamically significant stenosis disease states with more moderate precision with lesion identification, which can substantially reduce the need for manual review. The resulting cohort allows for efficient research and holds promise for similar reporting in other vascular diseases.

A natural language processing tool for automatic identification of new disease and disease progression: Parsing text in multi-institutional radiology reports to facilitate clinical trial eligibility screening.

1555 Background: Screening every patient for clinical trials is time-consuming, costly and inefficient. Developing an automated method for identifying patients who have potential disease progression, at the point where the practice first receives their radiology reports, but prior to the patient’s office visit, would greatly increase the efficiency of clinical trial operations and likely result in more patients being offered trial opportunities. Methods: Using Natural Language Processing (NLP) methodology, we developed a text parsing algorithm to automatically extract information about potential new disease or disease progression from multi-institutional, free-text radiology reports (CT, PET, bone scan, MRI or x-ray). We combined semantic dictionary mapping and machine learning techniques to normalize the linguistic and formatting variations in the text, training the XGBoost model particularly to achieve a high precision and accuracy to satisfy clinical trial screening requirements. In order to be comprehensive, we enhanced the model vocabulary using a multi-institutional dataset which includes reports from two academic institutions. Results: A dataset of 732 de-identified radiology reports were curated (two MDs agreed on potential new disease/dz progression vs stable) and the model was repeatedly re-trained for each fold where the folds were randomly selected. The final model achieved consistent precision (&gt;0.87 precision) and accuracy (&gt;0.87 accuracy). See the table for a summary of the results, by radiology report type. We are continuing work on the model to validate accuracy and precision using a new and unique set of reports. Conclusions: NLP systems can be used to identify patients who potentially have suffered new disease or disease progression and reduce the human effort in screening or clinical trials. Efforts are ongoing to integrate the NLP process into existing EHR reporting. New imaging reports sent via interface to the EHR will be extracted daily using a database query and will be provided via secure electronic transport to the NLP system. Patients with higher likelihood of disease progression will be automatically identified, and their reports routed to the clinical trials office for clinical trial screening parallel to physician EHR mailbox reporting. The over-arching goal of the project is to increase clinical trial enrollment. 5-fold cross-validation performance of the NLP model in terms of accuracy, precision and recall averaged across all the folds.[Table: see text]

Ontology-Based Approach to Semantically Enhanced Question Answering for Closed Domain: A Review

For many users of natural language processing (NLP), it can be challenging to obtain concise, accurate and precise answers to a question. Systems such as question answering (QA) enable users to ask questions and receive feedback in the form of quick answers to questions posed in natural language, rather than in the form of lists of documents delivered by search engines. This task is challenging and involves complex semantic annotation and knowledge representation. This study reviews the literature detailing ontology-based methods that semantically enhance QA for a closed domain, by presenting a literature review of the relevant studies published between 2000 and 2020. The review reports that 83 of the 124 papers considered acknowledge the QA approach, and recommend its development and evaluation using different methods. These methods are evaluated according to accuracy, precision, and recall. An ontological approach to semantically enhancing QA is found to be adopted in a limited way, as many of the studies reviewed concentrated instead on NLP and information retrieval (IR) processing. While the majority of the studies reviewed focus on open domains, this study investigates the closed domain.

Ambiguity Resolution : An Analytical Study

Natural language processing (NLP) is very much needed in today’s world to enhance human-machine interaction. It is an important concern to process textual data and obtain useful and meaningful information from these texts. NLP parses the texts and provides information to machine for further processing. The present status of NLP’s computational process of identifying the meaning (sense) of a word in a particular context is ambiguous, where the meaning of word in the context is not clear and may point to multiple senses. Ambiguity in understanding correct meaning of texts is hampering the growth and development in various fields of Natural language processing applications like Machine translation, Human Machine interface etc. The process of finding the correct meaning of the ambiguous texts in the given context is called as word sense disambiguation (WSD). WSD is perceived as one of the most challenging problem in the Natural language processing community and is still unsolved. It is evident that different ambiguities exist in natural languages and researchers are contributing to resolve the problem in different languages for successful disambiguation. These ambiguities must be resolved in order to understand the meaning of the text and help to boost NLP processing and applications. Objective is to investigate how WSD can be used to alleviate ambiguities, automatically determine the correct meaning of the ambiguous text and help to boost NLP processing and applications. Resolving ambiguity for translation involves working with various natural language processing techniques to investigate the structure of the languages, availability of lexical resources etc. Word Sense Disambiguation (WSD) in the field of computing linguistics is an area which is still unsolved. This paper focus on the in-depth analysis of such ambiguity, issues in Language Translation, how WSD resolves the ambiguity and contribute towards building a framework.

Transparency and accountability in AI decision support: Explaining and visualizing convolutional neural networks for text information

Proliferating applications of deep learning, along with the prevalence of large-scale text datasets, have revolutionized the natural language processing (NLP) field, thereby driving the recent explosive growth. Nevertheless, it is argued that state-of-the-art studies focus excessively on producing quantitative performances superior to existing models, by playing “the Kaggle game.” Hence, the field requires more effort in solving new problems and proposing novel approaches and architectures. We claim that one of the promising and constructive efforts would be to design transparent and accountable artificial intelligence (AI) systems for text analytics. By doing so, we can enhance the applicability and problem-solving capacity of the system for real-world decision support. It is widely accepted that deep learning models demonstrate remarkable performances compared to existing algorithms. However, they are often criticized for being less interpretable, i.e., the “black box.” In such cases, users tend to hesitate to utilize them for decision-making, especially in crucial tasks. Such complexity obstructs transparency and accountability of the overall system, potentially debilitating the deployment of decision support systems powered by AI. Furthermore, recent regulations are emphasizing fairness and transparency in algorithms to a greater extent, turning explanations more compulsory than voluntary. Thus, to enhance the transparency and accountability of the decision support system and preserve the capacity to model complex text data at the same time, we propose the Explaining and Visualizing Convolutional neural networks for Text information (EVCT) framework. By adopting and ameliorating cutting-edge methods in NLP and image processing, the EVCT framework provides a human-interpretable solution to the problem of text classification while minimizing information loss. Experimental results with large-scale, real-world datasets show that EVCT performs comparably to benchmark models, including widely used deep learning models. In addition, we provide instances of human-interpretable and relevant visualized explanations obtained from applying EVCT to the dataset and possible applications for real-world decision support.