Role Of Natural Language Processing Research Articles

Revolutionizing Healthcare: NLP, Deep Learning, and WSN Solutions for Managing the COVID-19 Crisis

The COVID-19 outbreak in 2020 catalyzed a global socio-economic upheaval, compelling nations to embrace digital technologies as a means of countering economic downturns and ensuring efficient communication systems. This paper delves into the role of Natural Language Processing (NLP) in harnessing wireless connectivity during the pandemic. The examination assesses how wireless networks have affected various facets of crisis management, including virus tracking, optimizing healthcare, facilitating remote education, and enabling unified communications. Additionally, the article underscores the importance of digital inclusion in mitigating disease outbreaks and reconnecting marginalized communities. To address these challenges, a Dual CNN-based BERT model is proposed. BERT model is used to extract the text features, the internal layers of BERT excel at capturing intricate contextual details concerning words and phrases, rendering them highly valuable as features for a wide array of text analysis tasks. The significance of dual CNN is capturing the unique capability to seamlessly integrate both character-level and word-level information. This fusion of insights from different levels of textual analysis proves especially valuable in handling text data that is noisy, complex, or presents challenges related to misspellings and domain-specific terminology. The proposed model is evaluated using the simulated WSN-based text data for crisis management.

Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research Directions.

Smart cities provide an efficient infrastructure for the enhancement of the quality of life of the people by aiding in fast urbanization and resource management through sustainable and scalable innovative solutions. The penetration of Information and Communication Technology (ICT) in smart cities has been a major contributor to keeping up with the agility and pace of their development. In this paper, we have explored Natural Language Processing (NLP) which is one such technical discipline that has great potential in optimizing ICT processes and has so far been kept away from the limelight. Through this study, we have established the various roles that NLP plays in building smart cities after thoroughly analyzing its architecture, background, and scope. Subsequently, we present a detailed description of NLP's recent applications in the domain of smart healthcare, smart business, and industry, smart community, smart media, smart research, and development as well as smart education accompanied by NLP's open challenges at the very end. This work aims to throw light on the potential of NLP as one of the pillars in assisting the technical advancement and realization of smart cities.

Muhasebe ve Denetimde Doğal Dil İşlemenin (NLP) Yeri (The Role of Natural Language Processing (NLP) in Accounting and Auditing)

Bu çalışma, yapay zekâteknolojisi tekniklerinden biri olan doğal dil işlemeninmuhasebe ve denetimde kullanımının mümkün olup-olamayacağı noktasından hareketle ortaya çıkmıştır. Bu doğrultuda çalışmada doğal dil işleme süreci ayrıntılarıyla açıklanmıştır. Doğal dil işlemenin kullanılmış olduğu uygulama örnekleri ve konuyla ilgili literatür kapsamlı bir şekilde incelenmiş ve sunulmuştur. Araştırma sonuçlarından ve doğal dil işlemenin uygulanabilme kapasitesinden yola çıkarak muhasebe ve denetim alanına yönelik öneriler sunulmuştur. Çalışma, yazılımcılar için yeni bir sektör alternatifi olmasının yanında muhasebe uygulayıcılarına, denetim firmalarınave işletmelere de farklı bir bakış açısı sunmaktadır.

The Role of Natural Language Processing during the COVID-19 Pandemic: Health Applications, Opportunities, and Challenges.

The COVID-19 pandemic is the most devastating public health crisis in at least a century and has affected the lives of billions of people worldwide in unprecedented ways. Compared to pandemics of this scale in the past, societies are now equipped with advanced technologies that can mitigate the impacts of pandemics if utilized appropriately. However, opportunities are currently not fully utilized, particularly at the intersection of data science and health. Health-related big data and technological advances have the potential to significantly aid the fight against such pandemics, including the current pandemic's ongoing and long-term impacts. Specifically, the field of natural language processing (NLP) has enormous potential at a time when vast amounts of text-based data are continuously generated from a multitude of sources, such as health/hospital systems, published medical literature, and social media. Effectively mitigating the impacts of the pandemic requires tackling challenges associated with the application and deployment of NLP systems. In this paper, we review the applications of NLP to address diverse aspects of the COVID-19 pandemic. We outline key NLP-related advances on a chosen set of topics reported in the literature and discuss the opportunities and challenges associated with applying NLP during the current pandemic and future ones. These opportunities and challenges can guide future research aimed at improving the current health and social response systems and pandemic preparedness.

Natural Language Processing: from Bedside to Everywhere

Owing to the rapid progress of natural language processing (NLP), the role of NLP in the medical field has radically gained considerable attention from both NLP and medical informatics. Although numerous medical NLP papers are published annually, there is still a gap between basic NLP research and practical product development. This gap raises questions, such as what has medical NLP achieved in each medical field, and what is the burden for the practical use of NLP? This paper aims to clarify the above questions. We explore the literature on potential NLP products/services applied to various medical/clinical/healthcare areas. This paper introduces clinical applications (bedside applications), in which we introduce the use of NLP for each clinical department, internal medicine, pre-surgery, post-surgery, oncology, radiology, pathology, psychiatry, rehabilitation, obstetrics, and gynecology. Also, we clarify technical problems to be addressed for encouraging bedside applications based on NLP. These results contribute to discussions regarding potentially feasible NLP applications and highlight research gaps for future studies.

Utilizing Natural Language Processing (NLP) to identify breast cancer associated-lung metastases from pathology reports to delineate characteristics and challenges of this common site of breast cancer recurrence.

e13592 Background: NLP (artificial intelligence) can automate the identification of records in large datasets. The purpose of this study was to evaluate the feasibility of NLP to identify breast cancer-associated lung metastases to understand clinical characteristics and challenges posed by this common site of breast cancer recurrence. Methods: Patients with pathologically confirmed breast cancer associated-lung metastases seen at a large academic center between 3/2012-5/2019 were identified using NLP of institutional pathology reports, with an IRB approved protocol. Chart review was performed to confirm breast cancer associated-lung metastases and determine clinical and pathological features. Results: Using NLP, 32 patients with pathology reports denoting breast cancer associated-lung metastases were identified, with pathologic confirmation of lung biopsy tissue in the majority of cases (24), and pleural fluid specimens (8) on the remainder. Ten of 32 (31%) were HR+/HER2-, 3/32 (9.3%) HER2+, and 19/32 (59%) TNBC. The majority were invasive ductal carcinoma (21/26) with the remainder invasive lobular carcinoma (2/26) or mixed histology (3/26). Median age at lung metastasis diagnosis was 62 years (range 31-88). The median time to development of lung metastasis following primary breast cancer was 5.6 years (range 0-24.8 years). Fifty six percent of lung metastases were detected on imaging and 44% by symptoms including dyspnea, cough, or pain. Tumor tissue genotyping results on the lung metastases were available for 8 patients showing PI3KCA (5), TP53 (3), SMARCA4 (2), ERBB2 (1), FGFR3 (1), ATM (1), CDK4 (1), MYC (1), and ESR1 (1). Treatment after diagnosis of lung metastases included hormone therapy (61%), chemotherapy (84%), lung irradiation (26%), and surgical resection of lung metastases (6%). Lung metastases were associated with considerable morbidity including pleural effusion (15%), dyspnea (6%), pneumothorax (3%), hemothorax (3%), and atelectasis (3%). Patients diagnosed with lung metastases had brain (32%), bone (35%), renal (6%), skin (3%) and adrenal (3%) metastases during disease course. Conclusions: NLP can help identify organ specific metastases from pathology reports, such as breast cancer associated-lung metastases, which can then facilitate observational, translational, and clinical research to characterize and address challenges posed by this common site of breast cancer recurrence. This cohort of patients highlights the morbidity of breast cancer associated-lung metastases and potential role of NLP for disease characterization and clinical research. (Support from ASCO Medical Student Rotation for Underrepresented Populations Award.)

WITHDRAWN: A study on the role of natural language processing in the healthcare sector

Abstract During the COVID-19 pandemic, Natural Language Processing (NLP) plays a vital role in detecting the disease, stabilizing the intensive care, discovering the drugs and controlling the spreading of infections. It energizes chat programs to assist in the reduction of outbreaks during the initial stages of coronavirus infection. NLP technologies have reached new heights in terms of utility, and are at the heart of the success of a multilingual conversation system, Chatbots, and Deep learning language models. It supports a total of 13 languages around the world. NLP powered AI such as Health map and Cobweb platforms track patient requests and perform incident detections. This chapter looks at the role of NLP and its technologies, challenges, and future possibilities using AI and machine learning for crisis mitigation and easier EHRs in the health care industry.

Identify Patients with Congestive Heart Failure through Analyzing Free-Text Clinical Notes

IntroductionA number of challenges exist in analyzing unstructured free text data in electronic medical records (EMRs). EMR text are difficult to represent and model due to their high dimensionality, heterogeneity, sparsity, incompleteness, random errors and the presence of noise.
 Objectives and ApproachStandard Natural Language Processing (NLP) tools make errors when applied to clinical notes due to physician use of unconventional language, involving polysemy, abbreviations, ambiguity, misspelling, variations, and negation.
 This paper presents a novel NLP framework, “Clinical Learning On Natural Expression” (CLONE), to automatically learn from a large primary care EMR database, analyzing free text clinical notes from primary care practices. CLONE’s predictive clinical models using text mining and neural network approach to extract features to identify patterns. To demonstrate effectiveness, we evaluate CLONE’s ability in a case study to identify patients with a specific chronic condition: congestive heart failure (CHF).
 ResultsA random selected sample of 7500 patients from Electronic Medical Record Administrative data Linked Database (EMRALD) is used. In this dataset, each patient’s medical chart includes a reference standard, manually reviewed by medical practitioners. Prevalence of CHF is approximately 2%. The low prevalence leads to another challenging problem in machine learning: imbalanced datasets. After pre-processing, we build deep learning models to represent and extract important medical information from free text to identify CHF patients through analyzing patient charts. We evaluated the effectiveness of CLONE by comparing the predicted labels with the standard references on a holdout test dataset. Comparing it with a number of alternative algorithms, we improve the overall accuracy to over 90% on a test dataset.
 Conclusion/ImplicationsAs the role of NLP in EMR data expands, the CLONE natural language processing framework can lead to substantial reduction in manual processing, while improving predictive accuracy.

Role of Natural Language Processing in Social Media

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Role of NLP in Indian Regional Languages

The NLP is closer for interfacing among the peoples knowing different languages. If we consider an example of India there are various peoples talking in various languages. Huge literature is available in different local languages which is not understandable to others in India itself. So we can use Information technology for Natural Language Processing. Natural language processing (NLP) is a field of computer science and linguistics concerned with the interactions between computers and human (natural) languages; it began as a branch of artificial intelligence. In theory, natural language processing is a very attractive method of human-computer interaction. Natural language understanding is sometimes referred to as an AI-complete problem because it seems to require extensive knowledge about the outside world and the ability to manipulate it. Modern NLP algorithms are grounded in machine learning, especially statistical machine learning. Research into modern statistical NLP algorithms requires an understanding of a number of disparate fields, including linguistics, computer science, and statistics. In this paper we want to study on Role of NLP for Indian Language conversions, like Marathi to Hindi, Hindi to Gujarati etc. If we observe the different languages in India they look similar in different aspects like Grammar, Words, and Alphabets. This paper will discuss the solutions available, problems and challenges in Indian Language conversions.

Differentiation of ileostomy from colostomy procedures: Assessing the accuracy of current procedural terminology codes and the utility of natural language processing

Large databases provide a wealth of information for researchers, but identifying patient cohorts often relies on the use of current procedural terminology (CPT) codes. In particular, studies of stoma surgery have been limited by the accuracy of CPT codes in identifying and differentiating ileostomy procedures from colostomy procedures. It is important to make this distinction because the prevalence of complications associated with stoma formation and reversal differ dramatically between types of stoma. Natural language processing (NLP) is a process that allows text-based searching. The Automated Retrieval Console is an NLP-based software that allows investigators to design and perform NLP-assisted document classification. In this study, we evaluated the role of CPT codes and NLP in differentiating ileostomy from colostomy procedures. Using CPT codes, we conducted a retrospective study that identified all patients undergoing a stoma-related procedure at a single institution between January 2005 and December 2011. All operative reports during this time were reviewed manually to abstract the following variables: formation or reversal and ileostomy or colostomy. Sensitivity and specificity for validation of the CPT codes against the mastery surgery schedule were calculated. Operative reports were evaluated by use of NLP to differentiate ileostomy- from colostomy-related procedures. Sensitivity and specificity for identifying patients with ileostomy or colostomy procedures were calculated for CPT codes and NLP for the entire cohort. CPT codes performed well in identifying stoma procedures (sensitivity 87.4%, specificity 97.5%). A total of 664 stoma procedures were identified by CPT codes between 2005 and 2011. The CPT codes were adequate in identifying stoma formation (sensitivity 97.7%, specificity 72.4%) and stoma reversal (sensitivity 74.1%, specificity 98.7%), but they were inadequate in identifying ileostomy (sensitivity 35.0%, specificity 88.1%) and colostomy (75.2% and 80.9%). NLP performed with greater sensitivity, specificity, and accuracy than CPT codes in identifying stoma procedures and stoma types. Major differences where NLP outperformed CPT included identifying ileostomy (specificity 95.8%, sensitivity 88.3%, and accuracy 91.5%) and colostomy (97.6%, 90.5%, and 92.8%, respectively). CPT codes can identify effectively patients who have had stoma procedures and are adequate in distinguishing between formation and reversal; however, CPT codes cannot differentiate ileostomy from colostomy. NLP can be used to differentiate between ileostomy- and colostomy-related procedures. The role of NLP in conjunction with electronic medical records in data retrieval warrants further investigation.

Realizing the full potential of electronic health records: the role of natural language processing

Meaningful use of electronic health records (EHRs) for patient care or for research requires data to be comparable. Many portions of EHRs continue to be unstructured, presenting significant challenges for biomedical informatics. This issue of the journal displays several solutions to this problem that are based on natural language processing (NLP) techniques. A high-level review by Nadkarni ( see page 544 ) is intended to introduce the main components of NLP for the novice, and to briefly describe machine learning methods that are successfully being employed in the field. It includes a discussion on Watson, a contestant on ‘Jeopardy!,’ a popular question-and-answer TV show, and the ensuing speculations about its potential extensions to medical NLP. However, despite some notable examples of successful NLP applications in clinical care, progress in the field has been relatively slow. Chapman and colleagues ( see page 540 ) discuss the need to steer current NLP research efforts so that new developments can be accelerated, and research products can become readily usable in healthcare applications. The authors advocate for a concerted, collaborative effort to develop open-source software components and infrastructure to share annotated data and tools. The guest editorial raises important questions …

Natural Language Processing for Radiology (Part 2)

Natural language processing (NLP) is a field of study focused on understanding the full meaning of spoken or written text, using various computational techniques. To fully realize the potential benefits of electronic medical records and radiology information systems that contain mostly unstructured text, the role of NLP in health care has expanded greatly in the previous decades and has permeated clinical practice. Generally, NLP in clinical applications is used for computerized decision making, evaluating appropriateness of care for quality improvement initiatives, structuring data for clinical research, and data processing to support educational programs. Several NLP applications focus specifically on recognizing clinical findings in radiology reports (eg, fractures, tumors) and identifying normal reports.

Natural language processing for the development of a clinical registry: a validation study in intraductal papillary mucinous neoplasms

BackgroundMedical natural language processing (NLP) systems have been developed to identify, extract and encode information within clinical narrative text. However, the role of NLP in clinical research and patient care remains limited. Pancreatic cysts are common. Some pancreatic cysts, such as intraductal papillary mucinous neoplasms (IPMNs), have malignant potential and require extended periods of surveillance. We seek to develop a novel NLP system that could be applied in our clinical network to develop a functional registry of IPMN patients. ObjectivesThis study aims to validate the accuracy of our novel NLP system in the identification of surgical patients with pathologically confirmed IPMN in comparison with our pre-existing manually created surgical database (standard reference). MethodsThe Regenstrief EXtraction Tool (REX) was used to extract pancreatic cyst patient data from medical text files from Indiana University Health. The system was assessed periodically by direct sampling and review of medical records. Results were compared with the standard reference. ResultsNatural language processing detected 5694 unique patients with pancreas cysts, in 215 of whom surgical pathology had confirmed IPMN. The NLP software identified all but seven patients present in the surgical database and identified an additional 37 IPMN patients not previously included in the surgical database. Using the standard reference, the sensitivity of the NLP program was 97.5% (95% confidence interval [CI] 94.8–98.9%) and its positive predictive value was 95.5% (95% CI 92.3–97.5%). ConclusionsNatural language processing is a reliable and accurate method for identifying selected patient cohorts and may facilitate the identification and follow-up of patients with IPMN.

Epidemiology of angina pectoris: Role of natural language processing of the medical record

The diagnosis of angina is challenging because it relies on symptom descriptions. Natural language processing (NLP) of the electronic medical record (EMR) can provide access to such information contained in free text that may not be fully captured by conventional diagnostic coding. To test the hypothesis that NLP of the EMR improves angina pectoris ascertainment over diagnostic codes. Billing records of inpatients and outpatients were searched for International Classification of Diseases, Ninth Revision (ICD-9) codes for angina pectoris, chronic ischemic heart disease, and chest pain. EMR clinical reports were searched electronically for 50 specific nonnegated natural language synonyms to these ICD-9 codes. The 2 methods were compared to a standardized assessment of angina by Rose questionnaire for 3 diagnostic levels: unspecified chest pain, exertional chest pain, and Rose angina. Compared with the Rose questionnaire, the true-positive rate of EMR-NLP for unspecified chest pain was 62% (95% CI 55-67) versus 51% (95% CI 44-58) for diagnostic codes (P < .001). For exertional chest pain, the EMR-NLP true-positive rate was 71% (95% CI 61-80) versus 62% (95% CI 52-73) for diagnostic codes (P = .10). Both approaches had 88% (95% CI 65-100) true-positive rate for Rose angina. The EMR-NLP method consistently identified more patients with exertional chest pain over a 28-month follow-up. EMR-NLP method improves the detection of unspecified and exertional chest pain cases compared to diagnostic codes. These findings have implications for epidemiological and clinical studies of angina pectoris.

Present and future trends with NLP

This paper reflects some desiderata on the role of Natural Language Processing (NLP) in the coming years as foreseen in the medical domain. During the Medical Informatics Europe (MIE) conference in 1997, the NLP track was composed by numerous papers on natural language, knowledge representation, nomenclatures and classifications. Indeed, the medical community is looking for solutions for the future which are going to emerge from more powerful desktop and for which the successful softwares of tomorrow are not yet identified. Presently this same community’s needs remains unsatisfied with healthcare professionals writing at best their patient medical records in a text processing system. We stand today closer to the typewriter than to any modern solution which could radically transform the treatment of information related to patients. What kind of hurdles are there still in front of us before we reach this new territory?

The role of natural-language processing in multimedia

The role of natural-language processing in multimedia

Natural language processing techniques in computer-assisted language learning: Status and instructional issues

The role of natural language processing (NLP) techniques, such as parsing and semantic analysis, is described within current language tutoring systems. Significant trends are distinguished in the exploitation of these techniques, design issues and tradeoffs are examined, and current and potential contributions of NLP technology are discussed with respect to instructional theory and educational practice. Limitations and problems are addressed in using NLP tools for teaching, and approaches to assessment are considered.