Natural Language Processing Algorithms Research Articles

Identifying Metabolic Dysfunction Associated Steatotic Liver Disease Using Natural Language Processing in a U.S National Cohort

Background and Aims: Although metabolic dysfunction-associated steatotic liver disease (MASLD) and MASLD with increased alcohol intake (MetALD) are identified as clinical entities, tools to identify patients from electronic health records (EHRs) to perform large outcome studies are lacking. Methods: In this retrospective study of participants from the Veterans Analysis of Liver Disease (VALID) cohort assembled from 1/1/2013 to 12/31/2022, a rule-based natural language processing (NLP) algorithm searched EHRs for imaging evidence of hepatic steatosis. This was combined with identification of cardiometabolic risk factors (CMRF) and harmful alcohol use. Algorithm-derived diagnoses of MASLD, MetALD, alcohol-associated steatotic liver disease (ALD), and no steatotic liver disease (SLD) were validated using a blinded review of randomly selected charts. Results: Among 817,657 eligible Veterans, SLD was present in over half (n=438,209, 53.5%), including MASLD in 299,259 (36.5%), 99,163 with MetALD (12.1%), 38,552 (4.7%) with ALD. The NLP algorithm had a high correlation with steatosis on chart review, with a kappa of 0.86 (95% CI 0.82-0.90), sensitivity of 0.96 and specificity of 0.90. Classification of MASLD, MetALD, ALD and no SLD by the algorithm also showed high correlation with chart review, with a kappa of 0.87 (95% CI 0.82-0.91). This algorithm identified 299,259 (36.5%) of the study cohort with MASLD, compared to 23,218 (2.8%) patients identified using ICD 9/10 codes. Conclusions and Relevance: An algorithm combining rule-based NLP with CMRFs and alcohol use from EHRs accurately identifies and classifies SLD and can be applied in large epidemiologic studies of SLD in the VHA.

Fair and Ethical Resume Screening: Enhancing ATS with JustScreen the ResumeScreeningApp

Abstract— In today's fast-paced job market, the efficiency and fairness of the resume screening process are paramount. "JustScreen" emerges as a cutting-edge solution leveraging advanced Natural Language Processing (NLP) to automate resume evaluation, thus eliminating biases and promoting merit-based candidate selection. This thesis explores JustScreen's innovative approach to integrating NLP and machine learning algorithms to enhance the recruitment workflow, ensuring a more streamlined, unbiased, and efficient candidate assessment process. The methodology involves several key components: data preprocessing, NLP information extraction, fairness metrics calculation, bias mitigation, and interpretability techniques. By utilizing frameworks such as spaCy for NLP tasks, JustScreen aims to overcome the challenges of traditional manual screening processes, improving both accuracy and fairness. This thesis explores the transition from developing a full Application Tracking System (ATS) to creating a powerful enhancement for existing ATS systems. The ResumeScreeningApp/ JustScreen integrates generative AI to provide comprehensive resume analysis, adding significant value to traditional ATS functionalities. Initial evaluations indicate a significant advancement in talent acquisition practices, promoting equal opportunities and reducing the impact of potentially discriminatory factors. This research signifies a transformative shift in recruitment, setting new standards for ethical and efficient hiring practices using Generative AI.

Race and Sex Disparities in Metabolic/Bariatric Surgery over 20 Years: A Cohort Study

Objectives: To determine how rates of discussion of metabolic and bariatric surgery (MBS) between patients with class 2 obesity and higher and their healthcare providers as well as subsequent progression to MBS have varied by race and sex over the last 20 years. Background: Obesity is a growing public health crisis. MBS remains the most effective treatment for long-term management of obesity. Methods: Retrospective cohort study of electronic health records from a large tertiary academic health system using a previously validated natural language processing algorithm. The primary outcomes were discussion of MBS between eligible patients and providers and progression to surgery. Multivariable logistic regression was used to determine the association between sociodemographic factors, time, and discussion and receipt of MBS. Results: Among 122,487 patients included in the analysis, 11,094 (9.1%) patients discussed MBS with their providers. Of these, 1348 (12.2%) progressed to MBS. Between 2000 and 2020 the annual fraction of patients who had a discussion of MBS with their providers rose from 3.2% to 10.0% (P < 0.001). Black people were equally likely to discuss MBS with providers as non-Black people (9.5 vs 9.0%, P = 0.09) but were less likely to progress to MBS (8.4 vs 12.6%, P < 0.001). These disparities in progression narrowed over the study period (P = 0.044). Men were less likely than women to both discuss (8.1% vs 9.6%, P < 0.001) and receive MBS (7.6% vs 14.6%, P < 0.001), and these disparities increased over the study period. Conclusions: Interventions to reduce racial disparities in utilization of MBS should focus on progression from discussion to MBS. Interventions to increase the uptake of MBS among men are required across the care cascade.

Leveraging Artificial Intelligence as a Safety Net for Incidentally Identified Lung Nodules at a Tertiary Center.

Artificial intelligence (AI)-powered platforms may be used to ensure that clinically significant lung nodules receive appropriate management. We studied the impact of a commercially available AI natural language processing tool on detection of clinically significant indeterminate pulmonary nodules (IPNs) based on radiology reports and provision of guideline-consistent care. All computed tomography (CT) scans performed at a single tertiary care center in the outpatient or emergency room setting between 20-Feb-2024 and 20-March-2024 were processed by the AI natural language processing algorithm. CT radiology reports mentioning a lung nodule or focal indeterminate lesion were flagged. All flagged reports were reviewed by a lung nodule expert two weeks after nodule identification. IPNs were classified as "appropriately followed" if follow-up imaging, referral to a nodule clinic, or other guideline-consistent care was ordered. IPNs were classified as "not appropriately followed" if no acknowledgement of the reported nodule was documented in the electronic health record within two weeks of being flagged. The AI software processed 76,507 unique radiology reports, identified 2,585 CT scans with chest imaging, and found 389 IPNs. Review determined that 272 (70%) nodules were appropriately followed while 117 (30%) were not appropriately followed. Of the 117 nodules without documented follow-up, 67 (57%) were > 8mm and 24 (20.5%) were > 15mm. IPNs that would not have received follow-up in the absence of the AI software generated 43 additional clinical appointments and 3 procedures. At a large tertiary care center, 30% of clinically significant incidental pulmonary nodules that would have otherwise been missed were brought to the attention of lung nodule clinicians by an AI software, allowing for initiation of appropriate follow-up.

Optimising source code vulnerability detection using deep learning and deep graph network

To enhance the effectiveness of vulnerability detection in software developed using C and C++ programming languages, our study introduces a novel correlation calculation method for analyzing and evaluating Code Property Graphs (CPG). The intelligent computation method proposed in this study comprises three key stages. In the first stage, we present a method for extracting features from the CPG source code. To accomplish this, we integrate three distinct data exploration methods: employing Graph Convolutional Neural (GCN) to extract node features from CPG, utilizing Convolutional Neural Network (CNN) to extract edge features from CPG, and finally employing the Doc2vec natural language processing algorithm to extract source code from CPG nodes. The second stage involves proposing a method for synthesizing CPG source code features. Building on the features acquired in the first stage, our paper introduces a synthesis and construction method to generate feature vectors for the source code. The final stage, stage three, executes the detection of source code vulnerabilities. The experimental results demonstrate that our proposed model in this study achieves higher efficiency compared to other studies, with an improvement ranging from 3% to 4%.

ChatGPT-4.0 in oral and maxillofacial radiology: prediction of anatomical and pathological conditions from radiographic images.

ChatGPT has the ability to generate human-like text, analyze and understand medical images using natural Language processing (NLP) algorithms. It can generate real-time diagnosis and recognize patterns and learn from previous cases to improve accuracy by combining patient history, symptoms, and image characteristics. It has been used recently for learning about maxillofacial diseases, writing and translating radiology reports, and identifying anatomical landmarks, among other things.

Porter 6: Protein Secondary Structure Prediction by Leveraging Pre-Trained Language Models (PLMs)

Accurately predicting protein secondary structure (PSSP) is crucial for understanding protein function, which is foundational to advancements in drug development, disease treatment, and biotechnology. Researchers gain critical insights into protein folding and function within cells by predicting protein secondary structures. The advent of deep learning models, capable of processing complex sequence data and identifying meaningful patterns, offer substantial potential to enhance the accuracy and efficiency of protein structure predictions. In particular, recent breakthroughs in deep learning—driven by the integration of natural language processing (NLP) algorithms—have significantly advanced the field of protein research. Inspired by the remarkable success of NLP techniques, this study harnesses the power of pre-trained language models (PLMs) to advance PSSP prediction. We conduct a comprehensive evaluation of various deep learning models trained on distinct sequence embeddings, including one-hot encoding and PLM-based approaches such as ProtTrans and ESM-2, to develop a cutting-edge prediction system optimized for accuracy and computational efficiency. Our proposed model, Porter 6, is an ensemble of CBRNN-based predictors, leveraging the protein language model ESM-2 as input features. Porter 6 achieves outstanding performance on large-scale, independent test sets. On a 2022 test set, the model attains an impressive 86.60% accuracy in three-state (Q3) and 76.43% in eight-state (Q8) classifications. When tested on a more recent 2024 test set, Porter 6 maintains robust performance, achieving 84.56% in Q3 and 74.18% in Q8 classifications. This represents a significant 3% improvement over its predecessor, outperforming or matching state-of-the-art approaches in the field.

Developing an AI-Powered Interactive Virtual Tutor for Enhanced Learning Experiences

The integration of artificial intelligence (AI) in education has opened new avenues for enhancing personalized learning experiences. This paper proposes the development of an AI-powered interactive virtual tutor designed to support students throughout their educational journey. The virtual tutor leverages advanced natural language processing (NLP) algorithms, sentiment analysis, and machine learning to engage students in real-time, providing tailored guidance, explanations, and feedback. By analyzing students' learning patterns, emotional states, and progress, the AI tutor offers personalized recommendations and interventions, enhancing both cognitive and emotional aspects of learning. The system’s interactive features, including voice recognition and conversational AI, allow students to interact naturally, facilitating a more engaging and immersive learning experience. This paper also presents the architecture of the proposed virtual tutor, key technologies involved, and its potential impact on student learning outcomes. Initial results demonstrate significant improvements in student engagement, satisfaction, and academic performance, suggesting that AI-driven virtual tutors could revolutionize personalized education..

Understanding Daily Care Experience Preferences Across the Lifespan of Older Adults: Application of Natural Language Processing.

Older adults are a heterogeneous group, and their care experience preferences are likely to be diverse and individualized. Thus, the aim of this study was to identify categories of older adults' care experience preferences and to examine similarities and differences across different age groups. The initial categories of older adults' care experience preferences were identified through a qualitative review of narrative text (n = 3134) in the ADVault data set. A natural language processing (NLP) algorithm was used to automatically and accurately define older adults' care experience preference categories. Descriptive statistics were used to examine similarities and differences in care experience preference categories across different age groups. The overall average performance of NLP algorithms was relatively high (average F-score = 0.88; range: 0.77-0.96). Through a qualitative review of 350 randomly selected texts, a total of 11 categories were identified. The most frequent category was music, followed by photographs, entertainment, family/friends, religion-related, atmosphere, flower/plants, pet, bed/bedding, hobby, and other. After applying the best performing NLP algorithm to each category, older adults' care experience preference categories were music (41.32%), followed by photographs (28.47%), entertainment (13.46%), religion-related (n = 12.69%), pet (12.22%), flower/plants (11.51%), family/friends (8.45%), atmosphere (7.75%), bed/bedding (6.12%), and hobby (5.45%). Young-old and old-old adults had similar care experience preferences with music being the most frequent category while old-old adults had photographs as the most frequent category for their care experience preference. Clinicians must understand the distinct categories of care experience preferences and incorporate them into personalized care planning.

Natural Language Processing to Identify Infants Aged 90 Days and Younger With Fevers Prior to Presentation.

Natural language processing (NLP) can enhance research studies for febrile infants by more comprehensive cohort identification. We aimed to refine and validate an NLP algorithm to identify and extract quantified temperature measurements from infants aged 90 days and younger with fevers at home or clinics prior to emergency department (ED) visits. We conducted a cross-sectional study using electronic health record (EHR) data from 17 EDs in 10 health systems that are part of the Pediatric Emergency Care Applied Research Network Registry. All visits between January 1, 2012, and May 31, 2023, for infants aged 90 days and younger were eligible, excluding those with trauma-related diagnoses. We iteratively refined a prespecified rules-based NLP algorithm in 7 successive samples of 200 visits and validated the algorithm on a held-out sample of 500 visits. The reference standard for pre-ED quantified temperature measurements was a temperature documented in clinical notes, excluding ED vital sign temperatures. In our final sample, 113 of 500 visits (23%) had quantified temperature measurements. The NLP algorithm had sensitivity 95% (95% CI: 88%-98%), specificity 96% (95% CI: 93%-97%), and positive predictive value 86% (95% CI: 78%-91%). When applying rules to exclude temperatures that may have been noted more than 24 hours previously, the NLP algorithm had lower sensitivity (88%; 95% CI: 81%-93%) but similar specificity (97%; 95% CI: 95%-98%). This highly accurate NLP algorithm can identify febrile infants without documented fevers in the ED to facilitate their inclusion in large studies using EHR data.

Artificial Intelligence Tools for Preservation and Popularization of Cultural Heritage

The purpose of the study is to analyze the possibilities of using artificial intelligence tools for the preservation and popularization of cultural heritage, as well as to study their impact on the efficiency of cultural institutions, such as libraries, museums and archives. Research methods. The work uses an interdisciplinary approach, which includes: analysis of modern AI technologies, such as natural language processing (NLP), computer vision and deep learning algorithms, as well as a review of cases of using artificial intelligence in cultural institutions, in particular the HYPERION project. Scientific novelty. The work systematizes data on the implementation of artificial intelligence technologies in cultural institutions for the first time, focusing on their impact on the preservation of digital resources. New aspects of the use of artificial intelligence have been identified, in particular for the automated creation of metadata, the restoration of damaged historical objects and the development of interactive exhibitions based on augmented and virtual reality. Conclusions. Artificial intelligence is becoming an integral element of modern cultural institutions. Its use contributes to the automation of cataloguing and digitization processes; increasing the accuracy of search systems and the relevance of results; creating innovative approaches to interacting with the audience through virtual tours and personalized recommendations. Despite significant advantages, it remains important to take into account ethical challenges, in particular ensuring the authenticity and confidentiality of data. The integration of AI into cultural heritage management has significant potential for its preservation and popularization, which makes culture accessible to a wider audience and ensures its adaptation to the needs of modern society.

Image Text to Speech Conversion in Desired Language and Summarization with Raspberry PI

In recent years, the integration of artificial intelligence and embedded systems has gained significant traction, enabling the development of efficient and user-friendly solutions. ourproject focuses on building a system capable of extracting text from images, converting the extracted content into speech in a desired language, and providing concise summaries, all powered by a Raspberry Pi. The system employs Optical Character Recognition (OCR) for text extraction, a text-to-speech engine for audio synthesis, and natural language processing techniques for summarization. Designed with accessibility and versatility in mind, the system can assist individuals with visual impairments, language barriers, or those seeking quick comprehension of extensive information. By leveraging the computational efficiency and affordability of the Raspberry Pi, the proposed solution aims to deliver a portable, cost- effective, and scalable platform for text and audio processing applications. Advancements in embedded systems and artificial intelligence have enabled the development of innovative solutions that enhance accessibility and improve user experience. ourproject presents a multifunctional system designed to process images, extract text, convert the text into speech in the desired language, and summarize the content, utilizing the Raspberry Pi as a cost-effective and portable platform. The system integrates Optical Character Recognition (OCR) for accurately identifying text within images, a text-to-speech synthesis engine for converting text to audio output, and natural language processing (NLP) algorithms to generate concise and meaningful summaries. Our approach offers significant benefits, including aiding visually impaired individuals, breaking down language barriers, and streamlining content consumption for users seeking quick comprehension of extensive material. The choice of the Raspberry Pi ensures affordability, energy efficiency, and scalability, making it suitable for various real-world applications. The proposed solution incorporates multilingual support, enabling audio output in a language of the user’s choice, thereby enhancing its versatility. The project also explores optimization techniques to ensure real-time performance, despite the hardware constraints of the Raspberry Pi. Applications of INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT (IJSREM) VOLUME: 08 ISSUE: 12 | DEC - 2024 SJIF RATING: 8.448 ISSN: 2582-3930 © 2024, IJSREM | www.ijsrem.com DOI: 10.55041/IJSREM39818 | Page 2 oursystem span education, accessibility tools, document digitization, and language learning. By leveraging open- source tools and libraries, the project underscores the potential of embedded systems in democratizing technology for a broader audience. Keywords Artificial Intelligence (AI),Embedded Systems,Raspberry Pi,Optical Character Recognition (OCR),Text-to-Speech (TTS),Natural Language Processing (NLP),Multilingual Support,Accessibility,Real-timeOptimization,Image Processing,ContentSummarization,Cost-effective Solutions,Scalable Systems,Open-source Tools,Document Digitization,Language Learning

Cuneiform Text Dialect Identification Using Machine Learning Algorithms and Natural Language Processing (NLP)

Due to a lack of resources and the tokenization issue, it is challenging to identify the languages inscribed in cuneiform symbols. Sumerian and six dialects of the Akkadian language-Old Babylonian, Middle Babylonian Peripheral, Standard Babylonian, Neo-Babylonian, Late Babylonian, and Neo-Assyrian-are among the seven languages and dialects written in cuneiform that need to be identified. This problem is addressed by the Cuneiform Language Identification task in VarDial 2019. This paper presents ten machine learning algorithms derived from four types of machine learning that were used (supervised, ensemble, instance-based, and Artificial Neural Network) learnings. The Support Vector Machine (SVM), Na Bayes (NB), Logistic Regression (LR), and Decision Tree (DT) algorithms within supervised learning, the K-Nearest Neighbors algorithm (KNN) within instance- based learning, the Random Forest (RF), Adaptive Boosting (Adaboost), Extreme Gradient Boosting (XGBoost), and Gradient Boosting (GB) algorithms within ensemble learning. Also, one of the natural language processing algorithms, n-gram, is used to identify the cuneiform dialect. The best result belongs to an ensemble of Random Forest classifiers working on character-level features with a macro averaged F1 score of 96%, and the best outcome for the n-grams algorithm is 0.82% of di-gram.

NLP-Driven Analysis of Pneumothorax Incidence Following Central Venous Catheter Procedures: A Data-Driven Re-Evaluation of Routine Imaging in Value-Based Medicine.

Background: This study presents a systematic approach using a natural language processing (NLP) algorithm to assess the necessity of routine imaging after central venous catheter (CVC) placement and removal. With pneumothorax being a key complication of CVC procedures, this research aims to provide evidence-based recommendations for optimizing imaging protocols and minimizing unnecessary imaging risks. Methods: We analyzed electronic health records from four university hospitals in Salzburg, Austria, focusing on X-rays performed between 2012 and 2021 following CVC procedures. A custom-built NLP algorithm identified cases of pneumothorax from radiologists' reports and clinician requests, while excluding cases with contraindications such as chest injuries, prior pneumothorax, or missing data. Chi-square tests were used to compare pneumothorax rates between CVC insertion and removal, and multivariate logistic regression identified risk factors, with a focus on age and gender. Results: This study analyzed 17,175 cases of patients aged 18 and older, with 95.4% involving CVC insertion and 4.6% involving CVC removal. Pneumothorax was observed in 106 cases post-insertion (1.3%) and in 3 cases post-removal (0.02%), with no statistically significant difference between procedures (p = 0.5025). The NLP algorithm achieved an accuracy of 93%, with a sensitivity of 97.9%, a specificity of 87.9%, and an area under the ROC curve (AUC) of 0.9283. Conclusions: The findings indicate no significant difference in pneumothorax incidence between CVC insertion and removal, supporting existing recommendations against routine imaging post-removal for asymptomatic patients and suggesting that routine imaging after CVC insertion may also be unnecessary in similar cases. This study demonstrates how advanced NLP techniques can support value-based medicine by enhancing clinical decision making and optimizing resources.

Beyond electronic health record data: leveraging natural language processing and machine learning to uncover cognitive insights from patient-nurse verbal communications.

Mild cognitive impairment and early-stage dementia significantly impact healthcare utilization and costs, yet more than half of affected patients remain underdiagnosed. This study leverages audio-recorded patient-nurse verbal communication in home healthcare settings to develop an artificial intelligence-based screening tool for early detection of cognitive decline. To develop a speech processing algorithm using routine patient-nurse verbal communication and evaluate its performance when combined with electronic health record (EHR) data in detecting early signs of cognitive decline. We analyzed 125 audio-recorded patient-nurse verbal communication for 47 patients from a major home healthcare agency in New York City. Out of 47 patients, 19 experienced symptoms associated with the onset of cognitive decline. A natural language processing algorithm was developed to extract domain-specific linguistic and interaction features from these recordings. The algorithm's performance was compared against EHR-based screening methods. Both standalone and combined data approaches were assessed using F1-score and area under the curve (AUC) metrics. The initial model using only patient-nurse verbal communication achieved an F1-score of 85 and an AUC of 86.47. The model based on EHR data achieved an F1-score of 75.56 and an AUC of 79. Combining patient-nurse verbal communication with EHR data yielded the highest performance, with an F1-score of 88.89 and an AUC of 90.23. Key linguistic indicators of cognitive decline included reduced linguistic diversity, grammatical challenges, repetition, and altered speech patterns. Incorporating audio data significantly enhanced the risk prediction models for hospitalization and emergency department visits. Routine verbal communication between patients and nurses contains critical linguistic and interactional indicators for identifying cognitive impairment. Integrating audio-recorded patient-nurse communication with EHR data provides a more comprehensive and accurate method for early detection of cognitive decline, potentially improving patient outcomes through timely interventions. This combined approach could revolutionize cognitive impairment screening in home healthcare settings.

Migration and Segregated Spaces: Analysis of Qualitative Sources Such as Wikipedia Using Artificial Intelligence

The scientific literature on residential segregation in large metropolitan areas highlights various explanatory factors, including economic, social, political, landscape, and cultural elements related to both migrant and local populations. This paper contrasts the impact of these factors individually, such as the immigrant rate and neighborhood segregation. To achieve this, a machine learning analysis was conducted on a sample of neighborhoods in the main Spanish metropolitan areas (Madrid and Barcelona), using a database created from a combination of official statistical sources and textual sources, such as Wikipedia. These texts were transformed into indexes using Natural Language Processing (NLP) and other artificial intelligence algorithms capable of interpreting images and converting them into indexes. The results indicate that the factors influencing immigrant concentration and segregation differ significantly, with crucial roles played by the urban landscape, population size, and geographic origin. While land prices showed a relationship with immigrant concentration, their effect on segregation was mediated by factors such as overcrowding, social support networks, and landscape degradation. The novel application of AI and big data, particularly through ChatGPT and Google Street View, has enhanced model predictability, contributing to the scientific literature on segregated spaces.

Using Natural Language Processing and Social Media Data to Understand the Lived Experience of People with Fibromyalgia.

Fibromyalgia has many unmet needs relating to treatment, and the delivery of effective and evidence-based healthcare is lacking. We analyzed social media conversations to understand the patients' perspectives on the lived experience of fibromyalgia, factors reported to trigger flares of pain, and the treatments being discussed, identifying barriers and opportunities to improve healthcare delivery. A non-interventional retrospective analysis accessed detail-rich conversations about fibromyalgia patients' experiences with 714,000 documents, including a fibromyalgia language tag, which were curated between May 2019 and April 2021. Data were analyzed via qualitative and quantitative analyses. Fibromyalgia conversations were found the most on Twitter and Reddit, and conversation trends remained stable over time. There were numerous environmental and modifiable triggers, ranging from the most frequent trigger of stress and anxiety to various foods. Arthritis and irritable bowel syndrome (IBS) were the most frequently associated comorbidities. Patients with fibromyalgia reported a wide range of symptoms, with pain being a cardinal feature. The massage, meditation and acupuncture domains were the most reported treatment modalities. Opportunities to improve healthcare delivered by medical providers were identified with current frustration relating to a lack of acknowledgement of their disease, minimization of symptoms and inadequately meeting their care needs. We developed a comprehensive, large-scale study which emphasizes advanced natural language processing algorithm application in real-world research design. Through the extensive encapsulation of patient perspectives, we outlined the habitual symptoms, triggers and treatment modalities which provide a durable foundation for addressing gaps in healthcare provision.

HealthSphere: Integrated AI-Powered and Blockchain Based Health Management and Support System

The healthcare sector is undergoing a significant transformation as it embraces advanced technologies to address some of the most pressing challenges related to accessibility, data security, and operational efficiency. Among these innovations, Artificial Intelligence (AI) and Blockchain technology have emerged as key enablers of enhanced healthcare delivery. AI-powered chatbots are being increasingly utilized to provide immediate, real-time assistance to patients, helping them assess symptoms, obtain basic medical advice, and access information without the need for in-person consultations. These chatbots, powered by Natural Language Processing (NLP) and Machine Learning (ML) algorithms, can efficiently handle a wide range of patient inquiries, improving accessibility and reducing the burden on healthcare providers. On the other hand, Blockchain technology is revolutionizing the way health records are managed. It offers a decentralized, secure, and transparent method of storing and sharing health data, ensuring data integrity, and empowering patients with control over their own medical information. By enabling smart contracts and secure data transactions, Blockchain ensures that health records are easily shared among authorized entities, reducing the risks of unauthorized access and data breaches. This paper provides an in-depth survey of the integration of these technologies into a unified healthcare management system. It explores the practical applications of AI chatbots in improving patient engagement, symptom management, and appointment scheduling, while also discussing how Blockchain can enhance the security, transparency, and interoperability of health records. The paper further addresses the challenges that come with implementing these technologies, including issues related to scalability, data privacy, and regulatory compliance. Finally, it proposes a scalable, secure, and user-friendly platform that combines the strengths of both AI chatbots and Blockchain to create an efficient and reliable healthcare system, poised to improve the overall delivery of healthcare services..

The construction of a digital dissemination platform for the intangible cultural heritage using convolutional neural network models.

In order to promote the digital dissemination and preservation of Chinese intangible cultural heritage, this work constructs a digital platform for its transmission. The platform integrates a range of advanced technologies, including the Densely Connected Convolutional Networks - Bottleneck and Compression model, a notable convolutional neural network, along with natural language processing algorithms, generative adversarial network algorithms, and neural collaborative filtering algorithms. The platform is validated with 224,055 publicly archived valid data records, ensuring its effectiveness and reliability. The results indicate that the heteroscedasticity-robust standard error is 0.15, and the platform achieves accuracies of 0.87 and 0.89 in 5-fold and 10-fold cross-validation, respectively, demonstrating the model's stability and predictive accuracy. Under various operating conditions, the platform's accuracy, recall, and F1-Score all exceed 0.85, with a root mean square error below 0.12, ensuring efficient and reliable recommendation performance. Regarding user experience, page load times remain steady between 6.45 and 10.25s, with bounce rates and error rates controlled between 40.88% to 58.33% and 0.05%-0.18%, respectively. A/B testing and heatmap analysis indicate that optimizing page layout significantly improves click-through and conversion rates. Additionally, in the fields of oral literature and traditional medicine, the platform achieves entity recognition accuracies of 90.2% and 91.3%, respectively. The platform also demonstrates strong coverage in the knowledge of folklore and traditional crafts, with rates reaching 92.5% and 89.8%, respectively, showcasing its advantages across different domains. This work aims to integrate cultural heritage conservation, cultural inheritance, and digital dissemination, fostering the broad transmission and preservation of intangible cultural heritage in the era of big data.

Using natural language processing and machine learning to replace human content coders.

Content analysis is a common and flexible technique to quantify and make sense of qualitative data in psychological research. However, the practical implementation of content analysis is extremely labor-intensive and subject to human coder errors. Applying natural language processing (NLP) techniques can help address these limitations. We explain and illustrate these techniques to psychological researchers. For this purpose, we first present a study exploring the creation of psychometrically meaningful predictions of human content codes. Using an existing database of human content codes, we build an NLP algorithm to validly predict those codes, at generally acceptable standards. We then conduct a Monte-Carlo simulation to model how four dataset characteristics (i.e., sample size, unlabeled proportion of cases, classification base rate, and human coder reliability) influence content classification performance. The simulation indicated that the influence of sample size and unlabeled proportion on model classification performance tended to be curvilinear. In addition, base rate and human coder reliability had a strong effect on classification performance. Finally, using these results, we offer practical recommendations to psychologists on the necessary dataset characteristics to achieve valid prediction of content codes to guide researchers on the use of NLP models to replace human coders in content analysis research. (PsycInfo Database Record (c) 2022 APA, all rights reserved).