Natural language processing and entrustable professional activity text feedback in surgery: A machine learning model of resident autonomy

Funding agencies such as the U.S. National Science Foundation (NSF), U.S. National Institutes of Health (NIH), and the Transportation Research Board (TRB) of The National Academies make their online grant databases publicly available which document a variety of information on grants that have been funded over the past few decades. In this paper, based on a quantitative analysis of the TRB’s Research In Progress (RIP) online database, we explore the feasibility of automatically estimating the appropriate funding level, given the textual description of a transportation research project. We use statistical Text Mining (TM) and Machine Learning (ML) technologies to build this model using the 14,000 or more records of the TRB’s RIP research grants big data. Several Natural Language Processing (NLP) based text representation models such as the Latent Dirichlet Allocation (LDA), Latent Semantic Indexing (LSI) and the Doc2Vec Machine Learning (ML) approach are used to vectorize the project descriptions and generate semantic vectors. Each of these representations is then used to train supervised regression models such as Random Forest (RF) regression. Out of the three latent feature generation models, we found LDA gives the least Mean Absolute Error (MAE) using 300 feature dimensions and RF regression model. However, based on the correlation coefficients, it was found that it is not very feasible to accurately predict the funding level directly from the unstructured project abstract, given the large variations in source agencies, subject areas, and funding levels. By using separate prediction models for different types of funding agencies, funding levels were better correlated with the project abstract.

In an era dominated by an unprecedented deluge of textual information, the need for effective methods to make sense of large datasets is more pressing than ever. This article takes a pragmatic approach to unraveling the intricacies of topic modeling, with a specific focus on the widely used Latent Dirichlet Allocation (LDA) algorithm. The initial segment of the article lays the groundwork by exploring the practical relevance of topic modeling in real-world scenarios. It addresses the everyday challenges faced by researchers and professionals dealing with vast amounts of unstructured text, emphasizing the potential of topic modeling to distill meaningful insights from seemingly chaotic data. Moving beyond theoretical abstraction, the article then delves into the mechanics of Latent Dirichlet Allocation. Developed in 2003 by Blei, Ng, and Jordan, LDA provides a probabilistic framework to identify latent topics within documents. The article takes a step-by-step approach to demystify LDA, offering a practical understanding of its components and the Bayesian principles governing its operation. A significant portion of the article is dedicated to the practical implementation of LDA. It provides insights into preprocessing steps, parameter tuning, and model evaluation, offering readers a hands-on guide to applying LDA in their own projects. Real-world examples and case studies showcase how LDA can be a valuable tool for tasks such as document clustering, topic summarization, and sentiment analysis. However, the journey through LDA is not without challenges, and the article candidly addresses these hurdles. Topics such as determining the optimal number of topics, the sensitivity of results to parameter settings, and the interpretability of outcomes are discussed. This realistic appraisal adds depth to the article, helping readers navigate the nuances and potential pitfalls of employing LDA in practice. Beyond the technical intricacies, the article explores the broad spectrum of applications where LDA has proven its efficacy. From text mining and information retrieval to social network analysis and healthcare informatics, LDA has left an indelible mark on diverse domains. Through practical examples, the article illustrates how LDA can be adapted to different contexts, showcasing its versatility as a tool for uncovering latent patterns. Keywords: Topic Modeling, Latent Dirichlet Allocation, Text Mining, Natural Language Processing, Document Clustering, Bayesian Inference.

Machine learning techniques have long been the foundations of speech processing. Bayesian classiflcation, decision trees, unsupervised clustering, the EM algorithm, maximum entropy, etc. are all part of existing speech recognition systems. The success of statistical speech recognition has led to the rise of statistical and empirical methods in natural language processing. Indeed, many of the machine learning techniques used in language processing, from statistical part-of-speech tagging to the noisy channel model for machine translation have roots in work conducted in the speech fleld. However, advances in learning theory and algorithmic machine learning approaches in recent years have led to signiflcant changes in the direction and emphasis of the statistical and learning centered research in natural language processing and made a mark on natural language and speech processing. Approaches such as memory based learning, a range of linear classiflers such as Boosting, SVMs and SNoW and others have been successfully applied to a broad range of natural language problems, and these now inspire new research in speech retrieval and recognition. We have seen an increasingly close collaboration between voice and language processing researchers in some of the shared tasks such as spontaneous speech recognition and understanding, voice data information extraction, and machine translation. The purpose of this special issue was to invite speech and language researchers to communicate with each other, and with the machine learning community on the latest machine learning advances in their work. The call for papers was met with great enthusiasm from the speech and natural language community. Thirty six submissions were received; each paper was reviewed by at least three reviewers. Only ten papers were selected re∞ecting not only some of the best work on machine learning in the areas of natural language and spoken language processing but also what we view as a collection of papers that represent current trends in these areas of research both from the perspective of

A density-based method for adaptive LDA model selection

This study explores how Artificial Intelligence (AI), particularly Natural Language Processing (NLP) and machine learning, can be leveraged to analyze public discourse on climate change and improve climate communication strategies. Focusing on four key questions, the research examines how AI classifies public sentiment, identifies dominant topics, detects framing structures, and generates actionable insights to inform targeted communication. Over one million climate-related posts were collected from Twitter and Facebook between January and June 2024. Sentiment analysis using a fine-tuned BERT model categorized posts into positive, negative, and neutral sentiments, while Latent Dirichlet Allocation (LDA) revealed key topics. Framing analysis employed supervised machine learning to classify posts into narrative frames, and AI-powered visualization tools were used for interpretation. The results indicate a polarized sentiment distribution: 45% negative, 35% positive, and 20% neutral. Negative posts centered on skepticism and political inaction, while positive posts supported renewable energy and activism. Thematic analysis highlighted five key topics: scientific evidence, activism, economic implications, political debate, and environmental justice. Framing analysis revealed four dominant frames—climate urgency, economic impact, political action, and environmental justice—that shape public perception. Temporal sentiment shifts aligned with major events, suggesting public discourse is responsive to political and activist developments. This research demonstrates the potential of AI to provide scalable, data-driven insights into public climate discourse. By integrating these insights into strategic planning, communicators can design more effective, emotionally resonant messages, enhancing public engagement and supporting collective climate action.

We present Topic View, an application for visually comparing and exploring multiple models of text corpora. Topic View uses multiple linked views to visually analyze both the conceptual content and the document relationships in models generated using different algorithms. To illustrate Topic View, we apply it to models created using two standard approaches: Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA). Conceptual content is compared through the combination of (i) a bipartite graph matching LSA concepts with LDA topics based on the cosine similarities of model factors and (ii) a table containing the terms for each LSA concept and LDA topic listed in decreasing order of importance. Document relationships are examined through the combination of (i) side-by-side document similarity graphs, (ii) a table listing the weights for each document's contribution to each concept/topic, and (iii) a full text reader for documents selected in either of the graphs or the table. We demonstrate the utility of Topic View's visual approach to model assessment by comparing LSA and LDA models of two example corpora.

Research publications related to the novel coronavirus disease COVID-19 are rapidly growing in number. However, current online literature hubs, even with artificial intelligence, are inadequate for identifying the relative strength of research topics. Hence, we aimed to develop a comprehensive Latent Dirichlet Allocation (LDA) topic model using natural language processing (NLP) techniques, provide visualisations for temporal trends, and apply our methodology to improve existing online literature hubs.Using the search term “COVID”, abstracts were extracted from PubMed®, from January to July 2020 (N=16346). An LDA topic model was trained on 81% of abstracts. Weekly temporal trends were visualised as a heatmap on all abstracts. Then, we tested our methodology on over 23,000 abstracts gathered from January 2020 to September 2020 from LitCovid, a literature hub from the National Center for Biotechnology Information. We use our topic model to subdivide LitCovid’s eight categories into corresponding LDA topics.The optimised LDA topic model, created using PubMed® data, produced 25 comprehensive topics with no significant overlap. There were temporal changes for topics: prominence of “Mental Health” and “Socioeconomic Impact” increased, “Genome Sequence” decreased, and “Epidemiology” remained relatively constant. We identified inadequate representation of “Airborne Transmission Protection”. Importantly, research on masks and PPE is skewed towards clinical applications with a lack of population-based epidemiological research. Our methodology, when applied to LitCovid, identified important topics within each LitCovid category. For example, “Case Report” was split into topics such as “Pulmonary” and “Oncology” as well as the under-represented topics “Haematology” and “Gastroenterology”. Our work allows for comprehensive topic identification and intuitive visualisation of temporal trends in COVID-19 research. Implementation of the methodology complements existing online literature hubs and identifies underrepresented topics such as population-based studies on masks that may be of significant public interest.Funding Statement: None to declare.Declaration of Interests: There are no conflicts of interest.

The paper focuses on the persistent conflict between Israel and Palestine. of world discussion, with such sites as Reddit containing a great deal medium for public expression. This study uses natural language processing (NLP) including valence aware dictionary and sentiment reasoner (VADER) for sentimental analysis and latent Dirichlet allocation (LDA) for topic modelling of geopolitical positions in comments of 400,000 posts belonging to the topics subreddits. A sentiment analysis using VADER shows that negative sentiments observed and indicate that such sentiments prevail throughout fear of aggression, human suffering, and threat. media biases. LDA analysis for topic modelling. The process of LDA reveals such important topics as humanitarian ones. media discourses, polemizing and appeals for calm. Furthermore, the study which divides comments into geopolitical attitudes – support for or yet, Israelis, supporters of Palestine, opponents of both, and neutrals, offering a contingency comprehensive conception of public perspectives. The results emphasize the effectiveness of large-scale. Of the big data pre-processing techniques, NLP can be used to understand geopolitics and its intricate dynamics. educate policy makers, researchers, and media planners.

IntroductionMoving evidence-based practices into the hands of practitioners requires the synthesis and translation of research literature. However, the growing pace of scientific publications across disciplines makes it increasingly difficult to stay abreast of research literature. Natural language processing (NLP) methods are emerging as a valuable strategy for conducting content analyses of academic literature. We sought to apply NLP to identify publication trends in the journal Implementation Science, including key topic clusters and the distribution of topics over time. A parallel study objective was to demonstrate how NLP can be used in research synthesis.MethodsWe examined 1711 Implementation Science abstracts published from February 22, 2006, to October 1, 2020. We retrieved the study data using PubMed’s Application Programming Interface (API) to assemble a database. Following standard preprocessing steps, we use topic modeling with Latent Dirichlet allocation (LDA) to cluster the abstracts following a minimization algorithm.ResultsWe examined 30 topics and computed topic model statistics of quality. Analyses revealed that published articles largely reflect (i) characteristics of research, or (ii) domains of practice. Emergent topic clusters encompassed key terms both salient and common to implementation science. HIV and stroke represent the most commonly published clinical areas. Systematic reviews have grown in topic prominence and coherence, whereas articles pertaining to knowledge translation (KT) have dropped in prominence since 2013. Articles on HIV and implementation effectiveness have increased in topic exclusivity over time.DiscussionWe demonstrated how NLP can be used as a synthesis and translation method to identify trends and topics across a large number of (over 1700) articles. With applicability to a variety of research domains, NLP is a promising approach to accelerate the dissemination and uptake of research literature. For future research in implementation science, we encourage the inclusion of more equity-focused studies to expand the impact of implementation science on disadvantaged communities.

We describe a novel approach to improve the accuracy of the IR which combines the tag-topic model with natural language processing. Tag-topic model extended the Latent Dirichlet We describe a novel approach to improve the accuracy of the IR which combines the tag-topic model with natural language processing. We get tag though semantic fingerprint. Tag-topic model extended the Latent Dirichlet Allocation(LDA) model by adding the data set with 901446 documents, and train the model with different number of topics, we can obtain three important distributions: the document-tag distribution, the tag-topic distribution and the topic-word distribution by using the Tag-topic model. Then through the matrix operation we can get the tag-word distribution which quantify the importance of each word of the document. Finally, based on this distribution these documents are re-ranked. Experiments on NTCIR-5 document collection for SLIR(Single Language IR) show that this method achieves an 13.6% and 19.6% improvement comparing to the initial retrieval method without any re-ranking. Keywords-Information Retrieval; Re-ranking; LDA;Tag-topic model

The way we communicate has been revolutionised by the widespread use of social networks. Any kind of online message can reach anyone in the world almost instantly. The speed with which information spreads is undoubtedly the strength of social networks, but at the same time, any user of these platforms can see how toxic messages spread in parallel with likes, comments and ratings about any person or entity. In such cases, the victim feels even more helpless and defenceless as a result of the rapid spread. For this reason, we have implemented an automatic detector of toxic messages on social media. This allows us to stop toxicity in its tracks and protect victims. In particular, the aim of the survey is to demonstrate how traditional Machine Learning methods of Natural Language Processing (NLP) work on equal terms with Deep Learning methods represented by a Transformer architecture and characterised by a higher computational cost. In particular, the paper describes the results obtained by testing different supervised Machine Learning classifiers (Logistic Regression, Random Forest and Support Vector Machine) combined with two topic-modelling techniques of NLP, (Latent Semantic Analysis and Latent Dirichlet Allocation). A pre-trained Transformer named BERTweet was also tested. All models performed well in this task, so much so that values close to or above 90% were achieved in terms of the F1 score evaluation metric. The best result achieved by Transformer BERTweet, 91.40%, was therefore not impressive in this context, as the performance gains are too small compared to the computational overhead.

In the context of deepening reform of higher education in the new era, integration of industry-education and science-education has become an important way to cultivate innovative talents. Graduation projects, an important part of a university education, currently suffer from a range of problems. To address these problems, this study innovatively proposes a dynamic optimization model for the selection of graduation project topics based on the dual-track synergy of “industry and education, science and education”. We build multi-source dynamic databases by deeply integrating Natural Language Processing (NLP), machine learning and intelligent recommendation technologies. First, the keywords are extracted from the input text using Bidirectional Encoder Representations from Transformers (BERT) and Term Frequency-Inverse Document Frequency (TF-IDF) fusion algorithms. Second, the topics were categorized according to university majors by means of the Latent Dirichlet Allocation (LDA) topic model and K-means clustering technique. Finally, we use collaborative filtering and content recommendation algorithms to achieve personalized matching of topics to students. After the matching of selected topics is completed, we have established a three-dimensional evaluation system of “supervisor-enterprisestudent”. Then the evaluation results are then fed back into the dynamic database, forming a closed-loop system. The study showed that the model significantly improved the cutting edge, practical value, and fitness of the selected topics. It provides an innovative solution to collaborative education between industry and academia, with significant theoretical and practical value for higher education development.

Latent Dirichlet Allocation (LDA) is a commonly used topic modeling method for text analysis and mining. Standard LDA treats documents as bags of words, ignoring the syntactic structures of sentences. In this paper, we propose a hybrid model that embeds hidden Markov models (HMMs) within LDA topics to jointly model both the topics and the syntactic structures within each topic. Our model is general and subsumes standard LDA and HMM as special cases. Compared with standard LDA and HMM, our model can simultaneously discover both topic-specific content words and background functional words shared among topics. Our model can also automatically separate content words that play different roles within a topic. Using perplexity as evaluation metric, our model returns lower perplexity for unseen test documents compared with standard LDA, which shows its better generalization power than LDA.

IntroductionRelatively few studies have focused on the wellbeing, experiences and needs of the siblings of children with a psychiatric diagnosis. However, the studies that have been conducted suggest that the impact of such circumstances on these siblings is significant. Studying narratives of diagnosed children or relatives has proven to be a successful approach to gain insights that could help improve care. Only a few attempts have been made to study narratives in psychiatry utilizing a machine learning approach.MethodIn this current study, 13 narratives of the experiences of siblings of children with a neurodevelopmental disorders were collected through largely unstructured interviews. The interviews were analyzed using the traditional qualitative, hermeneutic phenomenology method as well as latent Dirichlet allocation (LDA), an unsupervised machine learning method clustering words from documents into topics. One aim of this study was to evaluate the experiences of the siblings in order to find leads to improve care and support for these siblings. Furthermore, the outcomes of both analyses were compared to evaluate the role of machine learning in analyzing narratives.ResultsQualitative analysis of the interviews led to the formulation of nine main themes: confrontation with conflicts, coping strategies siblings, need for rest and time for myself, need for support and attention from personal circle, wish for normality, influence on personal choices and possibilities for development, doing things together, recommendations and advices, ambivalence and loyalty. Using unsupervised machine learning (LDA) 24 topics were formed that mostly overlapped with the qualitative themes found. Both the qualitative analysis and the LDA analysis detected themes that were unique to the respective analysis.ConclusionThe present study found that studying narratives of siblings of children with a neurodevelopmental disorder contributes to a better understanding of the subjects' experiences. Siblings cope with ambivalent feelings toward their brother or sister and this emotional conflict often leads to adapted behavior. Several coping strategies are developed to deal with the behavior of their brother or sister like seeking support or ignoring. Devoted support, time and attention from close relatives, especially parents, is needed. The LDA analysis didn't appear useful to distract meaning and context from the narratives, but it was proposed that machine learning could be a valuable and quick addition to the traditional qualitative methods by finding overlooked topics and giving a rudimental overview of topics in narratives.

If people with high risk of suicide can be identified through social media like microblog, it is possible to implement an active intervention system to save their lives. Based on this motivation, the current study administered the Suicide Probability Scale(SPS) to 1041 weibo users at Sina Weibo, which is a leading microblog service provider in China. Two NLP (Natural Language Processing) methods, the Chinese edition of Linguistic Inquiry and Word Count (LIWC) lexicon and Latent Dirichlet Allocation (LDA), are used to extract linguistic features from the Sina Weibo data. We trained predicting models by machine learning algorithm based on these two types of features, to estimate suicide probability based on linguistic features. The experiment results indicate that LDA can find topics that relate to suicide probability, and improve the performance of prediction. Our study adds value in prediction of suicidal probability of social network users with their behaviors.