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1.
Am J Prev Cardiol ; 18: 100678, 2024 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-38756692

RESUMO

Objectives: To investigate the potential value and feasibility of creating a listing system-wide registry of patients with at-risk and established Atherosclerotic Cardiovascular Disease (ASCVD) within a large healthcare system using automated data extraction methods to systematically identify burden, determinants, and the spectrum of at-risk patients to inform population health management. Additionally, the Houston Methodist Cardiovascular Disease Learning Health System (HM CVD-LHS) registry intends to create high-quality data-driven analytical insights to assess, track, and promote cardiovascular research and care. Methods: We conducted a retrospective multi-center, cohort analysis of adult patients who were seen in the outpatient settings of a large healthcare system between June 2016 - December 2022 to create an EMR-based registry. A common framework was developed to automatically extract clinical data from the EMR and then integrate it with the social determinants of health information retrieved from external sources. Microsoft's SQL Server Management Studio was used for creating multiple Extract-Transform-Load scripts and stored procedures for collecting, cleaning, storing, monitoring, reviewing, auto-updating, validating, and reporting the data based on the registry goals. Results: A real-time, programmatically deidentified, auto-updated EMR-based HM CVD-LHS registry was developed with ∼450 variables stored in multiple tables each containing information related to patient's demographics, encounters, diagnoses, vitals, labs, medication use, and comorbidities. Out of 1,171,768 adult individuals in the registry, 113,022 (9.6%) ASCVD patients were identified between June 2016 and December 2022 (mean age was 69.2 ± 12.2 years, with 55% Men and 15% Black individuals). Further, multi-level groupings of patients with laboratory test results and medication use have been analyzed for evaluating the outcomes of interest. Conclusions: HM CVD-LHS registry database was developed successfully providing the listing registry of patients with established ASCVD and those at risk. This approach empowers knowledge inference and provides support for efforts to move away from manual patient chart abstraction by suggesting that a common registry framework with a concurrent design of data collection tools and reporting rapidly extracting useful structured clinical data from EMRs for creating patient or specialty population registries.

2.
Data Brief ; 54: 110353, 2024 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-38590618

RESUMO

This paper presents the data collection method and introduces the dataset about consumers' consider-then-choose behaviors in the household vacuum cleaner market. First, we designed a questionnaire that collected participants' consideration and choice data, social network data, demographic information, and preferences for product features. In addition, we obtained data on vacuum cleaner product features through web scraping from online shopping websites. After data cleaning and processing, the resulting dataset enables investigation into customer preferences in two stages, namely the consideration and choice stages and the impact of social influence on the two-stage decision-making process. This dataset is unique as it is the first of its kind to collect both customers' revealed preferences in a two-stage decision-making process and their ego social networks. This enables the modeling of customer preferences while accounting for social influence. The published survey questionnaire can be used as a template to collect data on other products in support of customer preferences modeling and the design for market systems.

3.
Sensors (Basel) ; 24(7)2024 Mar 29.
Artigo em Inglês | MEDLINE | ID: mdl-38610412

RESUMO

Classical machine learning techniques have dominated Music Emotion Recognition. However, improvements have slowed down due to the complex and time-consuming task of handcrafting new emotionally relevant audio features. Deep learning methods have recently gained popularity in the field because of their ability to automatically learn relevant features from spectral representations of songs, eliminating such necessity. Nonetheless, there are limitations, such as the need for large amounts of quality labeled data, a common problem in MER research. To understand the effectiveness of these techniques, a comparison study using various classical machine learning and deep learning methods was conducted. The results showed that using an ensemble of a Dense Neural Network and a Convolutional Neural Network architecture resulted in a state-of-the-art 80.20% F1 score, an improvement of around 5% considering the best baseline results, concluding that future research should take advantage of both paradigms, that is, combining handcrafted features with feature learning.


Assuntos
Aprendizado Profundo , Música , Confiabilidade dos Dados , Emoções , Aprendizado de Máquina
4.
Rev Socionetwork Strateg ; 18(1): 27-47, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38646588

RESUMO

We summarize the 10th Competition on Legal Information Extraction and Entailment. In this tenth edition, the competition included four tasks on case law and statute law. The case law component includes an information retrieval task (Task 1), and the confirmation of an entailment relation between an existing case and a selected unseen case (Task 2). The statute law component includes an information retrieval task (Task 3), and an entailment/question-answering task based on retrieved civil code statutes (Task 4). Participation was open to any group based on any approach. Ten different teams participated in the case law competition tasks, most of them in more than one task. We received results from 8 teams for Task 1 (22 runs) and seven teams for Task 2 (18 runs). On the statute law task, there were 9 different teams participating, most in more than one task. 6 teams submitted a total of 16 runs for Task 3, and 9 teams submitted a total of 26 runs for Task 4. We describe the variety of approaches, our official evaluation, and analysis of our data and submission results.

5.
BMC Med Imaging ; 24(1): 86, 2024 Apr 10.
Artigo em Inglês | MEDLINE | ID: mdl-38600525

RESUMO

Medical imaging AI systems and big data analytics have attracted much attention from researchers of industry and academia. The application of medical imaging AI systems and big data analytics play an important role in the technology of content based remote sensing (CBRS) development. Environmental data, information, and analysis have been produced promptly using remote sensing (RS). The method for creating a useful digital map from an image data set is called image information extraction. Image information extraction depends on target recognition (shape and color). For low-level image attributes like texture, Classifier-based Retrieval(CR) techniques are ineffective since they categorize the input images and only return images from the determined classes of RS. The issues mentioned earlier cannot be handled by the existing expertise based on a keyword/metadata remote sensing data service model. To get over these restrictions, Fuzzy Class Membership-based Image Extraction (FCMIE), a technology developed for Content-Based Remote Sensing (CBRS), is suggested. The compensation fuzzy neural network (CFNN) is used to calculate the category label and fuzzy category membership of the query image. Use a basic and balanced weighted distance metric. Feature information extraction (FIE) enhances remote sensing image processing and autonomous information retrieval of visual content based on time-frequency meaning, such as color, texture and shape attributes of images. Hierarchical nested structure and cyclic similarity measure produce faster queries when searching. The experiment's findings indicate that applying the proposed model can have favorable outcomes for assessment measures, including Ratio of Coverage, average means precision, recall, and efficiency retrieval that are attained more effectively than the existing CR model. In the areas of feature tracking, climate forecasting, background noise reduction, and simulating nonlinear functional behaviors, CFNN has a wide range of RS applications. The proposed method CFNN-FCMIE achieves a minimum range of 4-5% for all three feature vectors, sample mean and comparison precision-recall ratio, which gives better results than the existing classifier-based retrieval model. This work provides an important reference for medical imaging artificial intelligence system and big data analysis.


Assuntos
Inteligência Artificial , Tecnologia de Sensoriamento Remoto , Humanos , Ciência de Dados , Armazenamento e Recuperação da Informação , Redes Neurais de Computação
6.
J Pathol Inform ; 15: 100375, 2024 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-38645985

RESUMO

Pathology images of histopathology can be acquired from camera-mounted microscopes or whole-slide scanners. Utilizing similarity calculations to match patients based on these images holds significant potential in research and clinical contexts. Recent advancements in search technologies allow for implicit quantification of tissue morphology across diverse primary sites, facilitating comparisons, and enabling inferences about diagnosis, and potentially prognosis, and predictions for new patients when compared against a curated database of diagnosed and treated cases. In this article, we comprehensively review the latest developments in image search technologies for histopathology, offering a concise overview tailored for computational pathology researchers seeking effective, fast, and efficient image search methods in their work.

7.
Rev Socionetwork Strateg ; 18(1): 101-121, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38646589

RESUMO

The challenge of information overload in the legal domain increases every day. The COLIEE competition has created four challenge tasks that are intended to encourage the development of systems and methods to alleviate some of that pressure: a case law retrieval (Task 1) and entailment (Task 2), and a statute law retrieval (Task 3) and entailment (Task 4). Here we describe our methods for Task 1 and Task 4. In Task 1, we used a sentence-transformer model to create a numeric representation for each case paragraph. We then created a histogram of the similarities between a query case and a candidate case. The histogram is used to build a binary classifier that decides whether a candidate case should be noticed or not. In Task 4, our approach relies on fine-tuning a pre-trained DeBERTa large language model (LLM) trained on SNLI and MultiNLI datasets. Our method for Task 4 was ranked third among eight participating teams in the COLIEE 2023 competition. For Task 4, We also compared the performance of the DeBERTa model with those of a knowledge distillation model and ensemble methods including Random Forest and Voting.

8.
J Dent Hyg ; 98(2): 51-56, 2024 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-38649289

RESUMO

This overview of the systematic review provides guidance regarding how and when to use this approach to a research question. High quality systematic reviews are essential to assist health care practitioners keep current with the large and rapidly growing body of scientific evidence. The systematic review is a transparent and reproducible synthesis of all the available evidence on a clearly defined research question or topic. Key stages in conducting a systematic review include clarification of aims and methods in a protocol, finding all of the relevant research, data collection, quality assessments, synthesizing evidence, and interpreting the findings. This short report provides examples for the various stages and steps of the systematic review research approach.


Assuntos
Projetos de Pesquisa , Revisões Sistemáticas como Assunto , Humanos , Coleta de Dados
9.
J Healthc Inform Res ; 8(2): 313-352, 2024 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-38681755

RESUMO

Clinical information retrieval (IR) plays a vital role in modern healthcare by facilitating efficient access and analysis of medical literature for clinicians and researchers. This scoping review aims to offer a comprehensive overview of the current state of clinical IR research and identify gaps and potential opportunities for future studies in this field. The main objective was to assess and analyze the existing literature on clinical IR, focusing on the methods, techniques, and tools employed for effective retrieval and analysis of medical information. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted an extensive search across databases such as Ovid Embase, Ovid Medline, Scopus, ACM Digital Library, IEEE Xplore, and Web of Science, covering publications from January 1, 2010, to January 4, 2023. The rigorous screening process led to the inclusion of 184 papers in our review. Our findings provide a detailed analysis of the clinical IR research landscape, covering aspects like publication trends, data sources, methodologies, evaluation metrics, and applications. The review identifies key research gaps in clinical IR methods such as indexing, ranking, and query expansion, offering insights and opportunities for future studies in clinical IR, thus serving as a guiding framework for upcoming research efforts in this rapidly evolving field. The study also underscores an imperative for innovative research on advanced clinical IR systems capable of fast semantic vector search and adoption of neural IR techniques for effective retrieval of information from unstructured electronic health records (EHRs). Supplementary Information: The online version contains supplementary material available at 10.1007/s41666-024-00159-4.

10.
Front Artif Intell ; 7: 1293084, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38601111

RESUMO

Recent advances in natural language processing enable more intelligent ways to support knowledge sharing in factories. In manufacturing, operating production lines has become increasingly knowledge-intensive, putting strain on a factory's capacity to train and support new operators. This paper introduces a Large Language Model (LLM)-based system designed to retrieve information from the extensive knowledge contained in factory documentation and knowledge shared by expert operators. The system aims to efficiently answer queries from operators and facilitate the sharing of new knowledge. We conducted a user study at a factory to assess its potential impact and adoption, eliciting several perceived benefits, namely, enabling quicker information retrieval and more efficient resolution of issues. However, the study also highlighted a preference for learning from a human expert when such an option is available. Furthermore, we benchmarked several commercial and open-sourced LLMs for this system. The current state-of-the-art model, GPT-4, consistently outperformed its counterparts, with open-source models trailing closely, presenting an attractive option given their data privacy and customization benefits. In summary, this work offers preliminary insights and a system design for factories considering using LLM tools for knowledge management.

11.
Bioengineering (Basel) ; 11(3)2024 Mar 08.
Artigo em Inglês | MEDLINE | ID: mdl-38534537

RESUMO

As available genomic interval data increase in scale, we require fast systems to search them. A common approach is simple string matching to compare a search term to metadata, but this is limited by incomplete or inaccurate annotations. An alternative is to compare data directly through genomic region overlap analysis, but this approach leads to challenges like sparsity, high dimensionality, and computational expense. We require novel methods to quickly and flexibly query large, messy genomic interval databases. Here, we develop a genomic interval search system using representation learning. We train numerical embeddings for a collection of region sets simultaneously with their metadata labels, capturing similarity between region sets and their metadata in a low-dimensional space. Using these learned co-embeddings, we develop a system that solves three related information retrieval tasks using embedding distance computations: retrieving region sets related to a user query string, suggesting new labels for database region sets, and retrieving database region sets similar to a query region set. We evaluate these use cases and show that jointly learned representations of region sets and metadata are a promising approach for fast, flexible, and accurate genomic region information retrieval.

12.
PeerJ Comput Sci ; 10: e1866, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38435583

RESUMO

In this article, we present CuentosIE (TalesEI: chatbot of tales with a message to develop Emotional Intelligence), an educational chatbot on emotions that also provides teachers and psychologists with a tool to monitor their students/patients through indicators and data compiled by CuentosIE. The use of "tales with a message" is justified by their simplicity and easy understanding, thanks to their moral or associated metaphors. The main contributions of CuentosIE are the selection, collection, and classification of a set of highly specialized tales, as well as the provision of tools (searching, reading comprehension, chatting, recommending, and classifying) that are useful for both educating users about emotions and monitoring their emotional development. The preliminary evaluation of the tool has obtained encouraging results, which provides an affirmative answer to the question posed in the title of the article.

13.
PeerJ Comput Sci ; 10: e1876, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38435589

RESUMO

Multilingual sentiment analysis (MSA) involves the task of comprehending people's opinions, sentiments, and emotions in multilingual written texts. This task has garnered considerable attention due to its importance in extracting insights for decision-making across diverse fields such as marketing, finance, and politics. Several studies have explored MSA using deep learning methods. Nonetheless, a majority of these studies depend on sequential-based approaches, which focus on capturing short-distance semantics within adjacent word sequences, but they overlook long-distance semantics, which can provide more profound insights for analysis. In this work, we propose an approach for multilingual sentiment analysis, namely MSA-GCN, leveraging a graph convolutional network to effectively capture both short- and long-distance semantics. MSA-GCN involves the comprehensive modeling of the multilingual sentiment analysis corpus through a unified heterogeneous text graph. Subsequently, a slightly deep graph convolutional network is employed to acquire predictive representations for all nodes by encouraging the transfer learning across languages. Extensive experiments are carried out on various language combinations using different benchmark datasets to assess the efficiency of the proposed approach. These datasets include Multilingual Amazon Reviews Corpus (MARC), Internet Movie Database (IMDB), Allociné, and Muchocine. The achieved results reveal that MSA-GCN significantly outperformed all baseline models in almost all datasets with a p-value < 0.05 based on student t-test. In addition, such approach shows prominent results in a variety of language combinations, revealing the robustness of the approach against language variation.

14.
Entropy (Basel) ; 26(3)2024 Mar 10.
Artigo em Inglês | MEDLINE | ID: mdl-38539757

RESUMO

We introduce the problem of deceptive information retrieval (DIR), in which a user wishes to download a required file out of multiple independent files stored in a system of databases while deceiving the databases by making the databases' predictions on the user-required file index incorrect with high probability. Conceptually, DIR is an extension of private information retrieval (PIR). In PIR, a user downloads a required file without revealing its index to any of the databases. The metric of deception is defined as the probability of error of databases' prediction on the user-required file, minus the corresponding probability of error in PIR. The problem is defined on time-sensitive data that keep updating from time to time. In the proposed scheme, the user deceives the databases by sending real queries to download the required file at the time of the requirement and dummy queries at multiple distinct future time instances to manipulate the probabilities of sending each query for each file requirement, using which the databases' make the predictions on the user-required file index. The proposed DIR scheme is based on a capacity achieving probabilistic PIR scheme, and achieves rates lower than the PIR capacity due to the additional downloads made to deceive the databases. When the required level of deception is zero, the proposed scheme achieves the PIR capacity.

15.
Front Res Metr Anal ; 9: 1300533, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38495828

RESUMO

Objectives: Studies on the impact of long COVID on work capacity are increasing but are difficult to locate in bibliographic databases, due to the heterogeneity of the terms used to describe this new condition and its consequences. This study aims to report on the effectiveness of different search strategies to find studies on the impact of long COVID on work participation in PubMed and to create validated search strings. Methods: We searched PubMed for articles published on Long COVID and including information about work. Relevant articles were identified and their reference lists were screened. Occupational health journals were manually scanned to identify articles that could have been missed. A total of 885 articles potentially relevant were collected and 120 were finally included in a gold standard database. Recall, Precision, and Number Needed to Read (NNR) of various keywords or combinations of keywords were assessed. Results: Overall, 123 search-words alone or in combination were tested. The highest Recalls with a single MeSH term or textword were 23 and 90%, respectively. Two different search strings were developed, one optimizing Recall while keeping Precision acceptable (Recall 98.3%, Precision 15.9%, NNR 6.3) and one optimizing Precision while keeping Recall acceptable (Recall 90.8%, Precision 26.1%, NNR 3.8). Conclusions: No single MeSH term allows to find all relevant studies on the impact of long COVID on work ability in PubMed. The use of various MeSH and non-MeSH terms in combination is required to recover such studies without being overwhelmed by irrelevant articles.

16.
J Dent Hyg ; 98(1): 78-82, 2024 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-38346895

RESUMO

This short report guides the reader through the types of narrative reviews and describes the narrative review process from conception to completion. This report is an overview on the topic of literature reviews and serves to provide guidance regarding how and when to use a narrative review approach. Authors have many purposes for selecting the narrative review of the literature including introducing an original research manuscript, reviewing a critical topic for a scholarly journal, creating an introductory chapter for a thesis, or completing a classroom assignment. Each purpose may include a specific format and may require different components to be included in the research and writing process. This short report provides examples for each section of the narrative review research and writing process.

17.
J Clin Epidemiol ; 169: 111300, 2024 Feb 24.
Artigo em Inglês | MEDLINE | ID: mdl-38402998

RESUMO

OBJECTIVES: To determine whether clinical trial register (CTR) searches can accurately identify a greater number of completed randomized clinical trials (RCTs) than electronic bibliographic database (EBD) searches for systematic reviews of interventions, and to quantify the number of eligible ongoing trials. STUDY DESIGN AND SETTING: We performed an evaluation study and based our search for RCTs on the eligibility criteria of a systematic review that focused on the underrepresentation of people with chronic kidney disease in cardiovascular RCTs. We conducted a combined search of ClinicalTrials.gov and the WHO International Clinical Trials Registry Platform through the Cochrane Central Register of Controlled Trials to identify eligible RCTs registered up to June 1, 2023. We searched Cochrane Central Register of Controlled Trials, EMBASE, and MEDLINE for publications of eligible RCTs published up to June 5, 2023. Finally, we compared the search results to determine the extent to which the two sources identified the same RCTs. RESULTS: We included 92 completed RCTs. Of these, 81 had results available. Sixty-six completed RCTs with available results were identified by both sources (81% agreement [95% CI: 71-88]). We identified seven completed RCTs with results exclusively by CTR search (9% [95% CI: 4-17]) and eight exclusively by EBD search (10% [95% CI: 5-18]). Eleven RCTs were completed but lacked results (four identified by both sources (36% [95% CI: 15-65]), one exclusively by EBD search (9% [95% CI: 1-38]), and six exclusively by CTR search (55% [95% CI: 28-79])). Also, we identified 42 eligible ongoing RCTs: 16 by both sources (38% [95% CI: 25-53]) and 26 exclusively by CTR search (62% [95% CI: 47-75]). Lastly, we identified four RCTs of unknown status by both sources. CONCLUSION: CTR searches identify a greater number of completed RCTs than EBD searches. Both searches missed some included RCTs. Based on our case study, researchers (eg, information specialists, systematic reviewers) aiming to identify all available RCTs should continue to search both sources. Once the barriers to performing CTR searches alone are targeted, CTR searches may be a suitable alternative.

18.
Med Ref Serv Q ; 43(1): 15-25, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38237019

RESUMO

This study sought to provide a protocol for searching complex medical cases of grand rounds. A clinical informationist was embedded in gastroenterology grand rounds to use comprehensive search strategies and summarize patients' information through concept mapping. Our proposed protocol classifies into three categories: (1) The general search strategy, (2) The protocol for searching for evidence about rare diseases, and (3) Identifying other resources more than routine medical databases. This approach represents a novel method beyond previous studies which were focused on usual ward rounds to facilitate evidence-based decision-making by providing and simplifying a comprehensive summary view of complex medical cases.


Assuntos
Gerenciamento de Dados , Hospitais
19.
Behav Res Methods ; 2024 Jan 30.
Artigo em Inglês | MEDLINE | ID: mdl-38286947

RESUMO

Selecting appropriate musical stimuli to induce specific emotions represents a recurring challenge in music and emotion research. Most existing stimuli have been categorized according to taxonomies derived from general emotion models (e.g., basic emotions, affective circumplex), have been rated for perceived emotions, and are rarely defined in terms of interrater agreement. To redress these limitations, we present research that served in the development of a new interactive online database, including an initial set of 364 music excerpts from three different genres (classical, pop, and hip/hop) that were rated for felt emotion using the Geneva Emotion Music Scale (GEMS), a music-specific emotion scale. The sample comprised 517 English- and German-speaking participants and each excerpt was rated by an average of 28.76 participants (SD = 7.99). Data analyses focused on research questions that are of particular relevance for musical database development, notably the number of raters required to obtain stable estimates of emotional effects of music and the adequacy of the GEMS as a tool for describing music-evoked emotions across three prominent music genres. Overall, our findings suggest that 10-20 raters are sufficient to obtain stable estimates of emotional effects of music excerpts in most cases, and that the GEMS shows promise as a valid and comprehensive annotation tool for music databases.

20.
Health Info Libr J ; 41(1): 76-83, 2024 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-37574776

RESUMO

BACKGROUND: Latin American and Caribbean Health Sciences Literature (LILACS) is the main reference database in the region; however, the way in which this resource is used in Cochrane systematic reviews has not been studied. OBJECTIVES: To assess the search methods of Cochrane reviews that used LILACS as a source of information and explore the Cochrane community's perceptions about this resource. METHODS: We identified all Cochrane reviews of interventions published during 2019, which included LILACS as a source of information, and analysed their search methods and also ran a survey through the Cochrane Community. RESULTS: We found 133 Cochrane reviews that reported the full search strategies, identifying heterogeneity in search details. The respondents to our survey highlighted many areas for improvement in the use of LILACS, including the usability of the search platform for this purpose. DISCUSSION: The use and reporting of LILACS in Cochrane reviews demonstrate inconsistencies, as evidenced by the analysis of search reports from systematic reviews and surveys conducted among members of the Cochrane community. CONCLUSION: With better guidance on how LILACS database is structured, information specialists working on Cochrane reviews should be able to make more effective use of this unique resource.


Assuntos
Serviços de Informação , Medicina , Humanos , Publicações , Inquéritos e Questionários
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