Data Structure Research Articles

Intelligent Monitoring Applications of Landslide Disaster Knowledge Graphs Based on ChatGLM2

Over the years, the field of landslide disaster research has amassed a wealth of data and specialized knowledge. However, these resources originate from a wide array of sources and often feature complex data structures, highlighting a persistent lack of methods to integrate multi-source, heterogeneous data. Traditional landslide monitoring methods typically focus on singular monitoring targets and data sources, which limits a comprehensive understanding of the complex processes involved in landslides. This paper introduces a landslide monitoring model based on a knowledge graph. This model employs P-Tuning to fine-tune ChatGLM2 for the extraction of triples. Differential InSAR (D-InSAR) is utilized to extract ground deformation data, which is then integrated with the knowledge graph for landslide monitoring and analysis. This study focuses on the co-seismic landslide in Jishishan, Gansu, China. By analyzing the landslide knowledge graph and the spatiotemporal deformation map, the results are as follows: (1) For this event, 106 entities and attributes were constructed, along with two recommended calculation routes. (2) The deformation at the earthquake’s central region reached up to 8.784 cm, with a slightly smaller deformation zone to the northwest peaking at 9.662 cm. Significant unilateral subsidence was observed in the mountain range to the southwest. (3) The area affected by the co-seismic landslide primarily includes farmland and villages, covering an area of 0.3408 square kilometers. (4) Analysis based on the knowledge graph indicates that this landslide was primarily caused by the rapid liquefaction of water-saturated soil layers due to the earthquake, resulting in instability. This study contributes to the analysis of post-disaster losses, attribution, and impacts.

Expressive rule-based modeling and fast simulation for dynamic compartments.

Compartmentalization is vital for cell biological processes. The field of rule-based stochastic simulation has acknowledged this, and many tools and methods have capabilities for compartmentalization. However, mostly, this is limited to a static compartmental hierarchy and does not integrate compartmental changes. Integrating compartmental dynamics is challenging for the design of the modeling language and the simulation engine. The language should support a concise yet flexible modeling of compartmental dynamics. Our work is based on ML-Rules, a rule-based language for multi-level cell biological modeling that supports a wide variety of compartmental dynamics, whose syntax we slightly adapt. To develop an efficient simulation engine for compartmental dynamics, we combine specific data structures and new and existing algorithms and implement them in the Rust programming language. We evaluate the concept and implementation using two case studies from existing cell-biological models. The execution of these models outperforms previous simulations of ML-Rules by two orders of magnitude. Finally, we present a prototype of a WebAssembly-based implementation to allow for a low barrier of entry when exploring the language and associated models without the need for local installation.

Research on 3D Model Calculation of Green and low-carbon New Buildings

Due to the widespread use of solid clay bricks in building houses in China, the annual destruction of farmland by burning bricks amounts to about 100000 acres. Faced with these problems, China has an extremely urgent demand for green and low-carbon buildings. Therefore, this article conducts computational research on the 3D model of a new type of green and low-carbon building. Due to the significant differences in the geometric form and texture representation of a large number of green and low-carbonl new buildings, it is difficult to uniformly quantify their level of detail. This article considers the usefulness and accessibility of 3D model calculation data separately. The shadow length is generally the distance from the selected building's facade roof to the end of the shadow, which can avoid the influence of building shape on its length. The determination of these two points and the calculation of the distance between them will directly affect the accuracy of shadow length calculation. Therefore, when users need to add an application subsystem to the system, they must add information such as the input and output data structures of the subsystem to the module. The height information calculation method uses the building and shadow imaging geometric modeling to calculate the building height by establishing the relationship between the shadow length and the corresponding building height.

Assessment of knowledge and perception of prescribers towards rational medicine use in the Ashanti Region of Ghana.

Prescribers must possess extensive knowledge and maintain a positive attitude towards the rational use of medicines to achieve desirable treatment outcomes and effectively prevent treatment failures, increased costs, drug toxicities, and interactions. The objective of this study was to evaluate prescribers' understanding and perception concerning the rational use of medicines in public hospitals. Additionally, the study aimed to identify the factors that influence rational prescribing practices. A structured data instrument was developed to collect demographic data and evaluate participants' knowledge and perception of rational medicine use, in line with the study objectives. Chi-squared statistics and Fisher's exact test were utilized to identify factors associated with good knowledge and perception among participants. Logistic regression was then employed to assess the strength of the associations, with odd ratios reported at a significant level of 0.05. Out of 192 participants, 85.4% held a positive view of rational medicine use, stressing patient safety and recognizing risks like antimicrobial resistance and polypharmacy. Perception was influenced by factors such as prescriber profession, access to references, and drug bulletin updates. Additionally, 65.6% demonstrated good knowledge of rational medicine use, which was notably influenced by factors like using standard prescribing guidelines, having a functional Drug and Therapeutics Committee, prescriber profession, and the frequency of drug bulletin updates. The study emphasizes the critical need to address knowledge gaps among healthcare professionals, especially nurses and other prescribers, to ensure the safe and effective use of medications. It highlights the positive influence of utilizing preferred prescribing references and the existence of functional Drug and Therapeutics Committees in hospitals on knowledge levels. However, the unexpected findings regarding the limited impact of frequent updates of drug bulletins require further investigation.

A Systematic Literature Review on The Role of LSTM Networks in Capturing Temporal Dependencies in Data Mining Algorithms

Data mining plays a crucial role in extracting meaningful insights from large and complex data sets, with broad applications in sectors like finance, health care, and market analysis. Traditional techniques—such as classification, clustering, association rule mining, regression, and anomaly detection—are effective for analyzing structured data but struggle with sequential data due to the challenges of modeling temporal dependencies. Long Short Term Memory (LSTM) networks, a specialized form of Recurrent Neural Networks (RNNs), provide a solution to these challenges. By incorporating memory cells and gating mechanisms, LSTM effectively manage long-term dependencies and address issues like vanishing and exploding gradients. This paper reviews the impact of LSTM networks on data mining, analyzing over 60 key publications. By synthesizing concepts and recent advancements, the review underscores how LSTMs enhance the ability of data mining algorithms to capture and predict temporal patterns, reflecting current research trends and innovations

ADOÇÃO DE BLOCKCHAIN E INTELIGÊNCIA ARTIFICIAL (IA) NAS CONTRATAÇÕES PÚBLICAS

It is clear that the world is experiencing a period of technological innovations in which systems need to be adapted and updated to new technologies in order to improve, streamline, and optimize the entire process. Therefore, this article discusses Artificial Intelligence (AI) and blockchain in the context of public administration. The objective was to verify the benefits provided to the government and society by these technologies when implemented in public procurement. The choice of the topic is justified by the fact that information technologies are a reality in all spheres, including public ones. As a methodology, this article involves qualitative, descriptive research, and a bibliographic review was chosen because it is a method that allows the use of material already published on the subject, serving as a theoretical basis. The results achieved showed that technologies – AI and blockchain, when adopted by the public administration, are capable of enabling the tracking of budget execution regarding payments made to suppliers and service providers who won the bid, greater transparency, the ability to structure data, assist in decision-making, the process of writing different types of reports, cross-referencing data, and organizing process documents automatically.

Development of a Practical Prediction Model for Adverse Neonatal Outcomes at the Start of the Second Stage of Labor.

To develop a prediction model for adverse neonatal outcomes using electronic fetal monitoring (EFM) interpretation data and other relevant clinical information known at the start of the second stage of labor. This was a retrospective cohort study of individuals who labored and delivered at two academic medical centers between July 2016 and June 2020. Individuals were included if they had a singleton gestation at term (more than 37 weeks of gestation), a vertex-presenting, nonanomalous fetus, and planned vaginal delivery and reached the start of the second stage of labor. The primary outcome was a composite of severe adverse neonatal outcomes. We developed and compared three modeling approaches to predict the primary outcome using factors related to EFM data (as interpreted and entered in structured data fields in the electronic health record by the bedside nurse), maternal comorbidities, and labor characteristics: traditional logistic regression, LASSO (least absolute shrinkage and selection operator), and extreme gradient boosting. Model discrimination and calibration were compared. Predicted probabilities were stratified into risk groups to facilitate clinical interpretation, and positive predictive values for adverse neonatal outcomes were calculated for each. A total of 22,454 patients were included: 14,820 in the training set and 7,634 in the test set. The composite adverse neonatal outcome occurred in 3.2% of deliveries. Of the three modeling methods compared, the logistic regression model had the highest discrimination (0.690, 95% CI, 0.656-0.724) and was well calibrated. When stratified into risk groups (no increased risk, higher risk, and highest risk), the rates of the composite adverse neonatal outcome were 2.6% (95% CI, 2.3-3.1%), 6.7% (95% CI, 4.6-9.6%), and 10.3% (95% CI, 7.6-13.8%), respectively. Factors with the strongest associations with the composite adverse neonatal outcome included the presence of meconium (adjusted odds ratio [aOR] 2.10, 95% CI, 1.68-2.62), fetal tachycardia within the 2 hours preceding the start of the second stage (aOR 1.94, 95% CI, 1.03-3.65), and number of prior deliveries (aOR 0.77, 95% CI, 0.60-0.99).

A Hierarchical Coarse–Fine Adaptive Fusion Network for the Joint Classification of Hyperspectral and LiDAR Data

Hyperspectral image (HSI) and light detection and ranging (LiDAR) data have gained significant attention due to their excellent complementarity, which can be combined to improve the accuracy of land cover classification. However, there are still many challenges, such as unrelated physical characteristics, different data structures, and a lack of labeled samples. Many methods fail to leverage the full potential of multi-source data, particularly hierarchical complementary information. To address these problems, a hierarchical coarse–fine adaptive (HCFA) fusion network with dynamic convolution and a transformer is proposed for multi-source remote sensing land cover classification. Although fusing hierarchical information can improve the classification accuracy of the model, improper hierarchical feature selection and optimization may negatively affect the classification results. Therefore, a coarse–fine mutual learning strategy is proposed to dynamically fuse hierarchical information. Additionally, the disparity between multi-source data continues to prevent the realization of effective fusion. To tackle this challenge, cross-tokenization and cross-token attention are implemented to enhance information interaction. Furthermore, to improve the model representation with limited computational cost, we combine the advantages of dynamic convolution with a transformer. Validation on three standard datasets demonstrates that HCFA achieves high accuracy with just 1% of the training set while maintaining low computational costs.

Algorithms and Data Structures for Numerical Computations with Automatic Precision Estimation

We introduce data structures and algorithms to count numerical inaccuracies arising from usage of floating numbers described in IEEE 754. Here we describe how to estimate precision for some collection of functions most commonly used for array manipulations and training of neural networks. For highly optimized functions like matrix multiplication, we provide a fast estimation of precision and some hint how the estimation can be strengthened.

A Computational Perspective into Binding Mechanism of a potent FAK Inhibitor as Anticancer Drug Candidate: Insights from Molecular Dynamics Simulations

Focal adhesion kinase (FAK) is an important non-receptor tyrosine kinase (nRTK) with crucial role in primary cell functions. Fundamental cellular processes, such as migration, adhesion, proliferation, and survival, are regulated by FAK. The tumorigenesis of FAK is very important since it is overexpressed in advanced cancers, causes malignant features and induces cancer metastasis. The available evidence proposes FAK as a promising target for cancer therapy. At the moment, there are no FAK inhibitors (FAKIs) in the market but several small molecule FAKIs have been developed and a few of them entered into clinical studies as anticancer agents. In spite of these achievements, development of selective and potent FAKIs may be partly hampered by the lack of crystallographic or structural data. This limitation could be resolved through proposing robust ligand–protein binding models to design new FAKIs. GSK2256098 is a small molecule ATP-competitive FAKI that undergoes clinical trials as a monotherapy or in combination with other medications against advanced solid tumors. GSK2256098 have been proven to be a selective and potent agent (IC[Formula: see text] 0.4 nM) with respect to other clinical candidates. With regard to appropriate characteristics, this molecule is particularly amenable for performing computational modeling studies. In this study, we try to propose an applicable binding model of GSK2256098 inside the FAK kinase domain. For this purpose, molecular docking, all-atom 200-ns molecular dynamics simulations and free-energy-calculation methods were applied as consecutive modeling techniques. Atomic insight and dynamic evolution of the system were used to attain a stable binding mode and infer chemical interactions of GSK2256098 inside the kinase domain of FAK. Although complementary investigations need to be conducted, obtained results may offer a structural basis for the development of potent and selective FAKIs.

Abnormal Granger causal connectivity based on altered gray matter volume and associated neurotransmitters of adolescents with internet gaming disorder revealed by a multimodal neuroimaging study

Although prior studies have revealed alterations in gray matter volume (GMV) among individuals with internet gaming disorder (IGD). The brain's multifaceted functions hinge crucially on the intricate connections and communication among distinct regions. However, the intricate interaction of information between brain regions with altered GMV and other regions, and how they synchronize with various neurotransmitter systems, remains enigmatic. Therefore, we aimed to integrate structural, functional and molecular data to explore the GMV-based Granger causal connectivity abnormalities and their correlated neurotransmitter systems in IGD adolescents. Voxel-based morphometry (VBM) analysis was firstly performed to investigate GMV differences between 37 IGD adolescents and 35 matched controls. Brain regions with altered GMV were selected as seeds for further Granger causality analysis (GCA). Two-sample t tests were performed using the SPM12 toolkit to compare the GMV and Granger causal connectivity between IGD and control groups (GRF corrected, Pvoxel<0.005, Pcluster<0.05). Then, GMV-based Granger causal connectivity was spatially correlated with PET- and SPECT-derived maps covering multifarious neurotransmitter systems. Multiple comparison correction was performed using false discovery rate (FDR). Compared with controls, IGD adolescents showed higher GMV in the caudate nucleus and lingual gyrus. For the GCA, IGD adolescents showed higher Granger causal connectivity from insula, putamen, supplementary motor area (SMA) and middle cingulum cortex (MCC) to the caudate nucleus, and lower Granger causal connectivity from superior/inferior parietal gyrus (SPG/IPG) and middle occipital gyrus (MOG) to the lingual gyrus. Besides, GMV-based Granger causal connectivity of IGD adolescents were associated with the dopaminergic, serotonergic, GABAergic and noradrenaline systems. This study revealed that the caudate nucleus and lingual gyrus may be the key sites of neuroanatomical changes in IGD adolescents, and whole-brain Granger causal connectivity abnormalities based on altered GMV involved large brain networks including reward, cognitive control, and visual attention networks, and these abnormalities are associated with a variety of neurotransmitter systems, which may be associated with higher reward sensitivity, cognitive control, and attention control dysfunction.

Metadata harvesting for digital library integration in Ukraine: a comparative study of the OAI-PMH protocol and VuFind’s efficacy

Purpose This paper aims to the problem of building an environment to support scientific research in connection with the development of Open Science in Ukraine. Design/methodology/approach An overview of modern portals for aggregating scientific data was conducted. Analysis of available tools and identifying problems that arise when collecting data from digital libraries and journals was conducted. The validity of choosing VuFind as a tool that allows building an extraction–transformation–loading (ETL) approach for data aggregation and bringing the format and values of metadata fields to one view was experimentally verified. Findings During the experimental verification, problems related to the fact that the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) protocol does not have strict requirements for the data structure, which lead to the complexity of integration, despite the fact that this protocol occupied a leading position, were noted. To simplify these problems, an ETL approach that allowed for the use of ontological methods (e.g. data mapping, linked data and dictionaries to improve the semantics of data for integration processes) was considered. A review of the possibilities of modern tools for OAI-PMH integration, which were actively supported and developed, was conducted. Originality/value This paper was an attempt to outline the problems that arose in integrating resources, with the aim of developing future integration protocols that would have simple means of semantic data validation and built-in ETL mechanism.

Ray Tracing Animated Displaced Micro‐Meshes

AbstractWe present a new method that allows efficient ray tracing of virtually artefact‐free animated displaced micro‐meshes (DMMs) [MMT23] and preserves their low memory footprint and low BVH build and update cost. DMMs allow for compact representation of micro‐triangle geometry through hierarchical encoding of displacements. Displacements are computed with respect to a coarse base mesh and are used to displace new vertices introduced during 1 : 4 subdivision of the base mesh. Applying non‐rigid transformation to the base mesh can result in silhouette and normal artefacts (see Figure 1) during animation. We propose an approach which prevents these artefacts by interpolating transformation matrices before applying them to the DMM representation. Our interpolation‐based algorithm does not change DMM data structures and it allows for efficient bounding of animated micro‐triangle geometry which is essential for fast tessellation‐free ray tracing of animated DMMs.

A goodness-of-fit measure for logistic regression under separation

Logistic regression models have a severe problem called separation. The maximum likelihood estimator does not exist in logistic regression models for data structures under separation. Under separation, the forcibly estimated maximum likelihood estimate may have an extremely large value. Separation often occurs when the size of dataset is small. Consequently, goodness-of-fit measures based on the likelihood ratio and those based on covariance functions using the maximum likelihood estimate indicate that the model is excessively good regardless of the cause of the separation. The Firth and exact logistic regression methods are valid estimation methods for separation problems. Therefore, we propose methods to reasonably evaluate the goodness-of-fit measures of statistical models under separation with dataset of a small sample size with the abovementioned methods. The goodness-of-fit measures based on covariance functions which are a generalization of the multiple correlation coefficient, referred to as the regression correlation coefficient and the entropy coefficient of determination are then used combined with the abovementioned methods for the separation data. In addition, we conducted a data analysis using the definition of the non separation ratio based on the regression depth.

Enhancing Blockchain Security by Developing the SHA256 Algorithm

Security plays a vital role in various domains, including blockchain technology. The Blockchain serves as a secure data structure for storing transactional records. Hash functions are employed in cryptography to ensure integrity and authentication within the blockchain. The widely used SHA256 algorithm has faced recent attacks, prompting the development of stronger hash functions. This paper presents a novel modification approach to enhance the performance of SHA256 by introducing an extended mechanism for generating a 288-bit message digest and reducing the number of rounds to 44 instead of 64 while preserving the diffusion of data through its complex iterative process, which involves multiple rounds of bitwise and logical operations. The change makes sure that even small changes to the input data cause noticeable variations in the output hash, thereby maintaining cryptographic properties. The suggested hash function SHA288 achieves improved security, collision resistance, and preimage resistance, while maintaining a faster execution time compared to SHA256. The tables and tests conducted on the suggested algorithm have revealed its remarkable safety and robustness in countering attacks as well as demonstrated outstanding performance in random tests, which further enhances its security measures.

Scalable Reading of Structured Data

En esta lección introduciremos la lectura escalable y cómo aplicar este flujo de trabajo a tus análisis de datos estructurados

Cerebellar Structural and N-Acetylaspartate, Choline, and Creatine Metabolic Profiles in Parkinson’s Disease and Essential Tremor

Background: The role of the cerebellum in Parkinson’s disease (PD), particularly in tremor-dominant subtypes, is increasingly recognized. Magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) provide anatomical and metabolic insights, suggesting compensatory hyperactivity or degenerative changes in the cerebellum in PD. Volumetric analysis of cerebellar structures in MRI images, combined with metabolic profiles from MRS, offers possibilities for differentiating PD from essential tremor (ET). The cerebellum may be a potential therapeutic target due to its role in neurocircuitry of PD and ET. Methods: Brain structural data were obtained using MRI, and cerebellar metabolic profiles, focusing on the quantification of N-acetylaspartate (NAA), choline, and creatine peaks were obtained using MRS. This study enrolled patients with ET and PD, both with and without tremor, as well as disease controls with cerebellar atrophy (including spinocerebellar ataxia and multiple system atrophy). Volumetric analysis of cerebellar structures was performed. Differences in MRI and MRS parameters were analyzed using one-way analysis of covariance with a significance threshold of p &lt; 0.05. Results: From November 2018 to March 2023, 111 patients were enrolled, including 29 ET, 29 cerebellar atrophy, 12 PD without tremor, and 41 PD with tremor. No significant differences in cerebellar volume and N-acetylaspartate/creatine and choline/creatine ratios were found between ET and PD with tremor. Conclusions: This preliminary retrospective study suggests similarities in cerebellar structures and metabolic profiles between ET and PD, highlighting the need for advanced imaging techniques to better differentiate between these conditions. Future research should integrate clinical data, such as tremor severity and cognitive assessments, to explore the relationships with cerebellar MRI parameters.

Household structure and dietary diversity among older adults in rural and urban China: a cross-sectional study

BackgroundHousehold structure can significantly affect older adults’ eating behaviours and diet quality. However, the difference in dietary diversity in various household structures in urban and rural has rarely been investigated. This study aimed to investigate the association between household structure and dietary diversity among older Chinese adults and examine whether the association differed by urban and rural.MethodsThe study used data from the 2018 wave of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). The participants were individuals aged 60 and over with available dietary and household structure data. Dietary diversity was defined as ‘high’ if respondents had a dietary diversity score (DDS) above the mean value. Household structures were classified into four mutually exclusive categories: (1) living alone; (2) spouse only; (3) at least with a great/grandchild(ren); (4) non-empty-nested. Binary logistic regression was applied to investigate the association between household structures and dietary diversity.ResultsNon-empty-nested older adults had the highest probability (OR = 1.64, 95% CI = 1.44–1.87) of having high dietary diversity. People who only lived with their spouse and at least lived with a great/grandchild(ren) were 1.45 (OR = 1.45, 95% CI = 1.23–1.71) and 1.23 (OR = 1.23, 95% CI = 1.08–1.41) times as likely to have diverse diets than older adults who lived alone. The difference in dietary diversity among various household structures is more evident in urban than rural areas. Solo-living older adults were the most disadvantaged regarding dietary diversity in urban and rural areas.ConclusionsHousehold structures are significantly associated with dietary diversity among older Chinese adults aged 60 years and over. The findings emphasise the need to reduce nutritional inequality, encourage dietary diversity, and consider both the household structures and residences when providing health intervention programs to the older population.

The Dragoman Renaissance Research Project in Library/DH Linked Data Partnerships

The Dragomans Renaissance Research Platform represents a long-standing collaboration between the authors. The project explores the role of dragomans (diplomatic interpreter-translators) in mediating relations between the Ottoman Empire and its European neighbours from ca. 1550 to ca. 1730. The project leverages surrogates and transcriptions from multiple archival and secondary sources to produce a structured data set that can be queried using SPARQL. The Dragomans use case illuminates the potential of library-partnered DH projects, like other unique collections, to become machine-readable research data sets made available through generalized, sustainable architectures that also suit researcher needs for UI-driven query and display.

Characterization of Telecare Conversations on Lifestyle Management and Their Relation to Health Care Utilization for Patients with Heart Failure: Mixed Methods Study.

Telehealth interventions where providers offer support and coaching to patients with chronic conditions such as heart failure (HF) and type 2 diabetes mellitus (T2DM) are effective in improving health outcomes. However, the understanding of the content and structure of these interactions and how they relate to health care utilization remains incomplete. This study aimed to characterize the content and structure of telecare conversations on lifestyle management for patients with HF and investigate how these conversations relate to health care utilization. We leveraged real-world data from 50 patients with HF enrolled in a postdischarge telehealth program, with the primary intervention comprising a series of telephone calls from nurse telecarers over a 12-month period. For the full cohort, we transcribed 729 English-language calls and annotated conversation topics. For a subcohort (25 patients with both HF and T2DM), we annotated lifestyle management content with fine-grained dialogue acts describing typical conversational structures. For each patient, we identified calls with unusually high ratios of utterances on lifestyle management as lifestyle-focused calls. We further extracted structured data for inpatient admissions from 6 months before to 6 months after the intervention period. First, to understand conversational structures and content of lifestyle-focused calls, we compared the number of utterances, dialogue acts, and symptom attributes in lifestyle-focused calls to those in calls containing but not focused on lifestyle management. Second, to understand the perspectives of nurse telecarers on these calls, we conducted an expert evaluation where 2 nurse telecarers judged levels of concern and follow-up actions for lifestyle-focused and other calls (not focused on lifestyle management content). Finally, we assessed how the number of lifestyle-focused calls relates to the number of admissions, and to the average length of stay per admission. In comparative analyses, lifestyle-focused calls had significantly fewer utterances (P=.01) and more dialogue acts (Padj=.005) than calls containing but not focused on lifestyle management. Lifestyle-focused calls did not contain deeper discussions on clinical symptoms. These findings indicate that lifestyle-focused calls entail short, intense discussions with greater emphasis on understanding patient experience and coaching than on clinical content. In the expert evaluation, nurse telecarers identified 24.2% (29/120) of calls assessed as concerning enough for follow-up. For these 29 calls, nurse telecarers were more attuned to concerns about symptoms and vitals (19/29, 65.5%) than lifestyle management concerns (4/29, 13.8%). The number of lifestyle-focused calls a patient had was modestly (but not significantly) associated with a lower average length of stay for inpatient admissions (Spearman ρ=-0.30; Padj=.06), but not with the number of admissions (Spearman ρ=-0.03; Padj=.84). Our approach and findings offer novel perspectives on the content, structure, and clinical associations of telehealth conversations on lifestyle management for patients with HF. Hence, our study could inform ways to enhance telehealth programs for self-care management in chronic conditions.