Artificial Intelligence in Kidney Disease: A Comprehensive Study and Directions for Future Research.

Artificial intelligence (AI) has emerged as a promising tool in the field of healthcare, with an increasing number of research articles evaluating its applications in the domain of kidney disease. To comprehend the evolving landscape of AI research in kidney disease, a bibliometric analysis is essential. The purposes of this study are to systematically analyze and quantify the scientific output, research trends, and collaborative networks in the application of AI to kidney disease. This study collected AI-related articles published between 2012 and 20 November 2023 from the Web of Science. Descriptive analyses of research trends in the application of AI in kidney disease were used to determine the growth rate of publications by authors, journals, institutions, and countries. Visualization network maps of country collaborations and author-provided keyword co-occurrences were generated to show the hotspots and research trends in AI research on kidney disease. The initial search yielded 673 articles, of which 631 were included in the analyses. Our findings reveal a noteworthy exponential growth trend in the annual publications of AI applications in kidney disease. Nephrology Dialysis Transplantation emerged as the leading publisher, accounting for 4.12% (26 out of 631 papers), followed by the American Journal of Transplantation at 3.01% (19/631) and Scientific Reports at 2.69% (17/631). The primary contributors were predominantly from the United States (n = 164, 25.99%), followed by China (n = 156, 24.72%) and India (n = 62, 9.83%). In terms of institutions, Mayo Clinic led with 27 contributions (4.27%), while Harvard University (n = 19, 3.01%) and Sun Yat-Sen University (n = 16, 2.53%) secured the second and third positions, respectively. This study summarized AI research trends in the field of kidney disease through statistical analysis and network visualization. The findings show that the field of AI in kidney disease is dynamic and rapidly progressing and provides valuable information for recognizing emerging patterns, technological shifts, and interdisciplinary collaborations that contribute to the advancement of knowledge in this critical domain.

mHealth Research for Weight Loss, Physical Activity, and Sedentary Behavior: Bibliometric Analysis.

BACKGROUND: Research into mobile health (mHealth) technologies on weight loss, physical activity, and sedentary behavior has increased substantially over the last decade; however, no research has been published showing the research trend in this field. OBJECTIVE: The purpose of this study was to provide a dynamic and longitudinal bibliometric analysis of recent trends of mHealth research for weight loss, physical activity, and sedentary behavior. METHODS: A comprehensive search was conducted through Web of Science to retrieve all existing relevant documents published in English between January 1, 2010, and November 1, 2021. We developed appropriate research questions; based on the proven bibliometric approaches, a search strategy was formulated to screen the title for eligibility. Finally, we conducted bibliometric analyses to explore the growth rate of publications; publication patterns; and the most productive authors, institutions, and countries, and visualized the trends in the field using a keyword co-occurrence network. RESULTS: The initial search identified 8739 articles, of which 1035 were included in the analyses. Our findings show an exponential growth trend in the number of annual publications of mHealth technology research in these fields. JMIR mHealth and uHealth (n=214, 20.67%), Journal of Medical Internet Research (n=71, 6.86%), and BMC Public Health (n=36, 3.47%) were the top 3 journals, publishing higher numbers of articles. The United States remained the leading contributor in these areas (n=405, 39.13%), followed by Australia (n=154, 14.87%) and England (n=125, 12.07%). Among the universities, the University of Sydney (n=36, 3.47%) contributed the most mHealth technology research in these areas; however, Deakin University (n=25, 2.41%) and the National University of Singapore (n=23, 2.22%) were in the second and third positions, respectively. CONCLUSIONS: Although the number of papers published on mobile technologies for weight loss, physical activity, and sedentary behavior was initially low, there has been an overall increase in these areas in recent years. The findings of the study indicate that mobile apps and technologies have substantial potential to reduce weight, increase physical activity, and change sedentary behavior. Indeed, this study provides a useful overview of the publication trends and valuable guidance on future research directions and perspectives in this rapidly developing field.

Association between Statin Use and Risk of Parkinson's Disease: Evidence from 18 Observational Studies Comprising 3.7 Million Individuals.

The potential impact of statins on the risk of Parkinson's disease (PD) is still controversial; therefore, we conducted a comprehensive meta-analysis of observational studies to examine the effect of statin use on the risk of PD. We searched electronic databases, such as PubMed, EMBASE, Scopus, and Web of Science, for articles published between 1 January 2000 and 15 March 2022. Cohort studies which examined the association between statins and PD risk in the general population were also included. Two authors assessed the data and extracted all potential information for analysis. Random effects meta-analyses were performed to measure the risk ratio (RR) and 95% confidence intervals (CIs). Eighteen cohort studies including 3.7 million individuals with 31,153 PD participants were identified. In statin users, compared with non-users, the RR for PD was 0.79 (95% CI: 0.68-0.91). In a subgroup analysis of PD, this association was observed with medium and high quality, and the studies were adjusted for age, gender, and smoking status. When the data were stratified according to the duration of exposure, long-duration statin use was associated with a decreased risk of PD (RR = 0.49; 95% CI: 0.26-0.92). There was no significant decrease in the risk of PD in short-term statin users (RR = 0.94; 95% CI: 0.67-1.31). Moreover, no significant difference in the reduction in the risk of PD was observed between men (RR = 0.80; 95% CI: 0.75-0.86) and women (RR = 0.80; 95% CI: 0.75-0.86). Although our findings confirm a reduction in the PD risk associated with statin treatment and suggest that statins play a clinically favorable role, these findings should be interpreted with caution. Future randomized control trials with an ad hoc design are needed to confirm the potential utility of statins in reducing the risk of PD.

Statin Use Is Associated with a Decreased Risk of Mortality among Patients with COVID-19.

BACKGROUND: Recent epidemiological studies remain controversial regarding the association between statin use and reducing the risk of mortality among individuals with COVID-19. OBJECTIVE: The objective of this study was to clarify the association between statin use and the risk of mortality among patients with COVID-19. METHODS: We conducted a systematic articles search of online databases (PubMed, EMBASE, Scopus, and Web of Science) between 1 February 2020 and 20 February 2021, with no restriction on language. The following search terms were used: "Statins" and "COVID-19 mortality or COVID19 mortality or SARS-CoV-2 related mortality". Two authors individually examined all articles and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for study inclusion and exclusion. The overall risk ratio (RRs) with 95% confidence interval (CI) was calculated to show the strength of the association and the heterogeneity among the studies was presented Q and I2 statistic. RESULTS: Twenty-eight studies were assessed for eligibility and 22 studies met the inclusion criteria. Statin use was associated with a significantly decreased risk of mortality among patients with COVID-19 (RR adjusted = 0.64; 95% CI: 0.57-0.72, p < 0.001). Moreover, statin use both before and after the admission was associated with lowering the risk of mortality among the COVID-19 patients (RR adjusted;before = 0.69; 95% CI: 0.56-0.84, p < 0.001 and RR adjusted;after = 0.57; 95% CI: 0.54-0.60, p < 0.001). CONCLUSION: This comprehensive study showed that statin use is associated with a decreased risk of mortality among individuals with COVID-19. A randomized control trial is needed to confirm and refute the association between them.

The impact of alcohol policies on alcohol-attributable diseases in Taiwan-A population-based study.

BACKGROUND: Taiwan has some of the strictest alcohol-related driving laws in the world. However, its laws continue to be toughened to reduce the ever-increasing social cost of alcohol-related harm. AIM: This study assumes that alcohol-related driving laws show a spillover effect such that behavioral changes originally meant to apply behind the wheel come to affect drinking behavior in other contexts. The effects of alcohol driving laws and taxes on alcohol-related morbidity are assessed; incidence rates of alcohol-attributable diseases (AAD) serve as our measure of morbidity. METHODS: Monthly incidence rates of alcohol-attributable diseases were calculated with data from the National Health Insurance Research Database (NHIRD) from 1996 to 2011. These rates were then submitted to intervention analyses using Seasonal Autoregressive Integrated Moving Average models (ARIMA) with multivariate adaptive regression splines (MARS). ARIMA is well-suited to time series analysis while MARS helps fit the regression model to the cubic curvature form of the irregular AAD incidence rates of hospitalization (AIRH). RESULTS: Alcoholic liver disease, alcohol abuse and dependence syndrome, and alcohol psychoses were the most common AADs in Taiwan. Compared to women, men had a higher incidence of AADs and their AIRH were more responsive to changes in the laws governing permissible blood alcohol. The adoption of tougher blood alcohol content (BAC) laws had significant effects on AADs, controlling for overall consumption of alcoholic beverages. CONCLUSION: Blood alcohol level laws and alcohol taxation effectively reduced alcohol-attributable morbidities with the exception of alcohol dependence and abuse, a disease to which middle-aged, lower income people are particularly susceptible. Attention should be focused on this cohort to protect this vulnerable population.

Design of an Electronic Reminder System for Supporting the Integerity of Nursing Records.

The integrity of electronic nursing records (ENRs) stands for the quality of medical records. But patients' conditions are varied (e.g. not every patient had wound or need fall prevention), to achieve the integrity of ENRs depends much on clinical nurses' attention. Our study site, an one 2,300-bed hospital in northern Taiwan, there are a total of 20 ENRs including nursing assessments, nursing care plan, discharge planning etc. implemented in the whole hospital before 2014. It become important to help clinical nurses to decrease their human recall burden to complete these records. Thus, the purpose of this study was to design an ENRs reminder system (NRS) to facilitate nursing recording process. The research team consisted of an ENR engineer, a clinical head nurse and a nursing informatics specialist began to investigate NRS through three phases (e.g. information requirements; design and implementation). In early 2014, a qualitative research method was used to identify NRS information requirements through both groups (e.g. clinical nurses and their head nurses) focus interviews. According to the their requirements, one prototype was created by the nursing informatics specialist. Then the engineer used Microsoft Visual Studio 2012, C#, and Oracle to designed a web-based NRS (Figure 1). Then the integrity reminder system which including a total of twelve electronic nursing records was designed and the preliminary accuracy validation of the system was 100%. NRS could be used to support nursing recording process and prepared for implementing in the following phase.

Semantic similarity measures in the biomedical domain by leveraging a web search engine.

Various researches in web related semantic similarity measures have been deployed. However, measuring semantic similarity between two terms remains a challenging task. The traditional ontology-based methodologies have a limitation that both concepts must be resided in the same ontology tree(s). Unfortunately, in practice, the assumption is not always applicable. On the other hand, if the corpus is sufficiently adequate, the corpus-based methodologies can overcome the limitation. Now, the web is a continuous and enormous growth corpus. Therefore, a method of estimating semantic similarity is proposed via exploiting the page counts of two biomedical concepts returned by Google AJAX web search engine. The features are extracted as the co-occurrence patterns of two given terms P and Q, by querying P, Q, as well as P AND Q, and the web search hit counts of the defined lexico-syntactic patterns. These similarity scores of different patterns are evaluated, by adapting support vector machines for classification, to leverage the robustness of semantic similarity measures. Experimental results validating against two datasets: dataset 1 provided by A. Hliaoutakis; dataset 2 provided by T. Pedersen, are presented and discussed. In dataset 1, the proposed approach achieves the best correlation coefficient (0.802) under SNOMED-CT. In dataset 2, the proposed method obtains the best correlation coefficient (SNOMED-CT: 0.705; MeSH: 0.723) with physician scores comparing with measures of other methods. However, the correlation coefficients (SNOMED-CT: 0.496; MeSH: 0.539) with coder scores received opposite outcomes. In conclusion, the semantic similarity findings of the proposed method are close to those of physicians' ratings. Furthermore, the study provides a cornerstone investigation for extracting fully relevant information from digitizing, free-text medical records in the National Taiwan University Hospital database.

An information technology framework for strengthening telehealthcare service delivery.

OBJECTIVE: Telehealthcare has been used to provide healthcare service, and information technology infrastructure appears to be essential while providing telehealthcare service. Insufficiencies have been identified, such as lack of integration, need of accommodation of diverse biometric sensors, and accessing diverse networks as different houses have varying facilities, which challenge the promotion of telehealthcare. This study designs an information technology framework to strengthen telehealthcare delivery. MATERIALS AND METHODS: The proposed framework consists of a system architecture design and a network transmission design. The aim of the framework is to integrate data from existing information systems, to adopt medical informatics standards, to integrate diverse biometric sensors, and to provide different data transmission networks to support a patient's house network despite the facilities. The proposed framework has been evaluated with a case study of two telehealthcare programs, with and without the adoption of the framework. RESULTS: The proposed framework facilitates the functionality of the program and enables steady patient enrollments. The overall patient participations are increased, and the patient outcomes appear positive. The attitudes toward the service and self-improvement also are positive. CONCLUSIONS: The findings of this study add up to the construction of a telehealthcare system. Implementing the proposed framework further assists the functionality of the service and enhances the availability of the service and patient acceptances.

Web-based healthcare hand drawing management system.

The paper addresses Medical Hand Drawing Management System architecture and implementation. In the system, we developed four modules: hand drawing management module; patient medical records query module; hand drawing editing and upload module; hand drawing query module. The system adapts windows-based applications and encompasses web pages by ASP.NET hosting mechanism under web services platforms. The hand drawings implemented as files are stored in a FTP server. The file names with associated data, e.g. patient identification, drawing physician, access rights, etc. are reposited in a database. The modules can be conveniently embedded, integrated into any system. Therefore, the system possesses the hand drawing features to support daily medical operations, effectively improve healthcare qualities as well. Moreover, the system includes the printing capability to achieve a complete, computerized medical document process. In summary, the system allows web-based applications to facilitate the graphic processes for healthcare operations.

Semantic similarity measure in biomedical domain leverage web search engine.

Semantic similarity measure plays an essential role in Information Retrieval and Natural Language Processing. In this paper we propose a page-count-based semantic similarity measure and apply it in biomedical domains. Previous researches in semantic web related applications have deployed various semantic similarity measures. Despite the usefulness of the measurements in those applications, measuring semantic similarity between two terms remains a challenge task. The proposed method exploits page counts returned by the Web Search Engine. We define various similarity scores for two given terms P and Q, using the page counts for querying P, Q and P AND Q. Moreover, we propose a novel approach to compute semantic similarity using lexico-syntactic patterns with page counts. These different similarity scores are integrated adapting support vector machines, to leverage the robustness of semantic similarity measures. Experimental results on two datasets achieve correlation coefficients of 0.798 on the dataset provided by A. Hliaoutakis, 0.705 on the dataset provide by T. Pedersen with physician scores and 0.496 on the dataset provided by T. Pedersen et al. with expert scores.

A newborn screening system based on service-oriented architecture embedded support vector machine.

The clinical symptoms of metabolic disorders are rarely apparent during the neonatal period, and if they are not treated earlier, irreversible damages, such as mental retardation or even death, may occur. Therefore, the practice of newborn screening is essential to prevent permanent disabilities in newborns. In the paper, we design, implement a newborn screening system using Support Vector Machine (SVM) classifications. By evaluating metabolic substances data collected from tandem mass spectrometry (MS/MS), we can interpret and determine whether a newborn has a metabolic disorder. In addition, National Taiwan University Hospital Information System (NTUHIS) has been developed and implemented to integrate heterogeneous platforms, protocols, databases as well as applications. To expedite adapting the diversities, we deploy Service-Oriented Architecture (SOA) concepts to the newborn screening system based on web services. The system can be embedded seamlessly into NTUHIS.

Newborn screening healthcare information system based on service-oriented architecture.

In this paper, we established a newborn screening system under the HL7/Web Services frameworks. We rebuilt the NTUH Newborn Screening Laboratory's original standalone architecture, having various heterogeneous systems operating individually, and restructured it into a Service-Oriented Architecture (SOA), distributed platform for further integrity and enhancements of sample collections, testing, diagnoses, evaluations, treatments or follow-up services, screening database management, as well as collaboration, communication among hospitals; decision supports and improving screening accuracy over the Taiwan neonatal systems are also addressed. In addition, the new system not only integrates the newborn screening procedures among phlebotomy clinics, referral hospitals, as well as the newborn screening center in Taiwan, but also introduces new models of screening procedures for the associated, medical practitioners. Furthermore, it reduces the burden of manual operations, especially the reporting services, those were heavily dependent upon previously. The new system can accelerate the whole procedures effectively and efficiently. It improves the accuracy and the reliability of the screening by ensuring the quality control during the processing as well.

A multi-voting enhancement for newborn screening healthcare information system.

The clinical symptoms of metabolic disorders during neonatal period are often not apparent. If not treated early, irreversible damages such as mental retardation may occur, even death. Therefore, practicing newborn screening is essential, imperative to prevent neonatal from these damages. In the paper, we establish a newborn screening model that utilizes Support Vector Machines (SVM) techniques and enhancements to evaluate, interpret the Methylmalonic Acidemia (MMA) metabolic disorders. The model encompasses the Feature Selections, Grid Search, Cross Validations as well as multi model Voting Mechanism. In the model, the predicting accuracy, sensitivity and specificity of MMA can be improved dramatically. The model will be able to apply to other metabolic diseases as well.