Effective Analysis Method Research Articles

Analytical modelling techniques for enhancing tacrolimus dosing in solid organ transplantation: a systematic review protocol.

Tacrolimus is an immunosuppressant commonly administered in transplant recipients. Given its narrow therapeutic range and susceptibility to various influencing variables, determining its optimal dosage is challenging. This systematic review seeks to identify effective analytical modelling techniques and methods for optimal tacrolimus dose prediction in solid transplant recipients. This review will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. The study will review the literature published from database inception to 11 March 2024, that assesses predictive models of tacrolimus dosing through analytical modelling techniques. We will include both randomised and non-randomised, as well as cross-sectional, qualitative and before-and-after studies and will perform our searches in four main databases-Ovid/MEDLINE, PubMed/MEDLINE, Scopus, Embase and Web of Science, and search engines including Centers for Disease Control (CDC) and Google Scholar. Papers that are not published in English or French are excluded from this study. A narrative synthesis and meta-analysis will be done if the extracted information permits such analysis. Conference abstracts will be ignored unless they are recent (published within 2 years of the search date). Ethics clearance is not required for this study as no primary data will be collected. The completed manuscript will be published, and the results of the study will be presented at conferences. International Prospective Register of Systematic Reviews (PROSPERO), CRD42024537212.

Firth-Type Penalized Methods of the Modified Poisson and Least-Squares Regression Analyses for Binary Outcomes.

The modified Poisson and least-squares regression analyses for binary outcomes have been widely used as effective multivariable analysis methods to provide risk ratio and risk difference estimates in clinical and epidemiological studies. However, there is no certain evidence that assessed their operating characteristics under small and sparse data settings and no effective methods have been proposed for these regression analyses to address this issue. In this article, we show that the modified Poisson regression provides seriously biased estimates under small and sparse data settings. In addition, the modified least-squares regression provides unbiased estimates under these settings. We further show that the ordinary robust variance estimators for both of the methods have certain biases under situations that involve small or moderate sample sizes. To address these issues, we propose the Firth-type penalized methods for the modified Poisson and least-squares regressions. The adjustment methods lead to a more accurate and stable risk ratio estimator under small and sparse data settings, although the risk difference estimator is not invariant. In addition, to improve the inferences of the effect measures, we provide an improved robust variance estimator for these regression analyses. We conducted extensive simulation studies to assess the performances of the proposed methods under real-world conditions and found that the accuracies of the point and interval estimations were markedly improved by the proposed methods. We illustrate the effectiveness of these methods by applying them to a clinical study of epilepsy.

A three-dimensional nitrogen-rich conjugated microporous polymer for solid-phase microextraction of nitroaromatic compounds from environmental water and soil samples

Nitroaromatic compounds (NACs), as a kind of important chemical intermediates, are widely used in industrial productions. However, NACs have carcinogenic effect and their residues may pose harm to human health. Therefore, there is necessity to set up effective analytical method to monitor them in some environmental samples. However, because of their low concentrations in real samples, they need to be enriched by an effective adsorbent before the subsequent instrumental detection. In this work, a three-dimensional nitrogen-rich conjugated microporous polymer (DCT-Try-CMP) was synthesized with 3,6-dichloro-1,2,4,5-tetrazine and triptycene as monomers via Friedel-Crafts reaction. It presented a good adsorption capability for the NACs. After optimization, a solid-phase microextraction with DCT-Try-CMP fiber combined with gas chromatography-flame ionization detection for the quantitation of trace NACs in environmental water and soil samples was developed. The limits of detection of the method (S/N = 3) for environmental water and soil samples were 0.08–0.30 μg L-1 and 1.00–5.00 ng g-1, and the limits of quantification (S/N = 9) were 0.27–0.90 μg L-1 and 3.00–15.0 ng g-1, respectively. The linear quantification response ranges for the analytes were 0.27–200 μg L-1 and 3.00–1000 ng g-1 for water and soil samples, respectively. For water samples at the analytes concentrations of 5.00, 50.0 and 100 μg L-1, the method recoveries ranged from 82.2% to 120% and for soil samples at the concentrations of 15.0, 200 and 400 ng g-1, the method recoveries fell in the range from 80.2% to 120%. The method provides a sensitive and effective approach for monitoring trace NACs in environmental water and soil samples.

Анализ методов многопараметрической оптимизации для механической обработки деталей

In the modern world, digitalization has become an integral part of various sectors of the economy, including manufacturing enterprises. However, for the efficient operation and development of the production process, it is necessary not only to be able to use the latest technologies, but also to be able to analyze and optimize the available data. One of the most effective methods of data analysis and management is multicriteria analysis. This method allows you to take into account many factors when making decisions, which is important for production processes where there are many variables and constraints. At the same time, large volumes of data collected and stored by the enterprise help improve the quality of decisions made and increase the efficiency of the production process. Data analysis allows you to identify patterns, predict the behavior of processes, and subsequently optimize their operation.

Application Of FMEA Method In Halal Risk Determination On Halal Poultry Slaughtering Operations

The importance of halal slaughtered chicken has become crucial in the era of globalization and complex supply chains. The rising awareness among Muslim communities regarding the integrity of slaughtered chicken products has led to a focus on adhering to halal compliance standards and Islamic law. This study aims to qualitatively analyze the halal risk points in chicken slaughtering operations, by using the Failure Mode and Effect Analysis (FMEA) method, in order to identify potential halal risks and ensure the integrity of halal chicken products. This qualitative study uses a analysis approach by examining secondary sources including journals, articles, guidelines and standards related to halal slaughtering process. In this study, seven Halal Risk Points (HRP) have been identified in the entire halal slaughtering process of broilers. Based on the results of the risk assessment through the FMEA method, each determined HRP will be assessed based on the severity, occurrence and detection rating scale. The final result of this study found that only six HRPs have a Risk Priority Number (RPN) value that exceeds 100. Therefore, corrective action recommendations are proposed to prevent any halal non-compliance from occurring on the process involved. Overall, the FMEA framework in this study is a new approach in guaranteeing the integrity of chicken slaughter products and is able to have a positive impact on the halal chicken slaughter industry players and also the implementers of the halal management system.

Comparative Analysis of K-Nearest Neighbors and Decision Tree Methods in Determining Students’ Purchase Interest in MacBook Laptops

In the context of increasingly competitive technology markets, companies need to know consumer preferences accurately to optimize product offerings and increase sales. Two classification methods that are often used in data mining, namely K-Nearest Neighbors and Decision Tree, have their own advantages and disadvantages. This study proposes a solution that involves processing student data using both classification methods to identify the most accurate and effective method for identifying purchase intentions. This study aims to compare the performance of the two methods in determining student purchase intentions for MacBook laptops. The research methodology includes collecting data from 100 students covering various factors such as user experience, design and portability, technical specifications, price, and security. This data is then classified using the K-Nearest Neighbors and Decision Tree methods. Furthermore, a confusion matrix is used to provide a more detailed picture of the performance of the two methods. The results of the study show that the Decision Tree method has a higher accuracy (91%) compared to K-Nearest Neighbors (88%). In addition, Decision Tree excels in other metrics such as precision (87.18% vs. 85.71%), recall (89.47% vs. 85.71%), specificity (91.94% vs. 89.66%), and F1-Score (88.31% vs. 85.71%). The decision tree also has a higher NPV value and lower FPR and FNR rates than K-Nearest Neighbors, indicating that it is superior in avoiding misclassification. The study's conclusion is that the Decision Tree method is more effective and accurate than K-Nearest Neighbors in determining students' purchase intentions for MacBook laptops. The decision tree shows better performance in almost all evaluation metrics, making it a more reliable method to use in consumer data analysis. The results of this study are expected to help companies choose a more appropriate and effective analysis method for their marketing strategies, as well as provide a basis for further research in the field of consumer purchase intention classification.

The Relationship between E-Health Literacy and Educational Participation Motivation among Elderly Individuals: The Chained Mediating Effects of Self-Identity and Social Capital.

Aims/Background In light of the increased utilization of digital technology among the elderly population, understanding the relationship between e-health literacy, self-identity, social capital, and educational participation motivation has become crucial. This study aims to investigate these relationships and explore the mediating effects of self-identity and social capital on the elderly population. By shedding light on these aspects, the study seeks to contribute to the existing knowledge base and inform intervention strategies to enhance the elderly individuals' overall well-being and engagement with digital health resources. Methods A questionnaire survey was conducted in a sample consisting of 578 elderly participants. Data of the surveyed variables were analyzed. Mediating effect analysis methods were employed to explore the mediating roles of self-identity and social capital in the relationship between e-health literacy and educational participation motivation among the elderly individuals. Results The results revealed a significant positive correlation between e-health literacy, self-identity, social capital, and educational participation motivation among elderly individuals. Furthermore, self-identity and social capital were found to play significant mediating roles between e-health literacy and educational participation motivation. Specifically, self-identity and social capital acted as complete mediators, with a mediating effect value of 0.61, between e-health literacy and educational participation motivation. Additionally, the chained mediating effect of self-identity and social capital was also significant. Conclusion This study demonstrated that e-health literacy and educational participation motivation are closely intertwined, with self-identity and social capital acting as the mediators in this association, in the elderly population, providing valuable guidance for enhancing the health and quality of life and offering insightful references for the development and implementation of relevant policies.

Large Group Failure Mode and Effects Analysis Method Considering Individual Concerns and Integrated Consensus Mechanism in Social Network Context: An Application to Mobile Medical Privacy Risk Evaluation

Large Group Failure Mode and Effects Analysis Method Considering Individual Concerns and Integrated Consensus Mechanism in Social Network Context: An Application to Mobile Medical Privacy Risk Evaluation

Analysis on the application of FMEA in 'instrument and equipment surface cleaning and disinfection' in hospitals based on standardization and cleaning and disinfection information system management.

To analyze the application of 'instrument and equipment surface cleaning and disinfection' in hospitals based on standardization and the management of cleaning and disinfection information systems. Employees and all cleaning and disinfected instruments and equipment from 56 inpatient departments in our hospital were selected as the subjects of observation. The period before the intervention (January 2023) was designated as the control group, while the period after the intervention (July 2023) was designated as the study group. In the control group, the instruments and equipment under routine management were disinfected. The research team applied the Failure Mode and Effects Analysis (FMEA) method to clean and disinfect the surfaces of instruments and equipment on the basis of standardization and cleaning and disinfection information system management. Employees' theoretical knowledge points and operational skill scores before and after the intervention were compared and evaluated. The changes in the risk priority coefficient (RPN) values of high-risk factors were analyzed. Fifty-six clinical medical staff from 56 inpatient departments in the hospital were selected to evaluate the clinical satisfaction of the cleaning and disinfection management of instruments and equipment before and after the intervention, and the clinical satisfaction of the two groups was compared. The scores of theoretical knowledge and operational skills of the staff in the research group were significantly higher than those in the control group. The passing rates of theoretical knowledge and operational skills in the control group and the research group were 44.64 and 94.64% respectively, and 55.36 and 96.43%, respectively. The qualified rate of theoretical knowledge and operational skills of staff in the study group was significantly higher than that in the control group (p < 0.05). The RPN scores of medical personnel, environment, system and system guarantee factors in the control group were 80, 80, 80, and 100, respectively. The RPN scores of medical personnel factors, environmental factors, system factors and system guarantee factors in the research group were 6, 24, 24, and 36, respectively. Through standardization and cleaning and disinfection information system management, the theoretical knowledge and technical operation capabilities of cleaning can be effectively improved.

Reliability Analysis of Coal Feeder Instrumentation Using Failure Mode and Effect Analysis (FMEA) at PT. PLN Nusantara Power UP Tenayan

PLN Nusantara Power UP Tenayan is a coal-fired power plant supplying electricity to the Riau region with a capacity of 2 x 110 MW. Operating 24/7, the plant faces frequent failures, particularly in the instrumentation components of the coal feeder. This research employs the Failure Mode and Effect Analysis (FMEA) method to identify and analyze these failures. The Risk Priority Number (RPN), calculated by multiplying severity, occurrence, and detection ratings, helps determine the failure level. Research stages include a preliminary study, problem identification, observations and interviews, data collection and processing, FMEA analysis, and conclusion drawing. The results indicate that the instrumentation components are generally reliable, with RPN values below the critical threshold of 200. The speed sensor, with the highest RPN value of 160, is still categorized as reliable. Failures in these components can lead to operational failures, suboptimal coal feeder performance, and monitoring difficulties. The study recommends preventive maintenance every six months to enhance reliability and minimize disruptions.

Analisa Keandalan Instrumentasi Boiler Feed Pump Menggunakan Metode Failure Mode and Effect Analysis (FMEA) di PT.PLN Nusantara Power UP Tenayan

&lt;p&gt;&lt;em&gt; &lt;/em&gt;&lt;strong&gt;PT. PLN Nusantara Power UP Tenayan is a company operating in the power generation sector and focuses on operating PLTUs Tenayan, Indonesia. One part of the machine that influences the operation of the generator is the boiler feed pump. Lack of optimal boiler feed pump instrumentation often results in failure in operating activities. This research uses the Failure Mode and Effect Analysis (FMEA) method with the research objective of finding out the causes of failures that occur, identifying the type of failure, and determining the RPN value and the impact that occurs. The analysis results, it shows that the Boiler Feed Pump instrumentation components still meet operating standards because the Risk Priority Number (RPN) value is below 200, even though the Speed Sensor has a fairly high RPN value but is still in the reliable category, the result of identifying the type of failure that occurs is that the indicator reading is not actual, the component is not functioning, the highest RPN value for the Boiler Feed Pump component is the Speed Sensor component with an RPN value of 160 and the lowest RPN value is for the pressure indicator component with an RPN value of 30. The most important recommended action for the Speed Sensor component is to carry out maintenance for 1 month very.&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Keywords&lt;/em&gt;&lt;/strong&gt; – &lt;em&gt;Boiler Feed Pump, FMEA, Keandalan, Pembangkit, RPN.&lt;/em&gt;&lt;/p&gt;

Challenges and effective tracking down strategies of antibiotic contamination in aquatic ecosystem.

A growing environmental concern revolves around the widespread use of medicines, particularly antibiotics, which adversely impact water quality and various life forms. The unregulated production and utilization of antibiotics not only affect non-targeted organisms but also exert significant evolutionary pressures, leading to the rapid development of antimicrobial resistance (AMR) in bacterial communities. To address this issue, global studies have been conducted to assess the prevalence and quantities of antibiotics in various environmental components including freshwater, ocean, local sewage, and fish. These studies aim to establish effective analytical methods for identifying and measuring antibiotic residues in environmental matrices that might enable authorities to establish norms for the containment and disposal of antibiotics. This article offers a comprehensive overview of methods used to extract antibiotics from environmental matrices exploring purification techniques such as liquid-liquid extraction, solid-phase extraction, green extraction techniques, and concentration methods like lyophilization and rotary evaporation. It further highlights qualitative and quantitative analysis methods, high-performance liquid chromatography, ultra-high-performance liquid chromatography, and liquid chromatography-tandem along with analytical methods such as UV-Vis and tandem mass spectrometry for detecting and measuring antibiotics. Urgency is underscored for proactive strategies to curb antibiotic contamination, safeguarding the integrity of aquatic ecosystems and public health on a global scale.

Green Analytical Method for Perfluorocarboxylic Acids (PFCAs) in Water of Stir Bar Sorptive Extraction Coupled with Thermal Desorption–Gas Chromatography—Mass Spectroscopy

Perfluoroalkyl carboxylic acids (PFCAs) are a significant group of per- and polyfluoroalkyl substances (PFASs). They are persistent organic chemicals manufactured for their resistance to heat, water, and stains. PFCAs are ubiquitous in the environment, particularly in surface water and wastewater, because they are widely used in everyday consumer products. This contamination poses a risk to drinking water supplies and human health, necessitating sensitive and effective analytical methods. Traditional liquid chromatography–tandem mass spectrometry (LC-MS/MS) is commonly used but involves complex sample handling and high costs. In this study, we developed an enhanced stir bar sorptive extraction (SBSE) method coupled with thermal desorption–gas chromatography–mass spectrometry (TD-GC-MS) for the analysis of PFCAs in water. This method demonstrates linearity, with R2 values from 0.9892 to 0.9988, and low limits of detection (LOD) between 21.17 ng/L and 73.96 ng/L. Recovery rates varied from 47 to 97%, suggesting efficient extraction. Compared to traditional methods, the developed SBSE technique requires only a 1 mL sample volume and minimal amounts of solvents, enhancing eco-friendliness and reducing potential contamination and handling errors. This method also demonstrated good precision and robustness across various water matrices. Overall, the developed method offers a precise, eco-friendly, and reliable approach for analyzing PFCAs in diverse water samples.

Identification of Degradation Reasons for a CO2 MEA Electrolyzer Using the Distribution of Relaxation Times Analysis.

The application of membrane electrode assembly (MEA) in electrocatalytic CO2 reduction (ECO2R) technology is an essential step toward industrialization. Nevertheless, the issue of ECO2R failure in MEA during extended operation hinders its industrial application. In this work, we employed in situ, nondestructive electrochemical impedance spectroscopy (EIS) techniques combined with distribution of relaxation times (DRT) methodology to diagnose the causes of the failure. By systematically investigating the variations in polarization resistance throughout the degradation process and utilizing a controlled variable approach to identify the origin of polarization, we diagnosed the degeneration of ionomers as the primary cause of the performance degradation of ECO2R in this instance. We further confirmed the reliability of our findings through material characterization and respraying ionomers onto the catalyst surface. This research provides an effective diagnostic method for the failure analysis of ECO2R performance in MEA, which is crucial for advancing industrialization of ECO2R technology.

Analisis Gagal Antar Surat dan Paket PT Pos Indonesia (Persero) Cabang Sekejati Bandung Menggunakan Metode Fault Tree Analysis (FTA) dan Failure Mode and Effect Analysis (FMEA)

PT Pos Indonesia (Persero) is a State-Owned Enterprise (BUMN) which is active in postal and logistics services. The Postal Processing Center itself is a postal institution whose mission is to plan, organize, implement and control and is responsible for the implementation of the CPTDR process.The occurrence of delivery failures at PT Pos Indonesia (Persero) Sekejati Bandung Branch reached 4% in a period of four months, this shows that DC Sekejati Bandung has not achieved the target determined by the company, where the company itself determines the maximum percentage for delivery failures of only 2%. In the research carried out by the author, the methods used were the Fault Tree Analysis (FTA) Method and also the Failure Mode and Effect Analysis (FMEA) Method to solve the problems that occurred. Based on the results of the analysis carried out, the author found that the most frequent problems were unclear addresses and recipients changing addresses.

A Risk Management Application by Failure Mode and Effect Analysis (FMEA) Method in A Road Drainage Channel Construction Project at H.M Ardan Street of South Sangatta District East Kutai, Indonesia

A construction project of road drainage channel in H.M Ardan Street, South Sangatta District (Multy Years) is included into type of construction project with high level of risk and work accidents since it has large number of workers, the use of sophisticated work tools and machines that require special methods and skills also strong supervision in its usage. These matters could bring undesirable impact potentiality in particular for the occupational safety and health aspect. A negligence of OSH implementation in the construction project can lead to work accident risk within the project. A work safety management is a part of management aspect with function to prevent any work accidents. Its prevention can be done by controlling the occurrence of high-potential work accidents whether in terms of consequences, occurrence probability, also simplicity in its detection. Many safety management methods have been introduced as method that able to be used for potential work accident identification, or measure the level of work accident’s risk, also for evaluating the work accidents; which leads to the Failure Mode and Effect Analysis (FMEA) method as the most appropriate method to achieve the objectives as described above. The selected method for this research was Failure Mode and Effect Analysis (FMEA) method, whereas the data source of this research were primary data obtained from interviews and questionnaires, and secondary data obtained from site plan drawings and identification data of the construction of drainage channels on H.M Ardan Street of South Sanggata District (Multi Years). According to FMEA analysis, there were 15 dominant risks with RPN values above the average value with 4 risks having a value of 48 as the highest RPN value found in this study. Type of risks with the highest RPN value were (a) stricken by heavy equipment and materials during loading and unloading construction materials; (b) fatigue due to an overly busy mobilization schedule; (c) workers fall while installing the formwork; (d) workers’ hand was pierced by nail or getting hit by hammer or wood. Furthermore, by the domino analysis conducted for this research, it produced several precautions and prevention recommendations that grouped into three main stages: Lack of Control, Basic Causes, and Immediate Causes.

On-Line Analysis of Cigarette Smoke Based on Microwave Plasma Torch Mass Spectrometry.

Cigarette smoke contains a large number of chemicals, including both flavor components and harmful substances. The mainstream smoke (MSS) generated by smoking is directly inhaled by individuals, making it crucial to establish an effective method for smoke detection and analysis. One promising technique for analyzing smoke is MPT-MS (Microwave plasma torch mass spectrometry). This approach offers several advantages in accurately detecting the composition of cigarette smoke. By combining MPT-MS with a smoke pumping device, we can achieve real-time online detection of smoke components. We successfully detected 22 flavor compounds present in the smoke. These compounds contribute to the distinct taste of cigarettes. Moreover, we identified 2 polycyclic aromatic hydrocarbons (PAHs) in the smoke. PAHs are known carcinogens and are of great concern in terms of their potential health risks. The successful detection and identification of flavor compounds and PAHs using our method confirm the online detection capability of MPT-MS. This approach provides an efficient and reliable means for analyzing the complex composition of cigarette smoke. By utilizing MPT-MS, we can gain valuable insights into the chemical composition of cigarette smoke and can inform the development of strategies and policies aimed at reducing the harmful effects of smoking and protecting public health.

Analysis of Liquefaction Susceptibility in the Nakdonggang Delta Region via Effective Stress Analysis

During the 2017 M5.4 Pohang earthquake, significant damage occurred because of liquefaction. However, South Korea is located in a stable continental region where earthquakes are rare, and the widely-used simplified liquefaction assessment methods have not been validated domestically. Hence, this study evaluates liquefaction at several locations in the Nakdonggang Delta region via effective stress analysis and compares the results with those of the simplified method of Boulanger and Idriss (2008). For the liquefaction assessment, a hypothetical earthquake scenario with a magnitude of 6.2 on the Dongnae Fault was assumed, and ground motions were generated via broadband hybrid ground motion simulation. The effective stress analysis was performed using OpenSeesPY and a multi-yield plasticity model. The results of the liquefaction assessment revealed significant differences between the two methods. The simplified method indicated that shallow sediments in the Nakdonggang Delta region were more susceptible to liquefaction, whereas the effective stress analysis indicated that loose, shallow sediments were more vulnerable. These results suggest that simplified liquefaction assessment methods must be carefully validated for application in South Korea. Future studies will need to accurately reflect dynamic soil properties based on experimental data to help validate effective stress analysis methods and assess the applicability of simplified methods for practical application in South Korea.

A non-uniform compound strip method for effective buckling analysis of stiffened structures

In this paper, a versatile compound strip method (CSM) with non-uniform spline functions is developed for buckling analysis of stiffened structures. The proposed method further extends the existing CSM by permitting the flexible modification of local knots to place the stiffeners along the strip arbitrarily. In addition, a reasonable knot arrangement can achieve an accurate solving procedure with improved convergence. The displacements of stiffeners are expressed compatibly by the fundamental parameters of skin according to the beam-plate model. Consequently, the reinforcement of stiffeners is naturally incorporated by adding the beam stiffness matrix to the corresponding strips. Based on the first-order shear deformation plate theory and non-uniform spline functions, the semi-analytical formulations of stiffened structures are established. The present method provides the possibility to achieve the local mesh refinement in the finite strip methods. The convergence and validation study is demonstrated through the comparisons with the results of the existing literature solution and finite element method. In conclusion, a more efficient and applicable CSM is proposed to analyze the buckling behaviors of stiffened structures.

What can we learn from multimorbidity? A deep dive from its risk patterns to the corresponding patient profiles

Multimorbidity, the presence of two or more chronic conditions within an individual, represents one of the most intricate challenges for global health systems. Traditional single-disease management often fails to address the multifaceted nature of multimorbidity. Network model emerges as a growing field for elucidating the interconnections among multimorbidity. However, the field lacks a standardized method to compute and visually represent of these networks. Given the challenges, this study proposes a three-stage methodology to decipher multimorbidity. First, we integrate the Failure Modes and Effects Analysis (FMEA) method with the multimorbidity encapsulation framework to develop the Multimorbidity Risk Network (MRN). Second, we use complex network techniques to identify high-risk patterns within MRN communities. Finally, we apply machine learning techniques to correlate these communities with the biological attributes of patients that have been marginalized in most studies. Our approach advocates a paradigm shift from the conventional focus on single diseases to a holistic, patient-centric approach, providing decision-makers with integrated information technology artifacts for deciphering the multimorbidity.