Data Mining in Healthcare: Applying Strategic Intelligence Techniques to Depict 25 Years of Research Development

Removal of Heavy Metals from Wastewaters and Other Aqueous Streams by Pressure-Driven Membrane Technologies: An Outlook on Reverse Osmosis, Nanofiltration, Ultrafiltration and Microfiltration Potential from a Bibliometric Analysis

A bibliometric study to analyze the scientific documents released until 2024 in the database Scopus related to the use of pressure-driven membrane technologies (microfiltration, ultrafiltration, nanofiltration and reverse osmosis) for heavy metal removal was conducted. The work aimed to assess the primary quantitative attributes of the research in this field during the specified period. A total of 2205 documents were identified, and the corresponding analysis indicated an exponential growth in the number of publications over time. The contribution of the three most productive countries (China, India and USA) accounts for more than 47.1% of the total number of publications, with Chinese institutions appearing as the most productive ones. Environmental Science was the most frequent knowledge category (51.9% contribution), followed by Chemistry and Chemical Engineering. The relative frequency of the keywords and a complete bibliometric network analysis allowed the conclusion that the low-pressure technologies (microfiltration and ultrafiltration) have been more deeply investigated than the high-pressure technologies (nanofiltration and reverse osmosis). Although porous low-pressure membranes are not adequate for the removal of dissolved heavy metals in ionic forms, the incorporation of embedded adsorbents within the membrane structure and the use of auxiliary chemicals to form metallic complexes or micelles that can be retained by this type of membrane are promising approaches. High-pressure membranes can achieve rejection percentages above 90% (99% in the case of reverse osmosis), but they imply lower permeate productivity and higher costs due to the required pressure gradients.

Diagnoses and medications associated with delayed ejaculation

Background

Delayed ejaculation (DE) is a disorder that can cause significant distress for sexually active men. The etiology of DE is largely idiopathic, with even less being known about clinical factors associated with the condition.

Aim

We sought to use data mining techniques to examine a broad group of health conditions and pharmaceutical treatments to identify factors associated with DE.

Methods

Using an insurance claims database, we evaluated all men with a diagnosis of DE and matched them to a cohort (1:1) of men with other male sexual disorders of urologic origin (ie, erectile dysfunction [ED] and Peyronie's disease [PD]). Given the low prevalence of DE, we incorporated the random forest approach for classification of DE vs controls, with a plethora of predictors and cross-validation with the least absolute shrinkage and selection operator (LASSO). We used both a high-performance generalized linear model and a multivariate logistic model. The area under the curve was reported to demonstrate classifier performance, and odds ratios were used to indicate risks of each predictor. We also evaluated for differences in the prevalence of conditions in DE by race/ethnicity.

Outcomes

Clinical factors (ie, diagnoses and medications) associated with DE were identified.

Results

In total, 11 602 men with DE were matched to a cohort of men with PD and ED. We focused on the 20 factors with the strongest association with DE across all models. The factors demonstrating positive associations with DE compared to other disorders of male sexual dysfunction (ie, ED and PD) included male infertility, testicular dysfunction, anxiety, disorders of lipid metabolism, alpha adrenergic blocker use, anemia, antidepressant use, and psychoses such as schizophrenia or schizoaffective disorder. In addition, the prevalence of several conditions varied by race/ethnicity. For example, male infertility was present in 5% of Asian men compared to <2% of men of other races.

Clinical Implications

Several medical conditions and pharmacologic treatments are associated with DE, findings that may provide insight into the etiology of DE and offer treatment options.

Strengths and Limitations

This study is to our knowledge the first to use using data mining techniques to investigate the association between medical conditions/pharmacologic agents and the development of subsequent DE. The generalizability of our findings is limited given that all men were commercially insured.

Conclusion

DE is associated with multiple medical conditions, a finding that may help identify the etiology for this disorder.

Studying infant mortality: A demographic analysis based on data mining models

Child mortality, particularly among infants below 5 years, is a significant community well-being concern worldwide. The health sector's top priority in emerging states is to minimize children's death and enhance infant health. Despite a substantial decrease in worldwide deaths of children below 5 years, it remains a significant community well-being concern. Children under five years of age died at 37 per 1,000 live birth globally in 2020. However, in underdeveloped countries such as Pakistan and Ethiopia, the fatality rate of children per 1,000 live birth is 65.2 and 48.7, respectively, making it challenging to reduce. Predictive analytics approaches have become well-known for predicting future trends based on previous data and extracting meaningful patterns and connections between parameters in the healthcare industry. As a result, the objective of this study was to use data mining techniques to categorize and highlight the important causes of infant death. Datasets from the Pakistan Demographic Health Survey and the Ethiopian Demographic Health Survey revealed key characteristics in terms of factors that influence child mortality. A total of 12,654 and 12,869 records from both datasets were examined using the Bayesian network, tree (J-48), rule induction (PART), random forest, and multi-level perceptron techniques. On both datasets, various techniques were evaluated with the aforementioned classifiers. The best average accuracy of 97.8% was achieved by the best model, which forecasts the frequency of child deaths. This model can therefore estimate the mortality rates of children under five years in Ethiopia and Pakistan. Therefore, an online model to forecast child death based on our research is urgently needed and will be a useful intervention in healthcare.

Critical Success Factors for Successful Implementation of Healthcare 4.0: A Literature Review and Future Research Agenda

The digitization of healthcare services is a major shift in the manner in which healthcare services are offered and managed in the modern era. The COVID-19 pandemic has speeded up the use of digital technologies in the healthcare sector. Healthcare 4.0 (H4.0) is much more than the adoption of digital tools, however; going beyond that, it is the digital transformation of healthcare. The successful implementation of H 4.0 presents a challenge as social and technical factors must be considered. This study, through a systematic literature review, expounds ten critical success factors for the successful implementation of H 4.0. Bibliometric analysis of existing articles is also carried out to understand the development of knowledge in this domain. H 4.0 is rapidly gaining prominence, and a comprehensive review of critical success factors in this area has yet to be conducted. Conducting such a review makes a valuable contribution to the body of knowledge in healthcare operations management. Furthermore, this study will also help healthcare practitioners and policymakers to develop strategies to manage the ten critical success factors while implementing H 4.0.

IMPLEMENTATION OF THE DECISION TREE METHOD IN EXPERT ANALYSIS OF THE MEDICAL ERRORS IN OBSTETRIC PRACTICE

OBJECTIVE

Aim: To identify expert patterns in cases of improper medical care in obstetric practice based on the analysis of the materials of judicial and investigative cases initiated against obstetrician-gynaecologists in cases of improper performance of their professional duties, using the decision tree method.

PATIENTS AND METHODS

Materials and methods: A retrospective review of all alleged medical malpractice cases (a total 350) between 2007 and 2016 handled at Ternopil Regional Bu¬reau of Forensic Medical Examination, Chernivtsi Regional Bureau of Forensic Medical Examination and Zhytomir Regional Bureau of Forensic Medical Examination (Ukraine) was performed.

RESULTS

Results: Expert commissions confirmed various shortcomings and omissions in provision of medical care in 232 (72.0%) of the investigated cases. Obstetricians were involved in claims in 82 (23.4%) cases. Application of intelligent data processing technology "Data Mining" with the use of the decision tree method revealed that inadequacies with regard to the medical records (attribute usage 100%) were the most informative attribute in the expert assessment of inappropriate medical care in obstetrics. Defects in the provision of obstetric care with a probability (P = 0.71) occur simultaneously both at pre-hospital and hospital levels and with a high probability (P = 0.83) result in severe consequences.

CONCLUSION

Conclusions: The use of modern technologies for data analysis and processing contributes to the formulation of mathematically substantiated statements that significantly enhance the reliability of expert opinions in cases of forensic medical examination attached to dereliction of duties by the medical practitioners.

Implementation of Health Information Systems to Improve Patient Identification

Wellbeing can be ensured in society through quality healthcare, a minimum of medical errors, and the improved performance of healthcare professionals. To this end, health information systems have been implemented in hospitals, with this implementation representing progress in medicine and information technologies. As a result, life expectancy has significantly increased, standards in healthcare have been raised, and public health has improved. This progress is influenced by the process of managing healthcare organizations and information systems. While hospitals tend to adapt health information systems to reduce errors related to patient misidentification, the rise in the occurrence and recording of medical errors in Lebanon resulting from failures to correctly identify patients reveals that such measures remain insufficient due to unknown factors. This research aimed to investigate the effect of health information systems (HISs) and other factors related to work-related conditions on reductions in patient misidentification and related consequences. The empirical data were collected from 109 employees in Neioumazloum Hospital in Lebanon. The results revealed a correlation between HISs and components and the effects of other factors on patient identification. These other factors included workload, nurse fatigue, a culture of patient safety, and lack of implementation of patient identification policies. This paper provides evidence from a Lebanese hospital and paves the way for further studies aiming to explore the role of information technologies in adopting HISs for work performance and patient satisfaction. Improved care for patients can help achieve health equality, enhance healthcare delivery performance and patient safety, and decrease the numbers of medical errors.

Diagnosis of brucellosis disease using data mining: A case study on patients of a hospital in Tehran

BACKGROUND

Brucellosis is a common zoonotic infection of humans from livestock. This bacterial infection is acquired from infected animals and their products. The pathogen of this disease is a genus of bacilli called Brucella, and no effective vaccine has been discovered yet for the prevention of human brucellosis.

OBJECTIVES

The present study is mainly conducted to diagnose brucellosis accurately and timely, using Data Mining techniques. Based on the knowledge discovered with Data Mining and opinions of specialist physicians, this study aims to propose instructions for diagnosing brucellosis.

MATERIALS AND METHODS

The dataset used in this study contains 340 samples and is extracted from the files of patients at Tehran Imam Khomeini Hospital from the years 2010-2020. Attributes of this dataset have been determined based on domain expert opinions, namely specialist physicians. After initial analysis and data pre-processing, various Data Mining techniques have been employed to diagnose brucellosis, including neural networks, Bayesian networks, and decision trees.

RESULTS

According to the recorded data, 270 people (approximately 79% of samples) had brucellosis. Some clinical symptoms were more prominent among infected patients, including fever, arthritis, tremor, decreased appetite, and nightly perspiration. Among all employed Data Mining techniques in this study, the decision tree with C5.0 pruning algorithm possessed the highest accuracy in diagnosing patients with brucellosis (approximately 99% accuracy). Based on the obtained final model, the most important factors for diagnosing brucellosis are the Wright test, Coombs Wright test, blood culture test, and living place.

DISCUSSION AND CONCLUSION

According to the results of this study, brucellosis can be diagnosed with a high accuracy using Data Mining techniques. Furthermore, the most significant factors for diagnosing brucellosis disease can be identified by Data Mining. Among all investigated techniques in this study, the decision tree with C5.0 pruning algorithm has the most accuracy in diagnosing brucellosis. Given the decision tree created by the C5.0 algorithm and the opinions of specialist physicians, some instructions are proposed based on a decision-making framework to classify referents into patient and non-patient groups. These instructions can accelerate the diagnosis, reduce therapeutic costs, and decrease treatment period.

A Bibliometric Analysis of Research on Selenium in Drinking Water during the 1990-2021 Period: Treatment Options for Selenium Removal

A bibliometric analysis based on the Scopus database was carried out to summarize the global research related to selenium in drinking water from 1990 to 2021 and identify the quantitative characteristics of the research in this period. The results from the analysis revealed that the number of accumulated publications followed a quadratic growth, which confirmed the relevance this research topic is gaining during the last years. High research efforts have been invested to define safe selenium content in drinking water, since the insufficient or excessive intake of selenium and the corresponding effects on human health are only separated by a narrow margin. Some important research features of the four main technologies most frequently used to remove selenium from drinking water (coagulation, flocculation and precipitation followed by filtration; adsorption and ion exchange; membrane-based processes and biological treatments) were compiled in this work. Although the search of technological options to remove selenium from drinking water is less intensive than the search of solutions to reduce and eliminate the presence of other pollutants, adsorption was the alternative that has received the most attention according to the research trends during the studied period, followed by membrane technologies, while biological methods require further research efforts to promote their implementation.

Extracting Significant Comorbid Diseases from MeSH Index of PubMed

Text mining is an important research area to be explored in terms of understanding disease associations and have an insight in disease comorbidities. The reason for comorbid occurrence in any patient may be genetic or molecular interference from any other processes. Comorbidity and multimorbidity may be technically different, yet still are inseparable in studies. They have overlapping nature of associations and hence can be integrated for a more rational approach. The association rule generally used to determine comorbidity may also be helpful in novel knowledge prediction or may even serve as an important tool of assessment in surgical cases. Another approach of interest may be to utilize biological vocabulary resources like UMLS/MeSH across a patient health information and analyze the interrelationship between different health conditions. The protocol presented here can be utilized for understanding the disease associations and analyze at an extensive level.

A Bibliometric Network Analysis of Recent Publications on Digital Agriculture to Depict Strategic Themes and Evolution Structure

The agriculture sector is one of the backbones of many countries’ economies. Its processes have been changing to enable technology adoption to increase productivity, quality, and sustainable development. In this research, we present a scientific mapping of the adoption of precision techniques and breakthrough technologies in agriculture, so-called Digital Agriculture. To do this, we used 4694 documents from the Web of Science database to perform a Bibliometric Performance and Network Analysis of the literature using SciMAT software with the support of the PICOC protocol. Our findings presented 22 strategic themes related to Digital Agriculture, such as Internet of Things (IoT), Unmanned Aerial Vehicles (UAV) and Climate-smart Agriculture (CSA), among others. The thematic network structure of the nine most important clusters (motor themes) was presented and an in-depth discussion was performed. The thematic evolution map provides a broad perspective of how the field has evolved over time from 1994 to 2020. In addition, our results discuss the main challenges and opportunities for research and practice in the field of study. Our findings provide a comprehensive overview of the main themes related to Digital Agriculture. These results show the main subjects analyzed on this topic and provide a basis for insights for future research.

Data mining and knowledge discovery in databases for urban solid waste management: A scientific literature review

The processes related to solid waste management (SWM) are being revised as new technologies emerge and are applied in the area to achieve greater environmental, social and economic sustainability for society. To achieve our goal, two robust review protocols (Population, Intervention, Comparison, Outcome, and Context (PICOC) and Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA)) were used to systematically analyze 62 documents extracted from the Web of Science database to identify the main techniques and tools for Knowledge Discovery in Databases (KDD) and Data Mining (DM) as applied to SWM and explore the technological potential to optimize the stages of collecting and transporting waste. Moreover, it was possible to analyze the main challenges and opportunities of KDD and DM for SWM. The results show that the most used tools for SWM are MATLAB (29.7%) and GIS (13.5%), whereas the most used techniques are Artificial Neural Networks (35.8%), Linear Regression (16.0%) and Support Vector Machine (12.3%). In addition, 15.3% of the studies were conducted with data from China, 11.1% from India and 9.7% of the studies analyzed and compared data from several other countries. Furthermore, the research showed that the main challenges in the field of study are related to the collection and treatment of data, whereas the opportunities appear to be linked mainly to the impact on the pillars of sustainable development. Thus, this study portrays important issues associated with the use of KDD and DM for optimal SWM and has the potential to assist and direct researchers and field professionals in future studies.