An autonomous cycle of data analysis tasks for the clinical management of dengue

Assessing the knowledge, attitudes, and practices (KAP) of dengue in Thailand: a systematic review and meta-analysis

AIM

Dengue cases are on the rise in Thailand, coinciding with increases in flooding events. Studies pertaining to public knowledge, attitudes, and practices (KAP) of dengue prevention have frequently been used to better understand the public's needs towards dengue. While these studies were conducted in different settings, it is necessary to initiate a systematic review and meta-analysis of relevant studies.

SUBJECTS AND METHOD

We conducted a systematic review and meta-analysis of prior studies in Thailand that assessed the KAP towards dengue. Eligibility criteria were established and independently used by reviewers to select nine studies for the systematic review and three for the meta-analysis. Collectively, the nine studies included 3,058 individuals and 2,519 households.

RESULTS

The overall estimate of the proportion of participants with good knowledge of dengue prevention is 35% (95% CI: 14-59%), suggesting the majority of the population in Thailand had low levels of knowledge towards dengue. The poor levels of practice in Thailand were also observed and confirmed for the majority of the population by meta-analysis, with the pooled estimate of the proportion of participants with good practice of dengue prevention being 25% (95% CI: 22-27%). In contrast, most of the studies included in the systematic review reported positive attitudes towards dengue prevention, and this finding was also affirmed by the meta-analysis, as the pooled estimate of the proportion of positive attitudes towards dengue prevention is 61% (95% CI: 43-77%).

CONCLUSION

Despite good attitudes towards dengue prevention, poor knowledge and poor practices predominate, highlighting the need for enhanced public health campaigns to educate the public on dengue risks and prevention methods.

An explainable analysis of diabetes mellitus using statistical and artificial intelligence techniques

BACKGROUND

Diabetes mellitus (DM) is a chronic disease prevalent worldwide, requiring a multifaceted analytical approach to improve early detection and subsequent mitigation of morbidity and mortality rates. This research aimed to develop an explainable analysis of DM by combining sociodemographic and clinical data with statistical and artificial intelligence (AI) techniques.

METHODS

Leveraging a small dataset that includes sociodemographic and clinical profiles of diabetic and non-diabetic individuals, we employed a diverse set of statistical and AI models for predictive purposes and assessment of DM risk factors. The statistical tests used were Student's t-test and Chi-square, while the AI techniques were fuzzy cognitive maps (FCM), artificial neural networks (ANN), support vector machines (SVM), and XGBoost.

RESULTS

Our statistical models facilitated an in-depth exploration of variable associations, while the resulting AI models demonstrated exceptional efficacy in DM classification. In particular, the XGBoost model showed superior performance in accuracy, sensitivity and specificity with values of 1 for each of these metrics. On the other hand, the FCM stood out for its explainability capabilities by allowing an analysis of the variables involved in the prediction using scenario-based simulations.

CONCLUSIONS

An integrated analysis of DM using a variety of methodologies is critical for timely detection of the disease and informed clinical decision-making.

Development of data-driven machine learning models and their potential role in predicting dengue outbreak

Dengue fever is one of the most widespread vector-borne viral infections in the world, resulting in increased socio-economic burden. WHO has reported that 2.5 billion people are infected with dengue fever across the world, resulting in high mortalities in tropical and subtropical regions. The current article endeavors to present an overview of predicting dengue outbreaks through data-based machine-learning models. This artificial intelligence model uses real world data such as dengue surveillance, climatic variables, and epidemiological data and combines big data with machine learning algorithms to forecast dengue. Monitoring and predicting dengue incidences has been significantly enhanced through innovative approaches. This involves gathering data on various climatic factors, including temperature, rainfall, relative humidity, and wind speed, along with monthly records of dengue cases. The study functions as an efficient warning system, enabling the anticipation of dengue outbreaks. This early warning system not only alerts communities but also aids relevant authorities in implementing crucial preventive measures.

Exploring trends and autonomy levels of adaptive business intelligence in healthcare: A systematic review

OBJECTIVE

In order to comprehensively understand the characteristics of Adaptive Business Intelligence (ABI) in Healthcare, this study is structured to provide insights into the common features and evolving patterns within this domain. Applying the Sheridan's Classification as a framework, we aim to assess the degree of autonomy exhibited by various ABI components. Together, these objectives will contribute to a deeper understanding of ABI implementation and its implications within the Healthcare context.

METHODS

A comprehensive search of academic databases was conducted to identify relevant studies, selecting AIS e-library (AISel), Decision Support Systems Journal (DSSJ), Nature, The Lancet Digital Health (TLDH), PubMed, Expert Systems with Application (ESWA) and npj Digital Medicine as information sources. Studies from 2006 to 2022 were included based on predefined eligibility criteria. PRISMA statements were used to report this study.

RESULTS

The outcomes showed that ABI systems present distinct levels of development, autonomy and practical deployment. The high levels of autonomy were essentially associated with predictive components. However, the possibility of completely autonomous decisions by these systems is totally excluded. Lower levels of autonomy are also observed, particularly in connection with prescriptive components, granting users responsibility in the generation of decisions.

CONCLUSION

The study presented emphasizes the vital connection between desired outcomes and the inherent autonomy of these solutions, highlighting the critical need for additional research on the consequences of ABI systems and their constituent elements. Organizations should deploy these systems in a way consistent with their objectives and values, while also being mindful of potential adverse effects. Providing valuable insights for researchers, practitioners, and policymakers aiming to comprehend the diverse levels of ABI systems implementation, it contributes to well-informed decision-making in this dynamic field.

Predictive health monitoring: Leveraging artificial intelligence for early detection of infectious diseases in nursing home residents through discontinuous vital signs analysis

This research addresses the problem of detecting acute respiratory, urinary tract, and other infectious diseases in elderly nursing home residents using machine learning algorithms. The study analyzes data extracted from multiple vital signs and other contextual information for diagnostic purposes. The daily data collection process encounters sampling constraints due to weekends, holidays, shift changes, staff turnover, and equipment breakdowns, resulting in numerous nulls, repeated readings, outliers, and meaningless values. The short time series generated also pose a challenge to analysis, preventing the extraction of seasonal information or consistent trends. Blind data collection results in most of the data coming from periods when residents are healthy, resulting in excessively imbalanced data. This study proposes a data cleaning process and then builds a mechanism that reproduces the basal activity of the residents to improve the classification of the disease. The results show that the proposed basal module-assisted machine learning techniques allow anticipating diagnostics 2, 3 or 4 days before doctors decide to start treatment with antibiotics, achieving a performance measured by the area-under-the-curve metric of 0.857. The contributions of this work are: (1) a new data cleaning process; (2) the analysis of contextual information to improve data quality; (3) the generation of a baseline measure for relative comparison; and (4) the use of either binary (disease/no disease) or multiclass classification, differentiating among types of infections and showing the advantages of multiclass versus binary classification. From a medical point of view, the anticipated detection of infectious diseases in institutionalized individuals is brand new.

Case studies of clinical decision-making through prescriptive models based on machine learning

BACKGROUND

The development of computational methodologies to support clinical decision-making is of vital importance to reduce morbidity and mortality rates. Specifically, prescriptive analytic is a promising area to support decision-making in the monitoring, treatment and prevention of diseases. These aspects remain a challenge for medical professionals and health authorities.

MATERIALS AND METHODS

In this study, we propose a methodology for the development of prescriptive models to support decision-making in clinical settings. The prescriptive model requires a predictive model to build the prescriptions. The predictive model is developed using fuzzy cognitive maps and the particle swarm optimization algorithm, while the prescriptive model is developed with an extension of fuzzy cognitive maps that combines them with genetic algorithms. We evaluated the proposed approach in three case studies related to monitoring (warfarin dose estimation), treatment (severe dengue) and prevention (geohelminthiasis) of diseases.

RESULTS

The performance of the developed prescriptive models demonstrated the ability to estimate warfarin doses in coagulated patients, prescribe treatment for severe dengue and generate actions aimed at the prevention of geohelminthiasis. Additionally, the predictive models can predict coagulation indices, severe dengue mortality and soil-transmitted helminth infections.

CONCLUSIONS

The developed models performed well to prescribe actions aimed to monitor, treat and prevent diseases. This type of strategy allows supporting decision-making in clinical settings. However, validations in health institutions are required for their implementation.