Data-Driven Fundraising: Strategic Plan for Medical Education.

Unlabelled: Higher education institutions, including medical schools, increasingly rely on fundraising to bridge funding gaps and support their missions. This paper presents a viewpoint on data-driven strategies in fundraising, outlining a 4-step approach for effective planning while considering ethical implications. It outlines a 4-step approach to creating an effective, end-to-end, data-driven fundraising plan, emphasizing the crucial stages of data collection, data analysis, goal establishment, and targeted strategy formulation. By leveraging internal and external data, schools can create tailored outreach initiatives that resonate with potential donors. However, the fundraising process must be grounded in ethical considerations. Ethical challenges, particularly in fundraising with grateful medical patients, necessitate transparent and honest practices prioritizing donors' and beneficiaries' rights and safeguarding public trust. This paper presents a viewpoint on the critical role of data-driven strategies in fundraising for medical education. It emphasizes integrating comprehensive data analysis with ethical considerations to enhance fundraising efforts in medical schools. By integrating data analytics with fundraising best practices and ensuring ethical practice, medical institutions can ensure financial support and foster enduring, trust-based relationships with their donor communities.

A unified approach for Parkinson's disease recognition: imbalance mitigation and grid search optimized boosting with LightGBM.

The work elucidates the importance of accurate Parkinson's disease classification within medical diagnostics and introduces a novel framework for achieving this goal. Specifically, the study focuses on enhancing disease identification accuracy utilizing boosting methods. A standout contribution of this work lies in the utilization of a light gradient boosting machine (LGBM) coupled with hyperparameter tuning through grid search optimization (GSO) on the Parkinson's disease dataset derived from speech recording signals. In addition, the Synthetic Minority Over-sampling Technique (SMOTE) has also been employed as a pre-processing technique to balance the dataset, enhancing the robustness and reliability of the analysis. This approach is a novel addition to the study and underscores its potential to enhance disease identification accuracy. The datasets employed in this work include both gender-specific and combined cases, utilizing several distinctive feature subsets including baseline, Mel-frequency cepstral coefficients (MFCC), time-frequency, wavelet transform (WT), vocal fold, and tunable-Q-factor wavelet transform (TQWT). Comparative analyses against state-of-the-art boosting methods, such as AdaBoost and XG-Boost, reveal the superior performance of our proposed approach across diverse datasets and metrics. Notably, on the male cohort dataset, our method achieves exceptional results, demonstrating an accuracy of 0.98, precision of 1.00, sensitivity of 0.97, F1-Score of 0.98, and specificity of 1.00 when utilizing all features with GSO-LGBM. In comparison to AdaBoost and XGBoost, the proposed framework utilizing LGBM demonstrates superior accuracy, achieving an average improvement of 5% in classification accuracy across all feature subsets and datasets. These findings underscore the potential of the proposed methodology to enhance disease identification accuracy and provide valuable insights for further advancements in medical diagnostics.

Enhancing data practices for Whole Health: Strategies for a transformative future.

We explored the challenges and solutions for managing data within the Whole Health System (WHS), which operates as a Learning Health System and a patient-centered healthcare approach that combines conventional and complementary approaches. Addressing these challenges is critical for enhancing patient care and improving outcomes within WHS. The proposed solutions include prioritizing interoperability for seamless data exchange, incorporating patient-centered comparative clinical effectiveness research and real-world data to personalize treatment plans and validate integrative approaches, and leveraging advanced data analytics tools to incorporate patient-reported outcomes, objective metrics, robust data platforms. Implementing these measures will enable WHS to fulfill its mission as a holistic and patient-centered healthcare model, promoting greater collaboration among providers, boosting the well-being of patients and providers, and improving patient outcomes.

Laser Doppler Fluximetry in Cutaneous Vasculature: Methods for Data Analyses.

INTRODUCTION: Acquisition of a deeper understanding of microvascular function across physiological and pathological conditions can be complicated by poor accessibility of the vascular networks and the necessary sophistication or intrusiveness of the equipment needed to acquire meaningful data. Laser Doppler fluximetry (LDF) provides a mechanism wherein investigators can readily acquire large amounts of data with minor inconvenience for the subject. However, beyond fairly basic analyses of erythrocyte perfusion (fluximetry) data within the cutaneous microcirculation (i.e., perfusion at rest and following imposed challenges), a deeper understanding of microvascular perfusion requires a more sophisticated approach that can be challenging for many investigators. METHODS: This manuscript provides investigators with clear guidance for data acquisition from human subjects for full analysis of fluximetry data, including levels of perfusion, single- and multiscale Lempel-Ziv complexity (LZC) and sample entropy (SampEn), and wavelet-based analyses for the major physiological components of the signal. Representative data and responses are presented from a recruited cohort of healthy volunteers, and computer codes for full data analysis (MATLAB) are provided to facilitate efforts by interested investigators. CONCLUSION: It is anticipated that these materials can reduce the challenge to investigators integrating these approaches into their research programs and facilitate translational research in cardiovascular science.

OHDSI-compliance: a set of document templates facilitating the implementation and operation of a software stack for real-world evidence generation.

Introduction: The open-source software offered by the Observational Health Data Science and Informatics (OHDSI) collective, including the OMOP-CDM, serves as a major backbone for many real-world evidence networks and distributed health data analytics platforms. While container technology has significantly simplified deployments from a technical perspective, regulatory compliance can remain a major hurdle for the setup and operation of such platforms. In this paper, we present OHDSI-Compliance, a comprehensive set of document templates designed to streamline the data protection and information security-related documentation and coordination efforts required to establish OHDSI installations. Methods: To decide on a set of relevant document templates, we first analyzed the legal requirements and associated guidelines with a focus on the General Data Protection Regulation (GDPR). Moreover, we analyzed the software architecture of a typical OHDSI stack and related its components to the different general types of concepts and documentation identified. Then, we created those documents for a prototypical OHDSI installation, based on the so-called Broadsea package, following relevant guidelines from Germany. Finally, we generalized the documents by introducing placeholders and options at places where individual institution-specific content will be needed. Results: We present four documents: (1) a record of processing activities, (2) an information security concept, (3) an authorization concept, as well as (4) an operational concept covering the technical details of maintaining the stack. The documents are publicly available under a permissive license. Discussion: To the best of our knowledge, there are no other publicly available sets of documents designed to simplify the compliance process for OHDSI deployments. While our documents provide a comprehensive starting point, local specifics need to be added, and, due to the heterogeneity of legal requirements in different countries, further adoptions might be necessary.

A comprehensive investigation of clustering algorithms for User and Entity Behavior Analytics.

Introduction: Government agencies are now encouraging industries to enhance their security systems to detect and respond proactively to cybersecurity incidents. Consequently, equipping with a security operation center that combines the analytical capabilities of human experts with systems based on Machine Learning (ML) plays a critical role. In this setting, Security Information and Event Management (SIEM) platforms can effectively handle network-related events to trigger cybersecurity alerts. Furthermore, a SIEM may include a User and Entity Behavior Analytics (UEBA) engine that examines the behavior of both users and devices, or entities, within a corporate network. Methods: In recent literature, several contributions have employed ML algorithms for UEBA, especially those based on the unsupervised learning paradigm, because anomalous behaviors are usually not known in advance. However, to shorten the gap between research advances and practice, it is necessary to comprehensively analyze the effectiveness of these methodologies. This paper proposes a thorough investigation of traditional and emerging clustering algorithms for UEBA, considering multiple application contexts, i.e., different user-entity interaction scenarios. Results and discussion: Our study involves three datasets sourced from the existing literature and fifteen clustering algorithms. Among the compared techniques, HDBSCAN and DenMune showed promising performance on the state-of-the-art CERT behavior-related dataset, producing groups with a density very close to the number of users.

Childhood vaccination trends during 2019 to 2022 in Tanzania and the impact of the COVID-19 pandemic.

The COVID-19 pandemic has significantly disrupted healthcare systems at all levels globally, notably affecting routine healthcare services, such as childhood vaccination. This study examined the impact of these disruptions on routine childhood vaccination programmes in Tanzania. We conducted a longitudinal study over four years in five Tanzanian regions: Mwanza, Dar es Salaam, Mtwara, Arusha, and Dodoma. This study analyzed the trends in the use of six essential vaccines: Bacille Calmette-Guérin (BCG), bivalent Oral Polio Vaccine (bOPV), Diphtheria Tetanus Pertussis, Hepatitis-B and Hib (DTP-HepB-Hib), measles-rubella (MR), Pneumococcal Conjugate Vaccine (PCV), and Rota vaccines. We evaluated annual and monthly vaccination trends using time-series and regression analyses. Predictive modeling was performed using an autoregressive integrated moving average (ARIMA) model. A total of 32,602,734 vaccination events were recorded across the regions from 2019 to 2022. Despite declining vaccination rates in 2020, there was a notable rebound in 2021, indicating the resilience of Tanzania's immunization program. The analysis also highlighted regional differences in vaccination rates when standardized per 1000 people. Seasonal fluctuations were observed in monthly vaccination rates, with BCG showing the most stable trend. Predictive modeling of BCG indicated stable and increasing vaccination coverage by 2023. These findings underscore the robustness of Tanzania's childhood immunization infrastructure in overcoming the challenges posed by the COVID-19 pandemic, as indicated by the strong recovery of vaccination rates post-2020. We provide valuable insights into the dynamics of vaccination during a global health crisis and highlight the importance of sustained immunization efforts to maintain public health.

Remote sensing image information extraction based on Compensated Fuzzy Neural Network and big data analytics.

Medical imaging AI systems and big data analytics have attracted much attention from researchers of industry and academia. The application of medical imaging AI systems and big data analytics play an important role in the technology of content based remote sensing (CBRS) development. Environmental data, information, and analysis have been produced promptly using remote sensing (RS). The method for creating a useful digital map from an image data set is called image information extraction. Image information extraction depends on target recognition (shape and color). For low-level image attributes like texture, Classifier-based Retrieval(CR) techniques are ineffective since they categorize the input images and only return images from the determined classes of RS. The issues mentioned earlier cannot be handled by the existing expertise based on a keyword/metadata remote sensing data service model. To get over these restrictions, Fuzzy Class Membership-based Image Extraction (FCMIE), a technology developed for Content-Based Remote Sensing (CBRS), is suggested. The compensation fuzzy neural network (CFNN) is used to calculate the category label and fuzzy category membership of the query image. Use a basic and balanced weighted distance metric. Feature information extraction (FIE) enhances remote sensing image processing and autonomous information retrieval of visual content based on time-frequency meaning, such as color, texture and shape attributes of images. Hierarchical nested structure and cyclic similarity measure produce faster queries when searching. The experiment's findings indicate that applying the proposed model can have favorable outcomes for assessment measures, including Ratio of Coverage, average means precision, recall, and efficiency retrieval that are attained more effectively than the existing CR model. In the areas of feature tracking, climate forecasting, background noise reduction, and simulating nonlinear functional behaviors, CFNN has a wide range of RS applications. The proposed method CFNN-FCMIE achieves a minimum range of 4-5% for all three feature vectors, sample mean and comparison precision-recall ratio, which gives better results than the existing classifier-based retrieval model. This work provides an important reference for medical imaging artificial intelligence system and big data analysis.

Establishment-level safety analytics: a scoping review.

The use of data analytics has seen widespread application in fields such as medicine and supply chain management, but their application in occupational safety has only recently become more common. The purpose of this scoping review was to summarize studies that employed analytics within establishments to reveal insights about work-related injuries or fatalities. Over 300 articles were reviewed to survey the objectives, scope and methods used in this emerging field. We conclude that the promise of analytics for providing actionable insights to address occupational safety concerns is still in its infancy. Our review shows that most articles were focused on method development and validation, including studies that tested novel methods or compared the utility of multiple methods. Many of the studies cited various challenges in overcoming barriers caused by inadequate or inefficient technical infrastructures and unsupportive data cultures that threaten the accuracy and quality of insights revealed by the analytics.

Tratamiento de la informacion de violencia de género. Con aportaciones de la inteligencia artificial / Treatment of the information on gender-based violence. With contributions from artificial intelligence

Introducción/objetivos la violencia contra la mujer sigue siendo un grave problema social y de salud a pesar de las medidas puestas en marcha en los últimos años. La exploración de las víctimas por el médico forense en los juzgados es de gran interés puesto que recibe información relacionada no solo con la agresión, sino también de su entorno social, familiar y económico. El objetivo es utilizar dicha información para identificar grupos de riesgo y mejorar/obtener las medidas necesarias. Material y métodos en este trabajo, el forense ha recogido, durante 8 años, una toma abundante de datos sobre las víctimas exploradas en L’Hospitalet de Llobregat. La muestra incluye 1.622 casos de mujeres víctimas de violencia de género. Se realiza un estudio descriptivo poblacional y de las lesiones. Resultados se exponen las principales variables estudiadas tanto socioeconómicas como referentes a la agresión en sí. Se trabaja también con base en la reentrada de las víctimas o repetición de las agresiones (revictimización), que son el 10,9% de la muestra. Finalmente, se presentan los resultados obtenidos tras aplicar técnicas de inteligencia artificial, en este caso, árboles de clasificación CaRT. Conclusiones con los resultados obtenidos concluimos que el tratamiento de la información recogida y sistematizada de la intervención médico-forense permite una mejor comprensión de la violencia sobre la mujer, de la que podemos extraer sugerencias sobre la adopción de medidas de atención y soporte a las víctimas y a los colectivos más vulnerables, así como sobre los recursos administrativos y la optimización de programas de prevención. (AU)

Introduction/objectives Violence against women is still a serious social and health problem, despite the measures implemented in recent years. The examination of the victims by the forensic doctor in the courts is of great interest since it provides information related not only to the aggression, but also to their social, family and economic environment. The objective is to use this information to identify groups at risk and improve/implement the necessary measures. Material and methods In this work, the forensic has collected, for eight years, abundant data on the victims examined in L'Hospitalet de Llobregat. The sample includes 1,622 cases of women who have been victims of gender violence. A descriptive study of the population and of the lesions has been carried out. Results The paper presents the main variables studied, both socioeconomic and referring to the aggression itself. This study also analyzes the reentry of the victims, the repetition of aggressions (revictimization), which are 10.9% of the sample. Finally, the results obtained after applying artificial intelligence techniques -in this case, CaRT classification trees- are presented. Conclusions With the results obtained, we conclude that the treatment of the information collected and systematized from the medical-forensic intervention allows a better understanding of Violence Against Women, from which we can extract suggestions on the adoption of care and support measures for the victims and the most vulnerable groups, as well as administrative resources and the optimization of prevention programs. (AU)

Effect of a non-reactive absorbent with or without environmentally oriented electronic feedback on anesthesia provider's fresh gas flow rates: A greening initiative.

STUDY OBJECTIVE: To examine the effects of a non-reactive carbon dioxide absorbent (AMSORB® Plus) versus a traditional carbon dioxide absorbent (Medisorb™) on the FGF used by anesthesia providers and an electronic educational feedback intervention using Carestation™ Insights (GE HealthCare) on provider-specific change in FGF. DESIGN: Prospective, single-center cohort study set in a greening initiative. SETTING: Operating room. PARTICIPANTS: 157 anesthesia providers (i.e., anesthesiology trainees, certified registered nurse anesthetists, and solo anesthesiologists). INTERVENTIONS: Intervention #1 was the introduction of AMSORB® Plus into 8 Aisys CS2, Carestation™ Insights-enabled anesthesia machines (GE HealthCare) at the study site. At the end of week 6, anesthesia providers were educated and given an environmentally oriented electronic feedback strategy for the next 12 weeks of the study (Intervention #2) using Carestation™ Insights data. MEASUREMENTS: The dual primary outcomes were the difference in average daily FGF during maintenance anesthesia between machines assigned to AMSORB® Plus versus Medisorb™ and the provider-specific change in average fresh gas flows after 12 weeks of feedback and education compared to the historical data. MAIN RESULTS: Over the 18-week period, there were 1577 inhaled anesthetics performed in the 8 operating rooms (528 for intervention 1, 1049 for intervention 2). There were 1001 provider days using Aisys CS2 machines and 7452 provider days of historical data from the preceding year. Overall, AMSORB® Plus was not associated with significantly less FGF (mean - 80 ml/min, 97.5% confidence interval - 206 to 46, P = .15). The environmentally oriented electronic feedback intervention was not associated with a significant decrease in provider-specific mean FGF (-112 ml/min, 97.5% confidence interval - 244 to 21, P = .059). CONCLUSIONS: This study showed that introducing a non-reactive absorbent did not significantly alter FGF. Using environmentally oriented electronic feedback relying on data analytics did not result in significantly reduced provider-specific FGF.

Combining pathological and cognitive tests scores: A novel data analytics process to improve dementia prediction models1.

BACKGROUND: The term 'dementia' covers a range of progressive brain diseases from which many elderly people suffer. Traditional cognitive and pathological tests are currently used to detect dementia, however, applications using Artificial Intelligence (AI) methods have recently shown improved results from improved detection accuracy and efficiency. OBJECTIVE: This research paper investigates the efficacy of one type of data analytics called supervised learning to detect Alzheimer's disease (AD) - a common dementia condition. METHODS: The aim is to evaluate cognitive tests and common biological markers (biomarkers) such as cerebrospinal fluid (CSF) to develop predictive classification systems for dementia detection. RESULTS: A data analytics process has been proposed, implemented, and tested against real data obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) repository. CONCLUSION: The models showed good power in predicting AD levels, notably from specified cognitive tests' scores and tauopathy related features.

Comparison of Blood Concentration and Weight-Based Heparin and Protamine Dosing Strategies for Cardiopulmonary Bypass: A Systematic Review and Meta-Analysis.

BACKGROUND:  The conventional method of heparin and protamine management during cardiopulmonary bypass (CPB) is based on total body weight which fails to account for the heterogeneous response to heparin in each patient. On the other hand, the literature is inconclusive on whether individualized anticoagulation management based on real-time blood heparin concentration improves post-CBP outcomes. METHODS:  We searched databases of Medline, Excerpta Medica dataBASE (EMBASE), PubMed, Cumulative Index to Nursing and Allied Health Literature (CINHL), and Google Scholar, recruiting randomized controlled trials (RCTs) and prospective studies comparing the outcomes of dosing heparin and/or protamine based on measured heparin concentration versus patient's total body weight for CPB. Random effects meta-analyses and meta-regression were conducted to compare the outcome profiles. Primary endpoints include postoperative blood loss and the correlation with heparin and protamine doses, the reversal protamine and loading heparin dose ratio; secondary endpoints included postoperative platelet counts, antithrombin III, fibrinogen levels, activated prothrombin time (aPTT), incidences of heparin rebound, and re-exploration of chest wound for bleeding. RESULTS:  Twenty-six studies, including 22 RCTs and four prospective cohort studies involving 3,810 patients, were included. Compared to body weight-based dosing, patients of individualized, heparin concentration-based group had significantly lower postoperative blood loss (mean difference (MD)=49.51 mL, 95% confidence interval (CI): 5.33-93.71), lower protamine-to-heparin dosing ratio (MD=-0.20, 95% CI: -0.32 ~ -0.12), and higher early postoperative platelet counts (MD=8.83, 95% CI: 2.07-15.59). The total heparin doses and protamine reversal were identified as predictors of postoperative blood loss by meta-regression. CONCLUSIONS:  There was a significant correlation between the doses of heparin and protamine with postoperative blood loss; therefore, précised dosing of both could be critical for reducing bleeding and transfusion requirements. Data from the enrolled studies indicated that compared to conventional weight-based dosing, individualized, blood concentration-based heparin and protamine dosing may have outcome benefits reducing postoperative blood loss. The dosing calculation of heparin based on the assumption of a one-compartment pharmacokinetic/pharmacodynamic (PK/PD) model and linear relationship between the calculated dose and blood heparin concentration may be inaccurate. With the recent advancement of the technologies of machine learning, individualized, precision management of anticoagulation for CPB may be possible in the near future.

Development and validation of HBV surveillance models using big data and machine learning.

BACKGROUND: The construction of a robust healthcare information system is fundamental to enhancing countries' capabilities in the surveillance and control of hepatitis B virus (HBV). Making use of China's rapidly expanding primary healthcare system, this innovative approach using big data and machine learning (ML) could help towards the World Health Organization's (WHO) HBV infection elimination goals of reaching 90% diagnosis and treatment rates by 2030. We aimed to develop and validate HBV detection models using routine clinical data to improve the detection of HBV and support the development of effective interventions to mitigate the impact of this disease in China. METHODS: Relevant data records extracted from the Family Medicine Clinic of the University of Hong Kong-Shenzhen Hospital's Hospital Information System were structuralized using state-of-the-art Natural Language Processing techniques. Several ML models have been used to develop HBV risk assessment models. The performance of the ML model was then interpreted using the Shapley value (SHAP) and validated using cohort data randomly divided at a ratio of 2:1 using a five-fold cross-validation framework. RESULTS: The patterns of physical complaints of patients with and without HBV infection were identified by processing 158,988 clinic attendance records. After removing cases without any clinical parameters from the derivation sample (n = 105,992), 27,392 cases were analysed using six modelling methods. A simplified model for HBV using patients' physical complaints and parameters was developed with good discrimination (AUC = 0.78) and calibration (goodness of fit test p-value >0.05). CONCLUSIONS: Suspected case detection models of HBV, showing potential for clinical deployment, have been developed to improve HBV surveillance in primary care setting in China. (Word count: 264).

This study has developed a suspected case detection model for HBV, which can facilitate early identification and treatment of HBV in the primary care setting in China, contributing towards the achievement of WHO's elimination goals of HBV infections.We utilized the state-of-art natural language processing techniques to structure the data records, leading to the development of a robust healthcare information system which enhances the surveillance and control of HBV in China.

Measuring the unmeasurable multidimensional poverty for economic development: Datasets, algorithms, and models from the poorest region of Luzon, Philippines.

Poverty is the oldest social problem that ever existed and is difficult to reverse. It is multidimensional and unmeasurable. Thus, measuring by decomposing rural multidimensional poverty is critical. Most poverty studies are usually generic, exposed to large sampling errors, and intended for macroeconomic decisions. Thus, measuring poverty for a specific locality with various configurations is crucial for economic development. This work presents a processed and analyzed dataset from a huge community-based monitoring system of Goa, Camarines Sur. The local is situated in the poorest district, of the poorest province, in the poorest region of Luzon, Philippines. Research about poverty in this area is limited and measuring poverty at specific locality is scarce. The datasets contain the multidimensional poverty indicators, health, and nutrition, housing and settlement, water and sanitation, basic education from elementary to senior high school, income classifications, employment and livelihood, peace and order, summary of calamity occurrences experienced by residents, disaster risk reduction preparedness, figures of diagnostic analytics, tables of descriptive analytics, poverty analytics, measurement of decomposed poverty, summary of disaggregated configurations, graphs of predictive and prescriptive analytics, and population dynamics. This work is vital in analyzing poverty in rural and multidimensional approaches through poverty incidence, poverty gap, severity statistics, watts index, and classifications. It may also serve as a basis for measuring poverty from nearby regions and nations that use complete enumeration of its households and members. By utilizing the analyzed and processed data, further classifications and regressions can be done. It can be freely used by the government, private organizations, charitable institutions, businesses, academia, and researchers to target policies. An advantage of utilizing the dataset is to address multifaceted poverty that requires different interventions. It will facilitate the creation of programs to alleviate poverty and promote local economic development.