Probabilistic domain-knowledge modeling of disorder pathogenesis for dynamics forecasting of acute onset

Huynh, Phat K.; Setty, Arveity R.; Thai, Hao Phan; Le, Trung Q.

doi:10.1016/j.artmed.2021.102056

Cited by 11 publications

(4 citation statements)

References 55 publications

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“…Cell functions such as immune signaling, metabolism, and apoptosis are dysregulated during tumor growth, resulting in catastrophic morphological and electrostatic changes that can be characterized by the multifractality of the KPFM images. The characterized morphological and surface potential images will facilitate our previous work on probabilistic modeling of disorder pathogenesis − by adding more details from cell and tissue levels. Multifractality in surface potential can also be used as a novel biomarker for drug delivery methods based on electrostatic activation.…”

Section: Discussionmentioning

confidence: 99%

Multifractality in Surface Potential for Cancer Diagnosis

et al. 2023

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Recent advances in high-resolution biomedical imaging have improved cancer diagnosis, focusing on morphological, electrical, and biochemical properties of cells and tissues, scaling from cell clusters down to the molecular level. Multiscale imaging revealed high complexity that requires advanced data processing methods of multifractal analysis. We performed label-free multiscale imaging of surface potential variations in human ovarian cancer cells using Kelvin probe force microscopy (KPFM). An improvement in the differentiation between nonmalignant and cancerous cells by multifractal analysis using adaptive versus median threshold for image binarization was demonstrated. The results reveal the multifractality of cancer cells as a new biomarker for cancer diagnosis.

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Section: Discussionmentioning

confidence: 99%

Multifractality in Surface Potential for Cancer Diagnosis

et al. 2023

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“…Secondly, while our proposed framework has proven successful under the conditions tested, its performance in real-world domains and applications, such as corporate environments, non-profit teams, or online collaboration platforms, needs to be extensively Thirdly, despite our model aiming to create balanced and diverse teams, it does not consider the dynamics of team interactions after the team assignment. Understanding how these teams function and adapt over time is a crucial aspect of the team formation process [76,77]. Lastly, our algorithm, while efficient, is computationally intensive, especially for large-scale applications.…”

Section: Discussionmentioning

confidence: 99%

Leveraging Multi-Criteria Integer Programming Optimization for Effective Team Formation

Singh,

Huynh,

Nguyen

et al. 2023

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The research addresses a critical aspect of organizational settings – team formation – which is vital for effective team-based learning. In many organizational environments, successful teamwork depends on the composition of teams that consider various criteria, such as skill levels, background, and personality traits. Meanwhile, maintaining fairness and equity among teams is crucial to ensure equal opportunities for all team members. Our research framework lies in its ability to model a diverse range of factors, while simultaneously maximizing within-team diversity and minimizing conflict levels. In this study, we introduce a novel application of multi-criteria integer programming (MCIP) that diverges from traditional and single-objective optimization methods to address the complexity of team formation with a focus on multiple criteria. This approach offers a comprehensive solution that accommodates multiple criteria simultaneously. The contributions of this paper are: 1) An innovative optimization model for team formation that addresses the complex task of maximizing intra-group diversity while minimizing inter-group diversity. 2) Addressing the challenge of forming balanced and diverse student teams and providing educators with a practical tool for effective team formation.

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“…Alternatively, the population risk is estimated using a Bayesian network when we have access to the domain knowledge that describes the relationships between the control factors and the infection risk at the population level and individual level. Here, the Bayesian network model [ 32 ] is employed to incorporate the domain knowledge that influences the virus spread. The network is formulated based on three subsets of factors from the literature that affect the risk of infection including 1) individual-level factors, 2) engineering control factors, and 3) administrative control factors (see Fig 1 ).…”

Section: Methodsmentioning

confidence: 99%

A domain-knowledge modeling of hospital-acquired infection risk in Healthcare personnel from retrospective observational data: A case study for COVID-19

et al. 2022

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Introduction Hospital-acquired infections of communicable viral diseases (CVDs) have been posing a tremendous challenge to healthcare workers globally. Healthcare personnel (HCP) is facing a consistent risk of viral infections, and subsequently higher rates of morbidity and mortality. Materials and methods We proposed a domain-knowledge-driven infection risk model to quantify the individual HCP and the population-level risks. For individual-level risk estimation, a time-variant infection risk model is proposed to capture the transmission dynamics of CVDs. At the population-level, the infection risk is estimated using a Bayesian network model constructed from three feature sets, including individual-level factors, engineering control factors, and administrative control factors. For model validation, we investigated the case study of the Coronavirus disease, in which the individual-level and population-level infection risk models were applied. The data were collected from various sources such as COVID-19 transmission databases, health surveys/questionaries from medical centers, U.S. Department of Labor databases, and cross-sectional studies. Results Regarding the individual-level risk model, the variance-based sensitivity analysis indicated that the uncertainty in the estimated risk was attributed to two variables: the number of close contacts and the viral transmission probability. Next, the disease transmission probability was computed using a multivariate logistic regression applied for a cross-sectional HCP data in the UK, with the 10-fold cross-validation accuracy of 78.23%. Combined with the previous result, we further validated the individual infection risk model by considering six occupations in the U.S. Department of Labor O*Net database. The occupation-specific risk evaluation suggested that the registered nurses, medical assistants, and respiratory therapists were the highest-risk occupations. For the population-level risk model validation, the infection risk in Texas and California was estimated, in which the infection risk in Texas was lower than that in California. This can be explained by California’s higher patient load for each HCP per day and lower personal protective equipment (PPE) sufficiency level. Conclusion The accurate estimation of infection risk at both individual level and population levels using our domain-knowledge-driven infection risk model will significantly enhance the PPE allocation, safety plans for HCP, and hospital staffing strategies.

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Probabilistic domain-knowledge modeling of disorder pathogenesis for dynamics forecasting of acute onset

Cited by 11 publications

References 55 publications

Multifractality in Surface Potential for Cancer Diagnosis

Multifractality in Surface Potential for Cancer Diagnosis

Leveraging Multi-Criteria Integer Programming Optimization for Effective Team Formation

A domain-knowledge modeling of hospital-acquired infection risk in Healthcare personnel from retrospective observational data: A case study for COVID-19

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