Non-obvious correlations to disease management unraveled by Bayesian artificial intelligence analyses of CMS data

Vemulapalli, Vijetha; Qu, Jiaqi; Garren, Jeonifer; Rodrigues, Leonardo O.; Kiebish, Michael A.; Sarangarajan, Rangaprasad; Narain, Niven R.; Akmaev, Viatcheslav R.

doi:10.1016/j.artmed.2016.11.001

Cited by 30 publications

(16 citation statements)

References 53 publications

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“…The method has been described in previous studies. 29,30 Demographic information was considered to be fixed information regarding the patients. Therefore, in the causal network, no other variables were permitted to drive changes in the demographic characteristics of the patients.…”

Section: Methodsmentioning

confidence: 99%

A Data-Driven Approach to Predicting Septic Shock in the Intensive Care Unit

Yee¹,

Narain²,

Akmaev³

et al. 2019

Biomed Inform Insights

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Early diagnosis of sepsis and septic shock has been unambiguously linked to lower mortality and better patient outcomes. Despite this, there is a strong unmet need for a reliable clinical tool that can be used for large-scale automated screening to identify high-risk patients. We addressed the following questions: Can a novel algorithm to identify patients at high risk of septic shock 24 hours before diagnosis be discovered using available clinical data? What are performance characteristics of this predictive algorithm? Can current metrics for evaluation of sepsis be improved using novel algorithm? Publicly available data from the intensive care unit setting was used to build septic shock and control patient cohorts. Using Bayesian networks, causal relationships between diagnosis groups, procedure groups, laboratory results, and demographic data were inferred. Predictive model for septic shock 24 hours prior to digital diagnosis was built based on inferred causal networks. Sepsis risk scores were augmented by de novo inferred model and performance was evaluated. A novel predictive model to identify high-risk patients 24 hours ahead of time, with area under curve of 0.81, negative predictive value of 0.87, and a positive predictive value as high as 0.65 was built. The specificity of quick sequential organ failure assessment, systemic inflammatory response syndrome, and modified early warning score was improved when augmented with the novel model, whereas no improvements were made to the sequential organ failure assessment score. We used a data-driven, expert knowledge agnostic method to build a screening algorithm for early detection of septic shock. The model demonstrates strong performance in the data set used and provides a basis for expanding this work toward building an algorithm that is used to screen patients based on electronic medical record data in real time.

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Section: Methodsmentioning

confidence: 99%

A Data-Driven Approach to Predicting Septic Shock in the Intensive Care Unit

Yee¹,

Narain²,

Akmaev³

et al. 2019

Biomed Inform Insights

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“…Developments in artificial intelligence (AI) for some aspects of tertiary care center management is predicted to lower costs. These may include machine learning algorithms in medical billing, supply chain management, scheduling efficiencies, virtual radiology (for image interpretation), and prevention of readmissions [71][72][73][74][75][76][77].…”

Section: Information Technology and Quality Benchmarksmentioning

confidence: 99%

Worldwide Network for Blood and Marrow Transplantation Recommendations for Establishing a Hematopoietic Stem Cell Transplantation Program in Countries with Limited Resources, Part II: Clinical, Technical, and Socioeconomic Considerations

Aljurf

Weisdorf

Hashmi

et al. 2019

Biology of Blood and Marrow Transplantation

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A B S T R A C T The development of hematopoietic stem cell transplantation (HSCT) programs can face significant challenges in most developing countries because such endeavors must compete with other government health care priorities, including the delivery of basic services. Although this is may be a limiting factor, these countries should prioritize development of the needed expertise to offer state-of-the-art treatments, including transplantation, by providing financial, technological, legal, ethical, and other needed support. This would prove beneficial in providing successful programs customized to the needs of their population and potentially provide long-term cost savings by circumventing the need for their citizens to seek care abroad. The costs of establishing an HSCT program and the costs of the HSCT procedure itself can be substantial barriers in developing countries. In addition, socioeconomic factors intrinsic to specific countries can influence access to HSCT, patient eligibility for HSCT, and timely utilization of HSCT center capabilities. This report describes recommendations from the Worldwide Network for Blood and Marrow Transplantation for establishing HSCT programs, with a specific focus on developing countries, and identifies challenges and opportunities for providing this specialized procedure in resource-constrained settings.

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“…In pharma and healthcare, when data are available but there are unmet needs for markers and therapeutic targets, causal inference has been successfully applied to generate knowledge from data, such as in the identification of novel biomarkers [4], disease regulators [5] and outcome predictors [6]. Similarly, the SARS-CoV-2 9 viral/host interaction necessitates a more comprehensive understanding which can be achieved by coupling protein-based interactome with a Bayesian network (BN) approach.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Model Infers Drug Repurposing Candidates for Treatment of COVID-19

et al. 2021

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The emergence of COVID-19 progressed into a global pandemic that has functionally put the world at a standstill and catapulted major healthcare systems into an overburdened state. The dire need for therapeutic strategies to mitigate and successfully treat COVID-19 is now a public health crisis with national security implications for many countries. The current study employed Bayesian networks to a longitudinal proteomic dataset generated from Caco-2 cells transfected with SARS-CoV-2 (isolated from patients returning from Wuhan to Frankfurt). Two different approaches were employed to assess the Bayesian models, a titer-center topology analysis and a drug signature enrichment analysis. Topology analysis identified a set of proteins directly linked to the SAR-CoV2 titer, including ACE2, a SARS-CoV-2 binding receptor, MAOB and CHECK1. Aligning with the topology analysis, MAOB and CHECK1 were also identified within the enriched drug-signatures. Taken together, the data output from this network has identified nodal host proteins that may be connected to 18 chemical compounds, some already marketed, which provides an immediate opportunity to rapidly triage these assets for safety and efficacy against COVID-19.

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Non-obvious correlations to disease management unraveled by Bayesian artificial intelligence analyses of CMS data

Cited by 30 publications

References 53 publications

A Data-Driven Approach to Predicting Septic Shock in the Intensive Care Unit

A Data-Driven Approach to Predicting Septic Shock in the Intensive Care Unit

Worldwide Network for Blood and Marrow Transplantation Recommendations for Establishing a Hematopoietic Stem Cell Transplantation Program in Countries with Limited Resources, Part II: Clinical, Technical, and Socioeconomic Considerations

Bayesian Model Infers Drug Repurposing Candidates for Treatment of COVID-19

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