scholarly journals The power of modeling in emergency preparedness for COVID-19: a moonshot moment for hospitals

2021 ◽  
pp. 1-10
Author(s):  
Kyan C. Safavi ◽  
Ann L. Prestipino ◽  
Ana Cecilia Zenteno Langle ◽  
Martin Copenhaver ◽  
Michael Hu ◽  
...  
Keyword(s):  
New England ◽  
Emergency Preparedness ◽  
Statistical Models ◽  
Operational Planning ◽  
Data Driven ◽  
Major Objective ◽  
Response Models ◽  
Specific Data ◽  
Clinical Leadership ◽  
Future Patient

Abstract Prior to COVID-19, few hospitals had fully tested emergency surge plans. Uncertainty in the timing and degree of surge complicates planning efforts, putting hospitals at risk of being overwhelmed. Many lack access to hospital-specific, data-driven projections of future patient demand to guide operational planning. Our hospital experienced one of the largest surges in New England. We developed statistical models to project hospitalizations during the first wave of the pandemic. We describe how we used these models to meet key planning objectives. To build the models successfully, we emphasize the criticality of having a team that combines data scientists with frontline operational and clinical leadership. While modeling was a cornerstone of our response, models currently available to most hospitals are built outside of their institution and are difficult to translate to their environment for operational planning. Creating data-driven, hospital-specific, and operationally relevant surge targets and activation triggers should be a major objective of all health systems.

Predicting matches in international football tournaments with random forests

2018 ◽  
Vol 18 (5-6) ◽  
pp. 460-482 ◽  
Author(s):  
Gunther Schauberger ◽  
Andreas Groll
Keyword(s):  
Random Forests ◽  
Statistical Models ◽  
Regression Models ◽  
Predictive Power ◽  
Statistical Modelling ◽  
World Cup ◽  
Fifa World Cup ◽  
Specific Data ◽  
Covariate Information ◽  
Different Types

Many approaches that analyse and predict results of international matches in football are based on statistical models incorporating several potentially influential covariates with respect to a national team's success, such as the bookmakers’ ratings or the FIFA ranking. Based on all matches from the four previous FIFA World Cups 2002–2014, we compare the most common regression models that are based on the teams’ covariate information with regard to their predictive performances with an alternative modelling class, the so-called random forests. Random forests can be seen as a mixture between machine learning and statistical modelling and are known for their high predictive power. Here, we consider two different types of random forests depending on the choice of response. One type of random forests predicts the precise numbers of goals, while the other type considers the three match outcomes—win, draw and loss—using special algorithms for ordinal responses. To account for the specific data structure of football matches, in particular at FIFA World Cups, the random forest methods are slightly altered compared to their standard versions and adapted to the specific needs of the application to FIFA World Cup data.

Event-Based Concepts for User-Driven Visualization

2009 ◽  
Vol 10 (1) ◽  
pp. 65-81 ◽  
Author(s):  
Christian Tominski
Keyword(s):  
General Model ◽  
Visual Representations ◽  
Data Driven ◽  
Relational Data ◽  
Temporal Data ◽  
Specific Data ◽  
Spatio Temporal ◽  
Visualization Technology ◽  
Event Based

Visualization has become an increasingly important tool to support exploration and analysis of the large volumes of data we are facing today. However, interests and needs of users are still not being considered sufficiently. The goal of this work is to shift the user into the focus. To that end, we apply the concept of event-based visualization that combines event-based methodology and visualization technology. Previous approaches that make use of events are mostly specific to a particular application case, and hence, can not be applied otherwise. We introduce a novel general model of event-based visualization that comprises three fundamental stages. (1) Users are enabled to specify what their interests are. (2) During visualization, matches of these interests are sought in the data. (3) It is then possible to automatically adjust visual representations according to the detected matches. This way, it is possible to generate visual representations that better reflect what users need for their task at hand. The model's generality allows its application in many visualization contexts. We substantiate the general model with specific data-driven events that focus on relational data so prevalent in today's visualization scenarios. We show how the developed methods and concepts can be implemented in an interactive event-based visualization framework, which includes event-enhanced visualizations for temporal and spatio-temporal data.

scholarly journals Inferring reaction network structure from single-cell, multiplex data, using toric systems theory

2019 ◽  
Author(s):  
Shu Wang ◽  
Jia-Ren Lin ◽  
Eduardo D. Sontag ◽  
Peter K. Sorger
Keyword(s):  
Single Cell ◽  
Chemical Reaction ◽  
Network Structure ◽  
Statistical Models ◽  
Control Cell ◽  
Toric Varieties ◽  
Chemical Reaction Networks ◽  
Data Driven ◽  
Cell Subpopulation ◽  
Reaction Networks

AbstractThe goal of many single-cell studies on eukaryotic cells is to gain insight into the biochemical reactions that control cell fate and state. In this paper we introduce the concept of effective stoichiometric space (ESS) to guide the reconstruction of biochemical networks from multiplexed, fixed time-point, single-cell data. In contrast to methods based solely on statistical models of data, the ESS method leverages the power of the geometric theory of toric varieties to begin unraveling the structure of chemical reaction networks (CRN). This application of toric theory enables a data-driven mapping of covariance relationships in single cell measurements into stoichiometric information, one in which each cell subpopulation has its associated ESS interpreted in terms of CRN theory. In the development of ESS we reframe certain aspects of the theory of CRN to better match data analysis. As an application of our approach we process cytomery- and image-based single-cell datasets and identify differences in cells treated with kinase inhibitors. Our approach is directly applicable to data acquired using readily accessible experimental methods such as Fluorescence Activated Cell Sorting (FACS) and multiplex immunofluorescence.Author summaryWe introduce a new notion, which we call the effective stoichiometric space (ESS), that elucidates network structure from the covariances of single-cell multiplexed data. The ESS approach differs from methods that are based on purely statistical models of data: it allows a completely new and data-driven translation of the theory of toric varieties in geometry and specifically their role in chemical reaction networks (CRN). In the process, we reframe certain aspects of the theory of CRN. As illustrations of our approach, we find stoichiometry in different single-cell datasets, and pinpoint dose-dependence of network perturbations in drug-treated cells.

scholarly journals Will Artificial Intelligence Translate Big Data Into Improved Medical Care or Be a Source of Confusing Intrusion? A Discussion Between a (Cautious) Physician Informatician and an (Optimistic) Medical Informatics Researcher

10.2196/16272 ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. e16272 ◽  
Author(s):  
Qing Zeng-Treitler ◽  
Stuart J Nelson
Keyword(s):  
Artificial Intelligence ◽  
New England ◽  
Medical Care ◽  
Data Driven ◽  
Single Individual ◽  
Physician Burnout ◽  
Problem Space ◽  
The Past ◽  
Challenges And Opportunities ◽  
Development And Utilization

Artificial intelligence (AI), the computerized capability of doing tasks, which until recently was thought to be the exclusive domain of human intelligence, has demonstrated great strides in the past decade. The abilities to play games, provide piloting for an automobile, and respond to spoken language are remarkable successes. How are the challenges and opportunities of medicine different from these challenges and how can we best apply these data-driven techniques to patient care and outcomes? A New England Journal of Medicine paper published in 1980 suggested that more well-defined “specialized” tasks of medical care were more amenable to computer assistance, while the breadth of approach required for defining a problem and narrowing down the problem space was less so, and perhaps, unachievable. On the other hand, one can argue that the modern version of AI, which uses data-driven approaches, will be the most useful in tackling tasks such as outcome prediction that are often difficult for clinicians and patients. The ability today to collect large volumes of data about a single individual (eg, through a wearable device) and the accumulation of large datasets about multiple persons receiving medical care has the potential to apply to the care of individuals. As these techniques of analysis, enumeration, aggregation, and presentation are brought to bear in medicine, the question arises as to their utility and applicability in that domain. Early efforts in decision support were found to be helpful; as the systems proliferated, later experiences have shown difficulties such as alert fatigue and physician burnout becoming more prevalent. Will something similar arise from data-driven predictions? Will empowering patients by equipping them with information gained from data analysis help? Patients, providers, technology, and policymakers each have a role to play in the development and utilization of AI in medicine. Some of the challenges, opportunities, and tradeoffs implicit here are presented as a dialog between a clinician (SJN) and an informatician (QZT).

Utilizing Program Specific Data to Develop Case Studies for Use With Preservice Teachers

2017 ◽  
pp. 1-20
Author(s):  
Heather M. Reynolds ◽  
A. Tina Wagle
Keyword(s):  
Preservice Teachers ◽  
Case Studies ◽  
Higher Order Thinking ◽  
Data Driven ◽  
Teacher Education Programs ◽  
Education Programs ◽  
Specific Data ◽  
Graduate Coursework ◽  
Relevant Case ◽  
Modeling Data

With a focus on data driven decision making, teacher education programs need to prepare preservice teachers to analyze data while modeling data driven practices in our own programs. Research has demonstrated the effectiveness of using case studies to promote critical thinking, analysis and interpretation, and higher order thinking. This study utilized the results from surveys of residents enrolled in a clinically rich residency program to develop and implement relevant case studies for use in program coursework. The utility of using case studies in graduate coursework was evaluated through a survey of current residents. The theoretical and practical value of creating case studies based on program specific challenges, and examples of the case studies that were generated from this data will be shared.

Can tongue be recovered from face? the answer of data-driven statistical models

2010 ◽  
Author(s):  
Atef Ben Youssef ◽  
Pierre Badin ◽  
Gérard Bailly
Keyword(s):  
Statistical Models ◽  
Data Driven
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