scholarly journals Data-driven prognosis for COVID-19 patients based on symptoms and age

2021 ◽  
Author(s):  
Subhendu Paul ◽  
Emmanuel Lorin
Keyword(s):  
Recovery Period ◽  
Relevant Information ◽  
Data Driven ◽  
Digital Code ◽  
Integer Variable ◽  
Average Recovery ◽  
Electronic Application ◽  
Novel Approach ◽  
Computational Code ◽  
Patient Prognosis

In this article, we develop an algorithm and a computational code to extract, analyze and compress the relevant information from the publicly available database of Canadian COVID-19 patients. We digitize the symptoms, that is, we assign a label/code as an integer variable for all possible combinations of various symptoms. We introduce a digital code for individual patient and divide all patients into a myriad of groups based on symptoms and age. In addition, we develop an electronic application (app) that allows for a rapid digital prognosis of COVID-19 patients, and provides individual patient prognosis on chance of recovery, average recovery period, etc. using the information, extracted from the database. This tool is aimed to assist health specialists in their decision regarding COVID-19 patients, based on symptoms and age of the patient. This novel approach can be used to develop similar applications for other diseases.

scholarly journals High-Fidelity Simulated Players for Interactive Narrative Planning

2018 ◽  
Author(s):  
Pengcheng Wang ◽  
Jonathan Rowe ◽  
Wookhee Min ◽  
Bradford Mott ◽  
James Lester
Keyword(s):  
State Of The Art ◽  
Data Driven ◽  
High Fidelity ◽  
Interactive Narrative ◽  
Interaction Data ◽  
Convolutional Networks ◽  
Novel Approach ◽  
Adaptation Policies ◽  
Narrative Planning ◽  
Prior State

Interactive narrative planning offers significant potential for creating adaptive gameplay experiences. While data-driven techniques have been devised that utilize player interaction data to induce policies for interactive narrative planners, they require enormously large gameplay datasets. A promising approach to addressing this challenge is creating simulated players whose behaviors closely approximate those of human players. In this paper, we propose a novel approach to generating high-fidelity simulated players based on deep recurrent highway networks and deep convolutional networks. Empirical results demonstrate that the proposed models significantly outperform the prior state-of-the-art in generating high-fidelity simulated player models that accurately imitate human players’ narrative interactions. Using the high-fidelity simulated player models, we show the advantage of more exploratory reinforcement learning methods for deriving generalizable narrative adaptation policies.

scholarly journals Cascading Forgetting in Product Development Challenges and Evaluation

2019 ◽  
Vol 1 (1) ◽  
pp. 2527-2536
Author(s):  
Patricia Kügler ◽  
Claudia Schon ◽  
Benjamin Schleich ◽  
Steffen Staab ◽  
Sandro Wartzack
Keyword(s):  
Product Design ◽  
Information Overload ◽  
Relevant Information ◽  
Knowledge Bases ◽  
Wear Behaviour ◽  
Combustion Engines ◽  
The Novel ◽  
Related Information ◽  
Intentional Forgetting ◽  
Novel Approach

AbstractVast amounts of information and knowledge is produced and stored within product design projects. Especially for reuse and adaptation there exists no suitable method for product designers to handle this information overload. Due to this, the selection of relevant information in a specific development situation is time-consuming and inefficient. To tackle this issue, the novel approach Intentional Forgetting (IF) is applied for product design, which aims to support reuse and adaptation by reducing the vast amount of information to the relevant. Within this contribution an IF-operator called Cascading Forgetting is introduced and evaluated, which was implemented for forgetting related information elements in ontology knowledge bases. For the evaluation the development process of a test-rig for studying friction and wear behaviour of the cam/tappet contact in combustion engines is analysed. Due to the interdisciplinary task of the evaluation and the characteristics of semantic model, challenges are discussed. In conclusion, the focus of the evaluation is to consider how reliable the Cascading Forgetting works and how intuitive ontology-based representations appear to engineers.

Psychophysics Study on LED Flicker Artefacts for Automotive Digital Mirror Replacement Systems

2020 ◽  
Vol 2020 (11) ◽  
pp. 234-1-234-6
Author(s):  
Nicolai Behmann ◽  
Holger Blume
Keyword(s):  
Light Source ◽  
Pulse Width ◽  
Digital Camera ◽  
Relevant Information ◽  
Human Vision ◽  
Light Sources ◽  
Traffic Light ◽  
Pulse Width Modulated ◽  
Novel Approach ◽  
Flicker Light

LED flicker artefacts, caused by unsynchronized irradiation from a pulse-width modulated LED light source captured by a digital camera sensor with discrete exposure times, place new requirements for both visual and machine vision systems. While latter need to capture relevant information from the light source only in a limited number of frames (e.g. a flickering traffic light), human vision is sensitive to illumination modulation in viewing applications, e.g. digital mirror replacement systems. In order to quantify flicker in viewing applications with KPIs related to human vision, we present a novel approach and results of a psychophysics study on the effect of LED flicker artefacts. Diverse real-world driving sequences have been captured with both mirror replacement cameras and a front viewing camera and potential flicker light sources have been masked manually. Synthetic flicker with adjustable parameters is then overlaid on these areas and the flickering sequences are presented to test persons in a driving environment. Feedback from the testers on flicker perception in different viewing areas, sizes and frequencies are collected and evaluated.

scholarly journals Integrated mechanistic and data-driven modelling for multivariate analysis of signalling pathways

2006 ◽  
Vol 3 (9) ◽  
pp. 515-526 ◽  
Author(s):  
Fei Hua ◽  
Sampsa Hautaniemi ◽  
Rayka Yokoo ◽  
Douglas A Lauffenburger
Keyword(s):  
Decision Tree ◽  
Mathematical Models ◽  
Large Scale ◽  
Therapeutic Interventions ◽  
Data Driven ◽  
Signalling Pathways ◽  
Apoptotic Pathway ◽  
Tree Analysis ◽  
Modelling Framework ◽  
Novel Approach

Mathematical models of highly interconnected and multivariate signalling networks provide useful tools to understand these complex systems. However, effective approaches to extracting multivariate regulation information from these models are still lacking. In this study, we propose a data-driven modelling framework to analyse large-scale multivariate datasets generated from mathematical models. We used an ordinary differential equation based model for the Fas apoptotic pathway as an example. The first step in our approach was to cluster simulation outputs generated from models with varied protein initial concentrations. Subsequently, decision tree analysis was applied, in which we used protein concentrations to predict the simulation outcomes. Our results suggest that no single subset of proteins can determine the pathway behaviour. Instead, different subsets of proteins with different concentrations ranges can be important. We also used the resulting decision tree to identify the minimal number of perturbations needed to change pathway behaviours. In conclusion, our framework provides a novel approach to understand the multivariate dependencies among molecules in complex networks, and can potentially be used to identify combinatorial targets for therapeutic interventions.

Calcineurin Inhibitors: A Novel Approach to Canine Atopic Dermatitis

2005 ◽  
Vol 41 (2) ◽  
pp. 92-97 ◽  
Author(s):  
Rosanna Marsella
Keyword(s):  
Atopic Dermatitis ◽  
Adverse Effects ◽  
Mechanism Of Action ◽  
Broad Spectrum ◽  
Skin Diseases ◽  
Calcineurin Inhibitors ◽  
Relevant Information ◽  
Novel Approach ◽  
Allergic Skin ◽  
Traditional Therapies

The introduction of calcineurin inhibitors represents a major addition to the armamentarium of drugs available to veterinary clinicians for the management of allergic skin diseases. Both cyclosporine and tacrolimus have been proven to be well tolerated and effective for the treatment of atopic dermatitis in dogs. Although broad spectrum in their mechanism of action, they lack the major adverse effects of glucocorticoids and provide an appealing alternative to traditional therapies. The purposes of this article are to review clinically relevant information regarding these agents and to provide tips for maximizing the benefit obtained from these therapies.

scholarly journals A Novel Approach to Data Driven Pandemic Recovery: The Pandemic Recovery Acceleration Model

2020 ◽  
Author(s):  
Jeffrey P Gold ◽  
Christopher Wichman ◽  
Kenneth Bayles ◽  
Ali S Khan ◽  
Christopher Kratochvil ◽  
...  
Keyword(s):  
Public Health ◽  
Health Care ◽  
Real Time ◽  
Local Community ◽  
The State ◽  
Data Driven ◽  
Real Time Analysis ◽  
Novel Approach ◽  
Acceleration Model ◽  
Care Systems

A data driven approach to guide the global, regional and local pandemic recovery planning is key to the safety, efficacy and sustainability of all pandemic recovery efforts. The Pandemic Recovery Acceleration Model (PRAM) analytic tool was developed and implemented state wide in Nebraska to allow health officials, public officials, industry leaders and community leaders to capture a real time snapshot of how the COVID-19 pandemic is affecting their local community, a region or the state and use this novel lens to aid in making key mitigation and recovery decisions. This is done by using six commonly available metrics that are monitored daily across the state describing the pandemic impact: number of new cases, percent positive tests, deaths, occupied hospital beds, occupied intensive care beds and utilized ventilators, all directly related to confirmed COVID-19 patients. Nebraska is separated into six Health Care Coalitions based on geography, public health and medical care systems. The PRAM aggregates the data for each of these geographic regions based on disease prevalence acceleration and health care resource utilization acceleration, producing real time analysis of the acceleration of change for each metric individually and also combined into a single weighted index, the PRAM Recovery Index. These indices are then shared daily with the state leadership, coalition leaders and public health directors and also tracked over time, aiding in real time regional and statewide decisions of resource allocation and the extent of use of comprehensive non-pharmacologic interventions.

scholarly journals Reaction Wheels Fault Isolation Onboard 3-Axis Controlled Satel-lite using Enhanced Random Forest with Multidomain Features

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
Afshin Rahimi ◽  
Mofiyinoluwa O. Folami
Keyword(s):  
Machine Learning ◽  
Fault Isolation ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Superior Performance ◽  
Alternative Methods ◽  
Model Parameters ◽  
High Fidelity ◽  
Novel Approach ◽  
Learning Techniques

As the number of satellite launches increases each year, it is only natural that an interest in the safety and monitoring of these systems would increase as well. However, as a system becomes more complex, generating a high-fidelity model that accurately describes the system becomes complicated. Therefore, imploring a data-driven method can provide to be more beneficial for such applications. This research proposes a novel approach for data-driven machine learning techniques on the detection and isolation of nonlinear systems, with a case-study for an in-orbit closed loop-controlled satellite with reaction wheels as actuators. High-fidelity models of the 3-axis controlled satellite are employed to generate data for both nominal and faulty conditions of the reaction wheels. The generated simulation data is used as input for the isolation method, after which the data is pre-processed through feature extraction from a temporal, statistical, and spectral domain. The pre-processed features are then fed into various machine learning classifiers. Isolation results are validated with cross-validation, and model parameters are tuned using hyperparameter optimization. To validate the robustness of the proposed method, it is tested on three characterized datasets and three reaction wheel configurations, including standard four-wheel, three-orthogonal, and pyramid. The results prove superior performance isolation accuracy for the system under study compared to previous studies using alternative methods (Rahimi & Saadat, 2019, 2020).

scholarly journals Investigation of factors contributing to accumulation of sediments in stormwater management ponds

2021 ◽  
Author(s):  
Sidra Yasir Siddiqui
Keyword(s):  
Literature Review ◽  
Stormwater Management ◽  
Sediment Accumulation ◽  
Relevant Information ◽  
Data Driven ◽  
Accumulation Rates ◽  
Additional Information ◽  
Modelling Approach

The purpose of this study was to investigate factors contributing to sediment accumulation rates in Stormwater Management ponds. For the purpose of this study almost fifty municipalities in Ontario were contacted and in collaboration with five of those municipalities this study was conducted. A questionnaire was developed and sent to municipalities through email and followup with in-person meetings were conducted. After collecting data and analyzing various characteristics of sediment accumulation rates in SWM ponds, a database was developed to systematically record the relevant information. Additional information on pond properties and drainage areas was sought through a questionnaire and meeting with stormwater managers, and supplemented with historic information. Data collected and used in the study was anonymized in all resulting publications. The calculated accumulated rates from the provided data were compared against the values extracted from the literature review. The developed approach will serve in the development of data-driven modelling approach in SWM ponds.

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