Implementing auralized CPB sounds on a pedestrian simulator

2021 ◽  
Vol 263 (3) ◽  
pp. 3085-3096
Author(s):  
Francisco Soares ◽  
Frederico Pereira ◽  
Emanuel Silva ◽  
Carlos Silva ◽  
Emanuel Sousa ◽  
...  
Keyword(s):  
Decision Making ◽  
Real World ◽  
Auditory Stimuli ◽  
Real World Data ◽  
Time To Collision ◽  
Close Proximity ◽  
Data Points ◽  
The Moment ◽  
Pedestrian Crossing ◽  
Determining Factor

Recently, several studies on pedestrian safety and particularly those addressing pedestrian crossing behaviour and decision-making, have been performed using virtual reality systems. The use of simulators to assess pedestrian behaviour is conditioned by the feeling of presence and immersion, for which the sound is a determining factor. This paper presents an implementation procedure in which tyre-road noise samples are auralized and presented as auditory stimuli in a virtual environment, for assessing pedestrian crossing decision-making. The auditory samples obtained through the Close Proximity (CPX) method and subsequently auralized to represent Controlled Pass-By (CPB) sounds reproduce the sounds of a vehicle approaching a crosswalk. The auralized sounds together with the presentation of visual stimuli composed an experiment which was carried out with 30 participants. Safety indicators, as the time-to-passage at the moment that participants decided to cross a virtual crosswalk and the minimum time-to-collision were registered and compared with data obtained in real-world road crossings. A comparison with real world data points to a close alignment between results obtained in virtual and real environments, indicating a good suitability of the approach for studying pedestrian crossing behaviour.

Quality criteria for Real-World Data in pharmaceutical research and healthcare decision making. An Austrian Expert Consensus (Preprint)

2021 ◽  
Author(s):  
Peter Klimek ◽  
Dejan Baltic ◽  
Martin Brunner ◽  
Alexander Degelsegger-Marquez ◽  
Gerhard Garhöfer ◽  
...  
Keyword(s):  
Decision Making ◽  
Medical Research ◽  
Real World ◽  
Pharmaceutical Medicine ◽  
Pharmaceutical Research ◽  
Quality Criteria ◽  
Clinical Decision ◽  
European Medicines Agency ◽  
Real World Data ◽  
World Data

UNSTRUCTURED Real-world data (RWD) collected in routine healthcare processes and transformed to real-world evidence (RWE) has become increasingly interesting within research and medical communities to enhance medical research and support regulatory decision making. Despite numerous European initiatives, there is still no cross-border consensus or guideline determining which quality RWD must meet in order to be acceptable for decision making within regulatory or routine clinical decision support. An Austrian expert group led by GPMed (Gesellschaft für Pharmazeutische Medizin, Austrian Society for Pharmaceutical Medicine) reviewed drafted guidelines, published recommendations or viewpoints to derive a consensus statement on quality criteria for RWD to be used more effectively for medical research purposes beyond registry-based studies discussed in the European Medicines Agency (EMA) guideline for registry-based studies

Automated Diagnosis through Ontologies and Logical Descriptions

2013 ◽  
pp. 117-134
Author(s):  
Alejandro Rodríguez-González ◽  
Ángel García-Crespo ◽  
Ricardo Colomo-Palacios ◽  
José Emilio Labra Gayo ◽  
Juan Miguel Gómez-Berbís ◽  
...  
Keyword(s):  
Decision Making ◽  
Health Systems ◽  
Real World ◽  
Information Age ◽  
Semantic Technologies ◽  
Automated Diagnosis ◽  
Real World Data ◽  
World Data ◽  
Degree Of Maturity ◽  
Automate Diagnosis

The combination of the burgeoning interest in efficient and reliable Health Systems and the advent of the Information Age represent both a challenge and an opportunity for new paradigms and cutting-edge technologies reaching a certain degree of maturity. Hence, the use of Semantic Technologies for Automated Diagnosis could leverage the potential of current solutions by providing inference-based knowledge and support on decision-making. This paper presents the ADONIS approach, which harnesses the use of ontologies and the underlying logical mechanisms to automate diagnosis and provide significant quality results in its evaluation on real-world data scenarios.

Network Data Characteristics

2011 ◽  
pp. 104-122
Author(s):  
Yu Wang
Keyword(s):  
Real World ◽  
Random Variables ◽  
Network Data ◽  
Traffic Data ◽  
Real World Data ◽  
Additional Information ◽  
Data Points ◽  
Key Features ◽  
Basic Concepts ◽  
Data Elements

Data represents the natural phenomena of our real world. Data is constructed by rows and columns; usually rows represent the observations and columns represent the variables. Observations, also called subjects, records, or data points, represent a phenomenon in the real world and variables, as also known as data elements or data fields, represent the characteristics of observations in data. Variables take different values for different observations, which can make observations independent of each other. Figure 4.1 illustrates a section of TCP/IP traffic data, in which the rows are individual network traffics, and the columns, separated by a space, are characteristics of the traffics. In this example, the first column is a session index of each connection and the second column is the date when the connection occurred. In this chapter, we will discuss some fundamental key features of variables and network data. We will present detailed discussions on variable characteristics and distributions in Sections Random Variables and Variables Distributions, and describe network data modules in Section Network Data Modules. The material covered in this chapter will help readers who do not have a solid background in this area gain an understanding of the basic concepts of variables and data. Additional information can be found from Introduction to the Practice of Statistics by Moore and McCabe (1998).

scholarly journals Neural Networks in Credit Risk Classification of Companies in the Construction Sector

2018 ◽  
Vol 2 (2) ◽  
pp. 63-77 ◽  
Author(s):  
Aleksandra Wójcicka
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Credit Risk ◽  
Financial Institutions ◽  
Real World ◽  
Decision Making Process ◽  
Construction Sector ◽  
Real World Data ◽  
Input Variables

The financial sector (banks, financial institutions, etc.) is the sector most exposed to financial and credit risk, as one of the basic objectives of banks' activity (as a specific enterprise) is granting credit and loans. Because credit risk is one of the problems constantly faced by banks, identification of potential good and bad customers is an extremely important task. This paper investigates the use of different structures of neural networks to support the preliminary credit risk decision-making process. The results are compared among the models and juxtaposed with real-world data. Moreover, different sets and subsets of entry data are analyzed to find the best input variables (financial ratios).

Abstract 154: Prevalence of Heart Team Decision-Making at Hospitals in Michigan

2014 ◽  
Vol 7 (suppl_1) ◽  
Author(s):  
Jeffrey Bruckel ◽  
Hitinder S Gurm ◽  
Milan Seth ◽  
Richard L Prager ◽  
Andrea Jensen ◽  
...  
Keyword(s):  
Decision Making ◽  
Real World ◽  
Teaching Hospitals ◽  
Case Conference ◽  
Heart Team ◽  
National Guidelines ◽  
Real World Data ◽  
Team Decision Making ◽  
Case Conferences ◽  
Team Decision

BACKGROUND: Heart Team decision-making for patients with complex coronary disease is a new concept in patient management, increasingly supported by guidelines. Although the Heart Team concept is supported by theory, there are little real-world data on implementation and prevalence. We sought to assess the prevalence of Heart Teams, and their impact on collaborative practice. METHODS: The Blue Cross Blue Shield of Michigan Cardiovascular Consortium and Michigan Society of Thoracic and Cardiovascular Surgeons performed a survey of 31 hospitals in Michigan where percutaneous coronary intervention (PCI) is performed. This survey was performed in 5/2011, before Heart Teams were recommended in National Guidelines. Physicians from each hospital were asked about the presence of a Heart Team or Case Conference, the frequency of collaboration, and the subjective level of collaboration (using a five-point Likert-type scale). We assessed the level of agreement between respondents from the same hospital about whether a Heart Team was active at their hospital. Due to disagreement at several hospitals, we classified them into Definite, Possible, or No Heart Team groups. RESULTS: Fifty-three physicians responded, representing 27/31 (87.1%) of hospitals surveyed. Seven hospitals out of 27 (25.9%) reported either a Heart Team or combined Case Conference, while 11/27 hospitals (40.7%) reported no Heart Team or Case Conference. There was disagreement about the presence of a Heart Team at 7/27 (25.9%) of hospitals and about Case Conferences at 9/27 (33.3%) of hospitals. There were few significant differences in the size or characteristics of hospitals between Heart Team groups. All Heart Team hospitals were teaching hospitals, compared with 73% (8/11) of non-Heart Team hospitals (p=0.056). The reported level of collaboration was significantly higher at Heart Team hospitals; this was reported as very good or best possible (4 or 5 out of 5) by 83.8% (31/37) of possible or definite Heart Team hospital respondents, compared with 50% (8/16) of respondents from hospitals without Heart Teams (p=0.017). Communication about patients undergoing multivessel PCI was higher at Heart Team hospitals (p=0.005). Discussion: This is the first report on the prevalence of Heart Teams in real-world practice, prior to the release of guidelines supporting their implementation. The most important findings of this study are the low initial prevalence of Heart Teams, and the level of disagreement between survey respondents about whether their hospital has a Heart Team or Case Conference. The survey shows higher levels of collaboration at hospitals with Heart Teams or Case Conferences. As Heart Teams become more widespread, more formal definition of the implementation and activities of a Heart Team will be crucial. Further study is needed to clarify the institutional characteristics that lead to a successful Heart Team.

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