An efficient security data-driven approach for implementing risk assessment

Abstract Motivation Generalized Read-Across (GenRA) is a data-driven approach to estimate physico-chemical, biological or eco-toxicological properties of chemicals by inference from analogues. GenRA attempts to mimic a human expert’s manual read-across reasoning for filling data gaps about new chemicals from known chemicals with an interpretable and automated approach based on nearest-neighbors. A key objective of GenRA is to systematically explore different choices of input data selection and neighborhood definition to objectively evaluate predictive performance of automated read-across estimates of chemical properties. Results We have implemented genra-py as a python package that can be freely used for chemical safety analysis and risk assessment applications. Automated read-across prediction in genra-py conforms to the scikit-learn machine learning library's estimator design pattern, making it easy to use and integrate in computational pipelines. We demonstrate the data-driven application of genra-py to address two key human health risk assessment problems namely: hazard identification and point of departure estimation. Availability and implementation The package is available from github.com/i-shah/genra-py.

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Comparison of an interpretable data-driven approach with state of the art classifiers: Application to cardiovascular risk assessment

2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI) ◽

10.1109/rtsi.2017.8065960 ◽

2017 ◽

Author(s):

Diana Mendes ◽

Paulo de Carvalho ◽

Jorge Henriques ◽

Simao Paredes ◽

Teresa Rocha ◽

...

Keyword(s):

Risk Assessment ◽

Cardiovascular Risk ◽

State Of The Art ◽

Data Driven ◽

Cardiovascular Risk Assessment ◽

Data Driven Approach

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A Data-Driven Approach to Cyber Risk Assessment

Security and Communication Networks ◽

10.1155/2019/6716918 ◽

2019 ◽

Vol 2019 ◽

pp. 1-8

Author(s):

Paolo Santini ◽

Giuseppe Gottardi ◽

Marco Baldi ◽

Franco Chiaraluce

Keyword(s):

Risk Assessment ◽

Quantitative Data ◽

Economic Loss ◽

Assessment Process ◽

Risk Assessments ◽

Data Driven ◽

Quantitative Methodology ◽

Data Driven Approach ◽

Cyber Risk ◽

A Company

Cyber risk assessment requires defined and objective methodologies; otherwise, its results cannot be considered reliable. The lack of quantitative data can be dangerous: if the assessment is entirely qualitative, subjectivity will loom large in the process. Too much subjectivity in the risk assessment process can weaken the credibility of the assessment results and compromise risk management programs. On the other hand, obtaining a sufficiently large amount of quantitative data allowing reliable extrapolations and previsions is often hard or even unfeasible. In this paper, we propose and study a quantitative methodology to assess a potential annualized economic loss risk of a company. In particular, our approach only relies on aggregated empirical data, which can be obtained from several sources. We also describe how the method can be applied to real companies, in order to customize the initial data and obtain reliable and specific risk assessments.

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An interpretable data-driven approach for rules construction: Application to cardiovascular risk assessment

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) ◽

10.1109/embc.2017.8037401 ◽

2017 ◽

Author(s):

D. Mendes ◽

S. Paredes ◽

T. Rocha ◽

P. Carvalho ◽

J. Henriques ◽

...

Keyword(s):

Risk Assessment ◽

Cardiovascular Risk ◽

Data Driven ◽

Cardiovascular Risk Assessment ◽

Data Driven Approach

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Data-Driven Approach to Improving the Risk Assessment Process of Medical Failures

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph15102069 ◽

2018 ◽

Vol 15 (10) ◽

pp. 2069 ◽

Cited By ~ 2

Author(s):

Shih-Heng Yu ◽

Emily Su ◽

Yi-Tui Chen

Keyword(s):

Risk Assessment ◽

Traditional Method ◽

Failure Modes ◽

Medical Center ◽

Assessment Process ◽

Data Driven ◽

Relative Advantage ◽

Data Envelopment ◽

Data Driven Approach ◽

Using Data

In recent decades, many researchers have focused on the issue of medical failures in the healthcare industry. A variety of techniques have been employed to assess the risk of medical failure and to generate strategies to reduce the frequency of medical failures. Considering the limitations of the traditional method—failure mode and effects analysis (FMEA)—for risk assessment and quality improvement, this paper presents two models developed using data envelopment analysis (DEA). One is called the slacks-based measure DEA (SBM-DEA) model, and the other is a novel data-driven approach (NDA) that combines FMEA and DEA. The relative advantages of the three models are compared. In this paper, an infant security case consisting of 16 failure modes at Western Wake Medical Center in Raleigh, North Carolina, U.S., was employed. The results indicate that both SBM-DEA and NDA may improve the discrimination and accuracy of detection compared to the traditional method of FMEA. However, NDA was found to have a relative advantage over SBM-DEA due to its risk assessment capability and precise detection of medical failures.

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