Risk and dangerousness

1999 ◽  
Vol 29 (2) ◽  
pp. 465-473 ◽  
Author(s):  
ALEC BUCHANAN
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
General Population ◽  
Risk Prediction ◽  
Financial Services ◽  
Clinical Prediction ◽  
Mathematical Methods ◽  
Mentally Disordered ◽  
Definition Of ◽  
Combining Information

The task of improving the ability of clinicians to predict which of their patients will be violent has come to be seen as one of establishing the relative merits of actuarial and clinical prediction. The meaning of these terms is unclear. ‘Clinical’ is usually defined by exclusion, that is, as something other than actuarial. The term ‘actuarial’ is often used to refer to the techniques of risk prediction in financial services. In the psychiatric and psychological literature relating to the assessment of dangerousness, three further meanings have emerged. That whereby actuarial refers to any mathematical means of combining information is the most widely accepted. Whichever definition is employed, the conclusion of most reviews has been that the future is actuarial. It is argued here that, while mathematical approaches have been successful in showing that risk factors for violence in the general population apply also to the mentally disordered, important questions remain unanswered. Mathematical methods address only one form of probability, that which arises from chance. A development of another form of probability, that which arises from causes, offers the prospect of improved risk assessment in psychiatry. It also offers a definition of clinical prediction that is not based on exclusion.

scholarly journals Diagnostics and monitoring of economic entities security

2019 ◽  
Vol 91 ◽  
pp. 08002 ◽  
Author(s):  
Elena V. Karanina ◽  
Olesya A. Ryazanova ◽  
Alexander N. Timin ◽  
Larisa P. Domracheva
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Final Stage ◽  
Small Businesses ◽  
Integrated Model ◽  
Economic Security ◽  
Management Decisions ◽  
Main Factors ◽  
Definition Of

The article shows the place and role of economic entities in the system of economic security of territories. Various approaches to the definition of the term “economic security of small businesses” are considered. The main factors and threats to the economic security of economic entities of the territories are presented. Presents the author’s system of basic indicators of estimation of economic safety of economic entities of the territory. Offers on carrying out diagnostics and monitoring of risks are given. Recommendations as a rating of economic security of economic entities of territories are given. The procedure for monitoring the economic security of economic entities of the territories based on a risk-based approach can be represented in the form of five interrelated stages. This is the stage of collecting data on enterprises and the calculation of the necessary indicators. The stage of formation of the system of indicators, they are risk factors. Stage of processing indicators. The stage of building an integrated model of potential and risk assessment. In addition, the final stage of assessment of the complex level of economic security of economic entities of the territories. This will allow making management decisions in the field of development and support of small businesses at the territorial level.

scholarly journals Assessing the Predictive Validity of Simple Dementia Risk Models in Harmonized Stroke Cohorts

Stroke ◽  
2020 ◽  
Vol 51 (7) ◽  
pp. 2095-2102
Author(s):  
Eugene Y.H. Tang ◽  
Christopher I. Price ◽  
Louise Robinson ◽  
Catherine Exley ◽  
David W. Desmond ◽  
...  
Keyword(s):  
Risk Factors ◽  
General Population ◽  
Risk Prediction ◽  
Prediction Models ◽  
Disease Risk ◽  
Risk Index ◽  
Area Under The Curve ◽  
Risk Prediction Models ◽  
C Statistic ◽  
Dementia Risk

Background and Purpose: Stroke is associated with an increased risk of dementia. To assist in the early identification of individuals at high risk of future dementia, numerous prediction models have been developed for use in the general population. However, it is not known whether such models also provide accurate predictions among stroke patients. Therefore, the aim of this study was to determine whether existing dementia risk prediction models that were developed for use in the general population can also be applied to individuals with a history of stroke to predict poststroke dementia with equivalent predictive validity. Methods: Data were harmonized from 4 stroke studies (follow-up range, ≈12–18 months poststroke) from Hong Kong, the United States, the Netherlands, and France. Regression analysis was used to test 3 risk prediction models: the Cardiovascular Risk Factors, Aging and Dementia score, the Australian National University Alzheimer Disease Risk Index, and the Brief Dementia Screening Indicator. Model performance or discrimination accuracy was assessed using the C statistic or area under the curve. Calibration was tested using the Grønnesby and Borgan and the goodness-of-fit tests. Results: The predictive accuracy of the models varied but was generally low compared with the original development cohorts, with the Australian National University Alzheimer Disease Risk Index (C-statistic, 0.66) and the Brief Dementia Screening Indicator (C-statistic, 0.61) both performing better than the Cardiovascular Risk Factors, Aging and Dementia score (area under the curve, 0.53). Conclusions: Dementia risk prediction models developed for the general population do not perform well in individuals with stroke. Their poor performance could have been due to the need for additional or different predictors related to stroke and vascular risk factors or methodological differences across studies (eg, length of follow-up, age distribution). Future work is needed to develop simple and cost-effective risk prediction models specific to poststroke dementia.

scholarly journals A Review on Automatic Mammographic Density and Parenchymal Segmentation

2015 ◽  
Vol 2015 ◽  
pp. 1-31 ◽  
Author(s):  
Wenda He ◽  
Arne Juette ◽  
Erika R. E. Denton ◽  
Arnau Oliver ◽  
Robert Martí ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Risk Assessment ◽  
Risk Prediction ◽  
Prediction Models ◽  
Prevention Measures ◽  
Clinical Environment ◽  
Tissue Segmentation ◽  
Risk Prediction Models ◽  
The Impact

Breast cancer is the most frequently diagnosed cancer in women. However, the exact cause(s) of breast cancer still remains unknown. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast cancer. There are more than 70 common genetic susceptibility factors included in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models). Image-based risk factors, such as mammographic densities and parenchymal patterns, have been established as biomarkers but have not been fully incorporated in the risk prediction models used for risk stratification in screening and/or measuring responsiveness to preventive approaches. Within computer aided mammography, automatic mammographic tissue segmentation methods have been developed for estimation of breast tissue composition to facilitate mammographic risk assessment. This paper presents a comprehensive review of automatic mammographic tissue segmentation methodologies developed over the past two decades and the evidence for risk assessment/density classification using segmentation. The aim of this review is to analyse how engineering advances have progressed and the impact automatic mammographic tissue segmentation has in a clinical environment, as well as to understand the current research gaps with respect to the incorporation of image-based risk factors in non-image-based risk prediction models.

scholarly journals Secondary use of clinical data in data-gathering, non-interventional research or learning activities: definition, types, and a framework for risk assessment (Preprint)

2020 ◽  
Author(s):  
Martin Jungkunz ◽  
Anja Köngeter ◽  
Katja Mehlis ◽  
Eva C. Winkler ◽  
Christoph Schickhardt
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Clinical Data ◽  
Data Gathering ◽  
Scientific Progress ◽  
Learning Activities ◽  
Research Quality ◽  
Future Research ◽  
Secondary Use ◽  
Definition Of

UNSTRUCTURED Background: The secondary use of clinical data in data-gathering, non-interventional research or learning activities (SeConts) bears great potential for scientific progress and health care improvement. At the same time, it poses relevant risks for privacy and informational self-determination of the patients whose data are used. A tailored framework for risk assessment in SeConts is still lacking and so does a clarification of the concept and practical scope of SeConts. Methods: (1) We analyze each element of the concept of SeConts to provide a synthetic definition. (2) We investigate the practical relevance and scope of SeConts through a literature review. (3) We operationalize the widespread definition of risk (as a harmful event of a certain magnitude that occurs with a certain probability) in order to conduct a tailored analysis of privacy risk factors typically implied in SeConts. Results: (1) We offer a conceptual clarification and a definition of SeConts. (2) We provide a list of types of research and learning activities that can be subsumed under the definition of SeConts. We also offer a proposal for the classification of SeConts types into the categories “non-interventional (observational) clinical research”, “quality control and improvement”, or “public health research”. (3) We provide a list of risk factors that determine either probability or magnitude of harm implied in SeConts. Discussion: The risk factors mentioned above provide a framework for assessing the privacy-related risks for patients implied in SeConts. We illustrate the usage of the risk assessment by applying it to a concrete example. Conclusion: In the future, research ethics committees and data use and access committees will be able to rely on and apply the framework offered here when reviewing projects of secondary use of clinical data for learning and research purposes.

scholarly journals Myocardial Biomarkers for Prediction of Cardiovascular Disease

2009 ◽  
Vol 26 (5-6) ◽  
pp. 235-246 ◽  
Author(s):  
Johan Sundström
Keyword(s):  
Risk Factors ◽  
Cardiovascular Disease ◽  
General Population ◽  
Risk Prediction ◽  
Cardiovascular Events ◽  
Troponin I ◽  
Adverse Outcome ◽  
Unmet Need ◽  
Vast Number ◽  
New Biomarkers

The identification of those persons in the population who have the highest risk of future cardiovascular events is important for targeting intensive preventive efforts. This can be reliably done using a handful of long since established risk factors. The unmet need for new molecular biomarkers for prediction of cardiovascular events in the general population is therefore low. In order for a new biomarker to be used clinically for risk prediction, a statistically significant association of levels of the biomarker to adverse outcome is not enough, but the biomarker should also be demonstrated to add discriminative capacity beyond established risk factors. In contrast to the limited value of new biomarkers for risk prediction, their usefulness for unraveling the pathophysiology of cardiovascular disease is large. The myocardium is the source of a vast number of interesting biomarkers, of which a few may be useful for risk prediction in the general population. Two of these, troponin-I and the N-terminal fragment of brain natriuretic peptide, have passed tests of added discriminatory value. Numerous other biomarkers produced by cardiomyocytes or non-cardiomyocytes in the myocardium are promising, and if they are not proven useful for risk prediction, they will unquestionably enhance our understanding of cardiovascular disease.

scholarly journals Prospective, longitudinal, cohort study for assessment of vascular ageing among adults of urban and rural area of Central India: research protocol

2017 ◽  
Vol 4 (9) ◽  
pp. 3273
Author(s):  
Vijay Bhagat ◽  
Shubhangi Baviskar ◽  
Abhay B. Mudey ◽  
Ramachandra Goyal
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Cohort Study ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Cvd Risk ◽  
Framingham Risk ◽  
Vascular Ageing ◽  
Vascular Age

Background: Considering the complex interaction of risk factors in causation of CVD; assessment of vascular ageing among the high risk group through non-interventional statistical models was useful in controlling CVD. While, many CVD risk assessment models were especially designed for application in the specific population or region such as SCORE scales for Europeans, ASSIGN scores for people of Scotland. The Framingham Risk Score were modified, validated and used in several countries. Though Indians have significantly higher predilection for CVD, no indigenous scores were developed or validated to assess the CV risk. The objective of the study were to determine vascular age of the study participants using Framingham risk prediction model, to assess its relationship with development of cardiovascular disease and to develop, validate and compare cardiovascular risk prediction model based on the follow up observations of the study participants.Methods: Community based cohort study will be conducted in large urban and rural population aged 31-60 years of age those who have no evidence of CVD. The study population will be followed up for three years and will be assessed for development of CVD. The vascular age will be determined using Framingham Risk Scores. Based on the risk factors associated with occurrence of CVD during the study period, the risk prediction model will be designed and tested for validity and accuracy. Results: The newly developed CVD risk prediction will be more accurate in assessment of CV risk among the study subjects. Conclusions: The newly developed and validated CV risk prediction model specific for Indians may be one of the first prospective CV risk assessment cohort study. 

Significant interactions between traditional risk factors affect cardiovascular risk prediction in healthy general population

2014 ◽  
Vol 47 (1) ◽  
pp. 53-60 ◽  
Author(s):  
Jussi A. Hernesniemi ◽  
Juho Tynkkynen ◽  
Aki S. Havulinna ◽  
Niku Oksala ◽  
Erkki Vartiainen ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cardiovascular Risk ◽  
General Population ◽  
Risk Prediction ◽  
Cardiovascular Risk Prediction ◽  
Traditional Risk Factors

scholarly journals Violence risk prediction

2000 ◽  
Vol 177 (4) ◽  
pp. 303-311 ◽  
Author(s):  
M. Dolan ◽  
M. Doyle
Keyword(s):  
Risk Assessment ◽  
Risk Prediction ◽  
Assessment Tools ◽  
Current Status ◽  
Violence Risk ◽  
Mentally Disordered Offenders ◽  
Mentally Disordered ◽  
Psychopathy Checklist ◽  
Violent Recidivism ◽  
Recent Developments

BackgroundViolence risk prediction is a priority issue for clinicians working with mentally disordered offenders.AimsTo review the current status of violence risk prediction research.MethodLiterature search (Medline). Key words: violence, risk prediction, mental disorder.ResultsSystematic/structured risk assessment approaches may enhance the accuracy of clinical prediction of violent outcomes. Data on the predictive validity of available clinical risk assessment tools are based largely on American and North American studies and further validation is required in British samples. The Psychopathy Checklist appears to be a key predictor of violent recidivism in a variety of settings.ConclusionsViolence risk prediction is an inexact science and as such will continue to provoke debate. Clinicians clearly need to be able to demonstrate the rationale behind their decisions on violence risk and much can be learned from recent developments in research on violence risk prediction.

scholarly journals Secondary Use of Clinical Data in Data-Gathering, Non-Interventional Research or Learning Activities: Definition, Types, and a Framework for Risk Assessment

10.2196/26631 ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. e26631
Author(s):  
Martin Jungkunz ◽  
Anja Köngeter ◽  
Katja Mehlis ◽  
Eva C Winkler ◽  
Christoph Schickhardt
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Clinical Data ◽  
Data Gathering ◽  
Scientific Progress ◽  
Learning Activities ◽  
Research Quality ◽  
Future Research ◽  
Secondary Use ◽  
Definition Of

Background The secondary use of clinical data in data-gathering, non-interventional research or learning activities (SeConts) has great potential for scientific progress and health care improvement. At the same time, it poses relevant risks for the privacy and informational self-determination of patients whose data are used. Objective Since the current literature lacks a tailored framework for risk assessment in SeConts as well as a clarification of the concept and practical scope of SeConts, we aim to fill this gap. Methods In this study, we analyze each element of the concept of SeConts to provide a synthetic definition, investigate the practical relevance and scope of SeConts through a literature review, and operationalize the widespread definition of risk (as a harmful event of a certain magnitude that occurs with a certain probability) to conduct a tailored analysis of privacy risk factors typically implied in SeConts. Results We offer a conceptual clarification and definition of SeConts and provide a list of types of research and learning activities that can be subsumed under the definition of SeConts. We also offer a proposal for the classification of SeConts types into the categories non-interventional (observational) clinical research, quality control and improvement, or public health research. In addition, we provide a list of risk factors that determine the probability or magnitude of harm implied in SeConts. The risk factors provide a framework for assessing the privacy-related risks for patients implied in SeConts. We illustrate the use of risk assessment by applying it to a concrete example. Conclusions In the future, research ethics committees and data use and access committees will be able to rely on and apply the framework offered here when reviewing projects of secondary use of clinical data for learning and research purposes.

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