Data-Mining and Expert Models for Predicting Injury Risk in Ski Resorts

2015 ◽  
pp. 46-60 ◽  
Author(s):  
Marko Bohanec ◽  
Boris Delibašić
Keyword(s):  
Data Mining ◽  
Injury Risk ◽  
Ski Resorts ◽  
Expert Models

A ski injury risk assessment model for ski resorts

2020 ◽  
Vol 23 (12) ◽  
pp. 1590-1602
Author(s):  
Delibašić Boris ◽  
Dragana Makajić-Nikolić ◽  
Marko Ćirović ◽  
Nataša Petrović ◽  
Milija Suknović
Keyword(s):  
Risk Assessment ◽  
Injury Risk ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Ski Resorts ◽  
Ski Injury

DATA MINING FOR OCCUPATIONAL INJURY RISK: A CASE STUDY

2010 ◽  
Vol 17 (04) ◽  
pp. 351-380 ◽  
Author(s):  
MAURIZIO BEVILACQUA ◽  
FILIPPO EMANUELE CIARAPICA ◽  
GIANCARLO GIACCHETTA
Keyword(s):  
Data Mining ◽  
Input Data ◽  
Injury Risk ◽  
Negative Binomial ◽  
Occupational Injury ◽  
Risk Level ◽  
Data Mining Technique ◽  
Economic Point ◽  
Interaction Detection ◽  
Neuro Fuzzy

The management of occupational injury is of strategic importance in a company from the organizational, engineering and economic point of view. This work is an attempt to apply data mining techniques to data regarding accidents in a medium-sized refinery. Several techniques were adopted in order to identify the important relationships between risk level and immediate/root causes and corrective actions. As a Data Mining technique were tested: Negative Binomial Regression (NBR), Chi-Squared Automatic Interaction Detection (CHAID); Exhaustive CHAID; Classification And Regression Trees (CART); Quick, Unbiased, Efficient Statistical Tree (QUEST), Artificial Neural Network (ANN) and Neuro-Fuzzy Systems (FIS). The comparison carried out in this study shows through a real application the flexibility and advantages of using the neuro-fuzzy network, a typical soft computing tool. Using these innovative techniques to analyse injury data this study aims to: • obtain a classification of input data according to their importance and/or influence on the risk level in injuries; • assess how a variation in one or more pieces of input data can effect occupational injury and subsequently carry out a sensitivity analysis concerning the probability, the consequences and risks of the injurie events; The analyses carried out indicated important relationships between the variables, providing useful decision-making rules which can be followed when adopting measures for improvement.

From Churn Models to Churn Solution

2014 ◽  
pp. 190-208
Keyword(s):  
Data Mining ◽  
Analytical Method ◽  
Time Dependent ◽  
Data Sets ◽  
Data Mining Technique ◽  
Model Classification ◽  
Mining Technique ◽  
Expert Models ◽  
Segmentation Models ◽  
Current Business

This chapter explains churn model classification, describes techniques for developing predictive churn models, and describes how to build churn segmentation models, churn time-dependent models, and expert models for churn reduction. Analysts (readers) are shown a holistic picture for churn modeling and presented an analytical method with techniques described as elements that could be used for building a final churn solution depending on current business problems and expected outputs. There are numerous ways for designing final churn models (solutions). The first criteria is to find solutions that will be in line with business needs. The problem is not applying some data mining technique; the problem is in choosing and preparing appropriate data sets. Applied techniques should show holistic solution pictures for churn, which are explainable and understandable for making decisions, which will help in churn understanding and churn mitigation.

Safety benefit evaluation of intelligent driving systems based on multisource data mining

2019 ◽  
Vol 233 (9) ◽  
pp. 2362-2370
Author(s):  
Long Chen ◽  
Chun-lei Yu ◽  
Yu-gong Luo ◽  
Man-jiang Hu ◽  
Ke-qiang Li
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Injury Risk ◽  
Evaluation Method ◽  
General System ◽  
Third Party ◽  
Test Scenario ◽  
Emergency Braking ◽  
Benefit Evaluation ◽  
Safety Benefit

Different manufacturers achieve intelligent driving system function diversely, which imposes higher impartiality requirements for the evaluation method of the third party. To this end, this article presents a safety benefit evaluation method of intelligent driving systems based on multi-source data mining. On the basis of the discussion over the nature of general system identification, this approach uses neural network to learn the behavior of the evaluated object using the running vehicle data collected and provided by the manufacturer. Combined with the trained network controller, the test scenario model and the car following model extracted from the field operational tests data, and with the occupant injury model obtained from the accident data, Monte Carlo random simulation is used to calculate the injury risk with or without the evaluated system, then the safety benefit by comparison is estimated. In this article, the adaptive cruise system and the automatic emergency braking system are evaluated. The results show that the neural network can accurately imitate the behavior of the object to be evaluated. There is only 0.01 error between the evaluation results using this network and the real object.

scholarly journals A Data Mining Approach to Predict Non-Contact Injuries in Young Soccer Players

2021 ◽  
Vol 20 (2) ◽  
pp. 147-163
Author(s):  
M. Mandorino ◽  
A.J. Figueiredo ◽  
G. Cima ◽  
A. Tessitore
Keyword(s):  
Risk Factors ◽  
Data Mining ◽  
Injury Risk ◽  
Classification Tree ◽  
Characteristic Curve ◽  
Soccer Players ◽  
Data Mining Algorithm ◽  
Total Quality ◽  
Tree Model ◽  
Classification Tree Model

Abstract Predicting and avoiding an injury is a challenging task. By exploiting data mining techniques, this paper aims to identify existing relationships between modifiable and non-modifiable risk factors, with the final goal of predicting non-contact injuries. Twenty-three young soccer players were monitored during an entire season, with a total of fifty-seven non-contact injuries identified. Anthropometric data were collected, and the maturity offset was calculated for each player. To quantify internal training/match load and recovery status of the players, we daily employed the session-RPE method and the total quality recovery (TQR) scale. Cumulative workloads and the acute: chronic workload ratio (ACWR) were calculated. To explore the relationship between the various risk factors and the onset of non-contact injuries, we performed a classification tree analysis. The classification tree model exhibited an acceptable discrimination (AUC=0.76), after receiver operating characteristic curve (ROC) analysis. A low state of recovery, a rapid increase in the training load, cumulative workload, and maturity offset were recognized by the data mining algorithm as the most important injury risk factors.

Clinical Update: Functional Capacity Evaluations—An Update

1999 ◽  
Vol 4 (5) ◽  
pp. 4-7 ◽  
Author(s):  
Laura Welch
Keyword(s):  
Job Performance ◽  
Functional Capacity ◽  
Injury Risk ◽  
Disability Evaluation ◽  
Time Period ◽  
Submaximal Effort ◽  
Lack Of Sleep ◽  
Overall Performance ◽  
Time Required ◽  
Specific Limitation

Abstract Functional capacity evaluations (FCEs) have become an important component of disability evaluation during the past 10 years to assess an individual's ability to perform the essential or specific functions of a job, both preplacement and during rehabilitation. Evaluating both job performance and physical ability is a complex assessment, and some practitioners are not yet certain that an FCE can achieve these goals. An FCE is useful only if it predicts job performance, and factors that should be assessed include overall performance; consistency of performance across similar areas of the FCE; consistency between observed behaviors during the FCE and limitations or abilities reported by the worker; objective changes (eg, blood pressure and pulse) that are appropriate relative to performance; external factors (illness, lack of sleep, or medication); and a coefficient of variation that can be measured and assessed. FCEs can identify specific movement patterns or weaknesses; measure improvement during rehabilitation; identify a specific limitation that is amenable to accommodation; and identify a worker who appears to be providing a submaximal effort. FCEs are less reliable at predicting injury risk; they cannot tell us much about endurance over a time period longer than the time required for the FCE; and the FCE may measure simple muscular functions when the job requires more complex ones.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Heart Rate Variability, Emotions, and Music

2010 ◽  
Vol 24 (2) ◽  
pp. 112-119 ◽  
Author(s):  
F. Riganello ◽  
A. Candelieri ◽  
M. Quintieri ◽  
G. Dolce
Keyword(s):  
Data Mining ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Vegetative State ◽  
Low Frequency ◽  
Emotional Reactions ◽  
Heart Beat ◽  
Healthy Controls ◽  
Frequency Spectra ◽  
Emotional Value

The purpose of the study was to identify significant changes in heart rate variability (an emerging descriptor of emotional conditions; HRV) concomitant to complex auditory stimuli with emotional value (music). In healthy controls, traumatic brain injured (TBI) patients, and subjects in the vegetative state (VS) the heart beat was continuously recorded while the subjects were passively listening to each of four music samples of different authorship. The heart rate (parametric and nonparametric) frequency spectra were computed and the spectra descriptors were processed by data-mining procedures. Data-mining sorted the nu_lf (normalized parameter unit of the spectrum low frequency range) as the significant descriptor by which the healthy controls, TBI patients, and VS subjects’ HRV responses to music could be clustered in classes matching those defined by the controls and TBI patients’ subjective reports. These findings promote the potential for HRV to reflect complex emotional stimuli and suggest that residual emotional reactions continue to occur in VS. HRV descriptors and data-mining appear applicable in brain function research in the absence of consciousness.

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