Implementing AI-Based Predictive Analytics for OHS
Expert-defined terms from the Advanced AI OHS Professional Certification (Part II) (Canada) course at LearnUNI. Free to read, free to share, paired with a professional course.
AI (Artificial Intelligence) – The simulation of human intelligence proce… #
Related terms: machine learning, deep learning, cognitive computing. In OHS predictive analytics, AI enables the analysis of complex safety data to uncover hidden risk patterns and support proactive interventions.
AI Ethics – Principles guiding the responsible development and deployment… #
Related terms: bias mitigation, algorithmic fairness, responsible AI. For OHS, ethical AI safeguards worker data and prevents discriminatory risk assessments.
AI Governance – Framework of policies, procedures, and oversight mechanis… #
Related terms: AI policy, AI oversight committee, AI risk register. Effective governance ensures predictive models align with occupational health regulations and corporate safety objectives.
Algorithmic Bias – Systematic and unfair discrimination embedded in AI mo… #
Related terms: bias detection, fairness metrics, mitigation strategies. In OHS analytics, unchecked bias could misrepresent risk for certain worker groups, leading to inequitable safety measures.
Algorithmic Transparency – The degree to which the inner workings of an A… #
Related terms: explainable AI, model interpretability, documentation. Transparent models enable safety managers to justify predictive alerts and regulatory filings.
Apache Spark – An open‑source distributed computing system for big data p… #
Related terms: RDD, DataFrame, Spark MLlib. Spark can ingest large OHS sensor streams, perform feature engineering, and feed predictive models at scale.
ArcGIS – Geographic information system (GIS) software for mapping and spa… #
Related terms: spatial clustering, heat maps, geocoding. Integrating ArcGIS with predictive analytics visualizes injury hotspots across worksites, guiding targeted interventions.
Asset Management – Systematic approach to maintaining and optimizing phys… #
Related terms: CMMS, predictive maintenance, reliability engineering. AI‑driven predictive analytics forecast equipment failures that could cause safety incidents, supporting proactive maintenance schedules.
Association Rule Mining – Data mining technique for discovering relations… #
Related terms: Apriori algorithm, confidence, support. In OHS, rule mining can reveal co‑occurring risk factors such as “night shift” and “manual handling” leading to higher injury rates.
Automation Bias – Tendency of users to over‑trust automated decisions, po… #
Related terms: human‑in‑the‑loop, decision support, verification. Safety officers must validate AI alerts to prevent reliance on false positives or negatives.
Balancing Scorecard – Strategic management tool linking performance metri… #
Related terms: KPI, leading indicators, lagging indicators. Predictive analytics enrich the “learning” quadrant with forward‑looking safety risk scores.
Bayesian Networks – Probabilistic graphical models representing variables… #
Related terms: inference, belief propagation, DAG. In OHS, Bayesian networks model causal chains from environmental exposure to injury outcomes, updating risk estimates as new data arrives.
Benchmarking – Process of comparing an organization’s performance metrics… #
Related terms: KPIs, gap analysis, performance dashboard. AI predictive scores can be benchmarked to identify safety performance relative to peers.
Big Data – Extremely large and complex data sets that exceed traditional… #
Related terms: volume, velocity, variety, veracity. OHS data streams from wearables, incident logs, and environmental sensors constitute big data for AI analytics.
Binary Classification – Machine‑learning task that assigns observations t… #
G., “High risk” vs “low risk”). Related terms: logistic regression, ROC curve, confusion matrix. Most OHS predictive models output a binary safety alert to trigger immediate action.
Black‑Box Model – AI model whose internal logic is not readily interpreta… #
Related terms: deep neural network, opacity, explainability. While powerful, black‑box models pose challenges for regulatory compliance in occupational safety reporting.
Bluetooth Low Energy (BLE) Sensors – Wireless devices that transmit low‑p… #
Related terms: IoT, proximity detection, signal strength. BLE sensor data feed predictive algorithms that detect unsafe proximity or fatigue‑related movement patterns.
CMMS (Computerized Maintenance Management System) – Software that tracks… #
Related terms: preventive maintenance, work order scheduling, asset lifecycle. Integrating CMMS data with AI models predicts equipment‑related injury risks before breakdowns occur.
Confusion Matrix – Tabular representation of classification outcomes #
True positives, false positives, true negatives, false negatives. Related terms: precision, recall, F1‑score. In OHS predictive analytics, the matrix quantifies model effectiveness in correctly flagging imminent hazards.
Continuous Integration/Continuous Deployment (CI/CD) – Automated pipeline… #
Related terms: DevOps, version control, automated testing. CI/CD ensures AI model updates for safety analytics are rolled out reliably without service interruption.
Correlation vs Causation – Distinction between variables that move togeth… #
Related terms: spurious correlation, causal inference, confounding. Predictive analytics must avoid mistaking correlated OHS factors for causal injury drivers.
Cross‑Validation – Technique for assessing model performance by partition… #
Related terms: k‑fold, hold‑out set, model stability. Robust cross‑validation prevents overfitting of OHS risk models to historical incident data.
Data Augmentation – Process of artificially expanding training datasets u… #
Related terms: SMOTE, oversampling, noise injection. For rare OHS events like severe injuries, augmentation helps AI models learn from limited examples.
Data Governance – Policies, standards, and processes that ensure data qua… #
Related terms: data stewardship, metadata management, data lineage. Strong governance protects worker privacy while enabling trustworthy predictive analytics.
Data Lake – Centralized repository that stores raw, unstructured, and str… #
Related terms: schema‑on‑read, Hadoop, object storage. OHS data from video feeds, sensor logs, and health records can be ingested into a data lake for downstream AI modeling.
Data Normalization – Scaling numeric features to a common range to improv… #
Related terms: min‑max scaling, z‑score, feature scaling. Normalizing exposure intensity measurements ensures fair weighting across diverse OHS variables.
Data Pipeline – Automated workflow that extracts, transforms, and loads (… #
Related terms: Airflow, Prefect, streaming ingestion. Real‑time OHS pipelines deliver sensor readings to predictive models with minimal latency.
Data Privacy Impact Assessment (DPIA) – Systematic evaluation of privacy… #
Related terms: GDPR, consent management, anonymization. Conducting a DPIA before deploying AI for worker health data safeguards compliance with Canadian privacy law.
Decision Support System (DSS) – Interactive software that assists users i… #
Related terms: dashboards, alerts, scenario modeling. AI‑driven OHS DSS provides safety managers with risk scores, recommended controls, and mitigation timelines.
Deep Learning – Subset of machine learning using multi‑layer neural netwo… #
Related terms: convolutional neural network, recurrent neural network, backpropagation. Deep learning excels at processing unstructured OHS data such as video footage of unsafe behaviors.
Deployment Architecture – Structural design of hardware, software, and ne… #
Related terms: microservices, containers, edge computing. Choosing an architecture that balances latency and security is critical for real‑time OHS hazard detection.
Dimensionality Reduction – Techniques that compress high‑dimensional data… #
Related terms: PCA, t‑SNE, feature selection. Reducing sensor feature space improves model speed for on‑site predictive analytics.
Discrete Event Simulation – Modeling approach that represents systems as… #
Related terms: Monte Carlo, queuing theory, process flow. Simulating worksite operations helps validate AI‑generated risk forecasts against realistic scenarios.
Docker – Platform for packaging applications and their dependencies into… #
Related terms: containerization, image registry, orchestration. Containerizing OHS predictive models ensures consistent execution across development, testing, and production environments.
Edge Computing – Processing data near its source rather than in centraliz… #
Related terms: IoT gateway, latency reduction, fog computing. Edge deployment of AI models enables instant hazard alerts from wearable sensors without network delays.
Ensemble Learning – Method that combines predictions from multiple models… #
Related terms: bagging, boosting, stacking. An ensemble of decision trees, logistic regressions, and neural networks can robustly predict injury likelihood across diverse OHS datasets.
Feature Engineering – Creation, transformation, and selection of variable… #
Related terms: one‑hot encoding, interaction terms, lag features. Crafting a “cumulative exposure index” from daily noise level readings enhances predictive power for hearing loss risk.
Feature Importance – Metric quantifying the contribution of each input va… #
Related terms: SHAP values, permutation importance, gain. Highlighting top risk factors (e.G., “Lifting weight > 30 kg”) guides targeted safety interventions.
Fisher Exact Test – Statistical significance test used for small sample s… #
Related terms: contingency table, p‑value, odds ratio. The test evaluates whether a rare injury event is disproportionately associated with a specific work shift.
FMEA (Failure Modes and Effects Analysis) – Systematic method for identif… #
Related terms: risk priority number, mitigation planning, reliability. AI‑enhanced FMEA incorporates predictive failure probabilities to prioritize safety controls.
Forecasting Horizon – Time span into the future for which predictions are… #
Related terms: lead time, short‑term horizon, long‑term planning. In OHS, a 7‑day horizon may trigger immediate PPE checks, whereas a 12‑month horizon informs strategic safety budgeting.
Frequentist vs Bayesian Statistics – Two philosophical approaches to infe… #
Related terms: posterior distribution, confidence interval, prior probability. Bayesian updating allows OHS risk models to refine predictions as new incident data become available.
General Data Protection Regulation (GDPR) – EU regulation governing perso… #
Related terms: right to erasure, data minimization, consent. While a Canadian course, understanding GDPR principles helps align AI OHS solutions with global privacy expectations.
Geofencing – Virtual boundary defined by GPS coordinates that triggers ac… #
Related terms: location‑based alerts, geo‑tags, perimeter monitoring. Geofencing can alert workers when they approach high‑risk zones identified by predictive models.
Gradient Boosting Machines (GBM) – Ensemble technique that builds sequent… #
Related terms: XGBoost, LightGBM, learning rate. GBM often yields high accuracy for binary OHS risk classification tasks.
Hazard Identification (HAZID) – Process of systematically recognizing pot… #
Related terms: risk matrix, JSA, safety audit. AI can augment HAZID by surfacing previously unnoticed patterns from historical incident data.
Hazardous Materials Management System (HMMS) – Software that tracks inven… #
Related terms: SDS, chemical safety, compliance reporting. Predictive analytics applied to HMMS data forecast chemical exposure incidents before they occur.
Health Surveillance – Ongoing monitoring of workers’ health status to det… #
Related terms: biomonitoring, medical exams, exposure tracking. AI models analyze trends in surveillance data to predict emerging health issues such as respiratory disorders.
Hierarchical Clustering – Unsupervised learning method that groups data p… #
Related terms: dendrogram, agglomerative, linkage criteria. Clustering incident reports can reveal natural groupings of injuries by department or equipment type.
Human‑in‑the‑Loop (HITL) – Design approach that retains human oversight o… #
Related terms: review workflow, escalation, validation. In OHS, HITL ensures that AI‑generated hazard alerts are examined by safety professionals before action.
Hyperparameter Tuning – Optimization of model settings that are not learn… #
Related terms: grid search, random search, Bayesian optimization. Proper tuning of tree depth, learning rate, and regularization improves OHS risk model generalization.
Incident Command System (ICS) – Standardized management framework for eme… #
Related terms: incident action plan, role assignment, escalation protocol. AI predictive alerts can be fed into the ICS to pre‑position resources before an incident escalates.
Incident Rate (IR) – Metric representing the number of occupational injur… #
G., Per 200,000 hours). Related terms: TRIR, lost‑time injury frequency rate, severity rate. Predictive models aim to reduce IR by identifying high‑risk periods in advance.
Inference Engine – Component of an AI system that applies logical rules o… #
Related terms: rule‑based system, Bayesian inference, decision logic. In OHS, the inference engine translates sensor inputs into actionable risk scores.
Information Security Management System (ISMS) – Set of policies and proce… #
Related terms: ISO 27001, confidentiality, access control. Protecting OHS data from breaches is essential when deploying AI models that contain personal health information.
IoT (Internet of Things) – Network of interconnected devices that collect… #
Related terms: sensor nodes, MQTT, edge analytics. Wearable IoT devices capture physiological and environmental metrics that feed predictive safety algorithms.
JIT (Just‑In‑Time) Maintenance – Maintenance strategy that performs repai… #
Related terms: predictive maintenance, condition‑based monitoring, reliability. AI forecasts equipment failure risk, enabling JIT interventions that also reduce injury exposure.
JSA (Job Safety Analysis) – Systematic examination of job tasks to identi… #
Related terms: SWMS, risk assessment, task breakdown. AI can auto‑populate JSA templates by extracting task‑specific risk factors from historical data.
K‑Fold Cross‑Validation – Specific form of cross‑validation that partitio… #
Related terms: validation set, overfitting prevention, model robustness. Using 5‑fold cross‑validation balances computational cost with reliable performance estimates for OHS models.
KPI (Key Performance Indicator) – Quantifiable measure used to evaluate s… #
Related terms: metric, target, dashboard. AI‑derived safety KPIs, such as “predicted high‑risk incidents per month,” provide early insight into program effectiveness.
Label Imbalance – Situation where one class (e #
G., Injuries) is far less frequent than the other (non‑injuries). Related terms: class weighting, SMOTE, under‑sampling. Addressing imbalance prevents models from defaulting to the majority class and missing rare but critical safety events.
Latent Variable – Unobserved factor inferred from observed data, often re… #
Related terms: hidden state, factor analysis, EM algorithm. In OHS, latent variables may capture “worker fatigue” derived from sleep patterns, heart rate variability, and shift length.
LightGBM – Gradient boosting framework that uses leaf‑wise tree growth fo… #
Related terms: GBDT, histogram‑based splitting, parallel learning. LightGBM’s low latency makes it suitable for real‑time OHS hazard prediction on streaming data.
Logistic Regression – Statistical model that estimates the probability of… #
Related terms: odds ratio, maximum likelihood, regularization. Logistic regression remains a baseline for OHS risk scoring due to interpretability and simplicity.
Machine Learning Operations (MLOps) – Set of practices that combine machi… #
Related terms: model registry, continuous training, monitoring. MLOps pipelines automate retraining of OHS predictive models as new incident data are recorded.
Meta‑Learning – “Learning to learn” approach where algorithms improve the… #
Related terms: few‑shot learning, model‑agnostic meta‑learning (MAML), transfer learning. Meta‑learning can accelerate OHS model adaptation when entering a new industry sector with limited data.
Missing Data Imputation – Techniques for estimating absent values in data… #
Related terms: mean substitution, multiple imputation, k‑NN imputer. Proper imputation ensures AI models do not bias risk predictions due to gaps in sensor logs or health records.
Model Drift – Degradation of model performance over time as underlying da… #
Related terms: concept drift, performance monitoring, retraining triggers. Detecting drift in OHS risk models is vital when workplace processes, equipment, or regulations evolve.
Model Explainability – Ability to articulate how an AI model reaches its… #
Related terms: SHAP, LIME, counterfactual analysis. Explainable OHS models help safety officers understand why a particular worker is flagged as high‑risk.
Monte Carlo Simulation – Computational technique that uses random samplin… #
Related terms: stochastic modeling, risk quantification, scenario analysis. Monte Carlo can assess the uncertainty of predicted injury rates under varying safety investment levels.
Multi‑Label Classification – Predictive task where each observation may b… #
Related terms: binary relevance, classifier chain, label powerset. OHS models may assign both “chemical exposure” and “ergonomic strain” labels to a single incident.
Natural Language Processing (NLP) – Suite of techniques for analyzing and… #
Related terms: sentiment analysis, entity extraction, topic modeling. NLP extracts risk indicators from free‑text incident reports, safety meeting minutes, and employee feedback.
Neural Architecture Search (NAS) – Automated process for discovering opti… #
Related terms: hyperparameter optimization, reinforcement learning, search space. NAS can tailor deep‑learning models to the specific sensor modalities used in OHS environments.
Normalization (Database) – Process of organizing data to reduce redundanc… #
Related terms: first normal form, relational schema, foreign key. Normalized OHS databases facilitate accurate joins between incident logs, personnel records, and equipment maintenance tables.
Ontology (Domain Knowledge) – Formal representation of concepts and relat… #
Related terms: semantic model, RDF, knowledge graph. Building an OHS ontology enables AI to reason about hierarchical hazard categories and regulatory requirements.
Outlier Detection – Identification of data points that deviate markedly f… #
Related terms: Z‑score, isolation forest, DBSCAN. Detecting outliers in sensor streams can signal abnormal equipment vibrations indicative of imminent failure.
Overfitting – Situation where a model captures noise instead of underlyin… #
Related terms: regularization, validation set, early stopping. Guarding against overfitting is essential when training OHS models on limited injury records.
Parameter Server – Distributed system that stores and updates model param… #
Related terms: distributed learning, synchronization, gradient aggregation. Parameter servers enable training of deep OHS models on massive sensor datasets across multiple sites.
Partial Dependence Plot (PDP) – Visualization that shows the marginal eff… #
Related terms: ICE plot, feature effect, model interpretation. PDPs help safety analysts understand how increasing noise exposure influences predicted hearing loss risk.
Personal Protective Equipment (PPE) Compliance Monitoring – Use of AI to… #
Related terms: computer vision, RFID tagging, compliance score. Real‑time alerts reduce incidents caused by PPE non‑use.
Predictive Maintenance – Strategy that anticipates equipment failures bef… #
Related terms: condition monitoring, remaining useful life, failure prediction. Preventing equipment breakdowns directly lowers the likelihood of injury in OHS contexts.
Predictive Modeling – Process of creating statistical or machine learning… #
Related terms: regression, classification, time‑series analysis. In OHS, predictive modeling estimates the probability of incidents based on historical and real‑time inputs.
Probabilistic Graphical Model – Framework that represents random variable… #
Related terms: Markov network, Bayesian network, factor graph. Such models capture complex interrelations among exposure, behavior, and injury outcomes in safety analytics.
Process Mining – Extraction of process models from event logs to discover… #
Related terms: conformance checking, bottleneck analysis, process discovery. Applying process mining to OHS work orders reveals deviations that may increase injury risk.
Python – High‑level programming language widely used for data science and… #
Related terms: Pandas, Scikit‑learn, TensorFlow. Python scripts automate data cleaning, feature extraction, and model training for OHS predictive analytics.
Quantum Machine Learning – Emerging field that leverages quantum computin… #
Related terms: qubits, quantum annealing, hybrid quantum‑classical. While still experimental, quantum approaches could eventually handle the combinatorial optimization problems in large‑scale OHS risk assessment.
Random Forest – Ensemble learning method that constructs multiple decisio… #
Related terms: bagging, feature importance, out‑of‑bag error. Random forests provide robust OHS risk classification with built‑in measures of variable relevance.
Recall (Sensitivity) – Metric measuring the proportion of actual positive… #
Related terms: true positive rate, false negative rate, detection rate. High recall in OHS models ensures most genuine hazards are flagged, even at the cost of some false alarms.
Regulatory Compliance – Adherence to laws, standards, and guidelines gove… #
Related terms: OSHA, CSA, audit trail. AI systems must generate documentation that demonstrates compliance with reporting and mitigation obligations.
Reinforcement Learning – Learning paradigm where an agent interacts with… #
Related terms: policy, Q‑learning, Markov decision process. In OHS, reinforcement learning can optimize the scheduling of safety inspections to maximize hazard reduction.
Remote Sensing – Acquisition of information about a surface without direc… #
Related terms: LIDAR, multispectral imaging, aerial surveys. Remote sensing data enrich predictive models with terrain and environmental risk factors such as slope steepness.
Resampling Techniques – Methods for creating multiple training/test split… #
Related terms: bootstrapping, jackknife, Monte Carlo cross‑validation. Resampling helps quantify uncertainty in OHS risk predictions.
RNN (Recurrent Neural Network) – Neural architecture designed to process… #
Related terms: LSTM, GRU, time‑series forecasting. RNNs model temporal patterns in worker fatigue signals derived from wearable heart‑rate monitors.
Root Cause Analysis (RCA) – Systematic investigation to determine underly… #
Related terms: 5 Whys, fishbone diagram, corrective action. AI can prioritize probable root causes by correlating incident attributes with historical RCA outcomes.
Safety Climate Survey – Questionnaire that gauges employees’ perceptions… #
Related terms: psychological safety, engagement index, Likert scale. AI aggregates survey responses to predict areas where low safety climate may precede incidents.
Safety Data Sheet (SDS) – Document providing detailed information about h… #
Related terms: GHS, hazard classification, exposure limits. NLP extracts hazard codes from SDS to enrich predictive models of chemical exposure incidents.
Scaling (Machine Learning) – Adjusting model complexity or computational… #
Related terms: horizontal scaling, GPU acceleration, distributed training. Scaling ensures OHS predictive pipelines remain responsive as sensor networks expand.
Scenario Analysis – Exploration of “what‑if” situations to assess potenti… #
Related terms: sensitivity analysis, stress testing, decision trees. Scenario analysis evaluates how changes in staffing levels affect predicted injury rates.
Seamless Integration – Design principle ensuring that AI components conne… #
Related terms: API, middleware, data interoperability. Seamless integration reduces data silos and accelerates adoption of predictive safety tools.
Sensor Fusion – Combination of data from multiple sensor modalities to pr… #
Related terms: Kalman filter, data assimilation, multimodal analytics. Fusing accelerometer, temperature, and ambient noise readings improves detection of unsafe conditions.
Sequential Pattern Mining – Identification of frequent subsequences withi… #
Related terms: prefixspan, SPADE, temporal rules. Mining sequential patterns from incident logs uncovers typical event chains that precede accidents.
Shapley Additive Explanations (SHAP) – Game‑theoretic method for assignin… #
Related terms: local interpretability, contribution plot, model agnostic. SHAP values enable OHS managers to see why a specific worker received a high‑risk score.
Signal‑to‑Noise Ratio (SNR) – Measure comparing the level of desired sign… #
Related terms: filtering, data quality, thresholding. High SNR in sensor streams ensures reliable inputs for AI hazard detection.
Singular Value Decomposition (SVD) – Matrix factorization technique used… #
Related terms: latent semantic analysis, PCA, low‑rank approximation. SVD can compress large OHS exposure matrices while preserving essential variance.
Skewed Distribution – Probability distribution where most observations cl… #
Related terms: log‑normal, power‑law, transformation. Many OHS metrics, such as injury severity, exhibit skewness, requiring appropriate modeling techniques.
Smart PPE – Protective equipment embedded with sensors and connectivity f… #
Related terms: IoT‑enabled helmets, vibration‑damping gloves, temperature‑sensing vests. Smart PPE streams data to AI models that predict fatigue or exposure exceedances.
Social Network Analysis (SNA) – Study of relationships and interactions a… #
Related terms: centrality, community detection, graph metrics. SNA reveals informal safety communication pathways that influence hazard awareness.
Software Development Kit (SDK) – Collection of tools, libraries, and docu… #
Related terms: API, sample code, integration guide. An OHS AI SDK accelerates custom development of risk dashboards and mobile alert apps.
Spatial Autocorrelation – Statistical property where nearby locations exh… #
Related terms: Moran’s I, Geary’s C, hotspot analysis. Accounting for spatial autocorrelation prevents overestimation of risk clusters in OHS predictive maps.
SQL (Structured Query Language) – Standard language for managing relation… #
Related terms: SELECT, JOIN, stored procedure. SQL queries extract historical incident records for training OHS machine‑learning models.
Standard Operating Procedure (SOP) – Documented step‑by‑step instructions… #
Related terms: work instruction, compliance checklist, procedural audit. AI can recommend SOP revisions when predictive analytics identify emerging hazards.
Statistical Process Control (SPC) – Methodology using control charts to m… #
Related terms: Shewhart chart, process capability, assignable cause. SPC charts integrated with AI predictions highlight when safety processes deviate from acceptable limits.
Stratified Sampling – Technique that divides a population into homogeneou… #
Related terms: cluster sampling, weighting, representation. Stratified sampling ensures OHS datasets capture diverse worker categories (e.G., Trades, office staff).
Supervised Learning – Machine‑learning paradigm where models are trained… #
Related terms: classification, regression, training set. Most OHS predictive tasks rely on supervised learning using historical incident labels.
Support Vector Machine (SVM) – Classification algorithm that finds the hy… #
Related terms: kernel trick, soft margin, support vectors. SVMs can separate high‑risk vs low‑risk scenarios when feature space is complex.
Survival Analysis – Statistical approach for modeling time‑to‑event data,… #
Related terms: Cox proportional hazards, Kaplan‑Meier, hazard function. Survival analysis predicts the time until a worker experiences a musculoskeletal injury based on exposure history.
Swarm Intelligence – Collective behavior algorithms inspired by natural g… #
Related terms: particle swarm optimization, ant colony optimization, decentralized search. Swarm techniques can optimize sensor placement across a large construction site for maximal hazard coverage.
Temporal Fusion Transformer (TFT) – Deep‑learning architecture for multi‑… #
Related terms: attention mechanism, variable selection network, quantile regression. TFT handles mixed static and dynamic OHS inputs, delivering accurate short‑ and long‑term risk forecasts.
Time‑Series Decomposition – Process of separating a series into trend, se… #
Related terms: STL, additive model, multiplicative model. Decomposition clarifies whether rising injury counts are driven by seasonal work peaks or underlying systemic issues.
Tree‑Based Model – Predictive model that uses hierarchical decision rules… #
Related terms: splitting criterion, leaf node, pruning. Tree‑based models are popular in OHS because they produce intuitive rule sets for safety managers.
Unstructured Data – Information that does not follow a predefined data mo… #
Related terms: free‑text, multimedia, raw logs. Unstructured OHS data (e.G., Safety photos) require NLP or computer‑vision techniques for analysis.
Under‑Sampling – Technique that reduces the majority class size to balanc… #
Related terms: random under‑sampling, Tomek links, cluster centroids. Under‑sampling helps prevent OHS models from being biased toward the abundant “no‑incident” records.
Uncertainty Quantification – Process of measuring the confidence or error… #
Related terms: prediction interval, Bayesian posterior, Monte Carlo dropout. Quantified uncertainty informs safety leaders about the reliability of AI‑generated risk scores.
Validation Set – Subset of data used to tune model hyperparameters withou… #
Related terms: hold‑out, cross‑validation, early stopping. A dedicated validation set guards against over‑optimistic OHS model evaluations.
Variable Importance – Ranking of input features based on their impact on… #
Related terms: Gini importance, permutation score, SHAP summary. Knowing which OHS variables drive risk empowers targeted mitigation strategies.
Version Control – System for tracking changes to code, data, and model ar… #
Related terms: Git, branching, commit history. Version control ensures reproducibility of OHS predictive analytics experiments.
Virtual Reality (VR) Safety Training – Immersive simulation used to teach… #
Related terms: simulation, immersive learning, scenario rehearsal. While not a hands‑on activity, AI can personalize VR scenarios based on predicted individual risk profiles.
Vision‑Based Hazard Detection – Use of computer‑vision algorithms to iden… #
Related terms: object detection, YOLO, semantic segmentation. Vision models flag missing guardrails or workers entering restricted zones in real time.
Weighted Loss Function – Modification of the loss calculation to give hig… #
Weighted Loss Function – Modification of the loss calculation to give higher penalty to certain classes.