Utilization of Machine Learning in Supporting Occupational Safety and Health Decisions in Hospital Workplace

Abstract

The prediction of possible future incidents or accidents and the efficiency assessment of the Occupational Safety and Health (OSH) interventions are essential for the effective protection of healthcare workers, as the occupational risks in their workplace are multiple and diverse. Machine learning algorithms have been utilized for classifying post-incident and post-accident data into the following 5 classes of events: Needlestick/Cut, Falling, Incident, Accident, and Safety. 476 event reports from Metaxa Cancer Hospital (Greece), during 2014-2019, were used to train the machine learning models. The developed models showed high predictive performance, with area under the curve range 0.950-0.990 and average accuracy of 93% on the 10-fold cross set, compared to the safety engineer’s study reports. The proposed DSS model can contribute to the prediction of incidents or accidents and efficiency evaluation of OSH interventions.

A Review on the Use of Machine Learning Against the Covid-19 Pandemic

“…This enormously heavy toll has generated the commitment of the research and development communities to alleviate this crisis. It turned out that ML and DL tools have been widely used in many applications during the last decade, including healthcare applications [69,70]. To render valuable guidance to researchers in evaluating the use of ML/DL models against Covid-19, this paper studied the available Covid-19 datasets, the indispensable fuel for any DL-based approach.…”

Predicting Diabetes Disease Occurrence Using Logistic Regression: An Early Detection Approach

“…Despite the impressive results reported by the models proposed in the literature [7][8][9][10][11][12][13][14][15][16], they fail to address the issue of predicting the likelihood of diabetes disease occurrence. Moreover, these models are designed to make general predictions such as categorizing patients into healthy or sick categories, and do not provide information on the disease progression of individual patients…”

“…People with diabetes can lead active, full lives with the right management, but this requires continual care and monitoring to avoid complications and maintain excellent health.Large databases of clinical and health-related data, such as electronic health records, medical imaging, and genetic data, can be analyzed using machine learning (ML) algorithms to find patterns and make predictions. [9][10][11][12][13]. Predicting and diagnosing diabetes is one of the key uses of machine learning in this field.…”

“…Large databases of clinical and health-related data, such as electronic health records, medical imaging, and genetic data, can be analyzed using machine learning (ML) algorithms to find patterns and make predictions. [9][10][11][12][13]. Predicting and diagnosing diabetes is one of the key uses of machine learning in this field.…”

Comparison of Accident Databases and Analysis of Past Industrial Accidents in the Chemical Process Industry

“…PAA gives important "intelligence of knowing the past" and techniques to forecast hazardous events or mitigate the effects of incidents [3,[12][13][14]. Active research is being carried out in the field of accident analysis with the help of management tools and machine learning, incorporating data management tools such as Business Intelligence (BI) in past accident analysis, which can provide valuable insight to develop business strategy and tactics [15][16][17][18].…”