Ahammed, Abu Shad
Preferred name
Ahammed, Abu Shad
Main Affiliation
ORCID
Scopus Author ID
57968178500
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Publication Access status: Open Access , Embedded AI for Real-time Health State Assessment and Treatment Recommendation in Rescue Operations(2025)Rescue emergencies are generally quite strenuous and challenging because they deal with human lives in a situation where it is difficult to apprehend precisely the health status of a distressed patient due to personal and work limitations. Often the rescue personnel have to deal with high levels of mental and physical stress, trauma, and emotional strain during rescue operations, especially when dealing with injured and vulnerable victims. This certainly impacts their decision-making abilities and overall well-being. Time, the main constraint in such rescue situations, also plays a vital role in decision-making. Therefore, it is necessary to recognize relevant situations i.e. health complications of the rescue patients on site, and to take appropriate first aid measures. The situation may change during the further course of initial treatment, with rescue workers judging this primarily by the visible condition of the emergency patient, the data from medical equipment (e.g., ventilator, ECG), and the mission description from the control room. Such changes are required to be responded to immediately, for example by resuscitation or appropriate medication. That's why, waiting for results from medical tests like MRI and ECG which are time-consuming and not suitable for emergency cases is not considered suitable for rescue cases. Considering these technical constraints, the doctoral thesis focuses on employing artificial intelligence (AI) models in two aspects that can expedite and improve the rescue process. Firstly, for the diagnosis of health complications in rescue situations and secondly to identify the correct medications as a part of initial treatment. A major part of this research is focused on advanced data analysis techniques that were used to extract information from 12 years of rescue records of 273,283 cases in the German city of Siegen-Wittgenstein. The initial data received from the rescue station was raw and in many cases contained incomprehensible information for which Natural Language Processing (NLP) techniques were applied to extract and interpolate relevant attributes. Subsequently, a detailed method for creating various AI models to promptly detect six key complications— Cardiovascular, Respiratory, Psychiatric, Neurological, Metabolic, and Abdominal—was conducted and is detailed in this dissertation. To develop the detection models for each complication, Artificial Intelligence(AI) algorithms like machine learning including both classical and deep learning approaches were used. To train these models attributes like patients' medical history, health diagnoses including neurological assessment, vital signs, initial impression of the rescue personnel, administered medications, and other treatment paths were used. During the course of development, one primary objective was to identify the model achieving the greatest accuracy and precision. Based on this research, Extreme Gradient Boosting (XGB) and Random Forest (RF) algorithms were found as the most promising, showcasing accuracy rates ranging from 80\% to 96\%. After recognizing health complications, further research was done to find out if AI can also be implemented to determine possible medications based on detected complications and patients' health vitals. The result achieved from it also was impressive with accuracy close to 80\%. AI models are further tested by deploying them into various accelerators, such as ARM processors, FPGAs and microcontrollers, to evaluate their performance based on inference time. The overall focus of this research is to overcome the rescue challenges in real-time by recognizing rescue situations and improving the quality of care and efficiency of rescue personnel.Source Type:Doctoral Thesis4 15 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Metadata only , Time-Efficient Identification Procedure for Neurological Complications of Rescue Patients in an Emergency Scenario Using Hardware-Accelerated Artificial Intelligence ModelsDuring an emergency rescue operation, rescuers have to deal with many different health complications like cardiovascular, respiratory, neurological, psychiatric, etc. The identification process of the common health complications in rescue events is not very difficult or time-consuming because the health vital symptoms or primary observations are enough to identify, but it is quite difficult with some complications related to neurology e.g., schizophrenia, epilepsy with non-motor seizures, or retrograde amnesia because they cannot be identified with the trend of health vital data. The symptoms have a wide spectrum and are often non-distinguishable from other types of complications. Further, waiting for results from medical tests like MRI and ECG is time-consuming and not suitable for emergency cases where a quick treatment path is an obvious necessity after the diagnosis. In this paper, we present a novel solution for overcoming these challenges by employing artificial intelligence (AI) models in the diagnostic procedure of neurological complications in rescue situations. The novelty lies in the procedure of generating input features from raw rescue data used in AI models, as the data are not like traditional clinical data collected from hospital repositories. Rather, the data were gathered directly from more than 200,000 rescue cases and required natural language processing techniques to extract meaningful information. A step-by-step analysis of developing multiple AI models that can facilitate the fast identification of neurological complications, in general, is presented in this paper. Advanced data analytics are used to analyze the complete record of 273,183 rescue events in a duration of almost 10 years, including rescuers’ analysis of the complications and their diagnostic methods. To develop the detection model, seven different machine learning algorithms-Support Vector Machine (SVM), Random Forest (RF), K-nearest neighbor (KNN), Extreme Gradient Boosting (XGB), Logistic Regression (LR), Naive Bayes (NB) and Artificial Neural Network (ANN) were used. Observing the model’s performance, we conclude that the neural network and extreme gradient boosting show the best performance in terms of selected evaluation criteria. To utilize this result in practical scenarios, the paper also depicts the possibility of embedding such machine learning models in hardware like FPGA. The goal is to achieve fast detection results, which is a primary requirement in any rescue mission. An inference time analysis of the selected ML models and VTA AI accelerator of Apache-TVM machine learning compiler used for the FPGA is also presented in this research.Source Type:Article1
