WSN‐ and IoT‐Based Smart Surveillance Systems for Patients with Closed‐Loop Alarm

Sundas, Amit; Badotra, Sumit; Rani, Shalli; Gajare, Chhabildas Madhukar

doi:10.1002/9781119816829.ch7

Cited by 5 publications

(4 citation statements)

References 18 publications

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“…GPS, heart rate, microphone, and ear clips [2] are some of the most common IoT sensors used in wearable devices like smartwatches and smartphones. Sensors and devices are used to identify autistic youngsters, rather than traditional techniques of diagnosis [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…Hence, the most important components of assisting the patient are early diagnosis and improving the QoL of the patients. Autistic children are frequently misdiagnosed until they are two years old [4]. As a result, they are still unable to carry out their daily routines.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, they are still unable to carry out their daily routines. Consequently, this article examines several IoT device techniques for children with ASD to evaluate and contrast novel ways of detecting the disorder or enhancing quality of life for individuals already diagnosed [4,5]. The Internet of Things is using artificial intelligence, machine learning, SS network, and deep learning to identify and protect patients from physical and emotional problems [5].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Autism Spectrum Disorder Based on the Healthcare by Using Artificial Intelligence Strategies

et al. 2023

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The behaviors of children with autism spectrum disorder (ASD) are often erratic and difficult to predict. Most of the time, they are unable to communicate effectively in their own language. Instead, they communicate using hand gestures and pointing phrases. Because of this, it can be difficult for caregivers to grasp their patients’ requirements, although early detection of the condition can make this much simpler. Assistive technology and the Internet of Things (IoT) can alleviate the absence of verbal and nonverbal communication in the community. The IoT-based solutions use machine Learning (ML) and deep learning (DL) algorithms to diagnose and enhance the lives of patients. A thorough review of ASD techniques in the setting of IoT devices is presented in this research. Identifying important trends in IoT-based health care research is the primary objective of this review. There is also a technical taxonomy for organizing the current articles on ASD algorithms and methodologies based on different factors such as AI, SS network, ML, and IoT. On the basis of criteria such as accuracy and sensitivity, the statistical and operational analyses of the examined ASD techniques are presented.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of Autism Spectrum Disorder Based on the Healthcare by Using Artificial Intelligence Strategies

et al. 2023

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“…These technologies derive their foundations from the comprehensive medical records of patients, utilizing this wealth of data to generate warnings and treatment recommendations [4]. However, it is noteworthy that the existing diagnostic methods for sepsis predominantly lack predictive capabilities [5][6][7], with the majority relying on rule-based processes to trigger alarms and provide recommendations.…”

Section: Introductionmentioning

confidence: 99%

Investigating the Role of Machine Learning Algorithms in Predicting Sepsis using Vital Sign Data

Sundas,

Badotra,

Singh

et al. 2023

IJACSA

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In hospitals, sepsis is a common and costly condition, but machine learning systems that utilize electronic health records can enhance the timely detection of sepsis. The purpose of this research is to verify the effectiveness of a machine learning tool that makes use of a gradient boosted ensemble for sepsis diagnosis and prediction in relation. San Francisco University of California, (SFUC) Medical Center and the Medical Information Mart for Intensive Care (MIMIC) databases were consulted for historical information. The study encompassed adult patients who were admitted without sepsis and had a minimum single logging of six vital signs (SpO2, temperature, heart rate, respiratory rate, diastolic blood pressure and systolic). Using the area under the receiver operating characteristic (AUROC) curve, the performance of the machine learning algorithm was compared to commonly used scoring systems, and its accuracy was determined. Performance of the MLA (machine learning algorithm) was evaluated at sepsis onset, as well as 24 and 48 hours before sepsis onset. The AUROC for the MLA was 0.88, 0.84, and 0.83 for sepsis onset, 24 hours prior, and 48 hours prior, respectively. At the time of onset, these values were superior to those of SOFA, MEWS, qSOFA, and SIRS. Using UCSF data for training and MIMIC data for testing, the sepsis onset AUROC was 0.89. The MLA can safely predict sepsis up to forty-eight hours before it occurs and the accuracy in detecting the onset of sepsis is higher in comparison to traditional instruments. When trained and evaluated on distinct datasets, the MLA maintains high performance for sepsis detection.

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Optimizing Length of Stay Prediction After Intubation: An Advanced Machine Learning Model with Real-time Vital Sign Integration

Sundas,

Singh,

Badotra

et al. 2023

2023 Seventh International Conference on Image Information Processing (ICIIP)

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WSN‐ and IoT‐Based Smart Surveillance Systems for Patients with Closed‐Loop Alarm

Cited by 5 publications

References 18 publications

Evaluation of Autism Spectrum Disorder Based on the Healthcare by Using Artificial Intelligence Strategies

Evaluation of Autism Spectrum Disorder Based on the Healthcare by Using Artificial Intelligence Strategies

Investigating the Role of Machine Learning Algorithms in Predicting Sepsis using Vital Sign Data

Optimizing Length of Stay Prediction After Intubation: An Advanced Machine Learning Model with Real-time Vital Sign Integration

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