Neural Correlate-Based E-Learning Validation and Classification Using Convolutional and Long Short-Term Memory Networks

Pathak, Dharmendra; Kashyap, Ramgopal

doi:10.18280/ts.400414

Cited by 88 publications

(2 citation statements)

References 28 publications

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“…To achieve this goal, we employ the Random Forest, Long Short-Term Memory, and k-Nearest Neighbors (k-NN) algorithms. Clinical notes, neuroimaging results, genetic data, and sensor readings from wearable devices are just some of the types of patient data we collect and organize first [17][18][19]. The data must go through normalization and feature extraction before it can be transformed into a structured dataset.…”

Section: Proposed Methodologymentioning

confidence: 99%

Machine Learning-Enhanced Decision Support for Neurological Disorders Management in Healthcare

Dr. Divya Jain, Dr. Vibha Tiwari, Akshra Tiwari

2023

tjjpt

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In a nutshell: Neurological illnesses, which include a wide range of ailments from Parkinson's disease to epilepsy, present considerable problems to the administration of healthcare. The purpose of this abstract is to present an overview of a unique technique called Machine Learning-Enhanced Decision Support (ML-EDS) for Neurological Disorders Management, which aims to improve patient treatment as well as patient outcomes. Within the scope of this investigation, we have suggested a complete ML-EDS framework that integrates cutting-edge machine learning strategies with more conventional approaches to medical treatment. The framework provides a comprehensive solution that makes use of several types of patient data, such as clinical records, neuroimaging data, genetic information, and data from wearable sensors. The Random Forest, Long Short-Term Memory (LSTM), and k-Nearest Neighbours (k-NN) algorithms are integrated into our methodology in order to improve illness progression prediction, seizure event detection, and individualized medication suggestion. In comparison to more conventional approaches, the suggested framework reveals noteworthy advantages. When compared to conventional algorithms, it demonstrates superior accuracy, sensitivity, and specificity in the prediction of illness development. Additionally, its F1 score is significantly higher. In addition, the application of LSTM for the identification of seizure episodes demonstrates the effectiveness of the method in determining crucial occurrences, which in turn enables prompt intervention. The possibility of tailored medical treatment based on k-means clustering is also highlighted in this research. Patients with comparable clinical characteristics can be grouped together to facilitate the development of personalized treatment methods, which can then provide individualized care pathways. The performance evaluation demonstrates that k-NN is capable of making therapy recommendations with high precision, hence contributing to an overall improvement in the health of the patient. Additionally, the proposed framework performs very well in terms of computational efficiency, reducing the amount of time required for both training and prediction, which positions it as a viable option for healthcare applications that are used in the real world. In summation, the use of machine learning in decision support for the management of neurological disorders in healthcare is a ground-breaking strategy that is reshaping the therapeutic landscape. It does this by using the power of machine learning, which enables it to provide more accurate illness progression predictions, rapid seizure event detection, and highly tailored medical care. The findings and efficiency metrics conclusively indicate that this strategy is superior to existing procedures, suggesting a paradigm change in the therapy of neurological disorders.

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Section: Proposed Methodologymentioning

confidence: 99%

Machine Learning-Enhanced Decision Support for Neurological Disorders Management in Healthcare

Dr. Divya Jain, Dr. Vibha Tiwari, Akshra Tiwari

2023

tjjpt

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“…Give a quick rundown of what artificial neural networks are and how they could improve medical care. Explore the use of neural networks in infectious disease management and antibiotic prescribing decisions [3][4][5]. The "neurons" in these networks are the linked nodes that perform the processing and transmission of data.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Antibiotic Prescriptions and Infectious Disease Management in Hospitals using Neural Networks

Vibha Tiwari,

Urmila S Soni,

Divya Jain

et al. 2023

J. Adv. Zool.

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This study introduces an innovative approach to antibiotic optimization and improved infectious disease management in healthcare facilities. Antibiotic stewardship and patient-specific outcomes are prioritized in the suggested strategy that uses neural networks to increase the precision and utility of antibiotic prescriptions. There are three primary algorithms at the heart of the technique. When it comes to identifying infectious illnesses from a picture, Algorithm 1 uses a Convolutional Neural Network (CNN). In order to provide educated antibiotic recommendations, Algorithm 2 uses a Recurrent Neural Network (RNN) containing Long Short-Term Memory (LSTM) cells. The third algorithm integrates reinforcement learning to automatically modify therapies based on patient results and antibiotic use. The outcomes prove that the suggested strategy is better than the status quo. The F1 score, recall, and precision all increase dramatically, and the overall diagnostic accuracy is much higher. Antibiotic stewardship also improves noticeably, leading to fewer antibiotic prescriptions, more effective measures against antibiotic resistance, better health outcomes for patients, and lower overall healthcare expenditures. Addressing the difficulties of fluctuating patient states and changing disease patterns, the suggested methodology provides a comprehensive strategy for managing infectious diseases. Using this method, antibiotic prescriptions may be optimized while still meeting all legal and ethical requirements. The ethical use of AI in healthcare is further ensured by constant monitoring and flexibility.

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Operational Excellence Deciphered Lessons from the Oracles of Order and Organization

Sapra,

Singh,

Saikia

et al. 2024

Lecture Notes in Networks and Systems

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Neural Correlate-Based E-Learning Validation and Classification Using Convolutional and Long Short-Term Memory Networks

Cited by 88 publications

References 28 publications

Machine Learning-Enhanced Decision Support for Neurological Disorders Management in Healthcare

Machine Learning-Enhanced Decision Support for Neurological Disorders Management in Healthcare

Optimizing Antibiotic Prescriptions and Infectious Disease Management in Hospitals using Neural Networks

Operational Excellence Deciphered Lessons from the Oracles of Order and Organization

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