Applications of Artificial Intelligence in E-Healthcare Systems
portes grátis
Applications of Artificial Intelligence in E-Healthcare Systems
Gayathri, N.; Sabharwal, Munish; Balusamy, B. Balamurugan; Rakesh Kumar, S.; Suvanov, Shakhzod
Institution of Engineering and Technology
09/2022
306
Dura
Inglês
9781839534492
15 a 20 dias
Descrição não disponível.
Chapter 1: Introduction to AI in E-healthcare
Chapter 2: The scope and future outlook of artificial intelligence in healthcare systems
Chapter 3: Class dependency-based learning using Bi-LSTM coupled with the transfer learning of VGG16 for the diagnosis of tuberculosis from chest X-rays
Chapter 4: Drug discovery clinical trial exploratory process and bioactivity analysis optimizer using deep convolutional neural network for E-prosperity
Chapter 5: An automated NLP methodology to predict ICU mortality CLINICAL dataset using multiclass grouping with LSTM RNN approach
Chapter 6: Applying machine learning techniques to build a hybrid machine learning model for cancer prediction
Chapter 7: AI in healthcare: challenges and opportunities
Chapter 8: Impression of artificial intelligence in e-healthcare medical applications
Chapter 9: Heterogeneous recurrent convolution neural network for risk prediction in the EHR dataset
Chapter 10: A narrative review and impacts on trust for data in the healthcare industry using artificial intelligence
Chapter 11: Analysis of COVID-19 outbreak using data visualization techniques: a review
Chapter 12: Artificial intelligence-based electronic health records for healthcare
Chapter 13: Automatic structuring on Chinese ultrasound report of Covid-19 diseases via natural language processing
Chapter 2: The scope and future outlook of artificial intelligence in healthcare systems
Chapter 3: Class dependency-based learning using Bi-LSTM coupled with the transfer learning of VGG16 for the diagnosis of tuberculosis from chest X-rays
Chapter 4: Drug discovery clinical trial exploratory process and bioactivity analysis optimizer using deep convolutional neural network for E-prosperity
Chapter 5: An automated NLP methodology to predict ICU mortality CLINICAL dataset using multiclass grouping with LSTM RNN approach
Chapter 6: Applying machine learning techniques to build a hybrid machine learning model for cancer prediction
Chapter 7: AI in healthcare: challenges and opportunities
Chapter 8: Impression of artificial intelligence in e-healthcare medical applications
Chapter 9: Heterogeneous recurrent convolution neural network for risk prediction in the EHR dataset
Chapter 10: A narrative review and impacts on trust for data in the healthcare industry using artificial intelligence
Chapter 11: Analysis of COVID-19 outbreak using data visualization techniques: a review
Chapter 12: Artificial intelligence-based electronic health records for healthcare
Chapter 13: Automatic structuring on Chinese ultrasound report of Covid-19 diseases via natural language processing
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
health care; natural language processing; electronic health records; learning (artificial intelligence); data analysis; diseases
Chapter 1: Introduction to AI in E-healthcare
Chapter 2: The scope and future outlook of artificial intelligence in healthcare systems
Chapter 3: Class dependency-based learning using Bi-LSTM coupled with the transfer learning of VGG16 for the diagnosis of tuberculosis from chest X-rays
Chapter 4: Drug discovery clinical trial exploratory process and bioactivity analysis optimizer using deep convolutional neural network for E-prosperity
Chapter 5: An automated NLP methodology to predict ICU mortality CLINICAL dataset using multiclass grouping with LSTM RNN approach
Chapter 6: Applying machine learning techniques to build a hybrid machine learning model for cancer prediction
Chapter 7: AI in healthcare: challenges and opportunities
Chapter 8: Impression of artificial intelligence in e-healthcare medical applications
Chapter 9: Heterogeneous recurrent convolution neural network for risk prediction in the EHR dataset
Chapter 10: A narrative review and impacts on trust for data in the healthcare industry using artificial intelligence
Chapter 11: Analysis of COVID-19 outbreak using data visualization techniques: a review
Chapter 12: Artificial intelligence-based electronic health records for healthcare
Chapter 13: Automatic structuring on Chinese ultrasound report of Covid-19 diseases via natural language processing
Chapter 2: The scope and future outlook of artificial intelligence in healthcare systems
Chapter 3: Class dependency-based learning using Bi-LSTM coupled with the transfer learning of VGG16 for the diagnosis of tuberculosis from chest X-rays
Chapter 4: Drug discovery clinical trial exploratory process and bioactivity analysis optimizer using deep convolutional neural network for E-prosperity
Chapter 5: An automated NLP methodology to predict ICU mortality CLINICAL dataset using multiclass grouping with LSTM RNN approach
Chapter 6: Applying machine learning techniques to build a hybrid machine learning model for cancer prediction
Chapter 7: AI in healthcare: challenges and opportunities
Chapter 8: Impression of artificial intelligence in e-healthcare medical applications
Chapter 9: Heterogeneous recurrent convolution neural network for risk prediction in the EHR dataset
Chapter 10: A narrative review and impacts on trust for data in the healthcare industry using artificial intelligence
Chapter 11: Analysis of COVID-19 outbreak using data visualization techniques: a review
Chapter 12: Artificial intelligence-based electronic health records for healthcare
Chapter 13: Automatic structuring on Chinese ultrasound report of Covid-19 diseases via natural language processing
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
