Kooperativer Bibliotheksverbund

UID:

Format: 1 online resource (352 pages)

Edition: 1st ed.

ISBN: 0-323-99376-1

Content: Artificial Intelligence, Machine Learning, and Deep Learning in Precision Medicine and Liver Diseases: Concept, Technology, Application, and Perspectives combines four major applications of artificial intelligence (AI) within the field of clinical medicine specific to liver diseases: radiology imaging, electronic health records, pathology, and multiomics. The book provides a state-of-the-art summary of AI in precision medicine in hepatology, clarifying the concept and technology of AI and pointing to the current and future applications of AI within the field of hepatology. Coverage includes data preparation, methodology and application within disease-specific cases in fibrosis, viral and steatohepatitis, cirrhosis, hepatocellular carcinoma, acute liver failure, liver transplantation, and more. The ethical and legal issues of AI and future challenges and perspectives are also discussed.

Note: 1 - Basics of artificial intelligence in medicine -- 1 - Artificial intelligence in health care: past and present -- Chapter outlines -- Clinical applications -- Introduction -- Past: a brief history of artificial intelligence in health care -- Present: artificial intelligence in health care today -- Image-based applications -- Electronic health record mining -- Reinforcement learning for identifying effective treatments -- Wearables -- Pandemic response -- Genomics -- Future: opportunities and challenges of artificial intelligence in health care -- Conclusion -- References -- 2 - Data-centric artificial intelligence in health care: progress, shortcomings, and remedies -- Introduction -- Training data generation and aggregation -- Data augmentation -- Federated learning -- Transfer representation learning -- Method specifications -- Empirical study -- Results of transfer representation learning for otitis media -- Results of transfer representation learning for otitis media -- Results of transfer representation learning for melanoma -- Results of transfer representation learning for melanoma -- Qualitative evaluation: visualization -- Qualitative evaluation: visualization -- Observations on transfer learning -- Observations on transfer learning -- Generative adversarial networks -- Method specifications -- Empirical study -- Experiment setup -- Experiment setup -- Experiment results -- Experiment results -- Fusing knowledge with generative adversarial networks -- Information from knowledge layers and structures. , Information from knowledge graph and dictionary -- Method specifications -- Empirical study -- Experiment setup -- Experiment results -- Concluding remarks -- References -- 2 - Fields of artificial intelligence in hepatology, by tools,data preparation,methodology andapplication -- 3 - Artificial intelligence in radiology and its application in liver disease -- Chapter outlines -- Clinical applications -- Introduction -- Radiomics -- Radiomics workflow -- Pre-processing -- Segmentation -- Radiomics feature extraction -- Feature selection and model building -- Clinical application of radiomics in liver imaging -- Chronic liver disease -- Classification of focal liver lesions -- Prognostication of hepatic malignancy -- Limitations and future perspectives of radiomics -- Deep learning -- Development and validation of deep learning algorithm -- Application of deep learning in liver imaging -- Liver and abdominal organ segmentation -- Quantification and classification of diffuse liver abnormalities -- Detection, segmentation, and classification of liver tumors -- Application of deep learning-based body composition analysis in liver disease -- Image quality improvement -- Limitations and future perspectives of deep learning -- Conclusion -- References -- 4 - Electronic health record for artificial intelligence health care, and application to liver disease -- Chapter outlines -- Clinical applications -- Introduction -- Electronic health records in precision health -- Precision medicine and precision health -- Earlier medicine -- Electronic health records applied to prediction of fatty liver -- Data preprocessing -- Variable selection -- Validation -- Time series data used to predict liver cancer risk -- Preprocessing: electronic health record images -- Convolutional neural network model building -- Validation. , Natural language processing for time-series coded data -- Conclusion -- References -- 5 - Artificial intelligence in pathology and application to liver disease -- Chapter outlines -- Clinical applications -- Introduction -- Role of pathology in liver disease diagnosis and staging -- Digital revolution of pathology -- Principles of artificial intelligence processing of whole-slide images -- Applications of artificial intelligence-based pathology -- Automated or assisted diagnosis -- Artificial intelligence-based prognostication -- Artificial intelligence-based pathology and the next generation of biomarkers -- Challenges to implementing artificial intelligence in pathology -- Conclusion -- References -- 6 - Artificial intelligence using multiomics/genetic tools and application in liver disease -- Introduction -- Multiomics data and their integration in hepatocellular carcinoma -- Cardinal data resources for multiomics analysis -- Applications of high-throughput multiomics hepatocellular carcinoma data -- Subtype and subgroup identification -- Diagnostic markers -- Prognostic markers -- Therapeutic markers -- Conclusion -- Clinical applications -- References -- 3 - Artificial intelligenc eapplication inspecific diseasesof liver -- 7 - Artificial intelligence in prediction of steatosis and fibrosis of nonalcoholic fatty liver disease -- Chapter outlines -- Clinical applications -- Introduction -- Current methods for assessing steatosis -- Artificial intelligence for predicting steatosis -- Current methods for assessing liver fibrosis -- Artificial intelligence for assessing histologic fibrosis -- Conclusions and the future -- References -- 8 - Artificial intelligence in the prediction of progression and outcomes in viral hepatitis -- Chapter outlines -- Clinical applications -- A brief introduction to artificial intelligence. , Artificial intelligence in the detection or prediction of liver fibrosis in chronic viral hepatitis -- Artificial intelligence in predicting gastroesophageal varices using computed tomography images -- Artificial intelligence in the diagnosis, prediction, and prognosis of hepatocellular carcinoma -- Artificial intelligence in predicting hepatocellular carcinoma occurrence -- Artificial intelligence in predicting survival of hepatocellular carcinoma based on multiomics data -- Artificial intelligence for clinical outcome prediction using histopathology images -- Artificial intelligence in identifying microvascular invasion for predicting clinical outcomes of hepatocellular carcinoma -- Future perspectives and limitations of artificial intelligence technology -- Conclusion -- References -- 9 - Artificial intelligence in cirrhosis complications and acute liver failure -- Chapter outlines -- Definition of terms -- Clinical applications -- Introduction -- Portal hypertension -- Gastroesophageal varices -- Ascites -- Hepatic encephalopathy -- Hepatorenal syndrome -- Portal vein thrombosis -- Transplantation and hepatocellular carcinoma -- Acute-on-chronic liver failure -- Acute liver failure -- Challenges -- References -- 10 - Artificial intelligence in liver transplantation -- Chapter outline -- Clinical applications -- Introduction -- Pretransplant -- Waiting list mortality -- Organ allocation -- Donor organ assessment -- Donor-recipient matching -- Summary -- Posttransplant -- Patient survival -- Prediction of graft rejection and failure -- Other post-transplant complications -- Recurrent hepatocellular carcinoma -- Metabolic disease -- Acute kidney injury -- Summary -- Future directions -- Conclusion -- References -- 11 - Artificial intelligence in liver cancer: diagnosis and management -- Chapter outlines -- Clinical applications -- Introduction. , Overview of main machine learning models used in field of hepatocellular carcinoma -- Artificial intelligence-based differential diagnosis of hepatocellular carcinoma -- Hepatocellular carcinoma diagnosis by artificial intelligence based on multiple biomarkers -- Differential diagnosis based on the findings of ultrasonography -- Differential diagnosis based on the findings of computed tomography -- Differential diagnosis based on findings of magnetic resonance imaging -- Artificial intelligence-based prediction of treatment response of hepatocellular carcinoma -- Artificial intelligence-based prediction of prognosis of hepatocellular carcinoma -- Conclusion -- References -- 12 - Predicting drug-induced liver injury with artificial intelligence-a minireview -- Disclaimer -- Chapter outlines -- Clinical applications -- What is drug-induced liver injury? -- Why drug-induced liver injury is important to drug development and public health -- Nonanimal approaches developed for drug-induced liver injury assessment -- How the drug-induced liver injury risk of a drug is determined -- Overview of computational methods for drug-induced liver injury prediction -- Discussion -- Conclusion -- Author contributions -- Conflict of interest -- References -- 13 - Artificial intelligence in precision medicine and liver disease monitoring -- Chapter outlines -- Clinical applications -- Precision medicine -- Four key steps to achieving precision medicine -- Data from multiple sources -- Methodology for data generation -- Artificial intelligence: tool to achieve precision medicine -- Applications: All of Us as an example -- From precision medicine to precision health and precision public health -- Artificial intelligence in monitoring liver disease -- Digital medicine -- Mobile health -- Digital tracking system -- Smart mirrors -- Applications. , Telemedicine and remote medicine.

Additional Edition: Print version: Su, Tung-Hung Artificial Intelligence, Machine Learning, and Deep Learning in Precision Medicine in Liver Diseases San Diego : Elsevier Science & Technology,c2023 ISBN 9780323991360

Language: English

UID:

Format: 1 online resource (352 pages)

Edition: 1st ed.

ISBN: 0-323-99376-1

Content: Artificial Intelligence, Machine Learning, and Deep Learning in Precision Medicine and Liver Diseases: Concept, Technology, Application, and Perspectives combines four major applications of artificial intelligence (AI) within the field of clinical medicine specific to liver diseases: radiology imaging, electronic health records, pathology, and multiomics. The book provides a state-of-the-art summary of AI in precision medicine in hepatology, clarifying the concept and technology of AI and pointing to the current and future applications of AI within the field of hepatology. Coverage includes data preparation, methodology and application within disease-specific cases in fibrosis, viral and steatohepatitis, cirrhosis, hepatocellular carcinoma, acute liver failure, liver transplantation, and more. The ethical and legal issues of AI and future challenges and perspectives are also discussed.

Note: 1 - Basics of artificial intelligence in medicine -- 1 - Artificial intelligence in health care: past and present -- Chapter outlines -- Clinical applications -- Introduction -- Past: a brief history of artificial intelligence in health care -- Present: artificial intelligence in health care today -- Image-based applications -- Electronic health record mining -- Reinforcement learning for identifying effective treatments -- Wearables -- Pandemic response -- Genomics -- Future: opportunities and challenges of artificial intelligence in health care -- Conclusion -- References -- 2 - Data-centric artificial intelligence in health care: progress, shortcomings, and remedies -- Introduction -- Training data generation and aggregation -- Data augmentation -- Federated learning -- Transfer representation learning -- Method specifications -- Empirical study -- Results of transfer representation learning for otitis media -- Results of transfer representation learning for otitis media -- Results of transfer representation learning for melanoma -- Results of transfer representation learning for melanoma -- Qualitative evaluation: visualization -- Qualitative evaluation: visualization -- Observations on transfer learning -- Observations on transfer learning -- Generative adversarial networks -- Method specifications -- Empirical study -- Experiment setup -- Experiment setup -- Experiment results -- Experiment results -- Fusing knowledge with generative adversarial networks -- Information from knowledge layers and structures. , Information from knowledge graph and dictionary -- Method specifications -- Empirical study -- Experiment setup -- Experiment results -- Concluding remarks -- References -- 2 - Fields of artificial intelligence in hepatology, by tools,data preparation,methodology andapplication -- 3 - Artificial intelligence in radiology and its application in liver disease -- Chapter outlines -- Clinical applications -- Introduction -- Radiomics -- Radiomics workflow -- Pre-processing -- Segmentation -- Radiomics feature extraction -- Feature selection and model building -- Clinical application of radiomics in liver imaging -- Chronic liver disease -- Classification of focal liver lesions -- Prognostication of hepatic malignancy -- Limitations and future perspectives of radiomics -- Deep learning -- Development and validation of deep learning algorithm -- Application of deep learning in liver imaging -- Liver and abdominal organ segmentation -- Quantification and classification of diffuse liver abnormalities -- Detection, segmentation, and classification of liver tumors -- Application of deep learning-based body composition analysis in liver disease -- Image quality improvement -- Limitations and future perspectives of deep learning -- Conclusion -- References -- 4 - Electronic health record for artificial intelligence health care, and application to liver disease -- Chapter outlines -- Clinical applications -- Introduction -- Electronic health records in precision health -- Precision medicine and precision health -- Earlier medicine -- Electronic health records applied to prediction of fatty liver -- Data preprocessing -- Variable selection -- Validation -- Time series data used to predict liver cancer risk -- Preprocessing: electronic health record images -- Convolutional neural network model building -- Validation. , Natural language processing for time-series coded data -- Conclusion -- References -- 5 - Artificial intelligence in pathology and application to liver disease -- Chapter outlines -- Clinical applications -- Introduction -- Role of pathology in liver disease diagnosis and staging -- Digital revolution of pathology -- Principles of artificial intelligence processing of whole-slide images -- Applications of artificial intelligence-based pathology -- Automated or assisted diagnosis -- Artificial intelligence-based prognostication -- Artificial intelligence-based pathology and the next generation of biomarkers -- Challenges to implementing artificial intelligence in pathology -- Conclusion -- References -- 6 - Artificial intelligence using multiomics/genetic tools and application in liver disease -- Introduction -- Multiomics data and their integration in hepatocellular carcinoma -- Cardinal data resources for multiomics analysis -- Applications of high-throughput multiomics hepatocellular carcinoma data -- Subtype and subgroup identification -- Diagnostic markers -- Prognostic markers -- Therapeutic markers -- Conclusion -- Clinical applications -- References -- 3 - Artificial intelligenc eapplication inspecific diseasesof liver -- 7 - Artificial intelligence in prediction of steatosis and fibrosis of nonalcoholic fatty liver disease -- Chapter outlines -- Clinical applications -- Introduction -- Current methods for assessing steatosis -- Artificial intelligence for predicting steatosis -- Current methods for assessing liver fibrosis -- Artificial intelligence for assessing histologic fibrosis -- Conclusions and the future -- References -- 8 - Artificial intelligence in the prediction of progression and outcomes in viral hepatitis -- Chapter outlines -- Clinical applications -- A brief introduction to artificial intelligence. , Artificial intelligence in the detection or prediction of liver fibrosis in chronic viral hepatitis -- Artificial intelligence in predicting gastroesophageal varices using computed tomography images -- Artificial intelligence in the diagnosis, prediction, and prognosis of hepatocellular carcinoma -- Artificial intelligence in predicting hepatocellular carcinoma occurrence -- Artificial intelligence in predicting survival of hepatocellular carcinoma based on multiomics data -- Artificial intelligence for clinical outcome prediction using histopathology images -- Artificial intelligence in identifying microvascular invasion for predicting clinical outcomes of hepatocellular carcinoma -- Future perspectives and limitations of artificial intelligence technology -- Conclusion -- References -- 9 - Artificial intelligence in cirrhosis complications and acute liver failure -- Chapter outlines -- Definition of terms -- Clinical applications -- Introduction -- Portal hypertension -- Gastroesophageal varices -- Ascites -- Hepatic encephalopathy -- Hepatorenal syndrome -- Portal vein thrombosis -- Transplantation and hepatocellular carcinoma -- Acute-on-chronic liver failure -- Acute liver failure -- Challenges -- References -- 10 - Artificial intelligence in liver transplantation -- Chapter outline -- Clinical applications -- Introduction -- Pretransplant -- Waiting list mortality -- Organ allocation -- Donor organ assessment -- Donor-recipient matching -- Summary -- Posttransplant -- Patient survival -- Prediction of graft rejection and failure -- Other post-transplant complications -- Recurrent hepatocellular carcinoma -- Metabolic disease -- Acute kidney injury -- Summary -- Future directions -- Conclusion -- References -- 11 - Artificial intelligence in liver cancer: diagnosis and management -- Chapter outlines -- Clinical applications -- Introduction. , Overview of main machine learning models used in field of hepatocellular carcinoma -- Artificial intelligence-based differential diagnosis of hepatocellular carcinoma -- Hepatocellular carcinoma diagnosis by artificial intelligence based on multiple biomarkers -- Differential diagnosis based on the findings of ultrasonography -- Differential diagnosis based on the findings of computed tomography -- Differential diagnosis based on findings of magnetic resonance imaging -- Artificial intelligence-based prediction of treatment response of hepatocellular carcinoma -- Artificial intelligence-based prediction of prognosis of hepatocellular carcinoma -- Conclusion -- References -- 12 - Predicting drug-induced liver injury with artificial intelligence-a minireview -- Disclaimer -- Chapter outlines -- Clinical applications -- What is drug-induced liver injury? -- Why drug-induced liver injury is important to drug development and public health -- Nonanimal approaches developed for drug-induced liver injury assessment -- How the drug-induced liver injury risk of a drug is determined -- Overview of computational methods for drug-induced liver injury prediction -- Discussion -- Conclusion -- Author contributions -- Conflict of interest -- References -- 13 - Artificial intelligence in precision medicine and liver disease monitoring -- Chapter outlines -- Clinical applications -- Precision medicine -- Four key steps to achieving precision medicine -- Data from multiple sources -- Methodology for data generation -- Artificial intelligence: tool to achieve precision medicine -- Applications: All of Us as an example -- From precision medicine to precision health and precision public health -- Artificial intelligence in monitoring liver disease -- Digital medicine -- Mobile health -- Digital tracking system -- Smart mirrors -- Applications. , Telemedicine and remote medicine.

Additional Edition: Print version: Su, Tung-Hung Artificial Intelligence, Machine Learning, and Deep Learning in Precision Medicine in Liver Diseases San Diego : Elsevier Science & Technology,c2023 ISBN 9780323991360

Language: English

UID:

Format: 1 online resource (352 pages)

Edition: 1st ed.

ISBN: 0-323-99376-1

Content: Artificial Intelligence, Machine Learning, and Deep Learning in Precision Medicine and Liver Diseases: Concept, Technology, Application, and Perspectives combines four major applications of artificial intelligence (AI) within the field of clinical medicine specific to liver diseases: radiology imaging, electronic health records, pathology, and multiomics. The book provides a state-of-the-art summary of AI in precision medicine in hepatology, clarifying the concept and technology of AI and pointing to the current and future applications of AI within the field of hepatology. Coverage includes data preparation, methodology and application within disease-specific cases in fibrosis, viral and steatohepatitis, cirrhosis, hepatocellular carcinoma, acute liver failure, liver transplantation, and more. The ethical and legal issues of AI and future challenges and perspectives are also discussed.

Note: 1 - Basics of artificial intelligence in medicine -- 1 - Artificial intelligence in health care: past and present -- Chapter outlines -- Clinical applications -- Introduction -- Past: a brief history of artificial intelligence in health care -- Present: artificial intelligence in health care today -- Image-based applications -- Electronic health record mining -- Reinforcement learning for identifying effective treatments -- Wearables -- Pandemic response -- Genomics -- Future: opportunities and challenges of artificial intelligence in health care -- Conclusion -- References -- 2 - Data-centric artificial intelligence in health care: progress, shortcomings, and remedies -- Introduction -- Training data generation and aggregation -- Data augmentation -- Federated learning -- Transfer representation learning -- Method specifications -- Empirical study -- Results of transfer representation learning for otitis media -- Results of transfer representation learning for otitis media -- Results of transfer representation learning for melanoma -- Results of transfer representation learning for melanoma -- Qualitative evaluation: visualization -- Qualitative evaluation: visualization -- Observations on transfer learning -- Observations on transfer learning -- Generative adversarial networks -- Method specifications -- Empirical study -- Experiment setup -- Experiment setup -- Experiment results -- Experiment results -- Fusing knowledge with generative adversarial networks -- Information from knowledge layers and structures. , Information from knowledge graph and dictionary -- Method specifications -- Empirical study -- Experiment setup -- Experiment results -- Concluding remarks -- References -- 2 - Fields of artificial intelligence in hepatology, by tools,data preparation,methodology andapplication -- 3 - Artificial intelligence in radiology and its application in liver disease -- Chapter outlines -- Clinical applications -- Introduction -- Radiomics -- Radiomics workflow -- Pre-processing -- Segmentation -- Radiomics feature extraction -- Feature selection and model building -- Clinical application of radiomics in liver imaging -- Chronic liver disease -- Classification of focal liver lesions -- Prognostication of hepatic malignancy -- Limitations and future perspectives of radiomics -- Deep learning -- Development and validation of deep learning algorithm -- Application of deep learning in liver imaging -- Liver and abdominal organ segmentation -- Quantification and classification of diffuse liver abnormalities -- Detection, segmentation, and classification of liver tumors -- Application of deep learning-based body composition analysis in liver disease -- Image quality improvement -- Limitations and future perspectives of deep learning -- Conclusion -- References -- 4 - Electronic health record for artificial intelligence health care, and application to liver disease -- Chapter outlines -- Clinical applications -- Introduction -- Electronic health records in precision health -- Precision medicine and precision health -- Earlier medicine -- Electronic health records applied to prediction of fatty liver -- Data preprocessing -- Variable selection -- Validation -- Time series data used to predict liver cancer risk -- Preprocessing: electronic health record images -- Convolutional neural network model building -- Validation. , Natural language processing for time-series coded data -- Conclusion -- References -- 5 - Artificial intelligence in pathology and application to liver disease -- Chapter outlines -- Clinical applications -- Introduction -- Role of pathology in liver disease diagnosis and staging -- Digital revolution of pathology -- Principles of artificial intelligence processing of whole-slide images -- Applications of artificial intelligence-based pathology -- Automated or assisted diagnosis -- Artificial intelligence-based prognostication -- Artificial intelligence-based pathology and the next generation of biomarkers -- Challenges to implementing artificial intelligence in pathology -- Conclusion -- References -- 6 - Artificial intelligence using multiomics/genetic tools and application in liver disease -- Introduction -- Multiomics data and their integration in hepatocellular carcinoma -- Cardinal data resources for multiomics analysis -- Applications of high-throughput multiomics hepatocellular carcinoma data -- Subtype and subgroup identification -- Diagnostic markers -- Prognostic markers -- Therapeutic markers -- Conclusion -- Clinical applications -- References -- 3 - Artificial intelligenc eapplication inspecific diseasesof liver -- 7 - Artificial intelligence in prediction of steatosis and fibrosis of nonalcoholic fatty liver disease -- Chapter outlines -- Clinical applications -- Introduction -- Current methods for assessing steatosis -- Artificial intelligence for predicting steatosis -- Current methods for assessing liver fibrosis -- Artificial intelligence for assessing histologic fibrosis -- Conclusions and the future -- References -- 8 - Artificial intelligence in the prediction of progression and outcomes in viral hepatitis -- Chapter outlines -- Clinical applications -- A brief introduction to artificial intelligence. , Artificial intelligence in the detection or prediction of liver fibrosis in chronic viral hepatitis -- Artificial intelligence in predicting gastroesophageal varices using computed tomography images -- Artificial intelligence in the diagnosis, prediction, and prognosis of hepatocellular carcinoma -- Artificial intelligence in predicting hepatocellular carcinoma occurrence -- Artificial intelligence in predicting survival of hepatocellular carcinoma based on multiomics data -- Artificial intelligence for clinical outcome prediction using histopathology images -- Artificial intelligence in identifying microvascular invasion for predicting clinical outcomes of hepatocellular carcinoma -- Future perspectives and limitations of artificial intelligence technology -- Conclusion -- References -- 9 - Artificial intelligence in cirrhosis complications and acute liver failure -- Chapter outlines -- Definition of terms -- Clinical applications -- Introduction -- Portal hypertension -- Gastroesophageal varices -- Ascites -- Hepatic encephalopathy -- Hepatorenal syndrome -- Portal vein thrombosis -- Transplantation and hepatocellular carcinoma -- Acute-on-chronic liver failure -- Acute liver failure -- Challenges -- References -- 10 - Artificial intelligence in liver transplantation -- Chapter outline -- Clinical applications -- Introduction -- Pretransplant -- Waiting list mortality -- Organ allocation -- Donor organ assessment -- Donor-recipient matching -- Summary -- Posttransplant -- Patient survival -- Prediction of graft rejection and failure -- Other post-transplant complications -- Recurrent hepatocellular carcinoma -- Metabolic disease -- Acute kidney injury -- Summary -- Future directions -- Conclusion -- References -- 11 - Artificial intelligence in liver cancer: diagnosis and management -- Chapter outlines -- Clinical applications -- Introduction. , Overview of main machine learning models used in field of hepatocellular carcinoma -- Artificial intelligence-based differential diagnosis of hepatocellular carcinoma -- Hepatocellular carcinoma diagnosis by artificial intelligence based on multiple biomarkers -- Differential diagnosis based on the findings of ultrasonography -- Differential diagnosis based on the findings of computed tomography -- Differential diagnosis based on findings of magnetic resonance imaging -- Artificial intelligence-based prediction of treatment response of hepatocellular carcinoma -- Artificial intelligence-based prediction of prognosis of hepatocellular carcinoma -- Conclusion -- References -- 12 - Predicting drug-induced liver injury with artificial intelligence-a minireview -- Disclaimer -- Chapter outlines -- Clinical applications -- What is drug-induced liver injury? -- Why drug-induced liver injury is important to drug development and public health -- Nonanimal approaches developed for drug-induced liver injury assessment -- How the drug-induced liver injury risk of a drug is determined -- Overview of computational methods for drug-induced liver injury prediction -- Discussion -- Conclusion -- Author contributions -- Conflict of interest -- References -- 13 - Artificial intelligence in precision medicine and liver disease monitoring -- Chapter outlines -- Clinical applications -- Precision medicine -- Four key steps to achieving precision medicine -- Data from multiple sources -- Methodology for data generation -- Artificial intelligence: tool to achieve precision medicine -- Applications: All of Us as an example -- From precision medicine to precision health and precision public health -- Artificial intelligence in monitoring liver disease -- Digital medicine -- Mobile health -- Digital tracking system -- Smart mirrors -- Applications. , Telemedicine and remote medicine.

Additional Edition: Print version: Su, Tung-Hung Artificial Intelligence, Machine Learning, and Deep Learning in Precision Medicine in Liver Diseases San Diego : Elsevier Science & Technology,c2023 ISBN 9780323991360

Language: English