In recent years, there has been an increase in interest in clinical applications of machine learning and artificial intelligence (AI). Despite many ethical discussions, these technologies have helped human decision makers in many contexts, for example, machine learning can aid in diagnosis, mapping of injury thresholds, treatment of diseases, classification of rapid tests, stratification risk and prediction models. These analysis models can help a lot to identify useful information from high-dimensional data, and with this, their use for evaluation and management of medical conditions. This Research Topic will focus on the use of artificial intelligence, algorithms, data Science, analytics and machine learning in the diagnosis, prognosis, prevention and treatment of patients with heart failure and stroke. We observed in the literature that a large amount of data is being generated from electronic health records and with this new methods of analysis can contribute to more reliable information. We intend with these advanced machine learning methods to help provide new discoveries from large data sets, which can, when compared to traditional methods in the context of epidemiology and biostatistics provide new discoveries that can be applied for the benefit of humanity. In this Research Topic, we intend that researchers present their results with a focus on translational and clinical investigations associated with heart failure and stroke. Studies on diagnosis, treatment, management, prevention, early diagnosis and prognosis are welcome.
This Research Topic will explore the themes previous mentioned with possible sub-topics including, but are not limited to:
1) Predictive analytics.
2) Risk stratification.
3) Treatment strategies.
4) Models of diagnosis and prevention.
5) Bioinformatics analysis.
6) New algorithms.
7) Patient values.
In recent years, there has been an increase in interest in clinical applications of machine learning and artificial intelligence (AI). Despite many ethical discussions, these technologies have helped human decision makers in many contexts, for example, machine learning can aid in diagnosis, mapping of injury thresholds, treatment of diseases, classification of rapid tests, stratification risk and prediction models. These analysis models can help a lot to identify useful information from high-dimensional data, and with this, their use for evaluation and management of medical conditions. This Research Topic will focus on the use of artificial intelligence, algorithms, data Science, analytics and machine learning in the diagnosis, prognosis, prevention and treatment of patients with heart failure and stroke. We observed in the literature that a large amount of data is being generated from electronic health records and with this new methods of analysis can contribute to more reliable information. We intend with these advanced machine learning methods to help provide new discoveries from large data sets, which can, when compared to traditional methods in the context of epidemiology and biostatistics provide new discoveries that can be applied for the benefit of humanity. In this Research Topic, we intend that researchers present their results with a focus on translational and clinical investigations associated with heart failure and stroke. Studies on diagnosis, treatment, management, prevention, early diagnosis and prognosis are welcome.
This Research Topic will explore the themes previous mentioned with possible sub-topics including, but are not limited to:
1) Predictive analytics.
2) Risk stratification.
3) Treatment strategies.
4) Models of diagnosis and prevention.
5) Bioinformatics analysis.
6) New algorithms.
7) Patient values.
