Artificial Intelligence (AI) has many possible applications in Oncology. One of the most promising is the potential for AI to represent and/or infer processes from real-world data. On one hand, the availability of the pattern of care is growing in opportunities (e.g. new markers, devices, drugs, etc.), constraints (guidelines, protocols, legal issues), and needs (e.g. investments, sustainable business plan, resource optimization, etc.). On the other hand, the growing availability of daily collected clinical (and administrative) data can potentially support the development and application of methods and tools to support the Decision Makers to face their challenges.
Starting from this background, process-oriented data analysis emerged and has been growing in attention and prospects in the past few years. This Research Topic is a high-quality forum for interdisciplinary researchers to propose novel methods for Process-Oriented Data Science in the Oncological Domain.
The main focus of this Research Topic is to act as a reference for the current status of the Process Oriented research that aims to provide frameworks, methodologies, and tools that can support healthcare staff in daily practice in oncology. The submitted papers should center around relevant problems experienced in the medical domain and propose innovative methods to deal with them.
In this Research Topic we aim to investigate the state-of-the-art, new methodologies, tools, frameworks, or experiences in AI applied to clinical or institutional pathways and processes to face emerging challenges in clinics or in management. We welcome papers that explore how methods coming from Process Mining for Healthcare, Computer Interpretable Clinical Guidelines, Cased Based Reasoning, Clinical pathways modeling, Business Process Management, time series analysis, and other process-oriented intelligent methodologies, tools, and theories can be addressed to:
- Study how the patients are expected to evolve during the time
- Design the best pattern of care
- Estimate the workload for the incoming future and;
- Optimize the institutional/clinical processes in terms of resource allocation strategy for saving money and time.
Descriptive and/or inferential analysis are accepted but the contributions are expected to present original and innovative methodologies OR to describe the application of existing techniques in situations where they have remarkable performances; in any case, they are expected to work with real-world data and considerations about the reproducibility should be explicitly placed in the text.
Please note: manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases, eg. SEER, which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) will not be accepted in this section.
Fernando Seoane is the founder of Z-Health Technologies AB and Wergonic AB. All other Topic Editors declare no conflict of interest in relation to the Research Topic theme.
Artificial Intelligence (AI) has many possible applications in Oncology. One of the most promising is the potential for AI to represent and/or infer processes from real-world data. On one hand, the availability of the pattern of care is growing in opportunities (e.g. new markers, devices, drugs, etc.), constraints (guidelines, protocols, legal issues), and needs (e.g. investments, sustainable business plan, resource optimization, etc.). On the other hand, the growing availability of daily collected clinical (and administrative) data can potentially support the development and application of methods and tools to support the Decision Makers to face their challenges.
Starting from this background, process-oriented data analysis emerged and has been growing in attention and prospects in the past few years. This Research Topic is a high-quality forum for interdisciplinary researchers to propose novel methods for Process-Oriented Data Science in the Oncological Domain.
The main focus of this Research Topic is to act as a reference for the current status of the Process Oriented research that aims to provide frameworks, methodologies, and tools that can support healthcare staff in daily practice in oncology. The submitted papers should center around relevant problems experienced in the medical domain and propose innovative methods to deal with them.
In this Research Topic we aim to investigate the state-of-the-art, new methodologies, tools, frameworks, or experiences in AI applied to clinical or institutional pathways and processes to face emerging challenges in clinics or in management. We welcome papers that explore how methods coming from Process Mining for Healthcare, Computer Interpretable Clinical Guidelines, Cased Based Reasoning, Clinical pathways modeling, Business Process Management, time series analysis, and other process-oriented intelligent methodologies, tools, and theories can be addressed to:
- Study how the patients are expected to evolve during the time
- Design the best pattern of care
- Estimate the workload for the incoming future and;
- Optimize the institutional/clinical processes in terms of resource allocation strategy for saving money and time.
Descriptive and/or inferential analysis are accepted but the contributions are expected to present original and innovative methodologies OR to describe the application of existing techniques in situations where they have remarkable performances; in any case, they are expected to work with real-world data and considerations about the reproducibility should be explicitly placed in the text.
Please note: manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases, eg. SEER, which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) will not be accepted in this section.
Fernando Seoane is the founder of Z-Health Technologies AB and Wergonic AB. All other Topic Editors declare no conflict of interest in relation to the Research Topic theme.
