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06 June 2022
Machine Learning for Medical Decision Making
In the recent past, Machine learning (ML) is considered as a key component of artificial intelligence (AI), with well-known significant popularity in various medical field applications. For healthcare, ML has an extensive impact on the enhancement of medical decision making effectively. Indeed, the growing availability of healthcare information and the progress of computation power has been considered as the prominent factors which allows rapid progress of “ML for medical decision” making community. Moreover, the deployment of ML techniques in healthcare assist physicians in making reliable medical decision and help healthcare practitioners in developing a consistent and cost-effective healthcare systems. In ML, supervised algorithms are typically produced using healthcare dataset that contains several variables to identify the characteristic of the disease for effective medical decision making. Further, unsupervised learning involves pattern algorithms to explore undefined patterns or clusters that occur in the healthcare datasets. Dimension reduction and principal component analysis are effective unsupervised ML prediction models plays a significant role in medical decision making that helps healthcare practitioners in developing a reliable healthcare system. State of the art ML intelligence methods are increasingly leveraged in medical predictive modelling to provide medical decision-making support to healthcare practitioners. Modern ML approaches such as artificial neural network and boosting decision making models perform better than tradition regression models. The characteristic prediction challenges in this emerging model has been resolved using applied interpretability ML models which emerged to potentially deploys explainable prediction for medical decision system with high performance.
Therefore, Diverse medical decisions can be supported by machine learning assistance, including screening, diagnosis, prognosis, monitoring, treatment, and management. Hence, ML has proven its efficiency in analyzing and interpreting medical decision making, using time series analysis to explore various classical data such as numerical and categorical ones. Since ML is unlocking limitations in terms of the equitable and reachable healthcare system. In reality, the rural medicine movement will take tremendous benefits from bringing together the world’s expertise through a recommender framework by providing support to healthcare practitioners through ML-based medical decision-making system
This special issue presents the venue with the ability to encourage up-to-date medical research ideas on the usage of ML for healthcare in decision-making, the research perspective is concerning all the ML objectives in medical decision making that includes classification, diagnosing, planning, therapy, screening, diagnosis, treatment, etc. Both theoretical and real-time case studies are welcome in the call for paper.
The topics of interest for the special issue include, but not limited to the following:
- Computer aided prognostic decision-making using ML
- Medical decision support for treatment and classification using ML
- Disease diagnosis and prediction system using ML
- Medical imaging decision analysis and prediction using ML
- E-health, m-health and wearable medical decision-making using ML
- Analysis and interpretation of medical decision-making using ML
- Decision support system for healthcare and well-being using ML
- Medical expert system using ML
- Interactive medical decision-making using ML
- Medical data mining decision making system using ML
- Intelligent computing platform for medical decision-making using ML
- Application of AI in medical decision-making systems
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