AIMC Topic: Decision Making

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ST-Tree with interpretability for multivariate time series classification.

Neural networks : the official journal of the International Neural Network Society
Multivariate time series classification is of great importance in practical applications and is a challenging task. However, deep neural network models such as Transformers exhibit high accuracy in multivariate time series classification but lack int...

Breast radiotherapy planning: A decision-making framework using deep learning.

Medical physics
BACKGROUND: Effective breast cancer treatment planning requires balancing tumor control while minimizing radiation exposure to healthy tissues. Choosing between intensity-modulated radiation therapy (IMRT) and three-dimensional conformal radiation th...

New Approaches of Generalised Fuzzy Soft sets on fuzzy Codes and Its Properties on Decision-Makings.

F1000Research
BACKGROUND: Several scholars defined the concepts of fuzzy soft set theory and their application on decision-making problem. Based on this concept, researchers defined the generalised fuzzy soft set and its applications. However, to the best of the a...

Machine learning algorithms mimicking specialists decision making on initial treatment for people with type 2 diabetes mellitus in Japan diabetes data management study (JDDM76).

Diabetes & metabolic syndrome
OBJECTIVE: To evaluate whether typical machine learning models that mimic specialists' care can successfully reproduce information, not only on whether to prescribe medications but also which hypoglycemic agents to prescribe as initial treatment for ...

R WE ready for reimbursement? A round up of developments in real-world evidence relating to health technology assessment: part 17.

Journal of comparative effectiveness research
In this update, we discuss a position statement from the National Institute of Health and Care Excellence (NICE) on the use of artificial intelligence for evidence generation and publications reviewing the use of real-world data as external control a...

Physicians' ethical concerns about artificial intelligence in medicine: a qualitative study: .

Frontiers in public health
BACKGROUND/AIM: Artificial Intelligence (AI) is the capability of computational systems to perform tasks that require human-like cognitive functions, such as reasoning, learning, and decision-making. Unlike human intelligence, AI does not involve sen...

Neural correlates of empathy in donation decisions: Insights from EEG and machine learning.

Neuroscience
Empathy is central to individual and societal well-being. Numerous studies have examined how trait of empathy affects prosocial behavior. However, little studies explored the psychological and neural mechanisms by which different dimensions of trait ...

Decision-making of autonomous vehicles in interactions with jaywalkers: A risk-aware deep reinforcement learning approach.

Accident; analysis and prevention
Jaywalking, as a hazardous crossing behavior, leaves little time for drivers to anticipate and respond promptly, resulting in high crossing risks. The prevalence of Autonomous Vehicle (AV) technologies has offered new solutions for mitigating jaywalk...

Development of a survey-based stacked ensemble predictive model for autonomy preferences in patients with periodontal disease.

Journal of dentistry
OBJECTIVES: This study aimed to develop a model to predict the autonomy preference (AP) and satisfaction after tooth extraction (STE) in patients with periodontal disease. Understanding of individual AP and STE is essential for improving patient sati...

Enhancing decision-making with linear diophantine multi-fuzzy set: application of novel information measures in medical and engineering fields.

Scientific reports
This study offers a comprehensive analysis of novel information for linear diophantine multi-fuzzy sets and illustrates its applications in practical scenarios. We introduce innovative similarity metrics tailored for linear diophantine multi-fuzzy se...