Related Experiment Video
Updated: Jul 1, 2025
07:05
Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents
Published on: September 10, 2018
6.0K
A new method of modeling the multi-stage decision-making process of CRT using machine learning with uncertainty
Kristoffer Larsen1, Chen Zhao2, Joyce Keyak3
1Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, USA.
Arxiv
|March 11, 2024
Summary
This study developed a multi-stage machine learning model to predict cardiac resynchronization therapy (CRT) response in heart failure (HF) patients. The model efficiently reduces the need for costly SPECT MPI data acquisition without compromising predictive performance.
Area of Science:
- Cardiology
- Medical Imaging
- Machine Learning
Background:
- Predicting patient outcomes for cardiac resynchronization therapy (CRT) in heart failure (HF) often involves extensive data collection, including single-photon emission computed tomography myocardial perfusion imaging (SPECT MPI).
- Current machine learning (ML) models typically utilize all available data, disregarding the significant costs and time associated with data acquisition.
Conclusions:
- The proposed multi-stage ML model effectively predicts CRT response while optimizing data acquisition strategies.
- Uncertainty quantification enables intelligent decision-making, reducing the reliance on expensive SPECT MPI data.
- This approach offers a cost-effective and efficient method for predicting patient outcomes in HF management.
Related Concept Videos
01:20Decision Making
109
Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
Automatic decision-making is fast, intuitive, and relies on gut feelings...
109
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
54
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
54
01:16Response Surface Methodology
129
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
The process of RSM involves several key steps:
129
01:37Reason and Intuition
6.4K
The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
6.4K
01:26Modeling in Therapy
72
Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...
72
02:16Stereotype Content Model
14.7K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
14.7K