Quantum-Fuzzy Expert Timeframe Predictor for Post-TAVR Monitoring
Abstract
:1. Introduction
1.1. Motivation
1.2. Related Work
- Timing Variability: Patients differ significantly in when they need PPMI. After TAVR, some patients could need treatment right away, while others might experience symptoms that need PPMI a few days later. Because of this unpredictability, standardizing predictions becomes more difficult because the models must be sensitive enough to cover a large temporal range.
- Complexity of Patient Data: Every patient has a different set of factors to consider, including underlying medical issues, body reactions to TAVR, and individual recovery paths. Predictive mistakes result from current models’ frequent inability to completely integrate and analyze these intricate datasets.
- Current Predictive Model Limitations: A lot of the predictive models that are now in use are limited by the following:
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- Data Simplification: A major information loss occurs when models oversimplify multidimensional, complex patient data.
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- Lack of Dynamism: In the crucial hours and days that follow TAVR, they frequently fail to take into account the dynamic changes in patient status.
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- Inadequate Integration of Clinical Expertise: Current models frequently fall short of incorporating the clinical knowledge required to properly analyze patient data.
1.3. Contributions
- Providing the time-based prediction architecture for PPMI requirement diagnosis following TAVR.
- Improving feature selection by incorporating expert knowledge to concentrate on more relevant predictors for results that are more accurate and understandable.
- Utilizing quantum computing to enhance high-dimensional data processing in PPMI requirement diagnosis following TAVR.
- Prompt healthcare decision-making provision by employing quantum algorithms for the ACO-ANFIS framework to increase prediction model accuracy and real-time performance.
- Developing model explainability using XAI, which sheds light on feature importance and interactions and meets the requirement for transparency in AI applications in the healthcare industry.
1.4. Paper Structure
2. Proposed Method
2.1. ANFIS Integrated with Classical ACO
- Evaporation: The existing pheromone level on each path is reduced by applying a factor of (1 ), where is the coefficient of evaporation of the pheromone. This weakening, in turn, reduces the level of the pheromone, and with time, the effect of a previously visited path weakens accordingly. This helps prevent the algorithm from becoming prematurely locked into a potentially suboptimal state.
- Ant Pheromone Deposits: Upon evaporation, the pheromone concentration is increased based on the pheromones deposited by the ants during their travels. More pheromone is precisely added by each ant k to the path that connects node i to node j. An ant’s experience with a trail determines the quality of this pheromone deposit. The overall pheromone level increase on this path sums up all the contributions from the ants, calculated as follows:
2.2. Quantum Entanglement Enhancement for ANFIS-ACO
Algorithm 1 Enhanced HQACO for ANFIS Parameter Optimization integrated with Quantum Circuit Generation |
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Ensure: Array of spherical angles .
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Ensure: Quantum circuit .
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- Quantum Circuit Setup: The quantum circuit is configured primarily using the GENERATEQUANTUMCIRCUIT(R) function from Algorithm 1. This function takes a set of probabilities and turns them into quantum angles that specify how the qubits are arranged in the circuit. These probabilities, which are represented by the symbol in Equation (6), stand for the possibility of moving from node i to node j. They are called simply in the PROBABILITYTOANGLEMAPPING(P) function, assuming a serial indexing that captures all node transitions.
- The role of classical registers
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- The C1 (System State Register) contains the node status and operational flags that direct the quantum computations, as well as the present state of the system.
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- Decision variables and the probabilities connected to each decision are stored in the C2 (Decision and Probability Register), which has a direct impact on updates and modifications made to the ANFIS model parameters in response to results from the quantum circuit.
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- The parameter adjustment register, or C3, keeps track of the changes made to the ANFIS parameters after quantum computation, making sure that the model’s tuning appropriately incorporates the feedback from the quantum measurements.
- Probability to Angle Conversion: The process of converting these probabilities into angles that quantum gates can use is handled by the function PROBABILITYTOANGLEMAPPING(P). The procedure entails the following:
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- The angle is set to 0, and succeeding entries are filled with , thereby nullifying those pathways in the quantum computation if = 0, signifying an impractical path.
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- In the case of non-zero probabilities, will determine the angle , assigning a corresponding quantum state angle to every likelihood.
- Quantum Decision-Making: The quantum state is initialized to reflect all possible paths weighted by their estimated probabilities when the quantum circuit is set up. This quantum state collapses upon measurement to a basis state , which represents the path selected according to quantum probability. Real-time optimization is made possible by this selection, which is not only faster than the classical approach but also more accurate.
- Feedback and Optimization: The quantum measurement results have a direct bearing on the ANFIS parameter adjustments. Figure 2 illustrates this real-time feedback loop, which continuously fine-tunes the model to guarantee ideal parameter settings, improving the patient monitoring system’s accuracy and effectiveness.
2.3. ANFIS Arrangement for Using QACO
3. Results and Discussion
3.1. Experimental Setup
3.2. Experimental Experience Results and Discussions
- Rule 1: Early DischargeIf ft1: No Baseline RBBB AND ft2: Absence of New ECG ChangesThen Early Discharge
- Rule 2: 24 h Inpatient MonitoringIf ft3: Baseline RBBB OR ft5: New-onset RBBB OR ft6: New-onset LBBB AND ft8: 130 ms < QRS < 160 msThen 24 h Inpatient Monitoring
- Rule 3: 48 Hours Inpatient MonitoringIf ft4: Baseline LBBB OR ft6: New-onset LBBB AND ft7: Post balloon dilatation OR ft9: Prolongation of PR > 20 msThen 48 h Inpatient Monitoring
- Rule 4: >48 h MonitoringIf ft11: Baseline Hypertension AND ft12: Diabetes Mellitus AND ft13: Male Sex OR ft14: Age significant factorThen > 48 h Monitoring
- Rule 5: 24 Hours Inpatient MonitoringIf ft10: QRS > 160 msOR ft3: Baseline RBBBThen 24 h Inpatient Monitoring
- Rule 6: 48 h Inpatient MonitoringIf ft3: Baseline RBBB OR ft4: Baseline LBBB OR ft5: New-onset RBBB OR ft6: New-onset LBBB AND ft9: Prolongation of PR > 20 ms OR ft14: Age > 80Then 48 h Inpatient Monitoring
3.3. Model Limitations
3.4. Future Works
- Cross-Domain Adaptation: To test and improve our model, we will use a variety of datasets from various medical centers. These datasets may have distinct feature relevancies and distributions. This modification will increase the model’s resilience to various clinical data features and enable us to apply the model more broadly.
- Feature Representation Learning: We seek to discover and encode more profound, abstract clinical data representations that apply to various tasks using sophisticated feature representation approaches. This will improve the model’s capacity to extract valuable information from hidden data.
- Task-Specific Tuning: Modifications to the model may be necessary for every clinical task. To optimize our model for particular tasks—like forecasting additional cardiovascular events—we intend to investigate ways to use less labeled data from the target domain while preserving the information we acquired from the source domain.
- Advanced Quantum Algorithms: Investigation of algorithms for complicated healthcare dataset optimization, such as Variational Quantum Eigensolvers (VQEs) and Quantum Annealing.
- Hybrid Quantum-Classical Models: These models have the potential to combine the benefits of both classical and quantum computing, providing the resilience and stability of classical computing with the speed and parallelism of quantum computing.
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
TAVR | Transcatheter Aortic Valve Replacement |
PPMI | Permanent Pacemaker Implantation |
QACO | Quantum Ant Colony Optimization |
ANFIS | Adaptive Neuro-Fuzzy Inference System |
XAI | eXplainable Artificial Intelligence |
ML | Machine Learning |
SLR | Simple Logistic Regression |
LWL | Locally Weighted Learner |
GBM | Gradient Boosting Machine |
ECG | Electrocardiogram |
SVI | Stroke Volume Index |
RBBB | Right Bundle Branch Block |
THV | Transcatheter Heart Valve |
BP | Backpropagation |
PSO | Particle Swarm Optimization |
ACO | Ant Colony Optimization |
BWOA | Black Widow Optimization Algorithm |
GA | Genetic Algorithm |
LAFB | Left Anterior Fascicular Block |
LVOT | Left Ventricular Outflow Tract |
QML | Quantum Machine Learning |
SHAP | SHapley Additive exPlanations |
LIME | Local Interpretable Model-Agnostic Explanations |
LSE | Least Squares Estimation |
NISQ | Noisy Intermediate-Scale Quantum |
LBBB | Left Bundle Branch Block |
VQE | Variational Quantum Eigensolvers |
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Author & Year | Objective | Sample Size | Main Predictors |
---|---|---|---|
[8] | Post-procedural prediction models for PPMI with pre-existing RBBB | 237 | Membranous septum, First degree AVB, implantation depth |
[5] | Pre- and post-procedural prediction models for PPMI | 336 | Prior RBBB, pre-procedural AVA, AVA ratio |
[9] | Predictors for PPM after TAVR | 447 | Transcatheter heart valve (THV) oversizing (30 days after TAVR) |
[10] | Identify predictors for PPM after TAVR | 357 | QRS > 120 ms, type II DM, supraventricular arrhythmias |
[11] | Identify predictors for PPM after TAVR in Chinese Population | 39 | New-onset LBBB and lead I T wave elevation |
[12] | Meta-analysis for identify predictors for PPM after TAVR | 981,168 | Male gender, age≥80, FDAVB, RBBB, AF, DM, CKD, Medtronic CoreValve, transfemoral TAVR, increased LVOT, and aortic annulus diameter |
[13] | Meta-analysis for identify predictors for PPM after TAVR | 31,261 | Male gender, Mobitz Type 1 HB, Bifascicular HB, LAFB, RBBB, Mechanically-expanding, ballon expanding 29 mm |
[7] | Meta-analysis for identify predictors for PPM after TAVR | 71,455 | Age, Male gender, DM, Baseline AV, post-procedure AV Left ventricular outflow tract calcification (left coronary cusp and non-coronary cusp), Valve size, balloon expanding, Pre-procedure balloon dilatation, Post-procedure balloon dilatation, Implantation depth |
[14] | Risk predictor model for pacemaker implantation after TAVR | 1071 | Self-expanding valve, RBBB, pre-existing first-degree AVB |
[15] | Predictors for PPM after TAVR | 182 | Previous RBBB and LVOT-EI |
[16] | Develop risk score for pacemaker implantation post-TAVR | 1266 | Syncope history, baseline RBBB, QRS duration, higher degree of oversizing |
[17] | Predictors for PPM after TAVR | 311 | High body mass index, bradycardia, RBBB |
[18] | Predictive model for pacemaker implantation post-TAVR | 18,400 | RBBB, LBBB, bradycardia, 2nd-AVB, transformal approach |
[19] | Predictive model for pacemaker implantation post-TAVR | 240 | RBBB, NCC-DLZ CA, MS length |
[6] | Identify predictors of PPMI post-TAVR | 229 | RBBB, Deep valve implantation, LVOT calcification |
[20] | Identify predictors of PPMI post-TAVR | 1973 | RBBB, the prosthesis to LV outflow tract diameter ratio and the LV end-diastolic diameter |
Input | Output | ||
---|---|---|---|
No. | Feature | No. | Decision |
1 | ft1: No Baseline RBBB | 1 | Early Discharge |
ft2: Absence of New ECG Changes | |||
2 | ft3: Baseline RBBB | 2 | 24 h Inpatient Monitoring |
ft4: Baseline LBBB | |||
ft5: New-onset RBBB | |||
ft6: New-onset LBBB | |||
ft7: post balloon dilatation | |||
ft8: 130 ms < QRS < 160 ms | |||
ft9: Prolongation of PR > 20 ms | |||
3 | ft3: Baseline RBBB | 3 | 48 h Inpatient Monitoring |
ft4: Baseline LBBB | |||
ft5: New-onset RBBB | |||
ft6: New-onset LBBB | |||
ft7: post balloon dilatation | |||
ft10: QRS>160 ms | |||
4 | ft11: Baseline Hypertension | 4 | >48 h monitoring |
ft4: Baseline RBBB | |||
ft12: Diabetes mellitus | |||
ft13: Male sex | |||
f14: Age |
Method | Key Hyperparameters | Value |
---|---|---|
ANFIS | MFi Type | Gaussian () |
No. of MFi | 2 | |
MFo | Linear | |
No of Fuzzy rules | 6 | |
ACO | No. of Ants | 1000 |
Weight of pheromone on decision | 0.4 | |
Weight of heuristic data on decision | 0.6 | |
Evaporation of pheromone per step | 0.05 | |
Max of iteration | 1000 | |
GA | population size | 100 |
Mutation rate | 0.5 | |
Crossover rate | 0.8 | |
Selection intensity | 0.5 | |
Max of iteration | 1000 | |
BWO | population size | 500 |
Procreation rate | 0.8 | |
Mutation rate | 0.7 | |
Cannibalism rate | 0.6 | |
Max of iteration | 1000 | |
PSO | population size | 100 |
Cognitive parameter | 1.5 | |
Social Parameter | 1.5 | |
Min of inertia weight | 0.4 | |
Max of inertia weight | 0.9 | |
Max of iteration | 1000 |
Train Dataset | Test Dataset | ||||||||
---|---|---|---|---|---|---|---|---|---|
Metrics | Accuracy | Precision | Recall | F1-Score | Accuracy | Precision | Recall | F1-Score | |
Method | |||||||||
ANFIS-BP | 0.68 | 0.73 | 0.7 | 0.71 | 0.67 | 0.72 | 0.7 | 0.71 | |
ANFIS-GA | 0.85 | 0.80 | 0.83 | 0.86 | 0.82 | 0.81 | 0.8 | 0.86 | |
ANFIS-PSO | 0.82 | 0.8 | 0.81 | 0.79 | 0.8 | 0.81 | 0.83 | 0.85 | |
ANFIS-ACO | 0.85 | 0.85 | 0.8 | 0.85 | 0.83 | 0.81 | 0.82 | 0.81 | |
ANFIS-BWOA | 0.89 | 0.88 | 0.89 | 0.89 | 0.9 | 0.90 | 0.90 | 0.89 | |
ANFIS-QACO | 0.99 | 0.97 | 0.96 | 0.97 | 0.97 | 0.96 | 0.97 | 0.96 |
No of Features | 3 | 5 | 8 | 14 | 18 | 20 | |
---|---|---|---|---|---|---|---|
Method | |||||||
ANFIS + Gradient—Abs(SD) | 4.2 | 4.5 | 4.8 | 5.5 | 6 | 12 | |
ANFIS + ACO—Abs(SD) | 1.8 | 1.8 | 1.5 | 1.5 | 2 | 2 | |
ANFIS + QACO—Abs(SD) | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
ANFIS + Gradient—Run Time | 18.3 | 18.5 | 20 | 25 | 25 | 30 | |
ANFIS + ACO—Run Time | 2.8 | 2.8 | 3 | 3 | 3.2 | 3.8 | |
ANFIS + QACO—Run Time | 1.04 | 1.06 | 1.08 | 1.10 | 1.12 | 1.14 |
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Tightiz, L.; Yoo, J. Quantum-Fuzzy Expert Timeframe Predictor for Post-TAVR Monitoring. Mathematics 2024, 12, 2625. https://doi.org/10.3390/math12172625
Tightiz L, Yoo J. Quantum-Fuzzy Expert Timeframe Predictor for Post-TAVR Monitoring. Mathematics. 2024; 12(17):2625. https://doi.org/10.3390/math12172625
Chicago/Turabian StyleTightiz, Lilia, and Joon Yoo. 2024. "Quantum-Fuzzy Expert Timeframe Predictor for Post-TAVR Monitoring" Mathematics 12, no. 17: 2625. https://doi.org/10.3390/math12172625