Sold by: MphasisÂ
Deployed on AWS
This quantum computing-based solution helps in categorization of patients for priority treatment within healthcare emergency department.
Overview
Patient triage categorization is a hybrid quantum computing-based solution for emergency assessment and priority treatment of incoming patients. The solution leverages quantum clustering based approach to prioritize patients using their vitals. The solution improves risk assesment and empowers healthcare facilities to optimize resource allocation, reduce wait times, and enhance the overall quality of care.
Highlights
- The solution uses a data driven approach to identify patients that require time-sensitive treatments, facilitating targeted interventions, reducing unnecessary delays, and optimizing resource allocation within healthcare systems.
- The solution uses quantum hybrid solvers from D-Wave to reduce the time and space required while providing better quality results.
- Need customized Quantum Computing solutions? Get in touch!
Details
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Delivery method
Latest version
Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.2xlarge Inference (Batch) Recommended | Model inference on the ml.m5.2xlarge instance type, batch mode | $15.00 |
ml.m5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.xlarge instance type, real-time mode | $10.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $15.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $15.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $15.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $15.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $15.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $15.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $15.00 |
ml.c4.2xlarge Inference (Batch) | Model inference on the ml.c4.2xlarge instance type, batch mode | $15.00 |
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
This is the first version.
Additional details
Inputs
- Summary
Supported content: zip file only with file name input.zip with three csv files: Patient_Unlabelled_Data.csv : patient's information, without any labels/categories. Patient_Labelled_Data.csv: patient's information with labels/categories. These labelled patients should be those who have been previously categorised by triage systems in emergency departments, and should be good representation of each label. User_Input.csv: user's credentials for acessing Dwave Leap,a quantum cloud service.
- Limitations for input type
- The fields mentioned in the input description are all mandatory and should follow the same naming convention.
- Input MIME type
- application/zip
https://github.com/Mphasis-ML-Marketplace/Patient-Triaging-with-Quantum-ML/tree/main/input
https://github.com/Mphasis-ML-Marketplace/Patient-Triaging-with-Quantum-ML/tree/main/input
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
age | Age of the patient. | Type: Continuous | Yes |
gender | Gender of the patient (Discrete). If the gender is 'male', then the value is '1', Else '0'. | Type: Categorical
Allowed values: 1,0 | Yes |
chest pain type | Patient's severity of the chest pain, if encountered (Discrete). If no chest pain is identified then the value is '0', Else the value is between '1' and '4' based on the severity. | Type: Categorical
Allowed values: 0,1,2,3,4 | Yes |
cholesterol | Cholesterol level of patient measured in mg/dL (Numerical). | Type: Continuous | Yes |
max heart rate | Maximum heart rate of patient measured in bpm (Numerical). | Type: Continuous | Yes |
exercise-induced angina | Patient's encountering of angina due to exertion (Discrete). If encountered then the value is '1', Else '0'. | Type: Categorical
Allowed values: 0,1 | Yes |
blood glucose | The blood glucose level of patient in mg/dL (Numerical) | Type: Continuous | Yes |
bmi | The body mass index of patient (Numerical). | Type: Continuous | Yes |
hypertension | The flag if patient is identified with hypertension or not.If the hypertension is identified, then the value is '1', Else '0'. | Type: Categorical
Allowed values: 0,1 | Yes |
heart_disease | Patient's history of heart disease. If the patient has a history of heart disease the value is '1', else '0'. | Type: Categorical
Allowed values: 0,1 | Yes |
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