Trial Success Metric System using Quantum Computers

Team QUBO

Archana Iyer

Soham Chatterjee

Why Quantum Computers?

  • Computation: Since quantum bits or qubits can exist in both states 0 and 1 as compared to a classical computer which can only exist either 0 or 1, the computation capabilities for a quantum computer is exponentially increased.
  • Quantum Search: The potential of quantum computers has taken a leap to understand problems in a given search space.
  • Solving Optimisation problems
  • Quantum Cryptography

Trial Success Metric System

  • Key Feature of the TPO. It helps in predicting the likelihood of a Trial being successful will be hugely beneficial during Trial Design.
  • New MIT Study Puts Clinical Research Success Rate at 14%. Only 14% of all drugs in clinical trials eventually win approval from the FDA 
  • Several factors which affect this include :
    • Recruiting patients: Finding the right patient for a clinical trial. An increase in attrition rates of patients during a trial is also high.
    • Lack of Infrastructure or Sites
    • This further leads to lower chances of the drug reaching the market.

Data Used 

  • Data used: 3k Citeline trial data that focuses on Oncology Leukemia. Input Parameters considered were Trial_phase (categorical), sponsor_academic (1 or 0), sponsor_cooperative_group (1 or 0), sponsor_government (1 or 0), sponsor_industry (1 or 0), sponsor_not_for_profit_funding_entity (1 or 0), sponsor_top_20_pharma (1 or 0), sponsor_all_other_pharma (1 or 0 )

  • Output : Whether the trial would be successful or not-i.e. can be either 0 or 1 .

The architecture of our System

RBM running on a Quantum Computer

Logistic Regression Model

Trial Success/Failure

Demo

Advantages​ of this Method

  • Tapping one of the most technologically advanced computing technique: Quantum Computing
  • QC took less than 30 seconds to train for 4 Epochs as compared to Classical Computer which takes more time
  • First application of Quantum for Clinical Trials (as per our research)
  • Train on Classical and Quantum Computer simultaneously
  • Further work could be done in understanding clinical drugs, drug-drug interaction and drug discovery

Bringing DaLIA to the Edge 

Why Bring DaLIA to the Edge?

  • Immense amount of data produced by IoT devices running parallel with the sheer increase in IoT devices across the world 
  • Real-World Interactions to DaLIA: DaLIA can be a tool that can be expanded and be integrated for conversations with people and their devices
  • DaLIA to connect the less privileged

DOSA: Dalia on Small Area 

  • A device that helps to run machine learning models on the edge with DaLIA that acts as an interface to the public
  • Currently, this device supports two features
    • Skin Cancer Detection through Image Processing
    • Detecting sickness sounds in patients
  • Use case: This device could be employed with doctors in rural areas where DaLIA could help act as an interface to patients and the deep learning models can help identify in real time  whether the patient is suffering from skin cancer/sickness 

Architecture of the System

UP board Microcontroller

Sickness and Cancer detection Neural Network models

Demo

Q & A

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