Quantum-based machine learning for oncology treatment

Patent metadata

Field	Value
Publication number	US-2018011981-A1
Application number	US-201715641431-A
Country	US
Kind code	A1
Filing date	Jul 5, 2017
Priority date	Jul 5, 2016
Publication date	Jan 11, 2018
Grant date	—

Abstract

A method and system may utilize a quantum information state analog to reinforcement learning techniques to determine whether to adapt a course of treatment for an oncology patient. A quantum-based reinforcement learning engine may represent a decision to adapt and a decision not to adapt the course of treatment for the oncology patient as quantum information states in a superposition. Each quantum information state has a corresponding amplitude indicative of the likelihood that the quantum information state has a higher expected clinical outcome for the oncology patient. Using a quantum search algorithm, the quantum-based reinforcement learning engine identifies amplitudes for each quantum information state in the superposition. The quantum-based reinforcement learning engine instructs a health care provider to adapt the course of treatment for the oncology patient when a likelihood corresponding to the decision to adapt state exceeds a likelihood threshold.

First claim

We claim:

1 . A method for adapting oncology treatment using quantum-based reinforcement learning, the method comprising:
receiving, at the one or more processors, a first set of patient data for an oncology patient including a plurality of patient variables collected at a first time; determining, by the one or more processors, a course of treatment for the oncology patient based on the first set of patient data; generating, by the one or more processors, a quantum adaptation model for determining whether to adapt the course of treatment, including representing a decision to adapt and a decision not to adapt the course of treatment as a superposition of quantum information states, wherein the decisions to adapt and not to adapt have associated likelihoods of improving a future clinical outcome for the oncology patient; receiving, at the one or more processors, an updated set of patient data for the oncology patient collected at a subsequent point in time after the first time, including at least some of the plurality of patient variables or including an indication of a current clinical outcome of the course of treatment; applying, by the one or more processors, the updated set of patient data to the quantum adaptation model to determine a likelihood that the decision to adapt improves the future clinical outcome; and when the likelihood corresponding to the decision to adapt exceeds a threshold likelihood, transmitting, by the one or more processors, an indication to a network-enabled device of a health care provider to administer an adapted course of treatment to the oncology patient.

2 . The method of claim 1 , wherein the updated set of patient data for the oncology patient is represented as a state, the quantum adaptation model includes a plurality of states, and the quantum adaptation model is used to determine likelihoods that the decision to adapt and the decision not to adapt the course of treatment improves the future clinical outcome for the oncology patient according to a particular state of the plurality of states corresponding to the oncology patient.

3 . The method of claim 2 , wherein when at least one of the plurality of patient variables or the current clinical outcome of the course of treatment is not collected at the subsequent point in time, the state corresponding to the oncology patient is unknown and the method further includes:
generating, by the one or more processors, a second superposition of quantum information states, wherein each quantum information state within the second superposition represents a possible state of the oncology patient; and determining, by the one or more processors, likelihoods that the decision to adapt and the decision not to adapt the course of treatment improves the future clinical outcome for the oncology patient according to the quantum adaptation model and the second superposition of quantum information states representing possible states of the oncology patient.

4 . The method of claim 1 , wherein determining a course of treatment for the oncology patient includes:
obtaining, at one or more processors, a set of training data including a plurality of patient variables associated with a plurality of oncology patients, a course of treatment applied to each oncology patient, and a current clinical outcome for each oncology patient; generating, by the one or more processors, a quantum predictive model for determining a course of treatment of a plurality of courses of treatment for an oncology patient having a highest expected clinical outcome for the oncology patient; and determining, by the one or more processors, the course of treatment of the plurality of courses of treatment having the highest expected clinical outcome for the oncology patient using the quantum predictive model.

5 . The method of claim 1 , wherein the current and future clinical outcomes are complication-free tumor control metrics.

6 . The method of claim 5 , wherein the complication-free tumor control metric is a product of a tumor control probability (TCP) and a normal tissues complications probability (NTCP).

7 . The method of claim 6 , wherein the likelihood for the decision to adapt is based on the TCP and NTCP for the oncology patient after receiving the course of treatment.

8 . The method of claim 5 , wherein the quantum adaptation model is generated by applying a time-dependent Schrödinger wave equation to the superposition of quantum information states to determine expected complication-free tumor control metrics discounted over time for the decision to adapt and the decision not to adapt.

9 . The method of claim 1 , wherein the likelihood that the decision to adapt improves the future clinical outcome is determined using a quantum search algorithm.

10 . The method of claim 1 , wherein the plurality of patient variables includes at least one of: clinical variables, biological variables, biopsy variables, physical variables, dosimetric variables, or laboratory variables.

11 . A computing device for adapting oncology treatment using quantum-based reinforcement learning, the computing device comprising:
a communication network, one or more processors; and a non-transitory computer-readable memory coupled to the communication network and the one or more processors and storing thereon instructions that, when executed by the one or more processors, cause the computing device to:
receive, via the communication network, a first set of patient data for an oncology patient including a plurality of patient variables collected at a first time;
determine a course of treatment for the oncology patient based on the first set of patient data;
generate a quantum adaptation model for determining whether to adapt the course of treatment, including representing a decision to adapt and a decision not to adapt the course of treatment as a superposition of quantum information states, wherein the decisions to adapt and not to adapt have associated likelihoods of improving a future clinical outcome for the oncology patient;
receive, via the communication network, an updated set of patient data for the oncology patient collected at a subsequent point in time after the first time, including at least some of the plurality of patient variables or including an indication of a current clinical outcome of the course of treatment;
apply the updated set of patient data to the quantum adaptation model to determine a likelihood that the decision to adapt improves the future clinical outcome; and
when the likelihood corresponding to the decision to adapt exceeds a threshold likelihood, transmit, via the communication network, an indication to a network-enabled device of a health care provider to administer an adapted course of treatment to the oncology patient.

12 . The computing device of claim 11 , wherein the updated set of patient data for the oncology patient is represented as a state, the quantum adaptation model includes a plurality of states, and the quantum adaptation model is used to determine likelihoods that the decision to adapt and the decision not to adapt the course of treatment improves the future clinical outcome for the oncology patient according to a particular state of the plurality of states corresponding to the oncology patient.

13 . The computing device of claim 12 , wherein when at least one of the plurality of patient variables or the current clinical outcome of the course of treatment is not collected at the subsequent point in time, the state corresponding to the oncology patient is unknown and the instructions further cause the computing device to:
generate a second superposition of quantum info

Assignees

• Univ Michigan Regents

Inventors

• El Naqa Issam
• Ten Haken Randall

Classifications

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06N99/005

  Physics · mapped topic

• G06F19/324

  Physics · mapped topic

• G06N5/04

  Inference or reasoning models · CPC title

• G06N99/002

  Physics · mapped topic