AI in eHealth

AI & eHealth

Noor Orfahly

Overview

Definition

Forms of eHealth

The 10 e’s in eHealth

AI-Based MDSS

Definition

eHealth is a new term dating back to 1999.

eHealth Strategy Office – UBC: Using modern information and communications technologies in the areas of health services, health education and health research.

The World Health Organization: The transfer of health resources and health care by electronic means. E-health provides a new method for using health resources – such as information, money, and medicines – and in time should help to improve efficient use of these resources.

Health Canada: An overarching term used today to describe the application of information and communications technologies in the health sector. It encompasses a whole range of purposes from purely administrative through to health care delivery.

http://www.who.int/trade/glossary/story021/en/index.html



Forms of eHealth

Primary care: The use of computer systems by general practitioners and

pharmacists for patient management, medical records and electronic prescribing.

Hospital care: ePatient administration systems Laboratory & radiology information systems Electronic messaging systems Telemedicine

Home care: Teleconsults Remote vital signs monitoring systems used for diabetes medicine,

asthma monitoring and home dialysis systems


efficiency

enhancing quality

evidence based

empowerment

encouragement

education

enabling

extending

ethics

equity


efficiency

enhancing quality

evidence based

empowerment

encouragement

education

enabling

extending

ethics

equity

Increase efficiency in health care:

Decreasing costs: avoiding duplicative or unnecessary diagnostic.

Therapeutic interventions: through enhanced communication possibilities between health care establishments, and through patient involvement.


efficiency

enhancing quality

evidence based

empowerment

encouragement

education

enabling

extending

ethics

equity

E-health may enhance the quality of health care for example by allowing comparisons between different providers, involving consumers as additional power for quality assurance, and directing patient streams to the best quality providers.


efficiency

enhancing quality

evidence based

empowerment

encouragement

education

enabling

extending

ethics

equity

eHealth interventions should be evidence-based in a sense that their effectiveness and efficiency should not be assumed but proven by rigorous scientific evaluation.


efficiency

enhancing quality

evidence based

empowerment

encouragement

education

enabling

extending

ethics

equity

By making the knowledge bases of medicine and personal electronic records accessible to consumers over the Internet, e-health opens new avenues for patient-centered medicine, and enables evidence-based patient choice.


efficiency

enhancing quality

evidence based

empowerment

encouragement

education

enabling

extending

ethics

equity

Encouragement of a new relationship between the patient and health professional, towards a true partnership, where decisions are made in a shared manner.


efficiency

enhancing quality

evidence based

empowerment

encouragement

education

enabling

extending

ethics

equity

Education of physicians through online sources (continuing medical education) and consumers (health education, tailored preventive information for consumers).


efficiency

enhancing quality

evidence based

empowerment

encouragement

education

enabling

extending

ethics

equity

Enabling information exchange and communication in a standardized way between health care establishments.


efficiency

enhancing quality

evidence based

empowerment

encouragement

education

enabling

extending

ethics

equity

Extending the scope of health care beyond its conventional boundaries.

Geographical sense: e-health enables consumers to easily obtain health services online from global providers.

Conceptual sense: These services can range from simple advice to more complex interventions or products such a pharmaceuticals.


efficiency

enhancing quality

evidence based

empowerment

encouragement

education

enabling

extending

ethics

equity

Online professional practice

Informed consent

Privacy


efficiency

enhancing quality

evidence based

empowerment

encouragement

education

enabling

extending

ethics

equity

Equitable health care is one of the promises of e-health.

People, who do not have the money, skills, and access to computers and networks, cannot use computers effectively.

The digital divide currently runs between

rural vs. urban rich vs. poor young vs. old male vs. female neglected/rare vs. common

diseases.

A FRAMEWORK FOR AI-BASED CLINICAL DECISION SUPPORT

Example

Enable medical decision making in the presence of partial information

AI-Based Clinical Decision Support Framework

Introduction

Medical decision support systems (MDSS) map patient information to promising diagnostic and treatment paths.

Knowledge-based systems can suffer a significant loss of performance when patient data is incomplete: Patients omit details Access restrictions prevent viewing of remote medical records

The output of learning-based systems cannot be easily explained.

Hybrid System


Knowledge Base

Patient Records

Prescription

Protocol

Drug Interaction Registry


Patient Records

Insomnia treatment.

Patient records drawn from the Center for Disease Control (CDC). Dataset: Behavioral Risk Factor Surveillance System (BRFSS) telephone survey for 2010.

BRFSS dataset contains: Respondent information: age, race, sex, and geographic

location. Common medical conditions: cancer, asthma, mental

illness, and diabetes. Behavioral risk factors: alcohol consumption, drug use,

and sleep deprivation. BRFSS: 450,000 individuals.

Relational database.


Patient Records


Prescription

Protocol

Identify a subset of sleep aids and apply the Mayo clinic sleep aid prescription protocol to identify the conditions under which each drug should be prescribed.

Inference Rules examples:1. drug-to-drug interaction rule:If a patient is currently taking an existing drug D1, and D1

cannot be given with drug D2, then the patient cannot be given drug D2.

2. drug-to-condition interaction rule:If a patient has some existing medical condition C, and a drug D has contraindication to the condition C, then the patient cannot be given drug D.

3. drug-to-disease interaction rule:If a patient has a disease E, and a drug D has contraindication to the disease E, then the patient cannot be given drug D.

Drug Interacti

on Registry


Imputation

Bayesian multiple imputation Assume a particular joint probability model over the feature values.

a1, a2, …, an , P(a1 = x, a2 = y, …, an = z) = prop., etc Draw imputed datasets from the posterior distribution of the

missing data given the observed data.

Make multiple imputed datasets, then take the average of the imputed values.

a1 a2 … an

id1 x y zz

id2 x yyy z

id3 xx yy zzzz

a1 a2 … an

id1 x y ?

id2 ? ? z

id3 ? yy zzzz


Experimental Comparison

Patients who should be given sleep aids were labeled as ‘positive’ exemplars and those who should not as ‘negative’ exemplars.

When a system labeled a patient correctly in response to a query, a ‘true positive’ (tp) or ‘true negative’ (tn) was produced.

Otherwise, a ‘false positive’ (fp) or ‘false negative’ (fn) was produced.

The results were evaluated in terms of: Sensitivity: rate of positive exemplars labeled as positive. Specificity: rate of negative exemplars labeled as negative. Balanced accuracy: simple average of specificity and sensitivity.


Experimental Comparison – Learning-based System

Evaluate the impact of missing information on the performance of the learning-based system by removing known values from the patient records.

Defined e as the average number of attribute values removed from a patient’s record.

For each value of e, train an AdaBoost-based classifier using 50 sets of 5000 exemplars from the partially-missing data.


Experimental Comparison – Learning-based SystemAdaBoost

AdaBoost helps you combine multiple “weak classifiers” into a single “strong classifier”.

A weak classifier is simply a classifier that performs poorly, but performs better than random guessing (accuracy is greater than 50%).

AdaBoost can be applied to any classification algorithm.

What does AdaBoost do for you?1. It helps you choose the training set for each new classifier that you train

based on the results of the previous classifier.2. It determines how much weight should be given to each classifier’s proposed

answer when combining the results.



Training Set Selection: Each weak classifier should be trained on a random subset of the total training

set. The subsets can overlap. AdaBoost assigns a “weight” to each training example, which determines the

probability that each example should appear in the training set. After training a classifier, AdaBoost increases the weight on the misclassified

examples so that these examples will make up a larger part of the next classifiers training set, and hopefully the next classifier trained will perform better on them.

Classifier Output Weights: After each classifier is trained, the classifier’s weight is calculated based on its

accuracy. A classifier with 50% accuracy is given a weight of zero A classifier with less than 50% accuracy is given negative weight.



Formal Definition: The equation for the final classifier:

No. of weak classifiers

Output of weak classifier ‘t’ {-1 , +1}

Weight applied to classifier ‘t’

We make our final decision simply by looking at the sign of

this sum



Formal Definition: Weight of classifier:

The first classifier (t = 1) is trained with equal probability given to all training examples.

After it’s trained, we compute the output weight (alpha) for that classifier.

error rate (e_t ) is just the number of misclassifications over the training set divided by the training set size.



Formal Definition: Updating examples’ weights:

If the predicted and actual output agree, y * h(x) will always be +1 (1*1 or -1*-1) If they disagree, y * h(x) will be negative. Misclassifications by a classifier with a positive alpha will cause this training example to be given

a larger weight. And vice versa. If a weak classifier misclassifies an input, we don’t take that as seriously as a strong classifier’s

mistake.

Vector of weightsTraining example

number

Sum of all the weights

(normalization)

Correct output

predicted output


Experimental Comparison – Learning-based System


Experimental Comparison – Knowledge-based System

Use EulerSharp semantic reasoner for the knowledge-based reasoning


Conclusion

This approach of Integrating machine learning with ontological reasoning makes use of the inherent advantages of both approaches in order to offer the required accuracy for the medical domain.

This approach supports interoperability between different health information systems. A decision making process should use all relevant data from many distributed systems instead of a single data source to maximize its effectiveness.

This approach provides a framework that is generic enough to be used in other medical applications.

Thank You

AI in eHealth

Technology

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