WELCOME!

NetHope Solutions Center

ICT4D Webinar Series:

AI and Machine Learning in Health

Programs

November 20, 2018

Speakers:

Neil Sahota, United Nations AI Subject Matter Expert, Faculty at University of California, Irvine, and IBM Master Inventor

Steve Hellen,

Director of ICT4D

and GIS,

Catholic Relief

Services

Moderator: Sonja Ruetzel, Catholic Relief Services

Dr Anuraj Shankar, Senior Research Scientist, Harvard T.H. Chan School of Public Health

Toby Norman, CEO, SimprintsTechnology

Discussion:

Toby Norman, CEO, Simprints Technology

What are the opportunities and risks of deploying AI in digital health care?

Background: Ethical biometrics for the ‘last mile’

11 countries

4.1m beneficiaries

Opportunity: Learning from our dataRisk: Learning from our biases

Error ~ 0.8% Error ~ 35%

https://www.media.mit.edu/articles/facial-recognition-is-accurate-if-you-re-a-white-guy/

AI is great… for white males

Opportunity: Incredible access to dataRisk: Incredible breaches of privacy

www.simprints.com

Opportunity: Truly customized technologyRisk: Vendor lock-in / no interoperability

www.simprints.com

Conclusion: For every complex problem there is an answer

that is clear, simple, and wrong

1. Learning vs. bias

1. Mass data vs. privacy

1. Customization vs. openness

Discussion:

Bridging the gap of data collection and analytics

Dr Anuraj Shankar, Senior Research Scientist, Harvard T.H. Chan School of Public Health

Open Smart Register Platform

www.smartregister.org

OpenSRP is a scalable Open-source Smart Register Platform that enables FHWs

to digitally register and track the health of all their patients through a single,

user-friendly mobile/tablet application, adaptable to different use cases.

AH Shankar, AI and ML in Health Programs 11 |

Challenges for AI and ML in Public Health

Use case is not clear

The right data is not collected

Assumption that AI/ML can ”clean up” bad data

The “Human Gap”

– Supervisors do not understand the analysis

– Workers don’t trust the conclusions

“Humans help humans, technology is simply a tool”

AH Shankar, AI and ML in Health Programs 12 |

Case study:

Optimize use of screening data to improve efficiency of

community health workers to detect tuberculosis cases

▪ cough

▪ fever

▪ haemoptysis

▪ night sweats

▪ weight loss

▪ fatigue

▪ shortness of breath

▪ chest pain

▪ diabetes

▪ family diabetes

▪ TB exposure

▪ kidney failure

▪ asthma

▪ COPD

▪ HIV

▪ active smoker

Self reported symptoms and conditions by verbal screening

Septiandri AA, Aditiawarman, Tjiong R, Burhan E, Shankar AH (2017). Cost-Sensitive

Machine Learning Classification for mass tuberculosis screening. In Latent Tuberculosis

Infection and Epidemiology of Disease, American Thoracic Society, A3991.

AH Shankar, AI and ML in Health Programs 13 |

Analytic approach to optimize accuracy of diagnosis

Data preparation▪ Cleaning

▪ Standardize data

▪ Deal with missing data

Select additional data▪ Clinic location

Apply analytic approaches▪ Support vector machine

▪ XGboost

▪ Logistic regression

Analytic method Accuracy Sensitivity Specificity

WHO Score

approach

30.5 91.6 20.6

XGboost w/out 40.5 92.9 31.9

Xgboost with clinic 48.2 92.1 41.1

SVM w/out clinic 37.7 94.3 28.3

SVM with clinic 45.9 94.3 38.1

Log Reg w/out clinic 48.8 88.6 42.2

Log Reg with clinic 51.0 93.0 44.2

AH Shankar, AI and ML in Health Programs 14 |

Implications of the analysis for active TB screening

Increasing the specificity by 20% or more, by considering clinic

location, means saving more money and time from doing

unnecessary tests.

This can be achieved without losing any sick people in the

screening process.

Uniform Manifold Approximation and Projection (UMAP)

visualization underscores case associations with clinics

Each dot is a

person screened

Each color is a

clinic

Each purple

dot is a

person

screened

Each yellow

dot is a TB

case

AH Shankar, AI and ML in Health Programs 15 |

Child’s baseline data, weight, height,

weight for age, height for age, weight for

height, exclusive breastfeeding, Vitamin

A (for child)

Immunizations

Child’s

baseline data,

assessment

results

Ris

k

Fla

g

Risk

FlagSDIDTKHOME

INVENTORYKARANA

Vitamin A

(for mother)

Pregnanc

y status

Immunizations

Weight, Height, Head

Circumference Risk Flag

MIDWIF

E APP

ECD

APP

VACCINATOR

APP

NUTRITION

APP

Ris

k

Fla

g

OpenSRP Indonesia cross-cadre data sharing:

enables system wide AI/ML analysis

AH Shankar, AI and ML in Health Programs 16 |

Indonesia – OpenSRP

AI/ML to improve client-centric care

Increase workforce productivity by

identifying incorrect or bogus data,

assess predictors of worker

productivity and client outcomes,

identify ways to improve

coordination among health

workers.

Increase client health by identifying

specific client care seeking

patterns and tendencies. Assessing

what types of interactions are

effective, what locations are high

risk

Clients

Pustu

(Village based health post)

Puskesmas

(Community Health Center)

Polindes (Village-based Community

Birth Center)

ECD CenterPosyandu

(Community Health Extension Clinic)

Discussion:

How is CRS using AI and machine learning in global programs?

Steve Hellen,

Director of ICT4D

and GIS,

Catholic Relief Services

Results: High confidence predictions of those at greatest risk are available 1-2 months in advance. Early warning information is continuously shared with communities to help them plan and target responses.

Solution: A low burden, high frequency data collection protocol gathers information about shocks, household characteristics, and food security. Machine learning algorithms identify predictors of food insecurity and examine household resilience.

Problem: The increased severity of natural disasters exacerbates food insecurity. Severe floods in 2015 followed by drought displaced hundreds of thousands in Malawi. The ability to respond to shocks varies significantly by household. Resilience is difficult to measure or predict.

Predicting Resilience in Malawi

Discussion:

What are the key questions you should ask yourself before starting an AI-based initiative? What are the small things you can do to make a difference?

Neil Sahota, United Nations AI Subject Matter Expert, Faculty at University of California, Irvine, and IBM Master Inventor

Speakers:

Neil Sahota, United Nations AI Subject Matter Expert, Faculty at University of California, Irvine, and IBM Master Inventor

Steve Hellen,

Director of ICT4D

and GIS,

Catholic Relief

Services

Moderator: Sonja Ruetzel, Catholic Relief Services

Dr Anuraj Shankar, Senior Research Scientist, Harvard T.H. Chan School of Public Health

Toby Norman, CEO, SimprintsTechnology

THANK YOU!

See you at the next ICT4D Webinar:

December 12: Digital financial tools for humanitarian response

www.ict4dconference.org

Call for Speaker ends November 30!

11th ICT4D Conference, April 30 to May 3, 2019 – Kampala, Uganda