Knowledge Vault 2/61 - ICLR 2014-2023
Aisha Walcott-Bryant ICLR 2020 - Invited Speaker - AI + Africa = Global Innovation
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Concept Graph & Resume using Claude 3 Opus | Chat GPT4 | Gemini Adv | Llama 3:

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ICLR 2020] --> A[Walcott-Bride: IBM Research Africa
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1.-Ayesha Walcott-Bride, a research scientist at IBM Research Africa in Nairobi, Kenya, discusses Africa's AI innovations for global impact.

2.-Her background includes degrees from Clark Atlanta University and MIT, and projects in Africa that influenced her decision to pursue research abroad.

3.-IBM Research Africa focuses on addressing Africa's grand challenges, transforming society, and having global impact through partnerships and AI/tech.

4.-Africa's rich diversity offers opportunities to address complex problems like optimal energy mix, crisis recovery, climate change impacts on disease spread.

5.-The talk focuses on IBM's AI research for global health, specifically disease intervention planning and characterizing subpopulations to understand health outcomes.

6.-Despite progress, malaria still kills a child every 30 seconds, with 90% of cases in sub-Saharan Africa. Treatment and prevention are possible.

7.-Malaria decision-makers must consider many complex factors and intervention models to reach targets. The intervention space is vast.

8.-IBM uses AI methods well-suited for the complex malaria intervention models to find more cost-effective intervention plans compared to current/expert plans.

9.-Multi-step intervention planning using reinforcement learning finds intervention paths over time that are more cost-effective for reaching malaria prevalence targets.

10.-Combining multiple malaria models provides a better uncertain future description. A case study applies this in Uganda to minimize deaths.

11.-The malaria research has been extended with deep RL, constraint optimization, explainability, and a trusted decision platform for optimal context-relevant intervention plans.

12.-The platform allows collaboration between decision-makers, AI developers and domain experts. It's replicable and scalable to other diseases like COVID-19, HIV/AIDS.

13.-For COVID-19, task forces are using common interventions to contain disease, limit disruption, reduce mortality, constrained by budgets, supplies, health workers.

14.-The malaria control work leveraging complex domain models and AI to explore vast intervention spaces directly applies to the COVID-19 pandemic.

15.-Characterizing subpopulations can enable targeted health interventions for bigger impact. Two examples are given in family planning and maternal/child health.

16.-In family planning, 214+ million women who want to avoid pregnancy are not using modern contraceptives. Discontinuation signals dissatisfaction.

17.-Discriminatory sub-sequence mining extends prefix span to find contraceptive use patterns unique to cohorts who discontinued for different reasons across countries.

18.-Results show common discriminatory sub-sequences for women who discontinued due to health concerns vs other reasons in Kenya, Nigeria, Ghana, Burkina Faso.

19.-Ethiopia lacked discriminatory patterns. Nigeria had data issues. Further analyses reveal patterns for discontinuation due to pregnancy while using contraceptives.

20.-The sub-sequence mining outputs inform causal analyses and predictive discontinuation models. An interactive dashboard explores family planning across countries.

21.-In maternal/child health, 99% of maternal deaths are in developing countries, child mortality is 15x higher in sub-Saharan Africa. Most are preventable.

22.-Predictive models found risk factors for maternal and child mortality as markers for vulnerability. Stratification identifies disproportionately susceptible subpopulations.

23.-In Nigeria, mothers with 3+ births in 5 years outside south-south had 6x increased odds of under-5 mortality (48%) vs 13% average.

24.-Protected populations had 1 birth in 5 years, age under 40, household size 3+, with 1/3 decreased odds of under-5 mortality.

25.-Automatic stratification efficiently explores feature combinations to find extreme vulnerability and protection cases, exploiting optimization function properties for polynomial time.

26.-Next steps: subset complexity penalties, explainable targeted interventions on modifiable factors, clinical applications in MNCH, HIV, COVID-19 susceptible populations.

27.-Interactive dashboards help understand subpopulations and inform targeted interventions. The work requires close collaborations with organizations and academia in Africa.

28.-IBM Research Africa also explores AI for climate, food security, financial inclusion, water access, and novel core AI algorithm development.

29.-These interconnected problems motivate cross-domain AI insights. IBM has several AI activities and demos at ICLR 2020.

30.-Africa's diversity makes it a parallel for AI innovations that can have global impact in understanding subpopulations and disease intervention planning.

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