Mihaela van der Schaar ICLR 2020 - Invited Speaker - Machine Learning: Changing the future of healthcare

**Concept Graph & Resume using Claude 3 Opus | Chat GPT4 | Gemini Adv | Llama 3:**

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ICLR 2020 ] --> B[Machine learning in medicine:

new methods needed 1] A --> C[Van der Schaar Lab:

transforming healthcare with AI 2] C --> D[Breast cancer system: integrates data,

interpretability, forecasting, monitoring 3] A --> E[Automated ML creates clinical

analytics across diseases 4] A --> F[Interpretability, trustworthiness, explainability

crucial for medical ML 5] F --> G[Symbolic metamodels: black-box to

white-box, tailored explanations 6] G --> H[Metamodels: discover hypotheses,

inform clinicians, policymakers 7] A --> I[Disease progression: model history,

risk, disease interactions 8] I --> J[Attentive state-space models: complex,

individualized disease trajectories 9] I --> K[Deep diffusion processes: personalized

comorbidity networks over time 10] A --> L[Personalized screening, monitoring

based on individual risk 11] A --> M[Individualized treatment effects ITE:

personalized causal inference 12] M --> N[ITE theory: rates depend on

response surface complexity 13] M --> O[ITE methods: flexible ML,

bias handling, shared representations 14] M --> P[Non-stationary GPs, ARD priors

for ITE with uncertainty 15] M --> Q[GANITE, counterfactual RNNs for

ITE with many treatments 16] A --> R[ML insights transform healthcare

practice, research, industry 17] R --> S[ML augments clinicians, researchers

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data-induced hypotheses 20] S --> V[Pharma: ML identifies

individualized treatments 20] A --> W[Effective learning integrates data

with expertise using ML 21] A --> X[Van der Schaar Lab: global

collaboration, empowering ML tools 22] D --> Y[Autoprognosis outperforms current

cancer survival models 23] G --> Z[Symbolic metamodels: interpretable

risk equations, transparency 24] Z --> AA[Inverse metamodels inform

risk reduction strategies 25] J --> AB[Individualized disease trajectories

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ICLR 2020 ] --> B[Machine learning in medicine:

new methods needed 1] A --> C[Van der Schaar Lab:

transforming healthcare with AI 2] C --> D[Breast cancer system: integrates data,

interpretability, forecasting, monitoring 3] A --> E[Automated ML creates clinical

analytics across diseases 4] A --> F[Interpretability, trustworthiness, explainability

crucial for medical ML 5] F --> G[Symbolic metamodels: black-box to

white-box, tailored explanations 6] G --> H[Metamodels: discover hypotheses,

inform clinicians, policymakers 7] A --> I[Disease progression: model history,

risk, disease interactions 8] I --> J[Attentive state-space models: complex,

individualized disease trajectories 9] I --> K[Deep diffusion processes: personalized

comorbidity networks over time 10] A --> L[Personalized screening, monitoring

based on individual risk 11] A --> M[Individualized treatment effects ITE:

personalized causal inference 12] M --> N[ITE theory: rates depend on

response surface complexity 13] M --> O[ITE methods: flexible ML,

bias handling, shared representations 14] M --> P[Non-stationary GPs, ARD priors

for ITE with uncertainty 15] M --> Q[GANITE, counterfactual RNNs for

ITE with many treatments 16] A --> R[ML insights transform healthcare

practice, research, industry 17] R --> S[ML augments clinicians, researchers

with powerful targeted tools 18] S --> T[Clinicians: ML for actionable,

patient-tailored intelligence 19] S --> U[Researchers: ML generates

data-induced hypotheses 20] S --> V[Pharma: ML identifies

individualized treatments 20] A --> W[Effective learning integrates data

with expertise using ML 21] A --> X[Van der Schaar Lab: global

collaboration, empowering ML tools 22] D --> Y[Autoprognosis outperforms current

cancer survival models 23] G --> Z[Symbolic metamodels: interpretable

risk equations, transparency 24] Z --> AA[Inverse metamodels inform

risk reduction strategies 25] J --> AB[Individualized disease trajectories

vs one-size-fits-all 26] L --> AC[Personalized cancer screening

schedules vs preset times 27] M --> AD[ITE moves beyond average

effects to optimize treatments 28] N --> AE[ITE difficulty depends on

response surface complexity 29] R --> AF[ML will augment clinicians,

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**Resume: **

**1.-**Machine learning for medicine is challenging due to poorly-defined problems, solutions and verification. New problem formulations and methods are needed.

**2.-**The van der Schaar Lab aims to transform healthcare with new machine learning and AI tools to address complex problems.

**3.-**A breast cancer decision support system was developed using autoprognosis (integrates data), InVase (interpretability), attentive state-space models (forecasts), and personalized monitoring.

**4.-**Automated machine learning methods can craft clinical analytics at scale across diseases. Structure kernel learning conquers the curse of dimensionality.

**5.-**Interpretability, trustworthiness and explainability are key for actionable machine learning models in medicine. Different users need tailored interpretations.

**6.-**Symbolic metamodels turn black-box models into interpretable white-box models, enabling forward and backward explanations for various users.

**7.-**Metamodels allow robust discovery of data-induced hypotheses from models. They help clinicians understand predictions and policymakers design screening programs.

**8.-**Disease progression should be modeled with history, individualized risk, and interactions between diseases, not just Markov models.

**9.-**Attentive state-space models combine probabilistic hidden Markov models with RNNs to model complex non-stationary individualized disease trajectories.

**10.-**Dynamic, personalized comorbidity networks are learned using deep diffusion processes to model disease interactions over time.

**11.-**Personalized screening and monitoring policies should be used instead of one-size-fits-all, based on individual patient risk.

**12.-**Estimating individualized treatment effects (ITE) moves from average effects to personalized causal inference. It's a complex problem due to unobserved counterfactuals.

**13.-**A Bayesian nonparametric theory for ITE estimation finds minimax rates depend on response surface complexity (sparsity, smoothness), not selection bias asymptotically.

**14.-**For large samples, flexible ML tunes hyperparameters; for small samples, selection bias handling and shared representations are key.

**15.-**Non-stationary Gaussian processes with ARD priors on response surfaces enable ITE estimation with limited data and uncertainty measures.

**16.-**GANITE uses GANs for ITE estimation with many treatments. Counterfactual recurrent networks optimize treatment plans over time.

**17.-**Machine learning provides insights on diseases, treatments, prevention, diagnosis, resource allocation, and care pathways to transform healthcare.

**18.-**We are at the beginning of using ML to augment clinicians and medical researchers with powerful targeted tools.

**19.-**Clinicians can use co-designed ML tools for actionable intelligence tailored to each patient to improve practice.

**20.-**Researchers can use ML to generate data-induced hypotheses. Pharma can identify individualized treatments with ML.

**21.-**Effective learning at scale integrates time series, omics, imaging data with clinical expertise using powerful ML methods.

**22.-**The van der Schaar Lab collaborates globally with clinicians to provide empowering ML tools and seeks collaborators and students.

**23.-**Autoprognosis automated ML significantly outperforms current models like PREDICT for breast cancer survival prediction, especially in young and old.

**24.-**Symbolic metamodels provide interpretable risk equations rivaling autoprognosis' accuracy that meet clinical requirements for transparency.

**25.-**Inverse metamodel equations can inform patients how to reduce risk through modifiable variables and lifestyle changes.

**26.-**One-size-fits-all disease progression ignores important factors like genetics. Individualized trajectories are modeled using attentive state space models.

**27.-**Sending post-op cancer patients for follow-up at preset times ignores individual risk. Personalized screening schedules are needed.

**28.-**Average treatment effects ignore individual differences. Estimating ITEs moves toward personalized causal inference to optimize treatments.

**29.-**ITE estimation theory shows problem difficulty depends on treatment response surface complexity. Shared data representations help with limited data.

**30.-**Machine learning will augment clinicians and scientists to improve healthcare at the individual patient level through collaboration and new tools.

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