# Development of Accurate and Interpretable Machine Learning Algorithms for their application in Medicine

> **NIH NIH K08** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2021 · $172,512

## Abstract

Project Summary
 The objective of this proposal is to provide a robust course of training for Gilmer Valdes, PhD, DABR, a
candidate with an excellent foundation in clinical and machine learning research, to enable him to become an
independent investigator. The proposed research aims to address a tradeoff between interpretability and
accuracy of modern machine learning algorithms which limits their use in clinical practice. The candidate’s
central hypothesis is that the current tradeoff is not a law of nature but rather a limitation of current
interpretable machine learning algorithms. Towards proving this hypothesis, the candidate, leading a
multidisciplinary team, have developed unique mathematical frameworks (MediBoost and the Conditional
Interpretable Super Learner) to build interpretable and accurate models. The proposed research will I)
implement and extensively benchmark these frameworks and II) use the algorithms develop to solve three
clinical problems where potentially suboptimal models are currently used to make clinical decisions: 1)
predicting mortality in the Intensive Care Unit, 2) predicting risk of Hospital Acquired Venous
Thromboembolism, 3) predicting which prostate cancer patients benefit the most from adjuvant radiotherapy.
The candidate’s training and research plan, multidisciplinary by nature, takes advantage of the proximity of UC
San Francisco, Stanford and UC Berkeley and proposes a training plan that cannot be easily replicated
elsewhere. Recognizing the multidisciplinary nature of the work proposed, the author will be mentored and
work closely with a stellar committee from three institutions and different scientific areas (Machine Learning,
Biostatistics, Statistics, Hospital Medicine, Cancer Research and Quality Assurance in Medicine): Jerome H.
Friedman PhD (Stanford Statistics Department), Mark Van der Laan PhD (Berkeley Biostatistics and Statistics
Department), Mark Segal (UCSF Epidimiology and Biostatistics Deparments), Andrew Auerbach MD (UCSF
Medicine Department), Felix Y. Feng MD (UCSF Radiation Oncology),and Timothy D. Solberg PhD (UCSF
Radiation Oncology). This committee will be coordinated by Dr Solberg. The candidate also counts with a
strong a multidisciplinary team of collaborators. Successful completion of the proposed research will develop
the next generation of accurate and interpretable Machine Learning algorithms and solve three important
clinical problems where linear models are currently used in clinical settings. This proposal has wide-ranging
implications across the healthcare spectrum. The intermediate-term goal is for the candidate to acquire the
knowledge, technical skills and expertise necessary to submit a successful R01 proposal.

## Key facts

- **NIH application ID:** 10241965
- **Project number:** 5K08EB026500-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Gilmer Valdes
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $172,512
- **Award type:** 5
- **Project period:** 2019-08-07 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10241965

## Citation

> US National Institutes of Health, RePORTER application 10241965, Development of Accurate and Interpretable Machine Learning Algorithms for their application in Medicine (5K08EB026500-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10241965. Licensed CC0.

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