The Healthy AI Lab at Chalmers University of Technology conducts academic research into machine learning and artificial intelligence motivated by challenges in healthcare: causality, decision-making and clinical applications. The lab is led by Fredrik Johansson.
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IncomeSCM: From tabular data set to time-series simulator and causal estimation benchmark
NeurIPS 2024 (To appear) [Paper URL]Sep 29, 2024
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Active Preference Learning for Ordering Items In- and Out-of-Sample
NeurIPS 2024 [Paper URL]Sep 29, 2024
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Pure Exploration in Bandits with Linear Constraints
AISTATS 2024 [Paper URL]Feb 4, 2024
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MINTY: Rule-based Models that Minimize the Need for Imputing Features with Missing Values
AISTATS 2024 [Paper URL]Oct 28, 2023
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Patterns in the Sequential Treatment of Patients With Rheumatoid Arthritis Starting a Biologic or Targeted Synthetic Disease-Modifying Antirheumatic Drug: 10-Year Experience From a US-Based Registry
ACR [Paper URL]Oct 26, 2023
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Fast Treatment Personalization with Latent Bandits in Fixed-Confidence Pure Exploration
TMLRPersonalizing treatments for patients often involves a period of trial-and-error search until an optimal choice is found. To minimize suffering and other costs, it is critical to make this process as short as possible. When treatments have primarily short-term effects, search can be performed with multi-armed bandits (MAB), but these typically require long exploration periods to guarantee optimality. In this work, we design MAB algorithms which provably identify optimal treatments quickly by leveraging prior knowledge of the types of decision processes (patients) we can encounter, in the form of a latent variable model. We present two algorithms, the Latent LP-based Track and Stop (LLPT) explorer and the Divergence Explorer for this setting: fixed-confidence pure-exploration latent bandits. We give a lower bound on the stopping time of any algorithm which is correct at a given certainty level, and prove that the expected stopping time of the LLPT Explorer matches the lower bound in the high-certainty limit. Finally, we present results from an experimental study based on realistic simulation data for Alzheimer's disease, demonstrating that our formulation and algorithms lead to a significantly reduced stopping time.May 3, 2023
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Time series of satellite imagery improve deep learning estimates of neighborhood-level poverty in Africa
IJCAI 2023 - AI for Social GoodTo combat poor health and living conditions, policymakers in Africa require temporally and geographically granular data measuring economic well-being. Machine learning (ML) offers a promising alternative to expensive and time-consuming survey measurements by training models to predict economic conditions from freely available satellite imagery. However, previous efforts have failed to utilize the temporal information available in earth observation (EO) data, which may capture developments important to standards of living. In this work, we develop an EO-ML method for inferring neighborhood-level material-asset wealth using multi-temporal imagery and recurrent convolutional neural networks. Our model outperforms state-of-the-art models in several aspects of generalization, explaining 72% of the variance in wealth across held-out countries and 75% held-out time spans. Using our geographically and temporally aware models, we created spatio-temporal material-asset data maps covering the entire continent of Africa from 1990 to 2019, making our data product the largest dataset of its kind. We showcase these results by analyzing which neighborhoods are likely to escape poverty by the year 2030, which is the deadline for when the Sustainable Development Goals (SDG) are evaluated.Apr 25, 2023
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Sharing pattern submodels for prediction with missing values
Proceedings of the AAAI Conference on Artificial Intelligence, 37(8), 9882-9890. [Paper URL]Oct 20, 2022
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NeurIPS 2022: Efficient learning of nonlinear prediction models with time-series privileged information
NeurIPS 2022 [Paper URL]Sep 15, 2022
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Seminar: Efficient learning of nonlinear prediction models with time-series privileged information
Chalmers machine learning seminarsSep 12, 2022
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Practicality of generalization guarantees for unsupervised domain adaptation with neural networks
TMLR [Paper URL]Sep 2, 2022
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Case-Based Off-Policy Evaluation Using Prototype Learning
UAI 2022 [Paper URL]Jul 27, 2022
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ADCB: An Alzheimer’s disease simulator for benchmarking observational estimators of causal effects
CHIL 2022 [Paper URL]Apr 7, 2022
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Learning using privileged time series information
AISTATS 2022 [Paper URL]Feb 1, 2022
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Predicting progression & cognitive decline in amyloid-positive patients with Alzheimer's disease
Alzheimer's Research & Therapy [Paper URL]Mar 15, 2021
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Tocilizumab treatment for rheumatoid arthritis
As of September 2020, the division of Data Science & AI have four brand new PhD students with Fredrik Johansson as main advisor. This marks the start of the Healthy AI lab at Chalmers University of Technology. Adam Breitholtz, Newton Mwai, Lena Stempfle and Anton Matsson begin their doctoral studies, funded by the Wallenberg AI, Autonomous Systems and Software programme.Feb 1, 2021
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Learning to search efficiently for causally near-optimal treatments
NeurIPS 2020 [Paper URL]Dec 31, 2020
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A new group: The Healthy AI lab!
As of September 2020, the division of Data Science & AI have four brand new PhD students with Fredrik Johansson as main advisor. This marks the start of the Healthy AI lab at Chalmers University of Technology. Adam Breitholtz, Newton Mwai, Lena Stempfle and Anton Matsson begin their doctoral studies, funded by the Wallenberg AI, Autonomous Systems and Software programme.Sep 1, 2020