ArticleNumerical algebra, control and optimization2025
A novel machine-learning based optimization: identifying new treatment regimens for tuberculosis.
Article in Numerical algebra, control and optimization, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- The eight pillars of within-host tuberculosis modelling.Frontiers in immunology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Identifying optimization in a system from any discipline requires identifying ways to either maximize or minimize objectives of interest-or even trade-offs between these choices where goals must be balanced. Traditionally, optimal control theory has been used specifically for applications that are represented by ordinary differential equations. Here, we introduce a new approach to optimization that can be applied not only to ordinary differential-equation based systems, but importantly to other more complex models, such as agent-based model systems. To this end, we create a novel machine learning optimization pipeline that uses a Kriging-based surrogate model to predict objective functions. We use a Pareto optimization algorithm to identify regimens that maximize improvement to the predicted optimal set and then rank these findings. As an example, we apply this to a model system that captures drug treatment of hosts during infection with
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.