Diet Planning with Machine Learning: Teacher-forced REINFORCE for Composition Compliance with Nutrition Enhancement

Lee, Changhun; Kim, Soohyeok; Lim, Chiehyeon; Kim, Jayun; Kim, Yeji; Jung, Minyoung

doi:10.1145/3447548.3467201

Cited by 7 publications

(1 citation statement)

References 17 publications

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“…[18] propose an original procedure based on combinatorial K-Means clustering designed to find the most appropriate clinical variables to efficiently separate similar patients into groups diagnosed with diabetes mellitus type 2 (DMT2) and underlying diseases. [19] use combinatorial optimization for dataset creation, used for diet planning and dietary healthcare with machine learning. [20] introduce a machine-learning-based prediction model capable of optimal treatment selection for hepatitis C virus (HCV).…”

Section: Related Workmentioning

confidence: 99%

Drug-Treatment Generation Combinatorial Algorithm Based on Machine Learning and Statistical Methodologies

Gishyan¹

2023

OJAppS

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Finding out the desired drug combinations is a challenging task because of the number of different combinations that exist and the adversarial effects that may arise. In this work, we generate drug combinations over multiple stages using distance calculation metrics from supervised learning, clustering, and a statistical similarity calculation metric for deriving the optimal treatment sequences. The combination generation happens for each patient based on the characteristics (features) observed during each stage of treatment. Our approach considers not the drug-to-drug (one-to-one) effect, but rather the effect of group of drugs with another group of drugs. We evaluate the combinations using an FNN model and identify future improvement directions.

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Section: Related Workmentioning

confidence: 99%