The idea behind weight manipulation is simple: as long as you'll be in a caloric deficit you'll lose weight and vice-versa: if you'll be in a caloric surplus you'll gain weight. The algorithm will use as input the foods that I want to consume in a given day and some data about me (e.g current weight, desired weight, activity level, goal (loose/gain weight), macronutrient ratio). We consider that every weight represents the quantity of food (in grams) that you should eat, from the corresponding food vector input. The idea is that the resulted macronutrients for the diet based on our weights to be as close as possible to your ideal diet's macronutrients. Then, I have used a free macronutrient calculator, which based on some personal information (e.g age, sex, current weight, desired weight, level of activity) told me that it will be ideal to eat 175 grams of protein, 359 of carbs, and 101 of fats.

Words: 4252 Keywords: self-monitoring data, type 2 diabetes, machine learning, phenotyping, precision medicine ABSTRACT Objective: To evaluate unsupervised clustering methods for identifying individual-level behavioral-clinical phenotypes that relate personal biomarkers and behavioral traits in type 2 diabetes (T2DM) self-monitoring data. Materials and Methods: We used hierarchical clustering (HC) to identify groups of meals with similar nutrition and glycemic impact for 6 individuals with T2DM who collected self-monitoring data. We evaluated clusters on: 1) correspondence to gold standards generated by certified diabetes educators (CDEs) for 3 participants; 2) face validity, rated by CDEs, and 3) impact on CDEs' ability to identify patterns for another 3 participants. Results: Gold standard (GS) included 9 patterns across 3 participants. Of these, all 9 were rediscovered using HC: 4 GS patterns were consistent with patterns identified by HC (over 50% of meals in a cluster followed the pattern); another 5 were included as subgroups in broader clusers. After reviewing clusters, CDEs identified patterns that were more consistent with data (70% reduction in contradictions between patterns and participants' records). Discussion: Hierarchical clustering of blood glucose and macronutrient consumption appears suitable for discovering behavioral-clinical phenotypes in T2DM. Most clusters corresponded to gold standard and were rated positively by CDEs for face validity. Cluster visualizations helped CDEs identify more robust patterns in nutrition and glycemic impact, creating new possibilities for visual analytic solutions. Conclusion: Machine learning methods can use diabetes self-monitoring data to create personalized behavioral-clinical phenotypes, which may prove useful for delivering personalized medicine.