New article in Journal of the American Medical Informatics Association

A new publication from Dr. Ameen Eetemadi and Dr. Ilias Tagkopoulos, about Algorithmic Lifestyle Optimization.

Algorithmic lifestyle optimization (ALO). ALO is designed to guide individuals in rapid discovery of lifestyle interventions (LIs) that are effective (potent) for them amongst many candidate LIs, for achieving a target health outcome. First, it builds the constrained adaptive group testing (CAGT) catalog, which is a lookup table for finding the maximum number of rounds needed by the CAGT algorithm for identifying between minimum l and maximum h number of potent LIs amongst n candidate LIs. Second, it partitions the LIs into disjoint sets given the potency probability of each LI, and determines whether the first step of the CAGT algorithm involves following all the LIs in a given set. These probabilities can be estimated from population wide studies that report the percentage of individuals that achieve the target health outcome following each LI. Third, the suggested LIs by the CAGT algorithm is followed by the individual in subsequent rounds. The CAGT algorithm stops once the potency of the LIs in each set is identified.

Abstract: A hallmark of personalized medicine and nutrition is to identify effective treatment plans at the individual level. Lifestyle interventions (LIs), from diet to exercise, can have a significant effect over time, especially in the case of food intolerances and allergies. The large set of candidate interventions, make it difficult to evaluate which intervention plan would be more favorable for any given individual. In this study, we aimed to develop a method for rapid identification of favorable LIs for a given individual. We have developed a method, algorithmic lifestyle optimization (ALO), for rapid identification of effective LIs. At its core, a group testing algorithm identifies the effectiveness of each intervention efficiently, within the context of its pertinent group. Evaluations on synthetic and real data show that ALO is robust to noise, data size, and data heterogeneity. Compared to the standard of practice techniques, such as the standard elimination diet (SED), it identifies the effective LIs 58.9%–68.4% faster when used to discover an individual’s food intolerances and allergies to 19–56 foods.

Reference: Eetemadi, Ameen, Ilias Tagkopoulos. “Algorithmic lifestyle optimization.” Journal of the American Medical Informatics Association (2022). doi: 10.1093/jamia/ocac186 (link), (pdf), (GitHub coming soon)