text: 78,210,1308492596285779969,1140066312380567553,Bryan Wilder,"I'm excited to share a project long in the making. We designed algorithms to find influential nodes in social networks, applied to HIV prevention for homeless youth. A trial with 713 youth over 2 years showed significant benefits. Paper just posted, <LINK> (1/9) Homeless youth have up to 10x HIV prevalence vs general population. One intervention is to recruit peer leaders from the youth to promote protective behaviors. But how to choose the most influential peer leaders? (2/9) There's tons of computer science work on finding influential nodes in a social network (""influence maximization""). But, mostly targeted at advertising/online social networks...not easily applicable to community health. (3/9) What are the new challenges? In a word, data. Who's connected to who? How will information diffuse? None of this is known. Gathering network structure = time consuming, face to face interviews with youth. (4/9) We developed algorithms to efficiently subsample the network, only requiring about 20% of the effort in data collection. Then, we designed a robust optimization algorithm to identify influential nodes even under uncertainty. (5/9) It worked in simulation but what about reality? We ran a clinical trial at centers for homeless youth in LA. Trial compared three arms: interventions with our algorithm, selecting highest-degree youth (standard baseline), and no intervention. 713 youth total over 2 years. (6/9) The results just out: in the algorithm arm, statistically significant reduction in key outcome, condomless anal sex (OR = 0.69). No significant change for the other arms. AI helped! (7/9) Key takeaways in the paper (<LINK>): simple, robust, data-efficient algorithms are critical for public health domains. Beyond the algorithm though, always requires community trust. (8/9) It was truly amazing to work with this close-knit team of social work/AI researchers: @MilindTambe_AI, @EricRicePhD, @onasch_vera, Graham Diguiseppe, @AmulyaYadav19 and many more at @CAIS_USC and @HCRCS. (9/9)",https://arxiv.org/abs/2009.09559,"Youth experiencing homelessness (YEH) are subject to substantially greater risk of HIV infection, compounded both by their lack of access to stable housing and the disproportionate representation of youth of marginalized racial, ethnic, and gender identity groups among YEH. A key goal for health equity is to improve adoption of protective behaviors in this population. One promising strategy for intervention is to recruit peer leaders from the population of YEH to promote behaviors such as condom usage and regular HIV testing to their social contacts. This raises a computational question: which youth should be selected as peer leaders to maximize the overall impact of the intervention? We developed an artificial intelligence system to optimize such social network interventions in a community health setting. We conducted a clinical trial enrolling 713 YEH at drop-in centers in a large US city. The clinical trial compared interventions planned with the algorithm to those where the highest-degree nodes in the youths' social network were recruited as peer leaders (the standard method in public health) and to an observation-only control group. Results from the clinical trial show that youth in the AI group experience statistically significant reductions in key risk behaviors for HIV transmission, while those in the other groups do not. This provides, to our knowledge, the first empirical validation of the usage of AI methods to optimize social network interventions for health. We conclude by discussing lessons learned over the course of the project which may inform future attempts to use AI in community-level interventions. ","Clinical trial of an AI-augmented intervention for HIV prevention in