Ada Thapa, Stellar Chibvunde, Leah Schwartz, Celina Trujillo, Gina Ferrari, Laura Drown, Apoorva Gomber, Paul H Park, Beatrice Matanje, Amos Msekandiana, Chiyembekezo Kachimanga, Gene Bukhman, Todd Ruderman, Alma J Adler
Appropriateness and acceptability of continuous glucose monitoring in people with type 1 diabetes at rural first-level hospitals in Malawi: a qualitative study
BMJ open
objective determine whether unsupervised machine learning identifies traumatic brain injury tbi phenotypes unique clinical profiles pilot selfreported survey data 10000 adults collected centers disease control prevention cdcs national concussion surveillance system ncss respondents selfreported head injury past 12 months n 1364 retained queried injury outcome clinical characteristics unsupervised machine learning algorithm partitioning around medoids pam employed gowers dissimilarity matrix used conduct cluster analysis pam grouped respondents five tbi clusters phenotypes ae phenotype c represented clinically severe tbis higher prevalence symptoms association worse outcomes compared individuals phenotype group tbirelated symptoms individuals phenotype c likely undergo medical evaluation odds ratio 98 95 confidence intervalci 58166 symptoms currently resolved resolved 8 days 106 95ci 62181 likely report least moderate impact social 547 95ci 2241334 work 254 95ci 112572 functioning machine learning used classify patients unique tbi phenotypes research might examine utility classifications supporting clinical diagnosis patient recovery complex health condition