<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>11</volume><submitter>Mukherjee D</submitter><pubmed_abstract>Over 250 million children in developing countries are at risk of not achieving their developmental potential, and unlikely to receive timely interventions because existing developmental assessments that help identify children who are faltering are prohibitive for use in low resource contexts. To bridge this "detection gap," we developed a tablet-based, gamified cognitive assessment tool named DEvelopmental assessment on an E-Platform (DEEP), which is feasible for delivery by non-specialists in rural Indian households and acceptable to all end-users. Here we provide proof-of-concept of using a supervised machine learning (ML) approach benchmarked to the Bayley's Scale of Infant and Toddler Development, 3rd Edition (BSID-III) cognitive scale, to predict a child's cognitive development using </pubmed_abstract><journal>Frontiers in psychology</journal><pagination>1202</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC7299081</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Proof of Concept of a Gamified DEvelopmental Assessment on an E-Platform (DEEP) Tool to Measure Cognitive Development in Rural Indian Preschool Children.</pubmed_title><pmcid>PMC7299081</pmcid><pubmed_authors>Thiagarajan TC</pubmed_authors><pubmed_authors>Bhavnani S</pubmed_authors><pubmed_authors>Verma D</pubmed_authors><pubmed_authors>Dasgupta J</pubmed_authors><pubmed_authors>Divan G</pubmed_authors><pubmed_authors>Parameshwaran D</pubmed_authors><pubmed_authors>Swaminathan A</pubmed_authors><pubmed_authors>Patel V</pubmed_authors><pubmed_authors>Sharma K</pubmed_authors><pubmed_authors>Mukherjee D</pubmed_authors></additional><is_claimable>false</is_claimable><name>Proof of Concept of a Gamified DEvelopmental Assessment on an E-Platform (DEEP) Tool to Measure Cognitive Development in Rural Indian Preschool Children.</name><description>Over 250 million children in developing countries are at risk of not achieving their developmental potential, and unlikely to receive timely interventions because existing developmental assessments that help identify children who are faltering are prohibitive for use in low resource contexts. To bridge this "detection gap," we developed a tablet-based, gamified cognitive assessment tool named DEvelopmental assessment on an E-Platform (DEEP), which is feasible for delivery by non-specialists in rural Indian households and acceptable to all end-users. Here we provide proof-of-concept of using a supervised machine learning (ML) approach benchmarked to the Bayley's Scale of Infant and Toddler Development, 3rd Edition (BSID-III) cognitive scale, to predict a child's cognitive development using </description><dates><release>2020-01-01T00:00:00Z</release><publication>2020</publication><modification>2026-07-15T21:50:01.577Z</modification><creation>2020-07-01T07:09:26Z</creation></dates><accession>S-EPMC7299081</accession><cross_references><pubmed>32587551</pubmed><doi>10.3389/fpsyg.2020.01202</doi></cross_references></HashMap>