FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2797485568> ?p ?o ?g. }

• W2797485568 endingPage "e0222907" @default.
• W2797485568 startingPage "e0222907" @default.
• W2797485568 abstract "The Centers for Disease Control and Prevention (CDC) coordinates a labor-intensive process to measure the prevalence of autism spectrum disorder (ASD) among children in the United States. Random forests methods have shown promise in speeding up this process, but they lag behind human classification accuracy by about 5%. We explore whether more recently available document classification algorithms can close this gap. We applied 8 supervised learning algorithms to predict whether children meet the case definition for ASD based solely on the words in their evaluations. We compared the algorithms' performance across 10 random train-test splits of the data, using classification accuracy, F1 score, and number of positive calls to evaluate their potential use for surveillance. Across the 10 train-test cycles, the random forest and support vector machine with Naive Bayes features (NB-SVM) each achieved slightly more than 87% mean accuracy. The NB-SVM produced significantly more false negatives than false positives (P = 0.027), but the random forest did not, making its prevalence estimates very close to the true prevalence in the data. The best-performing neural network performed similarly to the random forest on both measures. The random forest performed as well as more recently available models like the NB-SVM and the neural network, and it also produced good prevalence estimates. NB-SVM may not be a good candidate for use in a fully-automated surveillance workflow due to increased false negatives. More sophisticated algorithms, like hierarchical convolutional neural networks, may not be feasible to train due to characteristics of the data. Current algorithms might perform better if the data are abstracted and processed differently and if they take into account information about the children in addition to their evaluations." @default.
• W2797485568 created "2018-04-24" @default.
• W2797485568 creator A5054997173 @default.
• W2797485568 creator A5062759631 @default.
• W2797485568 creator A5069129539 @default.
• W2797485568 date "2019-09-25" @default.
• W2797485568 modified "2023-10-12" @default.
• W2797485568 title "A comparison of machine learning algorithms for the surveillance of autism spectrum disorder" @default.
• W2797485568 cites W1832693441 @default.
• W2797485568 cites W1969486090 @default.
• W2797485568 cites W1986873494 @default.
• W2797485568 cites W1996820190 @default.
• W2797485568 cites W2064675550 @default.
• W2797485568 cites W2091273188 @default.
• W2797485568 cites W2146292423 @default.
• W2797485568 cites W2159267296 @default.
• W2797485568 cites W2164404874 @default.
• W2797485568 cites W2318996262 @default.
• W2797485568 cites W2570301947 @default.
• W2797485568 cites W2911964244 @default.
• W2797485568 cites W2963626623 @default.
• W2797485568 cites W2964199361 @default.
• W2797485568 cites W4239510810 @default.
• W2797485568 cites W4244767238 @default.
• W2797485568 cites W4247665917 @default.
• W2797485568 cites W4298173112 @default.
• W2797485568 cites W4298369158 @default.
• W2797485568 cites W4300998128 @default.
• W2797485568 doi "https://doi.org/10.1371/journal.pone.0222907" @default.
• W2797485568 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6760799" @default.
• W2797485568 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31553774" @default.
• W2797485568 hasPublicationYear "2019" @default.
• W2797485568 type Work @default.
• W2797485568 sameAs 2797485568 @default.
• W2797485568 citedByCount "18" @default.
• W2797485568 countsByYear W27974855682019 @default.
• W2797485568 countsByYear W27974855682020 @default.
• W2797485568 countsByYear W27974855682021 @default.
• W2797485568 countsByYear W27974855682022 @default.
• W2797485568 countsByYear W27974855682023 @default.
• W2797485568 crossrefType "journal-article" @default.
• W2797485568 hasAuthorship W2797485568A5054997173 @default.
• W2797485568 hasAuthorship W2797485568A5062759631 @default.
• W2797485568 hasAuthorship W2797485568A5069129539 @default.
• W2797485568 hasBestOaLocation W27974855681 @default.
• W2797485568 hasConcept C107673813 @default.
• W2797485568 hasConcept C11413529 @default.
• W2797485568 hasConcept C118552586 @default.
• W2797485568 hasConcept C119857082 @default.
• W2797485568 hasConcept C12267149 @default.
• W2797485568 hasConcept C154945302 @default.
• W2797485568 hasConcept C169258074 @default.
• W2797485568 hasConcept C205778803 @default.
• W2797485568 hasConcept C207201462 @default.
• W2797485568 hasConcept C2778538070 @default.
• W2797485568 hasConcept C41008148 @default.
• W2797485568 hasConcept C50644808 @default.
• W2797485568 hasConcept C52001869 @default.
• W2797485568 hasConcept C64869954 @default.
• W2797485568 hasConcept C71924100 @default.
• W2797485568 hasConcept C81363708 @default.
• W2797485568 hasConceptScore W2797485568C107673813 @default.
• W2797485568 hasConceptScore W2797485568C11413529 @default.
• W2797485568 hasConceptScore W2797485568C118552586 @default.
• W2797485568 hasConceptScore W2797485568C119857082 @default.
• W2797485568 hasConceptScore W2797485568C12267149 @default.
• W2797485568 hasConceptScore W2797485568C154945302 @default.
• W2797485568 hasConceptScore W2797485568C169258074 @default.
• W2797485568 hasConceptScore W2797485568C205778803 @default.
• W2797485568 hasConceptScore W2797485568C207201462 @default.
• W2797485568 hasConceptScore W2797485568C2778538070 @default.
• W2797485568 hasConceptScore W2797485568C41008148 @default.
• W2797485568 hasConceptScore W2797485568C50644808 @default.
• W2797485568 hasConceptScore W2797485568C52001869 @default.
• W2797485568 hasConceptScore W2797485568C64869954 @default.
• W2797485568 hasConceptScore W2797485568C71924100 @default.
• W2797485568 hasConceptScore W2797485568C81363708 @default.
• W2797485568 hasIssue "9" @default.
• W2797485568 hasLocation W27974855681 @default.
• W2797485568 hasLocation W27974855682 @default.
• W2797485568 hasLocation W27974855683 @default.
• W2797485568 hasLocation W27974855684 @default.
• W2797485568 hasLocation W27974855685 @default.
• W2797485568 hasOpenAccess W2797485568 @default.
• W2797485568 hasPrimaryLocation W27974855681 @default.
• W2797485568 hasRelatedWork W2985924212 @default.
• W2797485568 hasRelatedWork W2996933976 @default.
• W2797485568 hasRelatedWork W3107218657 @default.
• W2797485568 hasRelatedWork W3168994312 @default.
• W2797485568 hasRelatedWork W3195168932 @default.
• W2797485568 hasRelatedWork W4221021152 @default.
• W2797485568 hasRelatedWork W4285225238 @default.
• W2797485568 hasRelatedWork W4375930479 @default.
• W2797485568 hasRelatedWork W4377964522 @default.
• W2797485568 hasRelatedWork W4384345534 @default.
• W2797485568 hasVolume "14" @default.
• W2797485568 isParatext "false" @default.
• W2797485568 isRetracted "false" @default.

• next