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Artificial Intelligence in Health                          Benchmarking ML imputation in mental health surveys



            volunteers of the SPARK Clinical Site Network and SFARI   results from an online questionnaire completed by 157 366
            for their invaluable contributions.                   participants: A reanalysis. BJPsych Open. 2020;6:e18.
                                                                  doi: 10.1192/bjo.2019.100
            Funding
                                                               3.   Ramirez  AH,  Sulieman  L,  Schlueter  DJ,  et al.  The  all  of
            This work is supported by Southern California         Us research program: Data quality, utility, and diversity.
            Environmental Health Sciences Center pilot grant      Patterns (N Y). 2022;3:100570.
            from  NIH/NIEHS,  grant  number  P30ES007048  (Rob      doi: 10.1016/j.patter.2022.100570
            McConnell), and The Tobacco-Related Disease Research
            Program, grant number T32IR5216 (Xuejuan Jiang) and   4.   Chesnut SR, Wei T, Barnard-Brak L, Richman DM. A meta-
            NIH/NIA, grant number 1RF1AG076124-01A1 (Hussein      analysis of the social communication questionnaire:
                                                                  Screening  for  autism  spectrum  disorder.  Autism.
            Yassine).
                                                                  2017;21:920-928.
            Conflict of interest                                  doi: 10.1177/1362361316660065

            The authors declare that they have no conflicts of interest.  5.   Hooker  JL,  Dow  D,  Morgan  L,  Schatschneider  C,
                                                                  Wetherby  AM. Psychometric  analysis  of the  repetitive
            Author contributions                                  behavior scale-revised using confirmatory factor analysis in
                                                                  children with autism. Autism Res. 2019;12:1399-1410.
            Conceptualization: Chang Shu
            Formal analysis: Preethi Prakash                      doi: 10.1002/aur.2159
            Investigation: Preethi Prakash, Chang Shu          6.   Van Damme T, Vancampfort D, Thoen A, Sanchez CPR, van
            Methodology: Preethi Prakash, Kelly Street, Yufeng Shen,   Biesen D. Evaluation of the Developmental Coordination
               Chang Shu                                          Questionnaire (DCDQ) as a screening instrument for
            Writing–original draft: Preethi Prakash, Chang Shu    co-occurring motor problems in children with autism
            Writing–review & editing:  Kelly  Street,  Shrikanth   spectrum disorder. J Autism Dev Disord. 2022;52:4079-4088.
               Narayanan, Bridget A. Fernandez, Yufeng Shen,      doi: 10.1007/s10803-021-05285-1
               Chang Shu                                       7.   Jebb AT, Ng V, Tay L. A review of key likert scale development
                                                                  advances: 1995-2019. Front Psychol. 2021;12:637547.
            Ethics approval and consent to participate
                                                                  doi: 10.3389/fpsyg.2021.637547
            Not applicable.
                                                               8.   Mirzaei A, Carter SR, Patanwala AE, Schneider CR. Missing
            Consent for publication                               data in surveys: Key concepts, approaches, and applications.
                                                                  Res Soc Adm Pharm. 2022;18:2308-2316.
            Not applicable.
                                                                  doi: 10.1016/j.sapharm.2021.03.009
            Availability of data                               9.   Mack C, Su Z, Westreich D. Managing Missing Data in Patient
                                                                  Registries: Addendum to Registries for Evaluating Patient
            SPARK  Phenotype  Dataset  is  accessible  through an   Outcomes:  A  User’s  Guide,  Third  Edition.  Rockville,  MD:
            application at SFARI Base (https://base.sfari.org). All   Agency for Healthcare Research and Quality (US); 2018.
            software used in this study is publicly available. The code
            for simulations and analysis can be found at https://github.  10.  Khan SI, Hoque ASM. SICE: An improved missing data
                                                                  imputation technique. J Big Data. 2020;7:37.
            com/AprilShuLab/MissingDataImputation.
                                                                  doi: 10.1186/s40537-020-00313-w
            Further disclosure                                 11.  Phiwhorm K, Saikaew C, Leung CK, Polpinit P, Saikaew KR.

            The  paper   has  been   uploaded  to  medRxiv        Adaptive multiple imputations of missing values using the
            (doi: 10.1101/2024.05.13.24307231).                   class center. J Big Data. 2022;9:52.
                                                                  doi: 10.1186/s40537-022-00608-0
            References
                                                               12.  De Goeij MCM, van Diepen M, Jager KJ,  Tripepi G,
            1.   Feliciano P, Daniels AM, Snyder LG, et al. SPARK: A US   Zoccali C, Dekker FW. Multiple imputation: Dealing with
               cohort  of  50,000  families  to  accelerate  autism  research.   missing data. Nephrol Dial Transplant. 2013;28:2415-2420.
               Neuron. 2018;97:488-493.
                                                                  doi: 10.1093/ndt/gft221
               doi: 10.1016/j.neuron.2018.01.015
                                                               13.  Van Buuren S, Groothuis-Oudshoorn K. Mice: Multivariate
            2.   Davis KAS, Coleman JRI, Adams M,  et al. Mental health   imputation by chained equations in R.  J  Stat Softw.
               in UK Biobank  -  development, implementation and   2011;45:1-67.


            Volume 2 Issue 1 (2025)                         91                               doi: 10.36922/aih.4406
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