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Aramaki E, Shikata S, Ayaya S, et al. Crowdsourced Identification of Possible Allergy-Associated Factors: Automated Hypothesis Generation and Validation Using Crowdsourcing Services. JMIR Res Protoc. 2017;6(5):e83doi: 10.2196/resprot.5851.
Aramaki, E., Shikata, S., Ayaya, S., & Kumagaya, S. I. (2017). Crowdsourced Identification of Possible Allergy-Associated Factors: Automated Hypothesis Generation and Validation Using Crowdsourcing Services. JMIR research protocols, 6(5), e83. https://doi.org/10.2196/resprot.5851
Aramaki, Eiji, et al. "Crowdsourced Identification of Possible Allergy-Associated Factors: Automated Hypothesis Generation and Validation Using Crowdsourcing Services." JMIR research protocols vol. 6,5 (2017): e83. doi: https://doi.org/10.2196/resprot.5851
Aramaki E, Shikata S, Ayaya S, Kumagaya SI. Crowdsourced Identification of Possible Allergy-Associated Factors: Automated Hypothesis Generation and Validation Using Crowdsourcing Services. JMIR Res Protoc. 2017 May 16;6(5):e83. doi: 10.2196/resprot.5851. PMID: 28512079; PMCID: PMC5449648.
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JMIR Res Protoc. 2017 May 16;6(5):e83. doi: 10.2196/resprot.5851.

Crowdsourced Identification of Possible Allergy-Associated Factors: Automated Hypothesis Generation and Validation Using Crowdsourcing Services.

JMIR research protocols

Eiji Aramaki, Shuko Shikata, Satsuki Ayaya, Shin-Ichiro Kumagaya

Affiliations

  1. Social Computing Lab, Graduate School of Information Science, Nara Institute of Science and Technology, Ikoma, Japan.
  2. Research Center for Advanced Science and Technology, University of Tokyo, Tokyo, Japan.

PMID: 28512079 PMCID: PMC5449648 DOI: 10.2196/resprot.5851

Abstract

BACKGROUND: Hypothesis generation is an essential task for clinical research, and it can require years of research experience to formulate a meaningful hypothesis. Recent studies have endeavored to apply crowdsourcing to generate novel hypotheses for research. In this study, we apply crowdsourcing to explore previously unknown allergy-associated factors.

OBJECTIVE: In this study, we aimed to collect and test hypotheses of unknown allergy-associated factors using a crowdsourcing service.

METHODS: Using a series of questionnaires, we asked crowdsourcing participants to provide hypotheses on associated factors for seven different allergies, and validated the candidate hypotheses with odds ratios calculated for each associated factor. We repeated this abductive validation process to identify a set of reliable hypotheses.

RESULTS: We obtained two primary findings: (1) crowdsourcing showed that 8 of the 13 known hypothesized allergy risks were statically significant; and (2) among the total of 157 hypotheses generated by the crowdsourcing service, 75 hypotheses were statistically significant allergy-associated factors, comprising the 8 known risks and 53 previously unknown allergy-associated factors. These findings suggest that there are still many topics to be examined in future allergy studies.

CONCLUSIONS: Crowdsourcing generated new hypotheses on allergy-associated factors. In the near future, clinical trials should be conducted to validate the hypotheses generated in this study.

©Eiji Aramaki, Shuko Shikata, Satsuki Ayaya, Shin-Ichiro Kumagaya. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 16.05.2017.

Keywords: Tohjisha-Kenkyu; allergy; automatic abduction; crowdsourcing; disease risk; self-support study

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