This paper is in the following e-collection/theme issue:

Infoveillance and Social Listening (258) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1089) Machine Learning (1769) Infodemiology and Infoveillance (1468) Public (e)Health, Digital Epidemiology and Public Health Informatics (871)

Published on in Vol 3 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/42721, first published .
Estimating Rare Disease Incidences With Large-scale Internet Search Data: Development and Evaluation of a Two-step Machine Learning Method

Estimating Rare Disease Incidences With Large-scale Internet Search Data: Development and Evaluation of a Two-step Machine Learning Method

Estimating Rare Disease Incidences With Large-scale Internet Search Data: Development and Evaluation of a Two-step Machine Learning Method

Authors of this article:

Jiayu Li1 Author Orcid Image ;   Zhiyu He1 Author Orcid Image ;   Min Zhang1 Author Orcid Image ;   Weizhi Ma2 Author Orcid Image ;   Ye Jin3 Author Orcid Image ;   Lei Zhang4 Author Orcid Image ;   Shuyang Zhang5 Author Orcid Image ;   Yiqun Liu1 Author Orcid Image ;   Shaoping Ma1 Author Orcid Image
Article Authors Cited by (2) Tweetations (2) Metrics

Journals

  1. Wang J, Ma F. Federated learning for rare disease detection: a survey. Rare Disease and Orphan Drugs Journal 2023;2(4) View
  2. Hindelang M, Sitaru S, Zink A. Tracking Public Interest in Rare Diseases and Eosinophilic Disorders in Germany: Web Search Analysis. JMIR Infodemiology 2025;5:e69040 View