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Skip search results from other journals and go to results- 4 JMIR Public Health and Surveillance
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Current Status and Future Directions of Ferroptosis Research in Breast Cancer: Bibliometric Analysis
forecast
Interact J Med Res 2025;14:e66286
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Once identified, weekly pain trajectories could be forecast. People living with chronic pain have reported that a pain forecast would reduce unpredictability and could be used to support planning daily activities, such as shopping, chores, and social participation [20,21].
JMIR Mhealth Uhealth 2024;12:e48582
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A Roadmap for Using Causal Inference and Machine Learning to Personalize Asthma Medication Selection
forecast
JMIR Med Inform 2024;12:e56572
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Participatory Surveillance for COVID-19 Trend Detection in Brazil: Cross-sectional Study
The combination model outperformed the baseline model for the 14-day forecast by up to 2.7%. The results for the 7-day forecast were ambiguous; while there were improvements of up to 4.1%, only 2 out of the 3 cities showed reduced RMSEs.
Forecasting errors using different models for 1-day, 7-day, and 14-day forecasts in the city of Teresina.
a The models used n=13 lagged components as independent variables.
b RMSE: root mean squared error.
c MAE: mean absolute error.
JMIR Public Health Surveill 2023;9:e44517
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There are few existing examples of machine learning used to forecast future events for persons with diabetes. In one example, the need for pharmacological therapy was forecast for patients with gestational diabetes [19]. In another study, infections and hypoglycemic events were accurately forecast for individuals with type 1 diabetes (T1 D) [20,21].
JMIR Diabetes 2022;7(2):e34624
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To forecast the DHR into the future, DHR(t)', we calculated a 2-week time-weighted average of the DHR and then assumed this DHR would persist throughout the forecast duration. Where t= 0 is the last day of the observed data, the weight of each DHR (t – n) for n = 0: 13 was determined as
The DHR is a convenient ratio that is obtainable from available data.
JMIR Public Health Surveill 2021;7(6):e27888
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Many epidemiological models that forecast future infection numbers have therefore suggested the role of NPIs in reducing infection rates [2,4,7,12], which can aid the implementation of national strategies and policy decision-making. Recent research incorporates publicly available data with machine learning for use cases such as reported infection case number forecasting [13-16].
J Med Internet Res 2021;23(4):e26628
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