Machine Learning for Risk Group Identification and User Data Collection in a Herpes Simplex Virus Patient Registry: Algorithm Development and Validation Study
نویسندگان
چکیده
Background Researching people with herpes simplex virus (HSV) is challenging because of poor data quality, low user engagement, and concerns around stigma anonymity. Objective This project aimed to improve collection for a real-world HSV registry by identifying predictors infection selecting limited number relevant questions ask new users determine their level risk. Methods The US National Health Nutrition Examination Survey (NHANES, 2015-2016) database includes the confirmed type 1 2 (HSV-1 HSV-2, respectively) status American participants (14-49 years) wealth demographic health-related data. questionnaires sets from this survey were used form two sets: one HSV-1 HSV-2. These train test model that random forest algorithm (devised using Python) minimize anonymous lifestyle-based needed identify risk groups HSV. Results selected reduced NHANES questionnaire predicted high accuracy scores 0.91 0.96 recall 0.88 0.98 HSV-2 sets, respectively. was 150 an average 40, depending on age gender. model, therefore, provided predictability minimal required input. Conclusions machine learning can be in evidence collect lifestyle individuals’ levels infection. A limitation absence real integration electronic medical records, which would enable improvement. Future work will explore adjustments, anonymization options, explicit permissions, standardized schema meet General Data Protection Regulation, Insurance Portability Accountability Act, third-party interface connectivity requirements.
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ژورنال
عنوان ژورنال: JMIRx med
سال: 2021
ISSN: ['2563-6316']
DOI: https://doi.org/10.2196/25560