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other·infectious disease, research methods, epidemiology, public health·PMC10414389

Machine Learning for Risk Group Identification and User Data Collection in a Herpes Simplex Virus Patient Registry: Algorithm Development and Validation Study

JMIRx Med · 6 authors, 6 centres

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TL;DR

The study developed and validated a machine learning algorithm to optimize data collection for a herpes simplex virus (HSV) patient registry.

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The study developed and validated a machine learning algorithm to optimize data collection for a herpes simplex virus (HSV) patient registry. Using the NHANES 2015-2016 dataset of US participants aged 14-49 years, researchers trained and tested a random forest model to identify the minimum number of survey questions needed to predict HSV-1 and HSV-2 infection risk. A key limitation is that the model was trained and tested solely on pre-collected survey data and has not yet been validated with real user data or integrated with electronic medical records. The findings suggest this approach could improve data collection for sensitive health topics by reducing user burden, though future work is needed to test the system in a real-world registry context.

STUDY TYPE

other

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infectious disease

SUMMARISED BY

AI pipeline

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ORIGINAL PAPER

PMC10414389

Machine Learning for Risk Group Identification and User Data Collection in a Herpes Simplex Virus Patient Registry: Algorithm Development and Validation Study

JMIRx Med

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ABSTRACT

Abstract Background Researching people with herpes simplex virus (HSV) is challenging because of poor data quality, low user engagement, and concerns around stigma and anonymity. Objective This project aimed to improve data collection for a real-world HSV registry by identifying predictors of HSV infection and selecting a limited number of relevant questions to ask new registry users to determine their level of HSV infection risk. Methods The US National Health and Nutrition Examination Survey (NHANES, 2015-2016) database includes the confirmed HSV type 1 and type 2 (HSV-1 and HSV-2, respectively) status of American participants (14-49 years) and a wealth of demographic and health-related data. The questionnaires and data sets from this survey were used to form two data sets: one for HSV-1 and one for HSV-2. These data sets were used to train and test a model that used a random forest algorithm (devised using Python) to minimize the number of anonymous lifestyle-based questions needed to identify risk groups for HSV. Results The model selected a reduced number of questions from the NHANES questionnaire that predicted HSV infection risk with high accuracy scores of 0.91 and 0.96 and high recall scores of 0.88 and 0.98 for the HSV-1 and HSV-2 data sets, respectively. The number of questions was reduced from 150 to an average of 40, depending on age and gender. The model, therefore, provided high predictability of risk of infection with minimal required input. Conclusions This machine learning algorithm can be used in a real-world evidence registry to collect relevant lifestyle data and identify individuals’ levels of risk of HSV infection. A limitation is the absence of real user data and integration with electronic medical records, which would enable model learning and improvement. Future work will explore model adjustments, anonymization options, explicit permissions, and a standardized data schema that meet the General Data Protection Regulation, Health Insurance Portability and Accountability Act, and third-party interface connectivity requirements. Keywords: data collection, herpes simplex virus, registries, machine learning, risk assessment, artificial intelligence, medical information system, user-centered design, predictor, risk

CITATION EXPORT

Svitlana Surodina, Ching Lam, Svetislav Grbich et al.. Machine Learning for Risk Group Identification and User Data Collection in a Herpes Simplex Virus Patient Registry: Algorithm Development and Validation Study. JMIRx Med. (2021). https://doi.org/10.2196/25560

AUTHORS

6 · 6 CENTRES

Svitlana Surodina, Ching Lam, Svetislav Grbich, Madison Milne-Ives, Michelle van Velthoven, Edward Meinert

1Skein Ltd, London, United Kingdom
2Department of Informatics, King’s College London, London, United Kingdom
3Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
4Centre for Health Technology, University of Plymouth, Plymouth, United Kingdom
5Nuffield Department of Primary Health Sciences, Medical Sciences Division, University of Oxford, Oxford, United Kingdom
6Department of Primary Care and Public Health, School of Public Health, Imperial College London, London, United Kingdom