It is different from other medical models
HIV prediction models are different from many other medical prediction models in several ways. Some of the key factors that make HIV prediction models unique include:
HIV is a genetic disease: HIV is caused by a virus that is transmitted through genetic material, such as DNA or RNA. This means that variations in a person's genetic makeup can influence their risk of developing HIV, and can be a useful input for a prediction model. Other medical conditions, such as diabetes or cancer, are not caused by genetic factors in the same way, and may not be as amenable to prediction using genetic data.
HIV is a highly variable disease: HIV can manifest itself in many different ways, and can affect different people in different ways. This variability can make it challenging to develop a prediction model that is able to accurately predict the course of the disease for any given individual. In contrast, many other medical conditions are more predictable, and may be easier to model using data-driven approaches.
HIV is a complex disease with many interacting factors: HIV is influenced by a wide range of factors, including genetic, environmental, and behavioral factors. This complexity can make it challenging to develop a prediction model that is able to accurately capture the full range of factors that influence a person's risk of developing HIV. In contrast, many other medical conditions may be influenced by fewer, simpler factors, which can make them easier to model using data-driven approaches.
HIV is a stigmatized disease: HIV is often associated with stigma and discrimination, which can make it difficult to collect and use data to train prediction models. Many people may be hesitant to disclose their HIV status or to provide information about their health and behavior, which can limit the availability of high-quality data for modeling purposes. In contrast, many other medical conditions may be less stigmatized, and may be easier to study and model using data-driven approaches.
HIV has a long latency period: HIV can take several years to develop into full-blown AIDS, during which time it may not cause any symptoms. This long latency period can make it difficult to accurately predict the progression of the disease, as it may not be possible to detect the virus until many years after infection. In contrast, many other medical conditions have shorter latency periods and may be easier to predict using data-driven approaches.
Overall, HIV prediction models are unique in many ways and are different from many other medical prediction models. These differences can make it challenging to develop accurate and reliable prediction models for HIV
Problems with EHRs
Electronic health records (EHRs) could potentially be a valuable source of data for training a HIV prediction model. EHRs typically contain a wide range of information about a person's health, including details about their medical history, current health status, and other relevant factors. This type of data can be useful for making predictions about a person's health and their risk of developing certain conditions, including HIV.
However, the use of EHRs as input data for a prediction model is not without its challenges. One major issue is that EHRs are not standardized, and their structure and content can vary greatly depending on the country or region in which they are used. This can make it difficult to compare and use EHR data from different sources, as the data may be organized in different ways and may not contain the same information.
Additionally, many countries do not yet use EHRs extensively, or may not have nationwide EHR systems in place. This means that the availability of EHR data for training prediction models may be limited, and may not be representative of the global population. This can make it difficult to develop prediction models that are accurate and reliable for use in a global context.
Overall, while EHRs can be a useful source of data for training a prediction model, including a model for predicting HIV risk, the lack of standardization and the limited availability of EHRs in many countries can present challenges for the development of accurate and reliable prediction models.
Getting an EHRs and demographic data from even a single country would be very challenging and even that would probably not be enough
If a prediction model is trained using data from a specific country, it is likely to be most accurate and reliable for use with the population of that country. This is because the model will have been trained on data that is specific to that population, and will have learned to identify patterns and trends that are relevant to that population.
If the model is then used to make predictions for a population that is different from the one used to train the model, the accuracy and reliability of the predictions may be reduced. This is because the model may not be able to accurately capture the patterns and trends that are specific to the new population, and may not have been trained on data that is relevant to that population.
For example, if a prediction model is trained using data from electronic health records (EHRs) and demographic data from the United States, the model is likely to be most accurate and reliable for use with the population of the United States. If the model is then used to make predictions for the population of another country, such as Brazil, the accuracy and reliability of the predictions may be reduced, as the model may not be able to accurately capture the patterns and trends that are specific to the Brazilian population.
Conclusion
Overall, the time and resources spent on developing a prediction model for HIV could potentially be better used on improving and refining existing medical prediction models, or on adding other useful and relevant features to an app. By focusing on these other areas, it may be possible to develop more effective and useful tools for improving health and well-being, without the need to invest significant time and resources in developing a new prediction model for HIV.
References
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2802664/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6933258/
https://www.hiv.lanl.gov/content/sequence/HIV/mainpage.html
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5019186/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7260108/#R2
https://towardsdatascience.com/9-reasons-why-machine-learning-models-not-perform-well-in-production-4497d3e3e7a5
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5171496/
https://www.ncbi.nlm.nih.gov/books/NBK551886/
Bernard Sovdat,
Data Scientist at Longevity InTime BioTech