We are Ready to Unleash the Power of Data Science and Mathematics in Medicine

The world of medicine is constantly changing and evolving, and data science and mathematics have become integral components of this progress. Data science and mathematics can provide powerful insights into the diagnosis and treatment of diseases and can help to create more personalized and effective treatments while also acting in resource-bounded settings. In this blog post, we explore some of the exciting examples of how data science and mathematics are being applied to medical research and healthcare delivery. In particular, using spatial data about cells to improve treatment protocols, the development of personalized medicine using socio-demographic data, and the use of pandemic models to predict future outbreaks.

Spatio Biology for Treatment Protocols

Spatial data analysis of cells can provide valuable insights into the underlying mechanisms of diseases and their response to treatment. By analyzing the location and behavior of cells within a tissue, researchers can identify patterns and associations that may not be apparent through traditional methods. This information can be used to improve treatment protocols, such as identifying specific cell types that are resistant to therapy and developing targeted therapies to overcome this resistance.

For example, Scott et al. (2016) use a mathematical model, called a hybrid cellular automaton, to study the effect of different vessel distributions on radiation therapy outcomes at the cellular level. The authors show that the correlation between radiation outcome and spatial organization of vessels changes signs between relatively low and high vessel density. Specifically, for relatively low vessel density, radiation efficacy is decreased when vessels are more homogeneously distributed, and the opposite is true, that radiation efficacy is improved when vessel organization is normalized in high densities. This result suggests an alteration to the vessel normalization hypothesis which states that the normalization of vascular beds should improve radio- and chemo-therapeutic response.

Personalized Medicine using Socio-demographic Data

Personalized medicine aims to provide tailored medical treatments based on an individual's unique genetic, clinical, and lifestyle factors. However, socio-demographic factors such as race, ethnicity, and socioeconomic status also play a crucial role in health outcomes and treatment response. Therefore, incorporating socio-demographic data into personalized medicine approaches can improve patient outcomes and reduce healthcare disparities.

For example, Lazebnik et al. (2021) proposed a clinically relevant mathematical model of Bacillus Calmette-Guerin (BCG) based immunotherapy for type 1 diabetes (T1D) treatment. The authors' model shows that BCG treatment is able to delay the T1D effects and provide long-term health benefits while it may modulate molecular and cellular manifestations of the disease but it does not eliminate T1D. The main advantage of the proposed model is the ability to personalize the treatment protocol according to the patient’s metabolism and diet. The authors show that individuals with different sugar consumption react statistically differently to the baseline treatment protocol. Thus, by altering the protocol using this knowledge, the treatments for different individuals were able to obtain better results.

Predicting future outbreaks

Predicting future outbreaks is a crucial aspect of disease control and prevention. Mathematical models recently gain popularity in this context, predicting the spread of infectious diseases and identifying strategies for control and prevention. For example, mathematical models were used during the COVID-19 pandemic to predict the potential spread of the virus and evaluate the effectiveness of control measures such as social distancing, lockdown, and vaccination.

Future work

As data science and mathematics continue to advance, the potential for their applications in medicine is vast. One area of future work is the development of more sophisticated predictive models that incorporate a wider range of data, including genetic, clinical, lifestyle, and socio-demographic factors. Current models and methods are usually limited to one or two of these data types, which narrows their ability to capture the patient's full picture. Additionally, the integration of home monitoring data, such as the one obtained from wearable devices, can provide a more comprehensive view of their patients' health status compared to the current doctor-visit system. This would allow for more personalized and timely interventions.

In order to achieve these goals, healthcare professionals would need to work together with technological individuals. Moreover, making more clinical data accessible would be an important step in the right direction.