Predictive Science in Disease Diagnostics : A Call for the Researchers

What if I inform you, ‘You would be having dementia after five years’, would you not take some actions to prevent it ?For sure you’d do.

Let me introduce you to a new branch of science called ‘Predictive Science’ and how I agog to use it to predict various diseases.

What is Predictive Science?

Predictive science is the art [at least for me]and science of forecasting future events using existing data. It involves sophisticated techniques and tools to analyze patterns, trends, and correlations within data to make informed predictions about future outcomes. In today’s digital age, we are blessed with data from various sources, including smartphones, wearables, medical devices, and other smart technologies.

This vast pool of data is a goldmine for predictive science,

providing the raw material needed to create accurate and actionable predictions.

Why Predictive Science is Needed

Simply, the reason made me an atheist is the reason I need Predictive Science

The prevalence of diseases, especially among children, is a matter of grave concern. Despite significant advancements in medical technology and healthcare, the incidence of chronic and acute diseases remains really high. [I consider this as a shame on Scientific Community which definitely includes me].

This paradox, where technological advancement coexists with persistent health challenges, is both frustrating and unacceptable.

It is this frustration that fuels my passion for developing a new sub-discipline within science: Predictive Science.

By leveraging the immense volumes of data at our disposal, predictive science aims to foresee the onset of diseases, allowing for early intervention and prevention. This proactive approach could revolutionize healthcare, reducing the incidence and severity of diseases and improving overall public health.

How Do We Evolve This Science?

The evolution of predictive science in disease diagnostics requires a multi-faceted approach.

Developing mathematical models involves using statistical techniques to identify patterns and correlations in historical health data, employing machine learning algorithms to analyze complex datasets and improve prediction accuracy, and creating simulations to test and refine predictive algorithms in controlled environments.

Data analysis entails identifying relevant data points such as genetic markers [this would predict several genetic diseases before hand], lifestyle factors, environmental exposures, and clinical history; utilizing big data tools to efficiently process and analyze vast amounts of information; and isolating predictive variables that significantly contribute to disease prediction, ensuring models are accurate and robust.

Data collection includes aggregating healthcare records from electronic health records to capture comprehensive patient histories, integrating data from wearable devices that monitor vital signs and other health metrics, collecting genomic data to understand hereditary disease risks, and incorporating environmental data on factors such as air quality and exposure to toxins.

Mapping data to models involves data integration, where disparate data sources are combined into a unified dataset for comprehensive analysis, model training on integrated datasets to improve accuracy and reliability, and continuous learning through machine learning techniques that allow models to adapt and improve over time based on new data.

How It Would Be Helpful in Disease Diagnosis

Predictive science has the potential to transform disease diagnosis and management in several ways.

It enables early detection and prevention by identifying early signs of chronic diseases like diabetes, heart disease, and cancer, which allows for timely intervention and lifestyle modifications, and predicting outbreaks of infectious diseases using environmental and epidemiological data for swift public health responses.

In personalized medicine, genetic risk assessments utilize genetic data to evaluate individual risk factors for hereditary diseases and provide personalized prevention strategies, while tailored treatments develop specific plans based on patient needs and risk profiles to improve outcomes and reduce side effects.

A Future I See

In the future I envision, healthcare will be fundamentally transformed by predictive science. Hospitals will primarily serve as emergency care centers, addressing acute injuries and urgent medical conditions. The majority of diseases will be detected and managed long before they become serious health issues.

Predictive science hrlps early detection and prevention by identifying early signs of chronic diseases such as diabetes, heart disease, and cancer, allowing for timely intervention and lifestyle modifications.

It also predicts outbreaks of infectious diseases using environmental and epidemiological data, facilitating swift public health responses. In personalized medicine, genetic risk assessments evaluate individual risk factors for hereditary diseases and provide personalized prevention strategies, while tailored treatments develop specific plans based on patient needs and risk profiles to improve outcomes and reduce side effects.

Finally I invite researchers to delve in this area further and find provisions to predict various diseases. While I am concentrating on Neuro Degenerative Diseases (Parkinsons & Dementia) , the horizons are open…

Let minimum Hospitals and Maximum Predictive Centres be our Mission !!

-Penned as ‘LLL’