I was recently inspired to write this article below, when I read a long while ago about how patients are most times treated as some sort of ‘average’.”Typically, a patient will receive a treatment based on what worked for most people, as reflected in large clinical studies. For example, for a long time, it was thought that Tamoxifen was roughly 80% effective for breast cancer patients. But now we know that it is 100% effective in 70% to 80% of the patients, and ineffective in the rest”.

Today, there is a huge opportunity for Data Scientists to put their Predictive Analytics / Statistical skills to work, as today, we have access to a new kind of data such as ‘DNA sequencing’ to tell whether it’s likely that a drug will be effective or ineffective in any given patient, and we can tell in advance whether to treat with the drug or to try something else.

Today, we can use predictive analytic techniques such as the Logistic Regression or Decision Trees to divide patients into groups and then determine the difference between those groups. With the Logistic Regression Modelling, we can tell who is likely to be cured with a particular treatment and also, the probability of being cured with that particular treatment. Decision Trees also offer good visualisation showing the breakdown on the different segments of patients who are likely to cured by a particular treatment or not.

Many focus on whether a treatment is effective or not. The fundamental question is, “for which patients is this treatment effective”? It’s all about asking the right questions….The question should always include the patient not just the treatment! A treatment that is only effective on 25% of patients might be very valuable if we can tell who that 25%  is.

One of Data Science’s many promises, is that, if we can collect enough data effectively, we will be able to predict more accurately which treatments will be effective for which patients and which treatments won’t.

If you are interested in “What treatments will work and on which type of patients?” or “Whether you are spending money effectively on your treatment?” Why not get help from Data Scientists, like myself, who can apply Predictive Analytics / Statistical Techniques to historical patient and treatment data and help you answer such questions.