I would like to recommend “The Formula” by Luke Dormehl for a good summer read.   I am enjoying this book so far.  I think it should be a must read for all of those interested in predictive analytics and predictive modelling.  A couple of passages from the beginning of the book are provided below.

“Algorithms sort, filter and select the information that is presented to us on a daily basis.”  “… are changing the way that we view … life, the universe, and everything.”

“To make sense of a big picture, we reduce it …  To take an abstract concept such as human intelligence and turn it into something quantifiable, we abstract it further, stripping away complexity and assigning it a seemingly arbitrary number, which becomes a person’s IQ.”

“What is new is the scale that this idea is now being enacted upon , to the point that it is difficult to think of a field of work or leisure that is not subject to algorithmization and The Formula.  This book is about how we reached this point, and how the age of the algorithm impacts and shapes subjects as varied as human creativity, human relationships, notions of identity, and matters of law.”

“Algorithms are very good at providing us with answers in all of these cases.  The real question is whether they give us the answers we want (my emphasis).”

This takes us back to George E.P. Box’s famous quote “all models are wrong, but some are useful”.   We can create algorithms for almost anything, but how useful are they.   Accurate models can be created that work really well on deterministic systems, but are much harder to develop on complex systems.   As you strip away features to be studied from that complex system, you lose the impact of that feature on the system. You try to select features that do not have a huge impact on the performance of the system, but this is often unknowable in advance.

One of the great challenges in clinical medicine is trying to determine or predict what is going to happen to a patient in the future.   We know generally that smoking is bad, too much alcohol is bad, being overweight is bad, not exercising is bad, not sleeping enough is bad.  We know these are bad for the overall population of people.  However we do not know how each of these effect a single patient, nor how they are interrelated.   We would like to develop models that can predict what will happen if you have certain conditions (predictive modeling), and then look at what would happen if you took certain courses of action/treatments/preventative actions(prescriptive modeling).  The results of these models would allow clinicians and patients to be better informed and choose the best pathway forward.

Of particular interest to me, I would like to be able to predict real-time what is going to happen to a patient I am seeing in the emergency room.    This is a complex situation.   Their current state – physiologic vital signs (level of consciousness, blood pressure, pulse, respiratory rate, temperature, blood oxygen level, respiratory variability, heart rate variability, ekg,  etc.), along with their current laboratory and radiological imaging findings will define their current problem or diagnosis.  The patients past medical history, medications, allergies, social support, living environment, etc.,  will have major impacts on how they respond to their current illness or injury.  We would like to aggregate all of this information into predictive and prescriptive models that could predict future states.   Are the patients safe to be discharged home or do they need to be admitted?  If they need to be admitted, can they go to the short stay unit, a bed with cardiac monitoring, a bed with cardiac monitoring, or the intensive care  unit?  Given the current treatment, what will their response to this treatment be – will they get better or worse?  Will they develop sepsis?  Will they develop respiratory failure and require a tube be placed down their throat and a ventilator to breathe for them?

A particularly exciting area ripe for development is the internet of things.   The internet of things is going to revolutionize how we collect data, both at home and in the hospital.   This much-needed capability will allow us to monitor patients at home,  detect illnesses much earlier, monitor responses to therapies, etc.,  and will be useful for a whole host of things we haven’t even imagined yet.

These are some of the complex questions that face us now in medicine.  I am excited to participate in this quest to answer some of these vexing questions using all of the analytical tools that are currently available – whether “small data”  using standard descriptive and inferential statistics, predictive analytics, and big data analytics.