Perspectives on Innovation

Dr. Torsten Hothorn has been on quite a run lately working on Prodigy “Big Data” Challenges. Recently, he won the $30,000 Cleveland Clinic Challenge, Build an Efficient Pipeline to Find the Most Powerful Predictors, and he earned a $10,000 award for his second place finish in The DREAM-Phil Bowen ALS Prediction Prize4Life Challenge. (We recently profiled Lilly Fang, a member of one of the two first place winning teams for the Prize4Life Challenge, and also posted a Seeker Spotlight featuring Prize4Life’s Neta Zach which dives into the background of the Challenge and final results). We’re happy to have Dr. Hothorn here to discuss his experience with these important Challenges.

I am a Professor of Biostatistics in the Department of Statistics at the University of Munich, Germany, and I’m interested in both methodological developments and applications of statistical models in medicine and biology. Research and teaching in Biostatistics ideally brings together practical problems and statistical theory. While I mainly teach students of statistics, I enjoy working with scientists from fields as diverse as oncology, ecology, and forestry. Because a statistical model is only useful when it actually can be applied to gain insights into aspects of data that otherwise would remain hidden, I spend a lot of time developing and implementing new statistical models. Some open source software packages to which I contributed are distributed via CRAN, the R package repository.

Developing statistical software always means pushing forward existing functionality. One of the best and most effective ways to find out where improvements are needed most is to work on the solution of practical problems, apply the software, and look at the results. While I’m not short of collaborators with interesting problems, I decided to give one of the Cleveland Clinic Challenges hosted by InnoCentive a shot when I first learned about InnoCentive in the Fall of 2011. In 2004 and 2006, I authored two scholarly papers about nonparametric survival models that also work in the presence of numerous potentially predictive variables. The Cleveland Clinic Challenge, “Build an Efficient Pipeline to Find the Most Powerful Predictors,” was an exact match for the models that I developed and described in these two papers. Luckily, I had already invested a fair amount of time into a software implementation, using the R add-on package “party,” and thus the solution was (almost) at my fingertips.

I must admit that “The DREAM-Phil Bowen ALS Prediction Prize4Life Challenge” was a little more challenging than I first thought. With the patient data coming from different clinical trials, it took a while to compile the data into a format suitable for statistical analysis. The relatively complex longitudinal structure of the data, the expected weak association between predictor variables and ALS disease progression, and the large amount of missing values in some of the potentially interesting predictor variables suggested that a nonparametric regression approach (e.g., random forests), might be a good candidate for a potential solution. However, the Challenge data gave me a hard time predicting ALS disease progression with good accuracy. Eventually, I went back and started from scratch. First, I slightly reformulated the Challenge objective by using an alternative statistical measure for describing the disease progression of a patient. In a second step, I collected as much information as I could about the disease progression in the first three months in which a patient was under observation. I observed that using these variables as predictors of the new ALS disease progression measure lead to better performing models.

Besides my interest in applying software that I developed and the thrill of competing with people from all over the world in this prediction Challenge – the InnoCentive leaderboard is really something one can get addicted to – I look forward to using the PRO-ACT database (a subset of which the Prize4Life Challenge was based on) in the classroom. Next spring, I’ll teach longitudinal data analysis and I intend to let my students work with the ALS patient data. That way, my students will be constantly reminded what the models and formulae presented on the blackboard are actually good for and what scientific obligation to society actually means to a statistician.

Mike Cirella recently won the Cleveland Clinic Challenge: Implantable Micro-sensor for Displacement & Mechanical Load. Previously, he received awards for three Challenges: Thresholds for Perception of Color Differences, Manufacturing of a Porous Film, and Task Light Charging.

Open innovation (OI) is a powerful platform that fosters creative thinking about problems that may be far outside a Solver’s daily routine. It provides an opportunity to apply diverse experiences that often lead to solutions never before considered. So often the ‘dumb’ questions are not asked by individuals studying problems from the perspective of someone inside an organization. The power of OI is much like a brainstorming session, where no question or suggested solution is off limits, thereby opening up the possibilities for a truly creative, even unique, solution.

It is precisely for these reasons that I am an active Solver. I have submitted many more proposed solutions than I have won, but each effort leads me down a new path and expands my knowledge for the next Challenge. The process allows me to ask “why not” instead of “why,” or worse, not ask at all since it is so far outside the normal approach.

For me, the common thread that links my winning solutions is the “Eureka moment” I experience after reading the Challenge description the first time and relate it to a past experience and solution to a problem in an entirely different field. Of course, many hours of research, organizing and fine-tuning my submission follows that moment, but the creative idea is formed by thinking laterally; searching my experience database for a tool or method that can be applied to a problem in a completely different area.

The Task Light Charging (aka Bogolight) Challenge sponsored by the Rockefeller Foundation triggered my memory of how modern coin acceptors in vending machines function reliably in harsh environments by eliminating moving parts that wear and corrode. Since the task light required a rugged, off-grid method for re-charging its batteries that supplemented the existing photocell method, I applied wind and water power, converted to electricity via permanent magnets spinning past induction coils embedded in a plastic housing. No metal parts exposed and high inherent reliability.

The Manufacturing of a Porous Film Challenge had an obvious solution (to me) by applying methods used in the paper and plastics web production industries. Again, a past life experience at a company that manufactured polarizers for sunglasses prompted me to apply my knowledge of web rollers and controls and create a simple, inexpensive solution. Read the rest of this entry »

Today we announced a new collaboration with the world renowned Cleveland Clinic, to advance medical and healthcare innovation.  The cornerstone of the partnership is the Cleveland Clinic Medical Innovation Pavilion, which will be home to a series of Challenges aimed at providing new advances in patient care.  Dr. Paul DiCorleto, Ph.D., Sherwin-Page Chair of the Cleveland Clinic Lerner Research Institute, led the initiative from the Cleveland Clinic side, bringing together a team of basic and clinical researchers with the aim of reaching outside of the traditional innovation process to uncover new ideas that could change the way the world approaches medical research.  We asked Dr. DiCorleto to give us his thoughts about the partnership and the role of open innovation in health care.

Hello Dr. DiCorleto, and thank you for speaking with us today. Can you tell us a bit about Cleveland Clinic and the mission of the Lerner Research Institute?

Cleveland Clinic is unique in that from its very beginning in 1921, the founders believed that research and education belonged with clinical care.  These elements remain in our mission statement today, and research is viewed as an integral part of patient care.  At the Lerner Research Institute, our goal is to understand the underlying causes of human diseases and to develop new treatments and cures.

Cleveland Clinic is a world renowned research institute.  Can you tell us about some of the specific innovations that have been developed since the organization’s founding? (more…)