I have made reference to "big data" in previous blog notes (see: Mount Sinai's Icahn School of Medicine Pioneers the Use of Big Data to Study Diabetes; More Discussion about Big Data; Relevance for Healthcare). A commonly accepted definition for big data is the use of extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations. I am particularly interested in the use of big data for predictive analytics in healthcare (see: Big data analytics in healthcare: promise and potential). Now comes an article about the use of big data to discover the co-morbidities associated with psoriasis (see: Researchers receive $6.5 million NIH grant to use big data to tackle psoriasis). Below is an excerpt from it:
An experienced interdisciplinary team of psoriasis and computational researchers from Case Western Reserve University School of Medicine (CWRU SOM) and University Hospitals Cleveland Medical Center (UHCMC) has received a $6.5M, 5-year grant from the National Institute of Arthritis, Musculoskeletal and Skin Diseases (NIAMS). The grant supports a Center of Research Translation in Psoriasis (CORT) at CWRU and UHCMC....The goal is to better identify and treat those psoriasis patients that are more susceptible to developing comorbidities ...associated with psoriasis, such as cardiovascular disease, diabetes, depression, and psoriatic arthritis. Currently it is difficult for physicians to determine which patients will develop these comorbidities. The researchers will cull data collected from blood and skin samples of UHCMC psoriasis patients and preclinical models, looking for new patterns and relationships developed using a systems biology approach.....This approach will use tailored computational processes to zero in on drug candidates or repurposed drugs matching the patient profiles from a large database of tens of thousands of drugs--and test the drugs in genetically engineered psoriasis mouse models.
Psoriasis has as very large number of comorbities (see: COMORBIDITIES ASSOCIATED WITH PSORIATIC DISEASE). It's important not only to identify the range and frequency of them but also the criteria to use to predict which patients will suffer from them in the future. Moreover and as noted above, big data can be used to discover which drug candidates or repurposed drugs would be effective in initially treating both the psoriasis and future associated diseases. We can thus look forward to a future when a set of lab tests will be ordered for a patient with psoriasis and from the data it can be predicted which comorbidities can be expected in the future for him or her and which drugs can be used to forestall or best treat these additional problems.