What is the Relationship between Informatics and Data Science?
Wednesday, August 2nd, 2017
When examining the connection between informatics and data science, the ratio is rather simple – 1:1. Informatics is the equivalent of data science.
Biomedical informatics is an amalgam of data science with both biomedicine and health components added in. Data science is a recent name that grew out of the emergence of big data, although biomedical informatics, i.e., data science in biomedicine and healthcare, has been around for several decades. The field of biomedical informatics is also an interdisciplinary field that involves:
- Clinical science and practice: medicine, nursing, dentistry, pharmacy, population health
- Public and community health
- Computer science and engineering: database, algorithm, programming, artificial intelligence, machine learning (including deep learning), neural network, cognitive computing, distributed computing, cloud computing, natural language processing and text processing, security, visualization, mobile devices, sensors, internet of things, etc.
- Cognitive science
- Mathematics and biostatistics
- Social and behavioral sciences
- Management science
- Health information technology policy and legal issues
Within biomedical informatics, there is an emphasis on certain key processes; acquisition, storage, communication, processing, integration, analysis, mining, retrieval, interpretation, and presentation. These processes transform data to information to knowledge to intelligence; these are entities.
Once researchers have entities for evaluation, the next step is to perform descriptive, predictive, and prescriptive tasks or functions. In general data science or informatics, these processes, entities, and functions can be applied to any domains. For biomedical informatics, the application domains are biomedical discovery, healthcare delivery, and disease prevention.
Focusing on innovations in these processes, entities, and functions, faculty, students and researchers at SBMI are performing in-depth research studies within the field of data science in biomedicine and healthcare.
Let’s take a closer look at how this framework works:
- Data is unintepreted, unprocessed, meaningless raw symbols, signals or pixels. For example, “101” could mean several things; the decimal number one hundred and one, the binary number five, the values of three pixels or even a label for a highway. Without context, most data types are meaningless.
- Information is interpreted data or data with meaning. For example, once we know that the metric for 101 is degrees in Fahrenheit, we immediate correlate the number to temperature. Information provides a descriptive function and tells you what happened, at what juncture and for whom.
- Knowledge is organized information that is justified or validated. Let’s say that we also know that 101 °F is an adult oral temperature. Immediately, we know that this indicates an abnormal body status (fever) and this relation is validated in medical practice and research. Knowledge provides a predictive function and tells you what might happen. An adult oral temperature of 101 °F predicts that the body status is irregular.
- Intelligence is actionable knowledge. An adult with a 101°F temperature should take fever medication, have further assessment and diagnosis performed and may need to see a doctor if it is not a simple cold. Intelligence provides prescriptive function and tells you what needs to be done. 101 °F adult oral temperature prescribes the action of taking fever medicine and further assessment.
Dr. Zhang is Dean, Professor, and Glassell Family Foundation Distinguished Chair in Informatics Excellence at the School of Biomedical Informatics (SBMI) at the University of Texas Health Science Center at Houston (UTHealth).