The biomedical healthcare industry dedicates its focus to biotechnologies that treat humans. It involves, as it relates to treatment, the pharmaceutical industry and outpatient and inpatient healthcare, among many others. It also includes the medical research industry to advance our understanding of health conditions and diseases.
Biotechnology is one of its main aspects. It gives us a way to carry out diagnostics, analyze and regulate biological functions, and create products, processes, and systems for treatment. Genomics and the development of gene therapy are examples to consider.
It goes without saying that the medical and healthcare industries are broad and full of variation. But just like other industries, as they grow, they accumulate and require massive amounts of data from various biomedical research, development, clinical, and patient data. Biomedical information continues to advance, and with the dramatic progress of technology and medicine, it has led to the development of biomedical informatics. With the huge amounts of data that are created and collected within these disciplines, the need for ever more developed data analytics and mining practices becomes much more necessary. Biomedical informatics contributes an environment for data integration across the different biomedical disciplines for handling information to help determining the best choices for clinicians and researchers.
The Challenge of Big Data
As a newly and continually developing industry, bioinformatics has evolved to the point to where it has become an elemental part of biomedical research and healthcare operations. Hospitals and outpatient clinics use information and documentation systems as a necessary part of their operations to input and organize high volumes of patient and treatment data. These systems and databases collect and organize data at impressive rates and scales that advances medical science further than could have been imagined when paper-based directories ruled the scene.
But with the data comes the need for rendering it useful in driving research and treatments. These systems and databases have multiplied over the course of the last decade and a half and now number well over a thousand. All of this must be done in compliance with in HIPAA privacy guidelines regarding patient privacy. A violation of HIPAA protocol could result in lawsuits or federal prosecution. Scrapers must be very careful in these techniques to respect patient privacy while at the same time collecting information that can further advance medicine.
Data Extraction, Aggregation, and Analysis
These databases were developed in response to lagging medical record and research informatics. Their number and the massive amount of information that they comprise remains, in many cases, uncoordinated and scattered because they don’t each integrate their systems and informatics in a way that empowers biotech research in an era of big data. For this reason, biomedical informaticians are turning to data mining and web scraping techniques as a way of aggregating and analyzing the massive amounts of data that the industry generates and collects.
The business of data mining and web scraping is far more involved than merely extracting data. Web scraping uses data analysis tools to analyze data and extract topical information from the data sample. Massive amounts of data are readily available on just about every network. Some aspects of that data can be critical, especially in the world of biotechnological research and development, where access to relevant information can be necessary for the development of further devices and treatments.
Data mining helps the informatician create strategies and convey useful information to improve organizational efficiency. This process begins with data scraping. Data scraping is a process where a web crawler robot mimics normal internet browsing methods to collect large amounts of data from websites, databases, systems, data repositories, documents, emails, forms, etc. That data is then warehoused and compiled until it can be “mined” for the information that is needed. The data mining step filters and analyzes the data specimen.
Data management systems then transform the data and search for patterns and correlations by applying algorithms and then merges it with relevant metadata in what is known as “machine learning” to interpret or transform the data into useful and actionable information. Together with information available in electronic document systems and databases, biomedical informatics is able to integrate information across various avenues. This has led to the development of what is now known as integrative bioinformatics.
Benefits of Integrated Data Analysis
The mapping of the human genome H, this means more data that must be processed and understood. Getting back to our example of genomics, genetic researchers are arming themselves with intelligent data analysis capabilities, referring to the transition of data into actionable information, to analyze genetic data for potential markers that could allow them to categorize as been of tremendous help and value to research in the way of discovering causes of certain illnesses. Howeverindividuals according to the results, productive or adverse, of current treatment methodology.
For Geneticists, this also improves a better understanding of possible genetic proneness towards certain conditions or diseases. In the case of pharmaceutical developers, researchers are able to move forward in conducting clinical trials. Such trials are enabled with the use of large volumes of healthcare data, pharmaceutical treatment outcomes data, and data gleaned from insurance claims to better pinpoint adverse effects of drug treatments and under more precisely determined conditions.
With more efficient biomedical research and data analysis tools at their disposal, healthcare researchers and practitioners can be better equipped to develop more and better drugs and therapies that can help prevent disease. Having this information at their fingertips in a format that is more easily accessed and cross-referenced speeds research and development. As a result, medicine and biomedical healthcare in general can become more proactive in its pathway to better health. With a more and better informed healthcare analysis, lifestyle counseling can suggest alternative paths that can diminish the possible effects of diseases or conditions which affect their patients.
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