Kansas City,
04
March
2019
|
08:03 PM
Europe/Amsterdam

MedLab: Impact of Data Science on Clinical Laboratory

By Dr. Mark Hoffman 

The diagnostic laboratory has always been a key source of data that informs clinical decisions. Clinical pathology tests generate discrete results with numeric or coded values that can be classified as normal or abnormal. Anatomic pathology analysis results in a report based on visual analysis of tissues based on the application of specialised stains, probes or other resources that help evaluate the sample for malignancy, inflammation or other clinically significant findings. Recent advances in molecular methods, including diagnostic genomic sequencing, as well as advanced imaging methods such as digital pathology, generate orders of magnitude more data than traditional methods. These advances have created exciting opportunities and some challenges for the laboratory community. The emerging discipline of data science offers a valuable toolkit to maximise the value of all modalities of laboratory data and to improve the diagnostic and operational functions of a modern lab. 

Laboratory professionals seeking to apply data science to address complex questions can take a number of approaches. The best approach is to form a collaborative team with computational and statistical experts to address a clearly defined problem. The team would identify and characterise the data available to them as they design their strategy. The team approach helps mitigate concerns that laboratorians have to become programmers to participate in data science. For those who do want to develop some of the technical skills, high quality online data science training resources such as those provided by Coursera or edX provide an excellent starting point for learning more about the principles and methods of data science. Open source applications such as R and Python are widely available to perform complex data analysis, as are commercial packages. Laboratories that embrace data science will be well positioned to engage in the next generation of diagnostic technologies and methods.

 

Read the full article via Medlab

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