Clinical Language Processing
Language Technology, which is Natural Language Processing in an industrial setting, is one of the critical technologies that HLA has developed. HLA has the ability to extract appropriate content from free-form text in radiology and pathology reports and clinical notes. Reliable information extraction from any type of prose requires specialised algorithms that understand the statistical behaviour of the language. That behaviour requires machine learning from a sample collection of reports. The combination of HLA’s linguistic analysis and statistical algorithms produces tailored information extraction engines for specific client needs.
In one Language Technology application, our team of software engineers designed a new technology for extracting targeted text content from documents and text fields in clinical information systems. These systems can be tailored through trial and error in the workplace under the management and control of the clinical staff. [read more]
Data Analytics is problematic in any clinical setting due to, among other factors, the high level of data held in unstructured text form. HLA can apply their Clinical Data Analytics technology (CliniDAL) to any clinical information system to support ad hoc questioning in the language of any medical speciality. The CliniDAL package can be installed on any clinical information system or multiple systems and provide combined analysis from the aggregated data sources. [read more]
Language engineering is the use of IT to analyse texts and extract pertinent content. To be able to create working systems of industrial quality there is a large amount of specific software engineering that needs to be built into the infrastructure. HLA are recognised international experts in building this infrastructure and have a lead on the rest of the industry in our efficiency at delivering language processing solutions for our clients.
HLA are particularly efficient at producing annotated training sets of linguistic content with our own specially crafted methods for building learning models, enhancing them with human corrections and regenerating the models in an iterative cycle of Knowledge Discovery – Knowledge Reuse. [read more]