AALIM: Advanced Analytics for Information Management       


 David Beymer photo Nalini K. Ratha photo Tanveer Syeda-Mahmood photo

AALIM: Advanced Analytics for Information Management - overview

In the last 3-4 years, we have been exploring a novel use of content-based search techniques in clinical decision support in a project called AALIM (Advanced Analytics for Information Management, it means the knowledgeable one in many Asian languages). The key hypothesis we are exploring is whether disease-specific similarity in raw modality data can reveal similarity in patient diagnosis, and hence treatments and outcomes. Our approach leverages the consensus opinions from other physicians who looked at similar patients to allow a clinician to get a refined insight into the patient's diagnosis and the comparative effectiveness of different treatments and outcomes.

Much of the work in AALIM has revolved around analyzing different modality data to extract disease-specific information and to develop spatio-temporal descriptors for comparing modality data. Our current emphasis is on cardiology data with analysis of echocardiogram videos, electrocardiogram (EKG) time series, heart sounds, doppler imaging, cardiac MRI, etc. A unique aspect of our work is the end-to-end addressing of the decision support problem not only using novel techniques for disease-specific similarity search but also in the fusion of information from multiple cardiac modalities.

AALIM (Advanced Analytics for Information Management) is an end-to-end clinical informatics platform being developed in the multimodal mining for healthcare group at Almaden Research. It showcases the latest technological advancements in medical image analysis, text analysis, and clinical informatics for various applications in clinical decision support, comparative effectiveness research, predictive analytics, data quality and cohort selection. Among the systems being developed in healthcare across IBM, the AALIM system is one of the oldest and most mature in terms of the comprehensive nature of tools and components being offered be it for data acquisition from HL7, DICOM, JDBC or other unconventional data sources to automatic data model creation and deep analytics to extract and use clinical information from multimodal clinical data sources (images, text, etc.). It also incorporated domain knowledge through extensive use of medical vocabularies such as SNOMED CT, ICD9, RxNorm, LOINC, CPT, etc. AALIM is finding its way into hospitals and research centers around the world where clinicians and investigators want to make meaningful use of the clinical data. The AALIM system was originally developed for cardiology data but is now finding its uses in other specialties such as oncology, neurology, etc.

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