Unstructured medical image query using big data – An epilepsy case study

Abstract

Big data technologies are critical to the medical field which requires new frameworks to leverage them. Such frameworks would benefit medical experts to test hypotheses by querying huge volumes of unstructured medical data to provide better patient care. The objective of this work is to implement and examine the feasibility of having such a framework to provide efficient querying of unstructured data in unlimited ways. The feasibility study was conducted specifically in the epilepsy field. The proposed framework evaluates a query in two phases. In phase 1, structured data is used to filter the clinical data warehouse. In phase 2, feature extraction modules are executed on the unstructured data in a distributed manner via Hadoop to complete the query. Three modules have been created, volume comparer, surface to volume conversion and average intensity. The framework allows for user-defined modules to be imported to provide unlimited ways to process the unstructured data hence potentially extending the application of this framework beyond epilepsy field. Two types of criteria were used to validate the feasibility of the proposed framework - the ability/accuracy of fulfilling an advanced medical query and the efficiency that Hadoop provides. For the first criterion, the framework executed an advanced medical query that spanned both structured and unstructured data with accurate results. For the second criterion, different architectures were explored to evaluate the performance of various Hadoop configurations and were compared to a traditional Single Server Architecture (SSA). The surface to volume conversion module performed up to 40 times faster than the SSA (using a 20 node Hadoop cluster) and the average intensity module performed up to 85 times faster than the SSA (using a 40 node Hadoop cluster). Furthermore, the 40 node Hadoop cluster executed the average intensity module on 10,000 models in 3h which was not even practical for the SSA. The current study is limited to epilepsy field and further research and more feature extraction modules are required to show its applicability in other medical domains. The proposed framework advances data-driven medicine by unleashing the content of unstructured medical data in an efficient and unlimited way to be harnessed by medical experts.

Can antiepileptic drug efficacy be studied from electronic health records? A review of current approaches

“…[30] For mesial temporal sclerosis, Chupin et al identified hippocampus and amygdala atrophy from raw imaging data to within approximately 10% of the “gold standard” based on structural volume. [31,32] For cerebral aneurysm, Castro et al improved on an initial screen of ICD codes with a simple dictionary and classifier NLP to achieve 86% PPV. [12] For venous thromboembolism, Heit et al combined ICD codes and NLP to achieve 100% PPV and 94% NPV.…”

“…[29] For cortical dysplasia, Kassubek extracted information from raw imaging data, successfully identifying dysplasia locations in seven out of seven patients, using 30 controls as a reference. [30] For mesial temporal sclerosis, Chupin et al identified hippocampus and amygdalaatrophy from raw imaging data to within approximately 10% of the "gold standard" based on structural volume [31,32]. For cerebral aneurysm, Castro et al improved on an initial screen of ICD codes with a simple dictionary and classifier NLP to achieve 86% PPV [12].…”

Inflammation Thread Runs across Medical Laboratory Specialities

“…Academic as well as business intelligence literature counsels are available for assisting transformation of big data into a selection of data assembled for care of single disease entities ( http://www.sas.com/ and http://www.aacc.org/ ). Structuring of such information for particular disease states is crucial [ 27 ] and platforms for gene sets might improve understanding different biological data types to reach meaningful outputs [ 28 ]. Web mapping and power-grid including care for people on a medical device that depends on electricity may be included.…”

Transforming big data into computational models for personalized medicine and health care

“…This framework has been used in the implementation of various applications, such as disease prediction in patients, diagnosis of cancer, patient emergency alerts, generation of disease decision rules, medical data quality assessment, and personalized recommendation systems. [4][5][6][7][8][9][10] In precision medicine, a patient's unique characteristics are used to tailor treatment in a manner that might be more elaborate than the standard course. For example, cardiologists currently use an algorithm that for a given patient predicts the occurrence of a myocardial infarction within 5 or 10 years based on body weight, arterial pressure, smoking status, blood lipid analysis results, and personal and family cardiovascular history.…”