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Toward a Learning Health-care System - Knowledge Delivery at the Point of Care Empowered by Big Data and NLP.

Kaggal VC, Elayavilli RK, Mehrabi S, Pankratz JJ, Sohn S, Wang Y, Li D, Rastegar MM, Murphy SP, Ross JL, Chaudhry R, Buntrock JD, Liu H - Biomed Inform Insights (2016)

Bottom Line: Additionally, significant clinical information is embedded in the free text, making natural language processing (NLP) an essential component in implementing an LHS.We compared the advantages of big data over two other environments.Big data infrastructure significantly outperformed other infrastructure in terms of computing speed, demonstrating its value in making the LHS a possibility in the near future.

View Article: PubMed Central - PubMed

Affiliation: Division of Information Management and Analytics, Mayo Clinic, Rochester, MN, USA.; Biomedical Informatics and Computational Biology, University of Minnesota, Rochester, MN, USA.

ABSTRACT
The concept of optimizing health care by understanding and generating knowledge from previous evidence, ie, the Learning Health-care System (LHS), has gained momentum and now has national prominence. Meanwhile, the rapid adoption of electronic health records (EHRs) enables the data collection required to form the basis for facilitating LHS. A prerequisite for using EHR data within the LHS is an infrastructure that enables access to EHR data longitudinally for health-care analytics and real time for knowledge delivery. Additionally, significant clinical information is embedded in the free text, making natural language processing (NLP) an essential component in implementing an LHS. Herein, we share our institutional implementation of a big data-empowered clinical NLP infrastructure, which not only enables health-care analytics but also has real-time NLP processing capability. The infrastructure has been utilized for multiple institutional projects including the MayoExpertAdvisor, an individualized care recommendation solution for clinical care. We compared the advantages of big data over two other environments. Big data infrastructure significantly outperformed other infrastructure in terms of computing speed, demonstrating its value in making the LHS a possibility in the near future.

No MeSH data available.


Average processing time of different server environments. Time taken for big data-empowered NLP to process 20,000 documents in three different server environments. On an average, (i) standalone server takes 23.97 minutes to complete the NLP process, (ii) data stage takes the maximum time of 85.67 minutes for the same, while (iii) big data take 20.03 minutes for the same task.
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f6-bii-suppl.1-2016-013: Average processing time of different server environments. Time taken for big data-empowered NLP to process 20,000 documents in three different server environments. On an average, (i) standalone server takes 23.97 minutes to complete the NLP process, (ii) data stage takes the maximum time of 85.67 minutes for the same, while (iii) big data take 20.03 minutes for the same task.

Mentions: Figure 6 shows the average processing time taken to process 20,000 clinical documents in the three server environments. The standalone server had an average processing time of 23.97 minutes; data stage averaged 85.67 minutes, while the big data averaged 20.13 minutes to process 20,000 clinical notes. The data stage had significantly higher processing times when compared with the other two environments. After further investigation, there may be two reasons for this low performance of such a very powerful server: (i) the specific configuration of the data stage server was not optimal for high throughput and (ii) the data stage server was configured to run in a shared environment. It was not possible for us to schedule a job for MEA processing in a controlled and isolated data stage environment at this time. On the big data server, all the computations during this run were concentrated on a single node. There was not any significant difference in the performance of the big data (shown in Fig. 6) when compared with the standalone server, while there is a significant performance gain over the data stage.


Toward a Learning Health-care System - Knowledge Delivery at the Point of Care Empowered by Big Data and NLP.

Kaggal VC, Elayavilli RK, Mehrabi S, Pankratz JJ, Sohn S, Wang Y, Li D, Rastegar MM, Murphy SP, Ross JL, Chaudhry R, Buntrock JD, Liu H - Biomed Inform Insights (2016)

Average processing time of different server environments. Time taken for big data-empowered NLP to process 20,000 documents in three different server environments. On an average, (i) standalone server takes 23.97 minutes to complete the NLP process, (ii) data stage takes the maximum time of 85.67 minutes for the same, while (iii) big data take 20.03 minutes for the same task.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4920204&req=5

f6-bii-suppl.1-2016-013: Average processing time of different server environments. Time taken for big data-empowered NLP to process 20,000 documents in three different server environments. On an average, (i) standalone server takes 23.97 minutes to complete the NLP process, (ii) data stage takes the maximum time of 85.67 minutes for the same, while (iii) big data take 20.03 minutes for the same task.
Mentions: Figure 6 shows the average processing time taken to process 20,000 clinical documents in the three server environments. The standalone server had an average processing time of 23.97 minutes; data stage averaged 85.67 minutes, while the big data averaged 20.13 minutes to process 20,000 clinical notes. The data stage had significantly higher processing times when compared with the other two environments. After further investigation, there may be two reasons for this low performance of such a very powerful server: (i) the specific configuration of the data stage server was not optimal for high throughput and (ii) the data stage server was configured to run in a shared environment. It was not possible for us to schedule a job for MEA processing in a controlled and isolated data stage environment at this time. On the big data server, all the computations during this run were concentrated on a single node. There was not any significant difference in the performance of the big data (shown in Fig. 6) when compared with the standalone server, while there is a significant performance gain over the data stage.

Bottom Line: Additionally, significant clinical information is embedded in the free text, making natural language processing (NLP) an essential component in implementing an LHS.We compared the advantages of big data over two other environments.Big data infrastructure significantly outperformed other infrastructure in terms of computing speed, demonstrating its value in making the LHS a possibility in the near future.

View Article: PubMed Central - PubMed

Affiliation: Division of Information Management and Analytics, Mayo Clinic, Rochester, MN, USA.; Biomedical Informatics and Computational Biology, University of Minnesota, Rochester, MN, USA.

ABSTRACT
The concept of optimizing health care by understanding and generating knowledge from previous evidence, ie, the Learning Health-care System (LHS), has gained momentum and now has national prominence. Meanwhile, the rapid adoption of electronic health records (EHRs) enables the data collection required to form the basis for facilitating LHS. A prerequisite for using EHR data within the LHS is an infrastructure that enables access to EHR data longitudinally for health-care analytics and real time for knowledge delivery. Additionally, significant clinical information is embedded in the free text, making natural language processing (NLP) an essential component in implementing an LHS. Herein, we share our institutional implementation of a big data-empowered clinical NLP infrastructure, which not only enables health-care analytics but also has real-time NLP processing capability. The infrastructure has been utilized for multiple institutional projects including the MayoExpertAdvisor, an individualized care recommendation solution for clinical care. We compared the advantages of big data over two other environments. Big data infrastructure significantly outperformed other infrastructure in terms of computing speed, demonstrating its value in making the LHS a possibility in the near future.

No MeSH data available.