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We provide big data analytics Techniques & Data Solutions

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Data Science

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Artificial Intelligence

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Machine Learning

  • Linear Regression
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We are master in data science& big data analysis

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Understanding patient complaint characteristics using contextual clinical BERT embeddings (42nd IEEE EMBC 2020)

To understand patient complaints in conversational applications, we propose a two-fold approach using Word2vec and BERT to detect the characterizations of entities like symptoms presented by general users in contexts where they would describe their symptoms to a clinician.

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Continuous Improvement of Medical Diagnostic Systems with Large Scale Patient Vignette Simulation (29th ACM CIKM 2020)

To address continuous large-scale evaluation of diagnostic systems, we proposed a novel patient vignette simulation algorithm within an iterative clinician-in-the-loop methodology for semi-automatically evaluating and deploying medical diagnostic systems in production.

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A Dictionary-based Oversampling Approach to Clinical Document Classification on Small and Imbalanced Dataset (19th IEEE/ACM WI-IAT 2020)

To address data imbalance in medical document classification, we propose a probabilistic dictionary-based data augmentation approach by oversampling on the minority class and creating new documents with high variety by using synonyms’ similarities with the original medical entity term.

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