Automated Adverse Drug Event (ADE) Detection from Text in Spark NLP with BioBert. The first end-to-end pretrained models and pipelines to detect Adverse Drug Reactions on scale with the help of Spark NLP and BioBert.
Adverse Drug Reactions (ADRs) or Adverse Drug Events (ADEs) are potentially very dangerous to patients and are amongst the top causes of morbidity and mortality . Many ADRs are hard to discover as they happen to certain groups of people in certain conditions and they may take a long time to expose. Healthcare providers conduct clinical trials to discover ADRs before selling the products but normally are limited in numbers. Thus, post-market drug safety monitoring is required to help discover ADRs after the drugs are sold on the market .
Recently unstructured data such as medical reports  or social network data  have been used to detect content that contains ADRs. Case reports published in the scientific biomedical literature are abundant and generated rapidly. Social networks are another source of redundant data with unstructured format. While an individual tweet or Facebook status that contains ADRs may not be clinically useful, a large volume of these data can expose serious or unknown consequences.
Given the need for collecting ADRs from various resources that are not composed in a structured manner (i.e. tweet, news, web forum etc.) as well as scientific papers (i.e. PubMed, arxiv, white papers, clinical trials, etc.), we wanted to build an end-2-end NLP pipeline to detect if a text contains possible ADRs, and extracting the ADR and Drug entities mentioned.
PySpark Tutorial For Beginners | Apache Spark With Python Tutorial will help you understand what PySpark is, the different features of PySpark, and the comparison of Spark with Python and Scala. Learn the various PySpark contents - SparkConf, SparkContext, SparkFiles, RDD, StorageLevel, DataFrames, Broadcast and Accumulator. You will get an idea about the various Subpackages in PySpark. You will look at a demo using PySpark SQL to analyze Walmart Stocks data
Apache Spark is a popular open-source data processing framework. This widely-known big data platform provides several exciting features, such as graph processing, real-time processing, in-memory processing, batch processing and more quickly and easily.
This video on Apache Spark Tutorial For Beginners - Apache Spark Full Course will help you learn the basics of Big Data, what Apache Spark is, and the architecture of Apache Spark. You will understand how to install Apache Spark on Windows and Ubuntu. You will look at the important components of Spark, such as Spark Streaming, Spark MLlib, and Spark SQL. You will get an idea about implement Spark with Python in PySpark tutorial and look at some of the important Apache Spark interview questions
In this Apache Spark For Beginners, we will have an overview of Spark in Big Data. An introduction to Apache Spark Programming. The Spark History. We'll learn why Spark is needed and covers everything that an individual needed to master its skill in this field. In this Apache Spark tutorial, you will not only learn Spark from the basics but also through this Apache Spark tutorial, you will get to know the Spark architecture and its components such as Spark Core, Spark Programming, Spark SQL, Spark Streaming, and much more.
You will learn what apache spark is, the features of Apache Spark, and the architecture of Apache Spark. You will understand the various components of Apache Spark, such as Spark Core, Spark SQL, Spark Streaming, Spark MLlib, and Spark GraphX. You will look into a case study of Spark for OpenTable company. Finally, you will do a demo on linear regression and logistic regression using PySpark.