Machine Learning in Healthcare

I attended a technical symposium yesterday hosted by the CIE-USA (Chinese Institute of Engineers-USA). One of the most popular plenary presentation was delivered by Dr. Jianying Hu (a Distinguished Research Staff Member and Senior Manger of Health Informatics Research at IBM T. J. Watson Research Center, NY) about "Data Driven Healthcare Analytics". Her team leveraged the data existing in the healthcare ecosystem, and developed various machine learning-based tools to help players such as doctors and medical directors in this ecosystem to make better decisions in care delivery and care management. For example, the Care Pathway Explorer is a system that mines and visualizes a set of frequent event sequences from patient EMR (Electronic Medical Records) data. The data of more than 200,000 patients over three years time frame has been analyzed in her research. 

Overview of the Care Pathway Explorer system: It contains four major modules: Event Database, Data Preprocessor, Frequent Pattern Analytics, and a Visual Interface.

"The goal is to utilize historical EMR data to extract common sequences of medical events such as diagnoses and treatments, and investigate how these sequences correlate with patient outcome." Patient similarity has been extracted through machine learning techniques to help decision making process. 

Care Pathway Explorer: A bubble chart and displays events of the most frequent patterns mined (left), and a flow visualization to show the most frequent patterns (right).

Insights of Care Pathway Explorer: Examples of insights reached interactively when mining patterns from patients with a hypertension pre-condition before the initial hyperlipidemi a diagnosis (2,800 patients, featuring 14,979 hyperlipidemia-related diagnoses and 24,898 medication events).

Machine learning has been integrated into the healthcare process and is ready for commercial deployment. I was so impressed to see how this new technology has started impacting the healthcare system. Though the speaker insisted that the system will only be a powerful tool for doctors to improve their efficiency, there were lots of feedback and comments about its potential to dramatically change (or even replace) doctors' roles in the healthcare system in the years to come.

I found a YouTube video and a paper in technical journal include the similar information shared by Dr. Jianying Hu yesterday. If you are interested in this field, you should take a look. Besides healthcare, machine learning has potential to change many other aspects of our lives. It encourages me to spend more time to catch up this fast-growing technology.