Over the last decade, American hospitals have adopted electronic health records (EHRs) widely. In the next decade, incorporating EHRs with clinical decision support (CDS) together into the process of medicine has the potential to change the way medicine has been practiced and advance the quality of patient care. It is a unique opportunity for machine learning (ML), with its ability to process massive datasets beyond the scope of human capability, to provide new clinical insights that aid physicians in planning and delivering care, ultimately leading to better outcomes, lower costs of care, and increased patient satisfaction. However, applying ML-based CDS has to face steep system and application challenges. No open platform is there to support ML and domain experts to develop, deploy, and monitor ML-based CDS; and no end-to-end solution is available for machine learning algorithms to consume heterogenous EHRs and deliver CDS in real-time. Build ML-based CDS from scratch can be expensive and time-consuming.
In this dissertation, CDS-Stack, an open cloud-based platform, is introduced to help ML practitioners to deploy ML-based CDS into healthcare practice. The CDS-Stack integrates various components into the infrastructure for the development, deployment, and monitoring of the ML-based CDS. It provides an ETL engine to transform heterogenous EHRs, either historical or online, into a common data model (CDM) in parallel so that ML algorithms can directly consume health data for training or prediction. It introduces both pull and push-based online CDS pipelines to deliver CDS in real-time. The CDS-Stack has been adopted by Johns Hopkins Medical Institute (JHMI) to deliver a sepsis early warning score since November 2017 and begins to show promising results. Furthermore, we believe CDS-Stack can be extended to outpatients too. A case study of outpatient CDS has been conducted which utilizes smartphones and machine learning to quantify the severity of Parkinson disease. In this study, a mobile Parkinson disease severity score (mPDS) is generated using a novel machine learning approach. The results show it can detect response to dopaminergic therapy, correlate strongly with traditional rating scales, and capture intraday symptom fluctuation.
Speaker Biography
Andong Zhan is a Ph.D. candidate in Computer Science whose mission is to build novel computer systems with machine learning to solve challenging healthcare problems. He has designed and deployed several computing systems for healthcare which directly influenced thousands of inpatients and outpatients as well. Thus far, he has published more than 20 peer-reviewed articles in the areas of computer science and clinical research including IEEE Communications, ACM SenSys, JAMA Neurology, Parkinsonism & Related Disorders, and Critical Care Medicine. His Parkinson disease remote monitoring project and mobile Parkinson disease severity score have been reported by Nature Outlook 2016, JHU Hub, medpagetoday.com, etc.; meanwhile, a machine learning based clinical decision system he mainly developed keeps monitoring inpatients in Johns Hopkins Hospitals and generating real-time alerts for those who may develop sepsis. He is going to graduate in May 2018 and is looking for his next adventure. His advisers are Prof. Andreas Terzis and Prof. Suchi Saria. Also, Prof. Yanif Ahmad gave him massive guidance on his research. He received his M.E from the Dept. of Computer Science at Nanjing University, Nanjing, China, in June 2010 and his B.S from the Dept. of Computer Science at Nanjing University, Nanjing, China, in June 2007.