Kartoun U. A Methodology to Generate Virtual Patient Repositories

Electronic medical records (EMR) contain sensitive personal information. For example, they may include details about infectious diseases, such as human immunodeficiency virus (HIV), or they may contain information about a mental illness. They may also contain other sensitive information such as medical details related to fertility treatments. Because EMRs are subject to confidentiality requirements, accessing and analyzing EMR databases is a privilege given to only a small number of individuals. Individuals who work at institutions that do not have access to EMR systems have no opportunity to gain hands-on experience with this valuable resource. Simulated medical databases are currently available; however, they are difficult to configure and are limited in their resemblance to real clinical databases. Generating highly accessible repositories of virtual patient EMRs while relying only minimally on real patient data is expected to serve as a valuable resource to a broader audience of medical personnel, including those who reside in underdeveloped countries.
Electronic medical records (EMR) contain sensitive personal information. For example, they may include details about infectious diseases, such as human immunodeficiency virus (HIV), or they may contain information about a mental illness. They may also contain other sensitive information such as medical details related to fertility treatments. Because EMRs are subject to confidentiality requirements, accessing and analyzing EMR databases is a privilege given to only a small number of individuals. Individuals who work at institutions that do not have access to EMR systems have no opportunity to gain hands-on experience with this valuable resource. Simulated medical databases are currently available; however, they are difficult to configure and are limited in their resemblance to real clinical databases. Generating highly accessible repositories of virtual patient EMRs while relying only minimally on real patient data is expected to serve as a valuable resource to a broader audience of medical personnel, including those who reside in underdeveloped countries.
Electronic medical records (EMR) contain sensitive personal information. For example, they may include details about infectious diseases, such as human immunodeficiency virus (HIV), or they may contain information about a mental illness. They may also contain other sensitive information such as medical details related to fertility treatments. Because EMRs are subject to confidentiality requirements, accessing and analyzing EMR databases is a privilege given to only a small number of individuals. Individuals who work at institutions that do not have access to EMR systems have no opportunity to gain hands-on experience with this valuable resource. Simulated medical databases are currently available; however, they are difficult to configure and are limited in their resemblance to real clinical databases. Generating highly accessible repositories of virtual patient EMRs while relying only minimally on real patient data is expected to serve as a valuable resource to a broader audience of medical personnel, including those who reside in underdeveloped countries.
 
Electronic medical records (EMR) contain sensitive personal information. For example, they may include details about infectious diseases, such as human immunodeficiency virus (HIV), or they may contain information about a mental illness. They may also contain other sensitive information such as medical details related to fertility treatments. Because EMRs are subject to confidentiality requirements, accessing and analyzing EMR databases is a privilege given to only a small number of individuals. Individuals who work at institutions that do not have access to EMR systems have no opportunity to gain hands-on experience with this valuable resource. Simulated medical databases are currently available; however, they are difficult to configure and are limited in their resemblance to real clinical databases. Generating highly accessible repositories of virtual patient EMRs while relying only minimally on real patient data is expected to serve as a valuable resource to a broader audience of medical personnel, including those who reside in underdeveloped countries.
 

Gangal R. Exploratory Statistical Analysis of EMR data Or Where Angels Fear to tread…

The value of any data lies not in its mere collection but what knowledge we can distill. "Give me a lever long enough and I shall move the Earth", so said Archimedes. Well, a data scientist can say the same about improving health outcomes, provided enough "Real World Evidence" data is accessible. Unfortunately, it's not easy to come by, what with legitimate privacy concerns and commercial interests galore. Btw, terms like "Race" are meant just as in the "Real World", only without the discrimination..So do chill and do not be offended!
 
It is thus with great interest, that I came across this simulated EMR data at www.EMRbots.org which claims the following:
 
The database contains the same characteristics that exist in a real medical database such as patients, admission details, demographics, socioeconomic details, labs, medications, etc.
The value of any data lies not in its mere collection but what knowledge we can distill. "Give me a lever long enough and I shall move the Earth", so said Archimedes. Well, a data scientist can say the same about improving health outcomes, provided enough "Real World Evidence" data is accessible. Unfortunately, it's not easy to come by, what with legitimate privacy concerns and commercial interests galore. Btw, terms like "Race" are meant just as in the "Real World", only without the discrimination..So do chill and do not be offended!
 
It is thus with great interest, that I came across this simulated EMR data at www.EMRbots.org which claims the following:
 
The database contains the same characteristics that exist in a real medical database such as patients, admission details, demographics, socioeconomic details, labs, medications, etc.
The value of any data lies not in its mere collection but what knowledge we can distill. "Give me a lever long enough and I shall move the Earth", so said Archimedes. Well, a data scientist can say the same about improving health outcomes, provided enough "Real World Evidence" data is accessible. Unfortunately, it's not easy to come by, what with legitimate privacy concerns and commercial interests galore. Btw, terms like "Race" are meant just as in the "Real World", only without the discrimination..So do chill and do not be offended!
 
It is thus with great interest, that I came across this simulated EMR data at www.EMRbots.org which claims the following:
 
The database contains the same characteristics that exist in a real medical database such as patients, admission details, demographics, socioeconomic details, labs, medications, etc.

Read more: https://www.linkedin.com/pulse/exploratory-statistical-analysis-emr-data-where-angels-rajeev-gangal

Bahrami M. and Singhal M. A Dynamic Cloud Computing Platform for eHealth Systems

Cloud Computing technology offers new opportunities for outsourcing data, and outsourcing computation to individuals, start-up businesses, and corporations in health care. Although cloud computing paradigm provides interesting, and cost effective opportunities to the users, it is not mature, and using the cloud introduces new obstacles to users. For instance, vendor lock-in issue that causes a healthcare system rely on a cloud vendor infrastructure, and it does not allow the system to easily transit from one vendor to another. Cloud data privacy is another issue and data privacy could be violated due to outsourcing data to a cloud computing system, in particular for a healthcare system that archives and processes sensitive data. In this paper, we present a novel cloud computing platform based on a ServiceOriented cloud architecture. The proposed platform can be ran on the top of heterogeneous cloud computing systems that provides standard, dynamic and customizable services for eHealth systems. The proposed platform allows heterogeneous clouds provide a uniform service interface for eHealth systems that enable users to freely transfer their data and application from one vendor to another with minimal modifications. We implement the proposed platform for an eHealth system that maintains patients’ data privacy in the cloud. We consider a data accessibility scenario with implementing two methods, AES and a light-weight data privacy method to protect patients’ data privacy on the proposed platform. We assess the performance and the scalability of the implemented platform for a massive electronic medical record. The experimental results show that the proposed platform have not introduce additional overheads when we run data privacy protection methods on the proposed platform.Electronic medical records (EMR) contain sensitive personal information. For example, they may include details about infectious diseases, such as human immunodeficiency virus (HIV), or they may contain information about a mental illness. They may also contain other sensitive information such as medical details related to fertility treatments. Because EMRs are subject to confidentiality requirements, accessing and analyzing EMR databases is a privilege given to only a small number of individuals. Individuals who work at institutions that do not have access to EMR systems have no opportunity to gain hands-on experience with this valuable resource. Simulated medical databases are currently available; however, they are difficult to configure and are limited in their resemblance to real clinical databases. Generating highly accessible repositories of virtual patient EMRs while relying only minimally on real patient data is expected to serve as a valuable resource to a broader audience of medical personnel, including those who reside in underdeveloped countries.

Baytas et al. Patient Subtyping via Time-Aware LSTM Networks

In the study of various diseases, the heterogeneity among patients usually leads to different progression patterns and may require different types of therapeutic intervention. Therefore, it is important to study patient subtyping, the grouping of patients into disease characterizing subtypes. Subtyping from complex patient data is challenging because of the information heterogeneity and temporal dynamics. Long-Short Term Memory (LSTM) has been successfully used in many domains for processing sequential data, and recently applied for analyzing longitudinal patient records. The LSTM units are designed to handle data with constant elapsed times between consecutive elements of the sequence. Given that time lapse between successive elements in patient records can vary from days to months, the design of traditional LSTM may lead to suboptimal performance. In this paper, we propose a novel LSTM unit called Time Aware LSTM (T-LSTM) to handle irregular time intervals in longitudinal patient records. We learn a subspace decomposition of the cell memory which enables time decay to discount the memory content according to the elapsed time. We propose a patient subtyping model that leverages the proposed T-LSTM in an auto-encoder to learn a powerful single representation for sequential records of patients, which are then used to cluster patients into clinical subtypes. Experiments on synthetic and real world datasets show that the proposed T-LSTM architecture captures the underlying structures in the sequences with time irregularities.

http://www.kdd.org/kdd2017/papers/view/patient-subtyping-via-time-aware-lstm-networks