Similarity Computing on Electronic Health Records

Similarity computing on real world applications like Electronic Health Records (EHRs) can reveal numerous interesting knowledge. Similarity measures the closeness between comparable things such as patients. Like similarity computing amongst Intensive Care Unit (ICU) patients can create various benefits, such as case based patient retrieval, unearthing of similar patient groups. However, many classical methods such as euclidean distance, cosine similarity can’t be directly applicable as similarity computing in EHRs is subjective and in many cases conditional. Also, many intrinsic relationships between the data are lost due to poor data representation and conversion. To address these challenges, firstly, we propose structural network representation for EHRs to preserve inherent relationship. And, to make them more comparable, we do data enrichment e.g. adding abstract information. Then, we extract different similarity feature sets to generate different similarity metrics and retrieve top similar patients. Finally, we perform experiment which shows promising results over classical methods

Processing of Electronic Health Records using Deep Learning: A review

Availability of large amount of clinical data is opening up new research avenues in a number of fields. An exciting field in this respect is healthcare, where secondary use of healthcare data is beginning to revolutionize healthcare. Except for availability of Big Data, both medical data from healthcare institutions (such as EMR data) and data generated from health and wellbeing devices (such as personal trackers), a significant contribution to this trend is also being made by recent advances on machine learning, specifically deep learning algorithms

Deepr: A Convolutional Net for Medical Records

Feature engineering remains a major bottleneck when creating predictive systems from electronic medical records. At present, an important missing element is detecting predictive regular clinical motifs from irregular episodic records. We present Deepr (short for Deep record), a new end-to-end deep learning system that learns to extract features from medical records and predicts future risk automatically. Deepr transforms a record into a sequence of discrete elements separated by coded time gaps and hospital transfers. On top of the sequence is a convolutional neural net that detects and combines predictive local clinical motifs to stratify the risk. Deepr permits transparent inspection and visualization of its inner working. We validate Deepr on hospital data to predict unplanned readmission after discharge. Deepr achieves superior accuracy compared to traditional techniques, detects meaningful clinical motifs, and uncovers the underlying structure of the disease and intervention space