Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records

Abstract

Secondary use of electronic health records (EHRs) promises to advance clinical research and better inform clinical decision making. Challenges in summarizing and representing patient data prevent widespread practice of predictive modeling using EHRs. Here we present a novel unsupervised deep feature learning method to derive a general-purpose patient representation from EHR data that facilitates clinical predictive modeling. In particular, a three-layer stack of denoising autoencoders was used to capture hierarchical regularities and dependencies in the aggregated EHRs of about 700,000 patients from the Mount Sinai data warehouse. The result is a representation we name “deep patient”. We evaluated this representation as broadly predictive of health states by assessing the probability of patients to develop various diseases. We performed evaluation using 76,214 test patients comprising 78 diseases from diverse clinical domains and temporal windows. Our results significantly outperformed those achieved using representations based on raw EHR data and alternative feature learning strategies. Prediction performance for severe diabetes, schizophrenia, and various cancers were among the top performing. These findings indicate that deep learning applied to EHRs can derive patient representations that offer improved clinical predictions, and could provide a machine learning framework for augmenting clinical decision systems.

Leveraging uncertainty information from deep neural networks for disease detection

“…By stacking layers of linear convolutions with appropriate non-linearities 4 , abstract concepts can be learnt from high-dimensional input alleviating the challenging and time-consuming task of hand-crafting algorithms. Such DNNs are quickly entering the field of medical imaging and diagnosis [5][6][7][8][9][10][11][12][13][14][15] , outperforming state-of-the-art methods at disease detection or allowing one to tackle problems that had previously been out of reach. Applied at scale, such systems could considerably alleviate the workload of physicians by detecting patients at risk from a prescreening examination.…”

L’intelligence artificielle à l’aide des myologues

“…Autre apport important, ces outils permettent une collecte exhaustive et standardisée de données médicales complexes et aident à leur interprétation dans le cadre de l'analyse et de la gestion de l'état de santé d'un individu et de potentielles atteintes à venir [19]. Les médecins sont aujourd'hui victimes de la progression vertigineuse des connaissances médicales (le temps de doublement des connaissances médicales en 1950 était de 50 ans, en 2010 de 3,5 ans et en 2020, il devrait être de seulement 73 jours) [20].…”

“…Pour cela on peut aborder les choses suivant deux approches diffé-rentes : la première est basée sur la quantité d'informations présentes dans les bases de données (« data-driven » [19]), la seconde sur la qualité de ces données (« goal-driven » [32] …”

Status and Direction of Healthcare Data in Korea for Artificial Intelligence

“…Recently, artificial intelligence (AI) has been highlighted in various areas including healthcare [1][2][3][4]. AI can be categorized into symbolic AI such as expert systems and machine learning (ML), which includes deep learning.…”