PCN286 GENERALIZABLE MACHINE LEARNING FRAMEWORK FOR PREDICTIVE MODELING OF PATIENT OUTCOMES USING ONCOLOGY ELECTRONIC HEALTH RECORDS

Abstract Background Severe sepsis and septic shock are among the leading causes of death in the United States and sepsis remains one of the most expensive conditions to diagnose and treat. Accurate early diagnosis and treatment can reduce the risk of adverse patient outcomes, but the efficacy of traditional rule-based screening methods is limited. The purpose of this study was to develop and validate a machine learning algorithm (MLA) for severe sepsis prediction up to 48 h before onset using a diverse patient dataset. Methods Retrospective analysis was performed on datasets composed of de-identified electronic health records collected between 2001 and 2017, including 510,497 inpatient and emergency encounters from 461 health centers collected between 2001 and 2015, and 20,647 inpatient and emergency encounters collected in 2017 from a community hospital. MLA performance was compared to commonly used disease severity scoring systems and was evaluated at 0, 4, 6, 12, 24, and 48 h prior to severe sepsis onset. Results 270,438 patients were included in analysis. At time of onset, the MLA demonstrated an AUROC of 0.931 (95% CI 0.914, 0.948) and a diagnostic odds ratio (DOR) of 53.105 on a testing dataset, exceeding MEWS (0.725, P < .001; DOR 4.358), SOFA (0.716; P < .001; DOR 3.720), and SIRS (0.655; P < .001; DOR 3.290). For prediction 48 h prior to onset, the MLA achieved an AUROC of 0.827 (95% CI 0.806, 0.848) on a testing dataset. On an external validation dataset, the MLA achieved an AUROC of 0.948 (95% CI 0.942, 0.954) at the time of onset, and 0.752 at 48 h prior to onset. Conclusions The MLA accurately predicts severe sepsis onset up to 48 h in advance using only readily available vital signs extracted from the existing patient electronic health records. Relevant implications for clinical practice include improved patient outcomes from early severe sepsis detection and treatment.

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Unifying Heterogenous Electronic Health Records Systems via Text-Based Code Embedding: Study of Predictive Modeling (Preprint)

10.2196/preprints.32523 ◽

2021 ◽

Author(s):

Kyunghoon Hur ◽

Jiyoung Lee ◽

Jungwoo Oh ◽

Wesley Price ◽

Young-Hak Kim ◽

...

Keyword(s):

Electronic Health Records ◽

Predictive Modeling ◽

Large Scale ◽

Representation Learning ◽

Superior Performance ◽

Health Records ◽

Learning Framework ◽

Learning Scenarios ◽

Electronic Health ◽

Medical Concepts

BACKGROUND Substantial increase in the use of Electronic Health Records (EHRs) has opened new frontiers for predictive healthcare. However, while EHR systems are nearly ubiquitous, they lack a unified code system for representing medical concepts. Heterogeneous formats of EHR present a substantial barrier for the training and deployment of state-of-the-art deep learning models at scale. OBJECTIVE The aim of this study is to suggest a novel text embedding approach to overcome heterogeneity of EHR structure among different EHR systems. METHODS We introduce Description-based Embedding, DescEmb, a code-agnostic description-based representation learning framework for predictive modeling on EHR. DescEmb takes advantage of the flexibility of neural language understanding models while maintaining a neutral approach that can be combined with prior frameworks for task-specific representation learning or predictive modeling. RESULTS Based on five prediction tasks with two heterogeneous EHR datasets, DescEmb achieves comparable or superior performance to the traditional code-based embedding approach, especially under the zero-shot and few-shot transfer learning scenarios. We also demonstrate that DescEmb enables us to train a single model on a pooled dataset from heterogeneous EHR systems and achieve the same, if not better performance compared to training separate models for each EHR system. CONCLUSIONS Based on the promising results, we believe the description-based embedding approach on EHR will open a new direction for large-scale predictive modeling in healthcare.

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Predicting dementia diagnosis from cognitive footprints in electronic health records: a case–control study protocol

BMJ Open ◽

10.1136/bmjopen-2020-043487 ◽

2020 ◽

Vol 10 (11) ◽

pp. e043487

Author(s):

Hao Luo ◽

Kui Kai Lau ◽

Gloria H Y Wong ◽

Wai-Chi Chan ◽

Henry K F Mak ◽

...

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Hong Kong ◽

Electronic Health Records ◽

Case Control Study ◽

Case Control ◽

Dementia Diagnosis ◽

Health Records ◽

Electronic Health ◽

Control Study

IntroductionDementia is a group of disabling disorders that can be devastating for persons living with it and for their families. Data-informed decision-making strategies to identify individuals at high risk of dementia are essential to facilitate large-scale prevention and early intervention. This population-based case–control study aims to develop and validate a clinical algorithm for predicting dementia diagnosis, based on the cognitive footprint in personal and medical history.Methods and analysisWe will use territory-wide electronic health records from the Clinical Data Analysis and Reporting System (CDARS) in Hong Kong between 1 January 2001 and 31 December 2018. All individuals who were at least 65 years old by the end of 2018 will be identified from CDARS. A random sample of control individuals who did not receive any diagnosis of dementia will be matched with those who did receive such a diagnosis by age, gender and index date with 1:1 ratio. Exposure to potential protective/risk factors will be included in both conventional logistic regression and machine-learning models. Established risk factors of interest will include diabetes mellitus, midlife hypertension, midlife obesity, depression, head injuries and low education. Exploratory risk factors will include vascular disease, infectious disease and medication. The prediction accuracy of several state-of-the-art machine-learning algorithms will be compared.Ethics and disseminationThis study was approved by Institutional Review Board of The University of Hong Kong/Hospital Authority Hong Kong West Cluster (UW 18-225). Patients’ records are anonymised to protect privacy. Study results will be disseminated through peer-reviewed publications. Codes of the resulted dementia risk prediction algorithm will be made publicly available at the website of the Tools to Inform Policy: Chinese Communities’ Action in Response to Dementia project (https://www.tip-card.hku.hk/).

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