scholarly journals Deep Multi-Modal Transfer Learning for Augmented Patient Acuity Assessment in the Intelligent ICU

2021 ◽  
Vol 3 ◽  
Author(s):  
Benjamin Shickel ◽  
Anis Davoudi ◽  
Tezcan Ozrazgat-Baslanti ◽  
Matthew Ruppert ◽  
Azra Bihorac ◽  
...  

Accurate prediction and monitoring of patient health in the intensive care unit can inform shared decisions regarding appropriateness of care delivery, risk-reduction strategies, and intensive care resource use. Traditionally, algorithmic solutions for patient outcome prediction rely solely on data available from electronic health records (EHR). In this pilot study, we explore the benefits of augmenting existing EHR data with novel measurements from wrist-worn activity sensors as part of a clinical environment known as the Intelligent ICU. We implemented temporal deep learning models based on two distinct sources of patient data: (1) routinely measured vital signs from electronic health records, and (2) activity data collected from wearable sensors. As a proxy for illness severity, our models predicted whether patients leaving the intensive care unit would be successfully or unsuccessfully discharged from the hospital. We overcome the challenge of small sample size in our prospective cohort by applying deep transfer learning using EHR data from a much larger cohort of traditional ICU patients. Our experiments quantify added utility of non-traditional measurements for predicting patient health, especially when applying a transfer learning procedure to small novel Intelligent ICU cohorts of critically ill patients.

2018 ◽  
Vol 146 (8) ◽  
pp. 1065-1069
Author(s):  
John C. O'Horo ◽  
Mikhail Dziadzko ◽  
Amra Sakusic ◽  
Rashid Ali ◽  
M. Rizwan Sohail ◽  
...  

AbstractThe definition of severe acute respiratory infection (SARI) – a respiratory illness with fever and cough, occurring within the past 10 days and requiring hospital admission – has not been evaluated for critically ill patients. Using integrated electronic health records data, we developed an automated search algorithm to identify SARI cases in a large cohort of critical care patients and evaluate patient outcomes. We conducted a retrospective cohort study of all admissions to a medical intensive care unit from August 2009 through March 2016. Subsets were randomly selected for deriving and validating a search algorithm, which was compared with temporal trends in laboratory-confirmed influenza to ensure that SARI was correlated with influenza. The algorithm was applied to the cohort to identify clinical differences for patients with and without SARI. For identifying SARI, the algorithm (sensitivity, 86.9%; specificity, 95.6%) outperformed billing-based searching (sensitivity, 73.8%; specificity, 78.8%). Automated searching correlated with peaks in laboratory-confirmed influenza. Adjusted for severity of illness, SARI was associated with more hospital, intensive care unit and ventilator days but not with death or dismissal to home. The search algorithm accurately identified SARI for epidemiologic study and surveillance.


2018 ◽  
Vol 84 (6) ◽  
pp. 875-880
Author(s):  
Timothy R. Romanauski ◽  
Erin E. Martin ◽  
Juraj Sprung ◽  
David P. Martin ◽  
Darrell R. Schroeder ◽  
...  

Postoperative delirium (POD) is common among surgical patients admitted to the intensive care unit (ICU) and is associated with increased resource utilization, morbidity, and death. Our primary aim was to compare rates of POD using administrative International Classification of Diseases, Ninth Revision, records and automated interrogation of electronic health records from Confusion Assessment Method for the ICU (CAM-ICU) screening. The secondary aim was to assess POD risk associated with patient and perioperative characteristics. Electronic health records of surgical patients admitted to the ICU during 2011 through 2014 were abstracted for POD assessment by CAM-ICU and by administrative codes, Charlson comorbidity index, surgical characteristics, and Acute Physiology, Age, Chronic Health Evaluation III scores. Of 6338 patients, CAM-ICU identified 606 (9.6%) and administrative records identified 55 (0.9%) POD cases, with agreement on 50 cases. In multivariable logistic regression based on POD identified with CAM-ICU, preexisting dementia had the strongest association with POD (odds ratio [95% confidence interval], 6.47 [3.68–11.37]; P < 0.001). Other associations found were older age, congestive heart failure, chronic pulmonary disease, increased surgical duration, emergency cases, blood transfusions, postoperative ventilation, and higher Acute Physiology, Age, Chronic Health Evaluation III scores (all P ≤ 0.01). POD cases had lengthier ICU and hospital stays and a higher mortality rate (all P < 0.001). CAM-ICU scores identified higher rates of POD than a search for POD based on administrative codes. Preoperative presence of dementia and major comorbidities were associated with POD. Delirium in surgical patients is associated with worse outcomes.


2019 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Saif Khairat ◽  
Stevan Whitt ◽  
Catherine K. Craven ◽  
Youngju Pak ◽  
Chi-Ren Shyu ◽  
...  

Author(s):  
Seungwon Lee ◽  
Yuan Xu ◽  
Adam G D'Souza ◽  
Elliot A Martin ◽  
Chelsea Doktorchik ◽  
...  

Electronic health records (EHRs), originally designed to facilitate health care delivery, are becoming a valuable data source for health research. EHR systems have two components: the front end, where the data is entered by healthcare workers including physicians and nurses, and the back-end electronic data warehouse where the data is stored in a relational database. EHR data elements can be of many types, which can be categorized as structured, unstructured free-text, and imaging data. The Sunrise Clinical Manager (SCM) EHR is one example of an inpatient EHR system, which covers the city of Calgary (Alberta, Canada). This system, under the management of Alberta Health Services, is now being explored for research use. The purpose of the present paper is to describe the SCM EHR for research purposes, showing how this generalizes to EHRs in general. We further discuss advantages, challenges (e.g. potential bias and data quality issues), and analytical capacities and requirements associated with using EHRs.


2021 ◽  
Vol 30 (10) ◽  
pp. 2221-2238
Author(s):  
Sarah B Peskoe ◽  
David Arterburn ◽  
Karen J Coleman ◽  
Lisa J Herrinton ◽  
Michael J Daniels ◽  
...  

While electronic health records data provide unique opportunities for research, numerous methodological issues must be considered. Among these, selection bias due to incomplete/missing data has received far less attention than other issues. Unfortunately, standard missing data approaches (e.g. inverse-probability weighting and multiple imputation) generally fail to acknowledge the complex interplay of heterogeneous decisions made by patients, providers, and health systems that govern whether specific data elements in the electronic health records are observed. This, in turn, renders the missing-at-random assumption difficult to believe in standard approaches. In the clinical literature, the collection of decisions that gives rise to the observed data is referred to as the data provenance. Building on a recently-proposed framework for modularizing the data provenance, we develop a general and scalable framework for estimation and inference with respect to regression models based on inverse-probability weighting that allows for a hierarchy of missingness mechanisms to better align with the complex nature of electronic health records data. We show that the proposed estimator is consistent and asymptotically Normal, derive the form of the asymptotic variance, and propose two consistent estimators. Simulations show that naïve application of standard methods may yield biased point estimates, that the proposed estimators have good small-sample properties, and that researchers may have to contend with a bias-variance trade-off as they consider how to handle missing data. The proposed methods are motivated by an on-going, electronic health records-based study of bariatric surgery.


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