Classification of postoperative surgical site infections from blood measurements with missing data using recurrent neural networks

Clinical measurements that can be represented as time series constitute an important fraction of the electronic health records and are often both uncertain and incomplete. Recurrent neural networks are a special class of neural networks that are particularly suitable to process time series data but, in their original formulation, cannot explicitly deal with missing data. In this paper, we explore imputation strategies for handling missing values in classifiers based on recurrent neural network (RNN) and apply a recently proposed recurrent architecture, the Gated Recurrent Unit with Decay, specifically designed to handle missing data. We focus on the problem of detecting surgical site infection in patients by analyzing time series of their blood sample measurements and we compare the results obtained with different RNN-based classifiers

Predicting infections using computational intelligence – A systematic review

Infections encompass a set of medical conditions of very diverse kinds that can pose a significant risk to health, and even death. As with many other diseases, early diagnosis can help to provide patients with proper care to minimize the damage produced by the disease, or to isolate them to avoid the risk of spread. In this context, computational intelligence can be useful to predict the risk of infection in patients, raising early alarms that can aid medical teams to respond as quick as possible. In this paper, we survey the state of the art on infection prediction using computer science by means of a systematic literature review. The objective is to find papers where computational intelligence is used to predict infections in patients using physiological data as features. We have posed one major research question along with nine specific subquestions. The whole review process is thoroughly described, and eight databases are considered which index most of the literature published in different scholarly formats. A total of 101 relevant documents have been found in the period comprised between 2003 and 2019, and a detailed study of these documents is carried out to classify the works and answer the research questions posed, resulting to our best knowledge in the most comprehensive study of its kind. We conclude that the most widely addressed infection is by far sepsis, followed by Clostridium difficile infection and surgical site infections. Most works use machine learning techniques, from which logistic regression, support vector machines, random forest and naive Bayes are the most common. Some machine learning works provide some ideas on the problems of small data and class imbalance, which can be of interest. The current systematic literature review shows that automatic diagnosis of infectious diseases using computational intelligence is well documented in the medical literature.publishedVersio

Automated Detection of External Ventricular and Lumbar Drain-Related Meningitis Using Laboratory and Microbiology Results and Medication Data

OBJECTIVE: Monitoring of healthcare-associated infection rates is important for infection control and hospital benchmarking. However, manual surveillance is time-consuming and susceptible to error. The aim was, therefore, to develop a prediction model to retrospectively detect drain-related meningitis (DRM), a frequently occurring nosocomial infection, using routinely collected data from a clinical data warehouse. METHODS: As part of the hospital infection control program, all patients receiving an external ventricular (EVD) or lumbar drain (ELD) (2004 to 2009; n = 742) had been evaluated for the development of DRM through chart review and standardized diagnostic criteria by infection control staff; this was the reference standard. Children, patients dying <24 hours after drain insertion or with <1 day follow-up and patients with infection at the time of insertion or multiple simultaneous drains were excluded. Logistic regression was used to develop a model predicting the occurrence of DRM. Missing data were imputed using multiple imputation. Bootstrapping was applied to increase generalizability. RESULTS: 537 patients remained after application of exclusion criteria, of which 82 developed DRM (13.5/1000 days at risk). The automated model to detect DRM included the number of drains placed, drain type, blood leukocyte count, C-reactive protein, cerebrospinal fluid leukocyte count and culture result, number of antibiotics started during admission, and empiric antibiotic therapy. Discriminatory power of this model was excellent (area under the ROC curve 0.97). The model achieved 98.8% sensitivity (95% CI 88.0% to 99.9%) and specificity of 87.9% (84.6% to 90.8%). Positive and negative predictive values were 56.9% (50.8% to 67.9%) and 99.9% (98.6% to 99.9%), respectively. Predicted yearly infection rates concurred with observed infection rates. CONCLUSION: A prediction model based on multi-source data stored in a clinical data warehouse could accurately quantify rates of DRM. Automated detection using this statistical approach is feasible and could be applied to other nosocomial infections

Automated Detection Of Surgical Adverse Events From Retrospective Clinical Data

University of Minnesota Ph.D. dissertation. August 2017. Major: Health Informatics. Advisors: GENEVIEVE MELTON-MEAUX, GYORGY SIMON. 1 computer file (PDF); iv 101 pages.The Detection of surgical adverse events has become increasingly important with the growing demand for quality improvement and public health surveillance with surgery. Event reporting is one of the key steps in determining the impact of postoperative complications from a variety of perspectives and is an integral component of improving transparency around surgical care and ultimately around addressing complications. Manual chart review is the most commonly used method in identification of adverse events. Though the manual chart review is the most commonly used method that is considered the “gold-standard” for detecting adverse events for many patient safety studies (research setting), it could be very labor-intensive and time-consuming and thus many hospitals have found it too expensive to routinely use. In this dissertation, aiming to accelerate the process of extracting postoperative outcomes from medical charts, an automated postoperative adverse events detection application has been developed by using structured electronic health record (EHR) data and unstructured clinical notes. First, pilot studies are conducted to test the feasibility by using only completed EHR data and focusing on three types of surgical site infection (SSI). The built models have high specificity as well as very high negative predictive values, reliably eliminating the vast majority of patients without SSI, thereby significantly reducing the chart reviewers’ burden. Practical missing data treatments have also been explored and compared. To address modeling challenges, such as high-dimensional dataset, and imbalanced distribution, several machine learning methods haven been applied. Particularly, one single-task and five multi-task learning methods are developed and compared for their detection performance. The models demonstrated high detection performance, which ensures the feasibility of accelerating the manual process of extracting postoperative outcomes from medical chart. Finally, the use of structured EHR data, clinical notes and the combination of these data types have been separately investigated. Models using different types of data were compared on their detection performance. Models developed with very high AUC score have demonstrated that supervised machine learning methods can be effective for automated detection of surgical adverse events

Information technology aspects of large-scale implementation of automated surveillance of healthcare-associated infections

PRAISE network: Maaike S. M. van Mourik, Stephanie M.van Rooden, Mohamed Abbas, Olov Aspevall, Pascal Astagneau, Marc J. M. Bonten, Elena Carrara, Aina Gomila-Grange, Sabine C. de Greeff , Sophie Gubbels, Wendy Harrison, Hilary Humphreys, Anders Johansson, Mayke B. G. Koek, Brian Kristensen, Alain Lepape, Jean-Christophe Lucet, Siddharth Mookerjee, Pontus Naucler, Zaira R. Palacios-Baena, Elisabeth Presterl, Miquel Pujol, Jacqui Reilly, Christopher Roberts, Evelina Tacconelli, Daniel Teixeira, Thomas Tängdén, John Karlsson Valik, Michael Behnke, PetraGastmeier.[Introduction] Healthcare-associated infections (HAI) are a major public health concern. Monitoring of HAI rates, with feedback, is a core component of infection prevention and control programmes. Digitalization of healthcare data has created novel opportunities for automating the HAI surveillance process to varying degrees. However, methods are not standardized and vary widely between different healthcare facilities. Most current automated surveillance (AS) systems have been confined to local settings, and practical guidance on how to implement large-scale AS is needed.[Methods] This document was written by a task force formed in March 2019 within the PRAISE network (Providing a Roadmap for Automated Infection Surveillance in Europe), gathering experts in HAI surveillance from ten European countries.[Results] The document provides an overview of the key e-health aspects of implementing an AS system of HAI in a clinical environment to support both the infection prevention and control team and information technology (IT) departments. The focus is on understanding the basic principles of storage and structure of healthcare data, as well as the general organization of IT infrastructure in surveillance networks and participating healthcare facilities. The fundamentals of data standardization, interoperability and algorithms in relation to HAI surveillance are covered. Finally, technical aspects and practical examples of accessing, storing and sharing healthcare data within a HAI surveillance network, as well as maintenance and quality control of such a system, are discussed.[Conclusions] With the guidance given in this document, along with the PRAISE roadmap and governance documents, readers will find comprehensive support to implement large-scale AS in a surveillance network.This network has been supported under the 7th transnational call within the Joint Programming Initiative on Antimicrobial Resistance (JPIAMR), Network Call on Surveillance (2018) and was thereby funded by ZonMw (grant 549007001). This project also received support from the COMBACTE MAGNET EPI-Net project funded by the Innovative Medicines Initiative Joint Undertaking under grant agreement 115523 | 115620 | 115737 | 777362, resources of which are composed of financial contribution from the European Union Seventh Framework Programme (FP7/2007-2013) and EFPIA companies in kind contribution. J.K.V. was supported by grants from Region Stockholm and Vinnova.Peer reviewe

PRAISE: providing a roadmap for automated infection surveillance in Europe

Introduction: Healthcare-associated infections (HAI) are among the most common adverse events of medical care. Surveillance of HAI is a key component of successful infection prevention programmes. Conventional surveillance - manual chart review - is resource intensive and limited by concerns regarding interrater reliability. This has led to the development and use of automated surveillance (AS). Many AS systems are the product of in-house development efforts and heterogeneous in their design and methods. With this roadmap, the PRAISE network aims to provide guidance on how to move AS from the research setting to large-scale implementation, and how to ensure the delivery of surveillance data that are uniform and useful for improvement of quality of care. Methods: The PRAISE network brings together 30 experts from ten European countries. This roadmap is based on the outcome of two workshops, teleconference meetings and review by an independent panel of international experts. Results: This roadmap focuses on the surveillance of HAI within networks of healthcare facilities for the purpose of comparison, prevention and quality improvement initiatives. The roadmap does the following: discusses the selection of surveillance targets, different organizational and methodologic approaches and their advantages, disadvantages and risks; defines key performance requirements of AS systems and suggestions for their design; provides guidance on successful implementation and maintenance; and discusses areas of future research and training requirements for the infection prevention and related disciplines. The roadmap is supported by accompanying documents regarding the governance and information technology aspects of implementing AS. Conclusions: Large-scale implementation of AS requires guidance and coordination within and across surveillance networks. Transitions to large-scale AS entail redevelopment of surveillance methods and their interpretation, intensive dialogue with stakeholders and the investment of considerable resources. This roadmap can be used to guide future steps towards implementation, including designing solutions for AS and practical guidance checklists