Detecting hospital-acquired infections : A document classification approach using support vector machines and gradient tree boosting

Hospital-acquired infections pose a significant risk to patient health, while their surveillance is an additional workload for hospital staff. Our overall aim is to build a surveillance system that reliably detects all patient records that potentially include hospital-acquired infections. This is to reduce the burden of having the hospital staff manually check patient records. This study focuses on the application of text classification using support vector machines and gradient tree boosting to the problem. Support vector machines and gradient tree boosting have never been applied to the problem of detecting hospital-acquired infections in Swedish patient records, and according to our experiments, they lead to encouraging results. The best result is yielded by gradient tree boosting, at 93.7percent recall, 79.7percent precision and 85.7percent F1 score when using stemming. We can show that simple preprocessing techniques and parameter tuning can lead to high recall (which we aim for in screening patient records) with appropriate precision for this task.Peer reviewe

Detecting hospital-acquired infections: A document classification approach using support vector machines and gradient tree boosting

Peer reviewe

Artificial intelligence-based tools to control healthcare associated infections: A systematic review of the literature

Background: Healthcare-associated infections (HAIs) are the most frequent adverse events in healthcare and a global public health concern. Surveillance is the foundation for effective HAIs prevention and control. Manual surveillance is labor intensive, costly and lacks standardization. Artificial Intelligence (AI) and machine learning (ML) might support the development of HAI surveillance algorithms aimed at understanding HAIs risk factors, improve patient risk stratification, identification of transmission pathways, timely or real-time detection. Scant evidence is available on AI and ML implementation in the field of HAIs and no clear patterns emerges on its impact. Methods: We conducted a systematic review following the PRISMA guidelines to systematically retrieve, quantitatively pool and critically appraise the available evidence on the development, implementation, performance and impact of ML-based HAIs detection models. Results: Of 3445 identified citations, 27 studies were included in the review, the majority published in the US (n = 15, 55.6%) and on surgical site infections (SSI, n = 8, 29.6%). Only 1 randomized controlled trial was included. Within included studies, 17 (63%) ML approaches were classified as predictive and 10 (37%) as retrospective. Most of the studies compared ML algorithms' performance with non-ML logistic regression statistical algorithms, 18.5% compared different ML models' performance, 11.1% assessed ML algorithms' performance in comparison with clinical diagnosis scores, 11.1% with standard or automated surveillance models. Overall, there is moderate evidence that ML-based models perform equal or better as compared to non-ML approaches and that they reach relatively high-performance standards. However, heterogeneity amongst the studies is very high and did not dissipate significantly in subgroup analyses, by type of infection or type of outcome. Discussion: Available evidence mainly focuses on the development and testing of HAIs detection and prediction models, while their adoption and impact for research, healthcare quality improvement, or national surveillance purposes is still far from being explored

Biosafety standards for working with Crimean-Congo haemorrhagic fever virus

In countries from which Crimean-Congo haemorrhagic fever (CCHF) is absent, the causative virus CCHF virus (CCHFV) is classified as a hazard group 4 agent and handled in containment level 4. In contrast, most endemic countries out of necessity have had to perform diagnostic tests under biosafety level (BSL) -2 or -3 conditions. In particular, Turkey and several of the Balkan countries have safely processed more than 100000 samples over many years in BSL-2 laboratories. It is therefore advocated that biosafety requirements for CCHF diagnostic procedures should be revised, to allow the required tests to be performed under enhanced BSL-2 conditions with appropriate biosafety laboratory equipment and personal protective equipment used according to standardized protocols in the affected countries. Downgrading of CCHFV research work from Cl-4, BSL-4 to Cl-3, BSL-3 should also be considered.Additional co-authors: Gülay Korukluoglu, Pieter Lyssen, Ali Mirazimi, Johan Neyts, Matthias Niedrig, Aykut Ozkul, Anna Papa, Janusz Paweska, Amadou A Sall, Connie S Schmaljohn, Robert Swanepoel, Yavuz Uyar, Friedemann Weber, Herve Zelle

One-Class Classification: Taxonomy of Study and Review of Techniques

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure

Use of matrix-assisted laser desorption ionization-time of flight mass spectrometry to identify vancomycin-resistant enterococci and investigate the epidemiology of an outbreak

The control of vancomycin-resistant enterococci (VRE) has become an increasing burden on health care resources since their discovery over 20 years ago. Current techniques employed for their detection include time-consuming and laborious phenotypic methods or molecular methods requiring costly equipment and consumables and highly trained staff. An accurate, rapid diagnostic test has the ability to greatly reduce the spread of this organism, which has the ability to colonize patients for long periods, potentially even lifelong. Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) is a technology with the ability to identify organisms in seconds and has shown promise in the identification of other forms of antimicrobial resistance in other organisms. Here we show that MALDI-TOF MS is capable of rapidly and accurately identifying vanB-positive Enterococcus faecium VRE from susceptible isolates. Internal validation of the optimal model generated produced a sensitivity of 92.4% and a specificity of 85.2%. Prospective validation results, following incorporation into the routine laboratory work flow, demonstrated a greater sensitivity and specificity at 96.7% and 98.1%, respectively. In addition, the utilization of MALDI-TOF MS to determine the relatedness of isolates contributing to an outbreak is also demonstrated

An Early Warning System for Hospital Acquired Pneumonia

Pneumonia is a dangerous, often fatal secondary disease acquired by patients during their stay at Intensive Care Units. ICU patients have scores of data collected on a real time basis. Based on two years of data for a large ICU, we develop an early warning system for the onset of pneumonia that is based on Alternating Decision Trees for supervised learning, Sequential Pattern Mining, and the stacking paradigm to combine the two. Mainly due to decreased stay, the system will save € 180000 in this hospital alone while at the same time increasing the quality and consistent standard of health care. The ultimate system relies on a rather small numeric data base alone and is thus amenable to integration in a treatment protocol and a newly conceived ICU workflow system

Inactivation of Bacteriophage Φ6 on Tyvek Suit Surfaces by Chemical Disinfection

The 2014 West Africa Ebola outbreak saw a substantial number of healthcare workers (HCWs) being infected, despite the use of personal protective equipment (PPE). PPE is intended to protect HCWs when caring for patients with Ebola virus disease (EVD), but PPE may play a role in the spread of Ebola in healthcare environments. Before the removal of PPE, chemical disinfection may prevent the transfer of pathogens to HCWs, but the efficacy of common disinfectants against enveloped viruses, such as Ebola, on PPE surfaces is relatively unknown. The purpose of this study is to assess the efficacy of two common disinfectants, chlorine bleach (Clorox® bleach) and quaternary ammonium (Micro-Chem Plus®), used in healthcare settings for inactivation of enveloped viruses on PPE. The virucidal activity of the two disinfectants were tested against bacteriophage Φ6, an enveloped, non-pathogenic surrogate for enveloped viruses, on Tyvek suit surfaces. Virus was dried onto Tyvek suit surface, exposed to the disinfectants at use-dilution for a contact time of one minute, and the surviving virus was quantified using a double agar layer (DAL) assay. The Clorox® bleach and Micro-Chem Plus® produced a \u3e3.21 log10 reduction and \u3e4.33 log10 reduction, respectively, in Φ6 infectivity. The results of this study suggest that chlorine bleach and quaternary ammonium are effective in the inactivation of enveloped viruses on Tyvek suit surfaces. Chemical disinfection of PPE should be considered as a viable method to reduce the spread of pathogenic, enveloped viruses to HCWs, patients, and other environmental surfaces in healthcare settings