Association of Electronic Health Records with Methicillin-Resistant Staphylococcus aureus Infection in a National Sample

This study examined the relationship between advanced electronic health record (EHR) use in hospitals and rates of Methicillin-resistant Staphylococcus aureus (MRSA) infection in an inpatient setting. National Inpatient Sample (NIS) and Health Information Management Systems Society (HIMSS) Annual Survey are combined in the retrospective, cross-sectional analysis. A twenty percent simple random sample of the combined 2009 NIS and HIMSS datasets included a total of 1,032,905 patient cases of MRSA in 550 hospitals. Results of the propensity-adjusted logistic regression model revealed a statistically significant association between advanced EHR and MRSA, with patient cases from an advanced EHR being less likely to report a MRSA diagnosis code

Graph Convolutional Networks for Predictive Healthcare using Clinical Notes

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2020. 8. 김선.Clinical notes in Electronic Health Record(EHR) system are recorded in free text forms with different styles and abbreviations of personal preference. Thus, it is very difficult to extract clinically meaningful information from EHR clinical notes. There are many computational methods developed for tasks such as medical text normalization, medical entity extraction, and patient-level prediction tasks. Existing methods for the patient-level prediction task focus on capturing the contextual or sequential information from clinical texts, but they are not designed to capture global and non-consecutive information in the clinical texts. Recently, graph convolutional neural networks(GCNs) are successfully used for text-based classification since GCN can extract the global and long-distance information among the whole texts. However, application of GCN for mining clinical notes is yet to be fully explored. In this study, we propose an end-to-end framework for the analysis of clinical notes using graph neural network-based techniques to predict whether a patient is with MRSA (Methicillin-Resistant Staphylococcus Aureus) positive infection or negative infection. For this MRSA infection prediction, it is critical to capture the patient-specific and global non-consecutive information from patient clinical notes. The clinical notes of a patient are processed to construct a patient-level graph, and each patient-level graph is fed into the GCN-based framework for graph-level supervised learning. The proposed framework consists of a graph convolutional network layer, a graph pooling layer, and a readout layer, followed by a fully connected layer. We tested various settings of the GCN-based framework with various combinations of graph convolution operations and graph pooling methods and we evaluated the performance of each variant framework. In experiments with MRSA infection data, all of the variant frameworks with graph structure information outperformed several baseline methods without using graph structure information with a margin of 2.93%∼11.81%. We also investigated graphs in the pooling step to conduct interpretable analysis in population-based statistical and patient-specific aspects, respectively. With this inspection, we found long-distance word pairs that are distinct for MRSA positive patients and we also showed the pooled graph of the patient that contributes to the patient-specific prediction. Moreover, the Adaboost algorithm was used to improve the performance further. As a result, the framework proposed in this paper reached the highest performance of 85.70%, which is higher than the baseline methods with a margin of 3.71%∼12.59%.전자 건강 기록은 디지털 형태로 체계적으로 수집된 환자의 건강 정보다. 전자 건강 기록이 환자의 상태를 표현 하는 단어들로 구성된 문서의 집합이기때문에 자연어 처리 분야에 적용되는 다양한 기계학습적 방법들이 적용되어왔다. 특히, 딥러닝 기술의 발전으로 인해, 이미지나 텍스트 분야에서 활용 되던 딥러닝 기술 들이생명정보및의학정보분야에점차적용되고있다.하지만,기존의이미지나 텍스트데이터와는 다르게, 전자 건강 기록 데이터는 작성자 및 환자 개개인의 상태에 따라서, 데이터의 환자 특이성이 높다. 또한, 유사한 의미를 지니는 건강 기록들간의 상관관계를 고려해야 할 필요가있다. 본연구에서는 전자 건강 기록 데이터의 환자특이성을 고려한 그래프 기반 딥러닝 모델을 고안하였다. 환자의 전자 건강 기록 데이터와 의료 문서들의 공통 출현 빈도를 활용 하여 환자 특이적 그래프를 생성하였다. 이를 기반으로, 그래프 컨볼루션 네트워크를 사용하여 환자의 병리학적상태를예측하는모델을고안하였다. 연구에서 사용한 데이터는 Methicillin-Resistant Staphylococcus Aureus(MRSA) 감염 여부를 측정한 데이터이다. 고안한 그래프기반 딥러닝 모델을 통해 환자의 내성을 예측한 결과, 그래프정보를 활용 하지 않은 기존모델들 보다 2.93%∼11.81% 뛰어난성능을보였다. 또한 해석 가능한 분석을 수행하기 위해 풀링 단계에서 그래프를 조사했다.이를 통해 MRSA 양성 환자에 대해 구별되는 장거리 단어패턴을 찾았으며 환자별 예측에 기여하는 환자의 합동 그래프를 보여 주었다. 성능을 더욱 향상시키기 위해 아다부스트 알고리즘을 사용하였다. 본 논문에서 제안된 결과는 85.70%로 가장 높은 성능을 기록했으며, 이는 기존 모델보다 3.71%∼12.59%의 향상 시켰음을 보여주었다.Chapter 1 Introduction 1 1.1 Background 1 1.1.1 EHR Clinical Text Data 1 1.1.2 Current methods and limitations 3 1.2 Problem Statement and Contributions 4 Chapter 2 Related Works 6 2.1 Traditional Methods 6 2.2 Deep Learning Methods 7 2.3 Graph Neural Networks 8 2.3.1 Graph Convolutional Networks 8 2.3.2 Graph Pooling Methods 9 2.3.3 Applications of GNN 10 Chapter 3 Methods and Materials 12 3.1 Notation and Problem Definition 12 3.2 Patient Graph Construction Process 14 3.2.1 Parsing and Filtering 15 3.2.2 Word Co-occurrence Finding 16 3.2.3 Patient-level Graph Representation 16 3.3 Word Embedding 17 3.4 Model Architecture 18 3.4.1 Graph Convolutional Network layer 19 3.4.2 Graph Pooling layer 22 3.4.3 Readout Layer 24 3.5 Prediction and Loss Function 25 3.6 Adaboost algorithm 25 Chapter 4 Experiments 27 4.1 EHR Dataset 27 4.1.1 Introduction to MIMIC-III Dataset 27 4.1.2 MRSA Data Collection 28 4.2 Hyper Parameter Settings 28 4.2.1 Model Training 29 4.3 Baseline Models 30 Chapter 5 Results 32 5.1 Performance Comparisons with baseline models 32 5.2 Performance Comparisons with graph networks 33 5.3 Interpretable analysis 34 5.4 Adaboost Result 38 Chapter 6 Conclusion 40 국문초록 49 감사의 글 50Maste

Applications of Machine Learning in Medical Prognosis Using Electronic Medical Records

Approximately 84 % of hospitals are adopting electronic medical records (EMR) In the United States. EMR is a vital resource to help clinicians diagnose the onset or predict the future condition of a specific disease. With machine learning advances, many research projects attempt to extract medically relevant and actionable data from massive EMR databases using machine learning algorithms. However, collecting patients\u27 prognosis factors from Electronic EMR is challenging due to privacy, sensitivity, and confidentiality. In this study, we developed medical generative adversarial networks (GANs) to generate synthetic EMR prognosis factors using minimal information collected during routine care in specialized healthcare facilities. The generated prognosis variables used in developing predictive models for (1) chronic wound healing in patients diagnosed with Venous Leg Ulcers (VLUs) and (2) antibiotic resistance in patients diagnosed with Skin and soft tissue infections (SSTIs). Our proposed medical GANs, EMR-TCWGAN and DermaGAN, can produce both continuous and categorical features from EMR. We utilized conditional training strategies to enhance training and generate classified data regarding healing vs. non-healing in EMR-TCWGAN and susceptibility vs. resistance in DermGAN. The ability of the proposed GAN models to generate realistic EMR data was evaluated by TSTR (test on the synthetic, train on the real), discriminative accuracy, and visualization. We analyzed the synthetic data augmentation technique\u27s practicality in improving the wound healing prognostic model and antibiotic resistance classifier. We achieved the area under the curve (AUC) of 0.875 in the wound healing prognosis model and an average AUC of 0.830 in the antibiotic resistance classifier by using the synthetic samples generated by GANs in the training process. These results suggest that GANs can be considered a data augmentation method to generate realistic EMR data

Risk of hospital-acquired infections and drug resistance caused by gram-negative bacteria in patients with multiple hospitalizations

Patients who experience multiple hospitalizations over short periods of time may be at greater risk of hospital-acquired infections (HAIs). While it is known that prior hospitalizations are associated with HAIs, there is a gap in knowledge regarding which factors of prior hospitalizations have an impact on the risk of HAIs in subsequent hospitalizations. HAIs caused by gram-negative bacteria (GNB) are of particular concern due to their propensity to develop drug resistance and the limited antibiotics available to treat them. The aims of this dissertation are to: 1) examine clinical and patient risk factors associated with acquiring at least one gram-negative hospital-acquired infection in adult patients with multiple hospitalizations; 2) systematically review the literature assessing the association between repeat gram-negative bacterial infections and changes in antibiotic susceptibility patterns; and 3) assess the association between repeat infections with three common gram-negative pathogens and risk of subsequent drug resistant infections with the same species among patients with multiple hospitalizations. A retrospective cohort study was conducted to identify risk factors from prior hospitalizations associated with incident HAIs caused by three common GNB. Of the 129,372 patients with multiple hospitalizations, 1,672 (1.3%) acquired K. pneumoniae, 1,127 (0.9%) acquired P. aeruginosa, and 262 (0.2%) acquired A. baumannii infections. In survival analyses, older age, mechanical ventilation, history of chronic diseases, and increasing days of use of antibiotics decreased the time to infection for all 3 pathogens. This study highlights potential modifiable risk factors for infection control. Patients with multiple hospitalizations are also inherently at greater risk for repeat HAIs which may result in decreased antibiotic susceptibility, making them more difficult to treat. A systematic review was conducted to evaluate if there is an association between repeat GNB HAIs and drug resistance. From 2000 to 2015, only seven studies explicitly examined repeat GNB HAIs and change in antibiotic susceptibility, five of which reported decreased susceptibility in later infections. The association between repeat GNB HAIs and risk of drug resistance among patients with multiple hospitalizations was then investigated with available electronic medical record data. The risk of a drug-resistant K. pneumoniae HAI increased by 1.14 times (95%CI: 1.04-1.24) with each prior K. pneumoniae HAI, after adjusting for potential confounders and antibiotic use. Similarly, patients with repeat P. aeruginosa infections had a 1.23 times increased risk of a subsequent drug-resistant infection (95%CI: 1.12-1.36) with each prior P. aeruginosa HAI as compared to patients with only one infection. Repeat A. baumannii infections were not analyzed due to limited sample size. The studies in this dissertation demonstrate that patients with multiple hospitalizations are a high-risk population for GNB HAIs. Prevention of GNB HAIs in this group is critical in order to reduce complications to medical care and limit transmission of infections to others in healthcare facilities and the community. Patient medical history can be used for infection risk assessment and to guide future medical care to reduce risk of infection in patients with multiple hospitalizations

Towards bioinformatics assisted infectious disease control

BACKGROUND: This paper proposes a novel framework for bioinformatics assisted biosurveillance and early warning to address the inefficiencies in traditional surveillance as well as the need for more timely and comprehensive infection monitoring and control. It leverages on breakthroughs in rapid, high-throughput molecular profiling of microorganisms and text mining. RESULTS: This framework combines the genetic and geographic data of a pathogen to reconstruct its history and to identify the migration routes through which the strains spread regionally and internationally. A pilot study of Salmonella typhimurium genotype clustering and temporospatial outbreak analysis demonstrated better discrimination power than traditional phage typing. Half of the outbreaks were detected in the first half of their duration. CONCLUSION: The microbial profiling and biosurveillance focused text mining tools can enable integrated infectious disease outbreak detection and response environments based upon bioinformatics knowledge models and measured by outcomes including the accuracy and timeliness of outbreak detection.9 page(s

Comparison of the sofa and qsofa scores in predicting in-hospital mortality among adult critical care patients with suspected Infection.

Background: Sepsis is global health priority and the commonest cause of death in critical care. SEPSIS 3 criteria introduced in 2016 is the latest tool in diagnosing sepsis, uses qSOFA and SOFA scores in place of the SIRS based definition for better prediction of mortality in patients with suspected infections. Their performance in predicting mortality in critical care units outside high- income countries remains largely unknown. Objective: We compared the SOFA and qSOFA scores in predicting the hospital mortality of adult critical care patients admitted with suspected infection at the Aga Khan University Hospital Nairobi, Kenya. Methods: We did a retrospective review of all admissions to the critical care units from January 2017 to December 2017, enrolling eligible patients. We censored on hospital discharge or death. We electronically collected clinical, demographic and outcome data. AUROC with 95 per cent confidence intervals for SOFA and qSOFA compares the scores. Results: There were a total of 450 patients with a mean age of 56 years and 57.60% were male. There were 92 deaths (20.44%) and 284 (63.1%) patients experienced a prolonged ICU stay (≥3 days). Majority (371, 82.44%) manifested a SOFA score of two or more and 190 patients (42.22%) had a qSOFA score of two or more. SOFA score had a better ability in predicting in hospital mortality compared to qSOFA (AUROC of 0.799 (95% CI, 0.752 - 0.846) Vs AUROC of 0.694 (95% CI, 0.691 -0.748), P \u3c 0.001). Conclusion and recommendations: Among adult critical care patients with suspected infection at admission to critical care, a SOFA score of two or more was better than qSOFA score of two or more in predicting in-hospital mortality

Simplified risk stratification criteria for identification of patients with MRSA bacteremia at low risk of infective endocarditis: implications for avoiding routine transesophageal echocardiography in MRSA bacteremia

The aim of this study was to identify patients with methicillin-resistant Staphylococcus aureus (MRSA) bacteremia with low risk of infective endocarditis (IE) who might not require routine trans-esophageal echocardiography (TEE). We retrospectively evaluated 398 patients presenting with MRSA bacteremia for the presence of the following clinical criteria: intravenous drug abuse (IVDA), long-term catheter, prolonged bacteremia, intra-cardiac device, prosthetic valve, hemodialysis dependency, vertebral/nonvertebral osteomyelitis, cardio-structural abnormality. IE was diagnosed using the modified Duke criteria. Of 398 patients with MRSA bacteremia, 26.4 % of cases were community-acquired, 56.3 % were health-care-associated, and 17.3 % were hospital-acquired. Of the group, 44 patients had definite IE, 119 had possible IE, and 235 had a rejected diagnosis. Out of 398 patients, 231 were evaluated with transthoracic echocardiography (TTE) or TEE. All 44 patients with definite IE fulfilled at least one criterion (sensitivity 100 %). Finally, a receiver operator characteristic (ROC) curve was obtained to evaluate the total risk score of our proposed criteria as a predictor of the presence of IE, and this was compared to the ROC curve of a previously proposed criteria. The area under the ROC curve for our criteria was 0.710, while the area under the ROC curve for the criteria previously proposed was 0.537 (p < 0.001). The p-value for comparing those 2 areas was less than 0.001, indicating statistical significance. Patients with MRSA bacteremia without any of our proposed clinical criteria have very low risk of developing IE and may not require routine TEE

Curr Opin Infect Dis

Purpose of reviewMathematical, statistical, and computational models provide insight into the transmission mechanisms and optimal control of healthcare-associated infections. To contextualize recent findings, we offer a summative review of recent literature focused on modeling transmission of pathogens in healthcare settings.Recent findingsThe COVID-19 pandemic has led to a dramatic shift in the modeling landscape as the healthcare community has raced to characterize the transmission dynamics of SARS-CoV-2 and develop effective interventions. Inequities in COVID-19 outcomes have inspired new efforts to quantify how structural bias impacts both health outcomes and model parameterization. Meanwhile, developments in the modeling of methicillin-resistant Staphylococcus aureus, Clostridioides difficile, and other nosocomial infections continue to advance. Machine learning continues to be applied in novel ways, and genomic data is being increasingly incorporated into modeling efforts.SummaryAs the type and amount of data continues to grow, mathematical, statistical, and computational modeling will play an increasing role in healthcare epidemiology. Gaps remain in producing models that are generalizable to a variety of time periods, geographic locations, and populations. However, with effective communication of findings and interdisciplinary collaboration, opportunities for implementing models for clinical decision-making and public health decision-making are bound to increase.U01 CK000585/CK/NCEZID CDC HHSUnited States/U01CK000538/ACL/ACL HHSUnited States/U01CK000585/ACL/ACL HHSUnited States/2022-08-22T00:00:00Z34039877PMC939421911808vault:4318

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