Use of an artificial neural network to predict risk factors of nosocomial infection in lung cancer patients

Neural networks for the prediction of bacterial and fungal infections: current evidence and implications

In the present narrative review, we discuss the use of artificial neural networks (ANNs) for predicting bacterial and fungal infections based on commonly available clinical and laboratory data, focusing on promises and challenges of these machine learning models. For predicting different bacterial or fungal infections from data commonly found in electronical medical records, ANN models may reach, based on current literature, an acceptable performance for discriminating between infected and non-infected patients, and outperformed other machine learning (ML)-based models in 38.3% of the retrieved studies evaluating at least another ML approach. In the near future, as for other ML models, the use of ANNs could be leveraged to provide real-time support to clinicians in clinical decision-making processes, although further research is needed in terms of quality of data and explainability of ANN model predictions to better understand whether and how these techniques can be safely adopted in everyday clinical practice.

A Holistic Approach to Implementing Artificial Intelligence in Lung Cancer

The application of artificial intelligence (AI) in lung cancer, particularly in surgical approaches, has significantly transformed the healthcare landscape. AI has demonstrated remarkable advancements in early lung cancer detection, precise medical image analysis, and personalized treatment planning, all of which are crucial for surgical interventions. By analyzing extensive datasets, AI algorithms can identify patterns and anomalies in lung scans, facilitating timely diagnoses and enhancing surgical outcomes. Furthermore, AI can detect subtle indicators that may be overlooked by human practitioners, leading to quicker intervention and more effective treatment strategies. The technology can also predict patient responses to surgical treatments, enabling tailored care plans that improve recovery rates. In addition to surgical applications, AI streamlines administrative tasks such as record management and appointment scheduling, allowing healthcare providers to concentrate on delivering high-quality care. The integration of AI with genomics and precision medicine holds the potential to further refine surgical approaches in lung cancer treatment by developing targeted strategies that enhance effectiveness and minimize side effects. Despite challenges related to data privacy and regulatory concerns, the ongoing advancements in AI, coupled with collaboration between healthcare professionals and AI experts, suggest a promising future for lung cancer care. This article explores how AI addresses the challenges of lung cancer treatment, focusing on current advancements, obstacles, and the future potential of surgical applications.

Clinical Impact and Risk Factors of Intensive Care Unit-Acquired Nosocomial Infection: A Propensity Score-Matching Study from 2018 to 2020 in a Teaching Hospital in China

Purpose

Nosocomial infection (NI) is associated with poor prognosis. The present study assessed the clinical and microbiological characteristics of NI patients in the intensive care unit (ICU) and investigated the clinical impact and risk factors for NI in ICU patients.

Patients and Methods

An observational study was conducted in an adult general ICU. The electronic medical records of all patients admitted to the ICU for >2 days from 2018-2020 were analyzed retrospectively. Multivariate regression models were used to analyze the risk factors for NI in ICU patients. Propensity score-matching (PSM) was used to control the confounding factors between the case and control groups, thus analyzing the clinical impact of NIs.

Results

The present study included 2425 patient admissions, of which 231 (9.53%) had NI. Acinetobacter baumannii (33.0%) was the most common bacteria. Long-term immunosuppressive therapy, disturbance of consciousness, blood transfusion, multiple organ dysfunction syndromes (MODS), treatment with three or more antibiotics, mechanical ventilation (MV), tracheotomy, the urinary catheter (UC), nasogastric catheter, and central venous catheter (CVC) were risk factors for NI in the ICU patients. After PSM, patients with NI had a prolonged length of stay (LOS) in the ICU and hospital, significant hospitalization expenses (all p<0.001), increased mortality (p=0.027), and predicted mortality (p=0.007). The differences in the ICU and hospital LOSs among three pathogens were statistically significant (p<0.001); the results of the Escherichia coli infection group were lower than the other two pathogenic groups.

Conclusion

NI was associated with poor outcomes. The risk factors for NI identified in this study provided further insight into preventing NI.

Using Artificial Neural Networks to Predict Intra-Abdominal Abscess Risk Post-Appendectomy

Objective

To determine if artificial neural networks (ANN) could predict the risk of intra-abdominal abscess (IAA) development post-appendectomy.

Background

IAA formation occurs in 13.6% to 14.6% of appendicitis cases with "complicated" appendicitis as the most common cause of IAA. There remains inconsistency in describing the severity of appendicitis with variation in treatment with respect to perforated appendicitis.

Methods

Two "reproducible" ANN with different architectures were developed on demographic, clinical, and surgical information from a retrospective surgical dataset of 1574 patients less than 19 years old classified as either negative (n = 1,328) or positive (n = 246) for IAA post-appendectomy for appendicitis. Of 34 independent variables initially, 12 variables with the highest influence on the outcome selected for the final dataset for ANN model training and testing.

Results

A total of 1574 patients were used for training and test sets (80%/20% split). Model 1 achieved accuracy of 89.84%, sensitivity of 70%, and specificity of 93.61% on the test set. Model 2 achieved accuracy of 84.13%, sensitivity of 81.63%, and specificity of 84.6%.

Conclusions

ANN applied to selected variables can accurately predict patients who will have IAA post-appendectomy. Our reproducible and explainable ANNs potentially represent a state-of-the-art method for optimizing post-appendectomy care.

The pathogenic characteristics and influencing factors of health care-associated infection in elderly care center under the mode of integration of medical care and elderly care service: A cross-sectional study

The aim of this study was to analyze the distribution of pathogenic bacteria in hospitalized patients in elderly care centers under the mode of integration of medical care and elderly care service, and explore the influencing factors to reduce the health care-associated infection rate of hospitalized patients.A total of 2597 inpatients admitted to elderly care centers from April 2018 to December 2019 were included in the study. The etiology characteristics of health care-associated infections (HCAI) was statistically analyzed, univariate analysis, and multivariate logistic regression analysis method were used to analyze the influencing factors of HCAI.A total of 98 of 2597 inpatients in the elderly care centers had HCAI, and the infection rate was 3.77%. The infection sites were mainly in the lower respiratory tract and urinary tract, accounting for 53.92% and 18.63%, respectively. A total of 53 pathogenic bacteria were isolated, 43 of which (81.13%) were Gram-negative, mainly Escherichia coli, Pseudomonas aeruginosa, and Klebsiella pneumoniae, which respectively accounted for 24.53, 16.98, and 13.21%. 9 (16.98%) strains were Gram-positive, mainly Staphylococcus aureus and Enterococcus faecium, respectively accounting for 7.55 and 5.66%. Only 1 patient (1.89%) had a fungal infection. Multivariate logistic regression analysis indicated that total hospitalization days, antibiotic agents used, days of central line catheter, use of urinary catheter and diabetes were independent risk factors of nosocomial infection in elderly care centers (P < .05).Many factors can lead to nosocomial infections in elderly care centers. Medical staff should take effective intervention measures according to the influencing factors to reduce the risk of infection in elderly care facilities.

COMPRIME - COnhecer Mais PaRa Intervir MElhor: Preliminary Mapping of Municipal Level Determinants of COVID-19 Transmission in Portugal at Different Moments of the 1st Epidemic Wave

<b><i>Background:</i></b> The role of demographic and socio-economic determinants of COVID-19 transmission is still unclear and is expected to vary in different contexts and epidemic periods. Exploring such determinants may generate a hypothesis about transmission and aid the definition of prevention strategies. <b><i>Objectives:</i></b> To identify municipality-level demographic and socio-economic determinants of COVID-19 in Portugal. <b><i>Methods:</i></b> We assessed determinants of COVID-19 daily cases at 4 moments of the first COVID-19 epidemic wave in Portugal, related with lockdown and post-lockdown measures. We selected 60 potential determinants from 5 dimensions: population and settlement, disease, economy, social context, and mobility. We conducted a multiple linear regression (MLR) stepwise analysis (<i>p</i> &#x3c; 0.05) and an artificial neural network (ANN) analysis with the variables to identify predictors of the number of daily cases. <b><i>Results:</i></b> For MLR, some of the identified variables were: resident population and population density, exports, overnight stays in touristic facilities, the location quotient of employment in accommodation, catering and similar activities, education, restaurants and lodging, some industries and building construction, the share of the population working outside the municipality, the net migration rate, income, and renting. In ANN, some of the identified variables were: population density and resident population, urbanization, students in higher education, income, exports, social housing buildings, production services employment, and the share of the population working outside the municipality of residence. <b><i>Conclusions:</i></b> Several factors were identified as possible determinants of COVID-19 transmission at the municipality level. Despite limitations to the study, we believe that this information should be considered to promote communication and prevention approaches. Further research should be conducted.

Machine learning in infection management using routine electronic health records: tools, techniques, and reporting of future technologies

BACKGROUND

Machine learning (ML) is increasingly being used in many areas of health care. Its use in infection management is catching up as identified in a recent review in this journal. We present here a complementary review to this work.

OBJECTIVES

To support clinicians and researchers in navigating through the methodological aspects of ML approaches in the field of infection management.

SOURCES

A Medline search was performed with the keywords artificial intelligence, machine learning, infection∗, and infectious disease∗ for the years 2014-2019. Studies using routinely available electronic hospital record data from an inpatient setting with a focus on bacterial and fungal infections were included.

CONTENT

Fifty-two studies were included and divided into six groups based on their focus. These studies covered detection/prediction of sepsis (n = 19), hospital-acquired infections (n = 11), surgical site infections and other postoperative infections (n = 11), microbiological test results (n = 4), infections in general (n = 2), musculoskeletal infections (n = 2), and other topics (urinary tract infections, deep fungal infections, antimicrobial prescriptions; n = 1 each). In total, 35 different ML techniques were used. Logistic regression was applied in 18 studies followed by random forest, support vector machines, and artificial neural networks in 18, 12, and seven studies, respectively. Overall, the studies were very heterogeneous in their approach and their reporting. Detailed information on data handling and software code was often missing. Validation on new datasets and/or in other institutions was rarely done. Clinical studies on the impact of ML in infection management were lacking.

IMPLICATIONS

Promising approaches for ML use in infectious diseases were identified. But building trust in these new technologies will require improved reporting. Explainability and interpretability of the models used were rarely addressed and should be further explored. Independent model validation and clinical studies evaluating the added value of ML approaches are needed.

Noninvasive Evaluation of Liver Fibrosis Reverse Using Artificial Neural Network Model for Chronic Hepatitis B Patients

The diagnostic performance of an artificial neural network model for chronic HBV-induced liver fibrosis reverse is not well established. Our research aims to construct an ANN model for estimating noninvasive predictors of fibrosis reverse in chronic HBV patients after regular antiviral therapy. In our study, 141 consecutive patients requiring liver biopsy at baseline and 1.5 years were enrolled. Several serum biomarkers and liver stiffness were measured during antiviral therapy in both reverse and nonreverse groups. Statistically significant variables between two groups were selected to form an input layer of the ANN model. The ROC (receiver-operating characteristic) curve and AUC (area under the curve) were calculated for comparison of effectiveness of the ANN model and logistic regression model in predicting HBV-induced liver fibrosis reverse. The prevalence of fibrosis reverse of HBV patients was about 39% (55/141) after 78-week antiviral therapy. The Ishak scoring system was used to assess fibrosis reverse. Our study manifested that AST (aspartate aminotransferase; importance coefficient = 0.296), PLT (platelet count; IC = 0.159), WBC (white blood cell; IC = 0.142), CHE (cholinesterase; IC = 0.128), LSM (liver stiffness measurement; IC = 0.125), ALT (alanine aminotransferase; IC = 0.110), and gender (IC = 0.041) were the most crucial predictors of reverse. The AUC of the ANN model and logistic model was 0.809 ± 0.062 and 0.756 ± 0.059, respectively. In our study, we concluded that the ANN model with variables consisting of AST, PLT, WBC, CHE, LSM, ALT, and gender may be useful in diagnosing liver fibrosis reverse for chronic HBV-induced liver fibrosis patients.

Use of an Artificial Neural Network to Construct a Model of Predicting Deep Fungal Infection in Lung Cancer Patients

BACKGROUND

The statistical methods to analyze and predict the related dangerous factors of deep fungal infection in lung cancer patients were several, such as logic regression analysis, meta-analysis, multivariate Cox proportional hazards model analysis, retrospective analysis, and so on, but the results are inconsistent.

MATERIALS AND METHODS

A total of 696 patients with lung cancer were enrolled. The factors were compared employing Student's t-test or the Mann-Whitney test or the Chi-square test and variables that were significantly related to the presence of deep fungal infection selected as candidates for input into the final artificial neural network analysis (ANN) model. The receiver operating characteristic (ROC) and area under curve (AUC) were used to evaluate the performance of the artificial neural network (ANN) model and logistic regression (LR) model.

RESULTS

The prevalence of deep fungal infection from lung cancer in this entire study population was 32.04%(223/696), deep fungal infections occur in sputum specimens 44.05% (200/454). The ratio of candida albicans was 86.99% (194/223) in the total fungi. It was demonstrated that older (≥65 years), use of antibiotics, low serum albumin concentrations (≤37.18 g /L), radiotherapy, surgery, low hemoglobin hyperlipidemia (≤93.67 g /L), long time of hospitalization (≥14 days) were apt to deep fungal infection and the ANN model consisted of the seven factors. The AUC of ANN model (0.829±0.019) was higher than that of LR model (0.756±0.021).

CONCLUSIONS

The artificial neural network model with variables consisting of age, use of antibiotics, serum albumin concentrations, received radiotherapy, received surgery, hemoglobin, time of hospitalization should be useful for predicting the deep fungal infection in lung cancer.