The development an artificial intelligence algorithm for early sepsis diagnosis in the intensive care unit

Association between comorbidities at ICU admission and post-Sepsis physical impairment: A retrospective cohort study

PURPOSE

Few studies have measured the association between pre-existing comorbidities and post-sepsis physical impairment. The study aimed to estimate the risk of physical impairment at hospital discharge among sepsis patients, adjusting for pre-existing physical impairment prior to ICU admission and in-hospital mortality.

MATERIALS AND METHODS

We analyzed all consecutive adult patients admitted to an ICU in a tertiary community hospital, Kameda Medical Center, with sepsis diagnosis from September 2014 to October 2020. Inverse probability attrition weighting using machine learning was employed to estimate the risk of physical impairment at hospital discharge for sepsis patients with and without pre-existing comorbidities at ICU admission. This estimation was adjusted for baseline covariates, pre-ICU physical impairment, and in-hospital mortality.

RESULTS

Of 889 sepsis patients analyzed, 668 [75.1%] had at least one comorbidity and 221 [24.9%] had no comorbidities at ICU admission. Upon adjusting for baseline covariates, pre-ICU physical impairment, and in-hospital mortality, pre-existing comorbidities were not associated with an elevated risk of physical impairment at hospital discharge (RR: 1.02, 95% CI: 0.92, 1.14).

CONCLUSIONS

Pre-existing comorbidities prior to ICU admission were not associated with an increased risk of physical impairment at hospital discharge among sepsis patients after adjusting for baseline covariates and in-hospital mortality.

Machine learning application in otology

This review presents a comprehensive history of Artificial Intelligence (AI) in the context of the revolutionary application of machine learning (ML) to medical research and clinical utilization, particularly for the benefit of researchers interested in the application of ML in otology. To this end, we discuss the key components of ML-input, output, and algorithms. In particular, some representation algorithms commonly used in medical research are discussed. Subsequently, we review ML applications in otology research, including diagnosis, influential identification, and surgical outcome prediction. In the context of surgical outcome prediction, specific surgical treatments, including cochlear implantation, active middle ear implantation, tympanoplasty, and vestibular schwannoma resection, are considered. Finally, we highlight the obstacles and challenges that need to be overcome in future research.

Role of artificial intelligence in critical care nutrition support and research

Nutrition plays a key role in the comprehensive care of critically ill patients. Determining optimal nutrition strategy, however, remains a subject of intense debate. Artificial intelligence (AI) applications are becoming increasingly common in medicine, and specifically in critical care, driven by the data-rich environment of intensive care units. In this review, we will examine the evidence regarding the application of AI in critical care nutrition. As of now, the use of AI in critical care nutrition is relatively limited, with its primary emphasis on malnutrition screening and tolerance of enteral nutrition. Despite the current scarcity of evidence, the potential for AI for more personalized nutrition management for critically ill patients is substantial. This stems from the ability of AI to integrate multiple data streams reflecting patients' changing needs while addressing inherent heterogeneity. The application of AI in critical care nutrition holds promise for optimizing patient outcomes through tailored and adaptive nutrition interventions. A successful implementation of AI, however, necessitates a multidisciplinary approach, coupled with careful consideration of challenges related to data management, financial aspects, and patient privacy.

Association Between Loop Diuretics and Mortality in Patients With Cardiac Surgery-Associated Acute Kidney Injury: A Retrospective Propensity Score-Weighted Analysis

BACKGROUND

Although loop diuretics (LDs) have been widely used in clinical practice, their effect on mortality when administered to patients experiencing cardiac surgery-associated acute kidney injury (CS-AKI) remains unknown. The study aimed to investigate the effectiveness of LD use in patients with CS-AKI.

METHODS

Patients who underwent cardiac surgery with AKI were identified from the Medical Information Mart for Intensive Care III. Postoperative LD use in intensive care units (ICUs) was exposure. There were 2 primary outcome measures, the in-hospital mortality and ICU mortality; both were treated as time-to-event data and were analyzed via multivariable Cox proportional hazard models. Inverse probability weighting (IPW) was used to minimize bias.

RESULTS

The study enrolled a total of 5478 patients, with a median age of 67 years, among which 2205 (40.3%) were women. The crude in-hospital and ICU mortality rates were significantly lower in the LD use group (525 of 4150 [12.7%] vs 434 of 1328 [32.7%], P < .001; 402 of 4150 [9.69%] vs 333 of 1328 [25.1%], P < .001). Adjusted hazard ratios suggested significant reductions in both in-hospital (hazard ratio [HR], 0.428; 95% confidence interval [CI], 0.374-0.489) and ICU mortality (HR, 0.278; 95% CI, 0.238-0.327). The IPW data showed a similar reduction, in-hospital mortality (HR, 0.434; 95% CI, 0.376-0.502) and ICU mortality (HR, 0.296; 95% CI, 0.251-0.349). Such association may act differently for patients with different fluid balance ( P value for interaction < .001).

CONCLUSIONS

LD use is associated with lower hospital and ICU mortality in CS-AKI patients in general. Patients under different conditions showed diverse responses toward such treatment indicating that personalized management is needed.

Machine learning for screening and predicting the availability of medications for children: a cross-sectional survey study

Objective

The aim of the study was to explore the factors influencing the availability of medications for children, and establish a machine learning model to provide an empirical basis for the subsequent formulation and improvement of relevant policies.

Methods

Design: Cross-sectional survey. Setting: 12 provinces, China. Medical doctors from 25 public hospitals were enrolled. All data were randomly divided into a training set and a validation set at a ratio of 7:3. Three prediction models, namely random forest (RF), logistic regression (LR), and extreme gradient boosting (XGBoost), were developed and compared. The receiver operating characteristic curve (ROC) and the associated area under the curve (AUC) were used to evaluate the three models. A nomogram and clinical impact curve (CIC) for availability of medication were developed.

Results

Fifteen of 29 factors in the database that were most likely to be selected were considered to establish the prediction model. The XGBoost model (AUC = 0.915) demonstrated better performance than the RF model (AUC = 0.902) and the LR model (AUC = 0.890). According to the Shapley additive explanation values, the five factors that most significantly affected the availability of medications for children in the XGboost model were as follows: the relatively small number of specialized dosage forms for children; unaffordable medications for children; public education on the accessibility and safety of medication for children; uneven distribution of medical resources, leading to insufficient access to medication for children; and years of service as a doctor. The CIC was used to assess the practical applicability of the factor prediction nomogram.

Conclusions

The XGBoost model can be used to establish a prediction model to screen the factors associated with the availability of medications for children. The most important contributing factors to the models were the following: the relatively small number of specialized dosage forms for children; unaffordable medications for children; public education on the accessibility and safety of medication for children; uneven distribution of medical resources, leading to insufficient access to medication for children; and years of service as a doctor.

A scoping review of machine learning for sepsis prediction- feature engineering strategies and model performance: a step towards explainability

BACKGROUND

Sepsis, an acute and potentially fatal systemic response to infection, significantly impacts global health by affecting millions annually. Prompt identification of sepsis is vital, as treatment delays lead to increased fatalities through progressive organ dysfunction. While recent studies have delved into leveraging Machine Learning (ML) for predicting sepsis, focusing on aspects such as prognosis, diagnosis, and clinical application, there remains a notable deficiency in the discourse regarding feature engineering. Specifically, the role of feature selection and extraction in enhancing model accuracy has been underexplored.

OBJECTIVES

This scoping review aims to fulfill two primary objectives: To identify pivotal features for predicting sepsis across a variety of ML models, providing valuable insights for future model development, and To assess model efficacy through performance metrics including AUROC, sensitivity, and specificity.

RESULTS

The analysis included 29 studies across diverse clinical settings such as Intensive Care Units (ICU), Emergency Departments, and others, encompassing 1,147,202 patients. The review highlighted the diversity in prediction strategies and timeframes. It was found that feature extraction techniques notably outperformed others in terms of sensitivity and AUROC values, thus indicating their critical role in improving sepsis prediction models.

CONCLUSION

Key dynamic indicators, including vital signs and critical laboratory values, are instrumental in the early detection of sepsis. Applying feature selection methods significantly boosts model precision, with models like Random Forest and XG Boost showing promising results. Furthermore, Deep Learning models (DL) reveal unique insights, spotlighting the pivotal role of feature engineering in sepsis prediction, which could greatly benefit clinical practice.

Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosis

Today's AI systems for medical decision support often succeed on benchmark datasets in research papers but fail in real-world deployment. This work focuses on the decision making of sepsis, an acute life-threatening systematic infection that requires an early diagnosis with high uncertainty from the clinician. Our aim is to explore the design requirements for AI systems that can support clinical experts in making better decisions for the early diagnosis of sepsis. The study begins with a formative study investigating why clinical experts abandon an existing AI-powered Sepsis predictive module in their electrical health record (EHR) system. We argue that a human-centered AI system needs to support human experts in the intermediate stages of a medical decision-making process (e.g., generating hypotheses or gathering data), instead of focusing only on the final decision. Therefore, we build SepsisLab based on a state-of-the-art AI algorithm and extend it to predict the future projection of sepsis development, visualize the prediction uncertainty, and propose actionable suggestions (i.e., which additional laboratory tests can be collected) to reduce such uncertainty. Through heuristic evaluation with six clinicians using our prototype system, we demonstrate that SepsisLab enables a promising human-AI collaboration paradigm for the future of AI-assisted sepsis diagnosis and other high-stakes medical decision making.

Artificial Intelligence in the Intensive Care Unit: Current Evidence on an Inevitable Future Tool

Artificial intelligence (AI) is a technique that attempts to replicate human intelligence, analytical behavior, and decision-making ability. This includes machine learning, which involves the use of algorithms and statistical techniques to enhance the computer's ability to make decisions more accurately. Due to AI's ability to analyze, comprehend, and interpret considerable volumes of data, it has been increasingly used in the field of healthcare. In critical care medicine, where most of the patient load requires timely interventions due to the perilous nature of the condition, AI's ability to monitor, analyze, and predict unfavorable outcomes is an invaluable asset. It can significantly improve timely interventions and prevent unfavorable outcomes, which, otherwise, is not always achievable owing to the constrained human ability to multitask with optimum efficiency. AI has been implicated in intensive care units over the past many years. In addition to its advantageous applications, this article discusses its disadvantages, prospects, and the changes needed to train future critical care professionals. A comprehensive search of electronic databases was performed using relevant keywords. Data from articles pertinent to the topic was assimilated into this review article.

Creation of a machine learning-based prognostic prediction model for various subtypes of laryngeal cancer

Depending on the source of the blastophore, there are various subtypes of laryngeal cancer, each with a unique metastatic risk and prognosis. The forecasting of their prognosis is a pressing issue that needs to be resolved. This study comprised 5953 patients with glottic carcinoma and 4465 individuals with non-glottic type (supraglottic and subglottic). Five clinicopathological characteristics of glottic and non-glottic carcinoma were screened using univariate and multivariate regression for CoxPH (Cox proportional hazards); for other models, 10 (glottic) and 11 (non-glottic) clinicopathological characteristics were selected using least absolute shrinkage and selection operator (LASSO) regression analysis, respectively; the corresponding survival models were established; and the best model was evaluated. We discovered that RSF (Random survival forest) was a superior model for both glottic and non-glottic carcinoma, with a projected concordance index (C-index) of 0.687 for glottic and 0.657 for non-glottic, respectively. The integrated Brier score (IBS) of their 1-year, 3-year, and 5-year time points is, respectively, 0.116, 0.182, 0.195 (glottic), and 0.130, 0.215, 0.220 (non-glottic), demonstrating the model's effective correction. We represented significant variables in a Shapley Additive Explanations (SHAP) plot. The two models are then combined to predict the prognosis for two distinct individuals, which has some effectiveness in predicting prognosis. For our investigation, we established separate models for glottic carcinoma and non-glottic carcinoma that were most effective at predicting survival. RSF is used to evaluate both glottic and non-glottic cancer, and it has a considerable impact on patient prognosis and risk factor prediction.

Using Artificial Intelligence to Predict Mechanical Ventilation Weaning Success in Patients with Respiratory Failure, Including Those with Acute Respiratory Distress Syndrome

The management of mechanical ventilation (MV) remains a challenge in intensive care units (ICUs). The digitalization of healthcare and the implementation of artificial intelligence (AI) and machine learning (ML) has significantly influenced medical decision-making capabilities, potentially enhancing patient outcomes. Acute respiratory distress syndrome, an overwhelming inflammatory lung disease, is common in ICUs. Most patients require MV. Prolonged MV is associated with an increased length of stay, morbidity, and mortality. Shortening the MV duration has both clinical and economic benefits and emphasizes the need for better MV weaning management. AI and ML models can assist the physician in weaning patients from MV by providing predictive tools based on big data. Many ML models have been developed in recent years, dealing with this unmet need. Such models provide an important prediction regarding the success of the individual patient's MV weaning. Some AI models have shown a notable impact on clinical outcomes. However, there are challenges in integrating AI models into clinical practice due to the unfamiliar nature of AI for many physicians and the complexity of some AI models. Our review explores the evolution of weaning methods up to and including AI and ML as weaning aids.

Emerging applications of machine learning in genomic medicine and healthcare

The integration of artificial intelligence technologies has propelled the progress of clinical and genomic medicine in recent years. The significant increase in computing power has facilitated the ability of artificial intelligence models to analyze and extract features from extensive medical data and images, thereby contributing to the advancement of intelligent diagnostic tools. Artificial intelligence (AI) models have been utilized in the field of personalized medicine to integrate clinical data and genomic information of patients. This integration allows for the identification of customized treatment recommendations, ultimately leading to enhanced patient outcomes. Notwithstanding the notable advancements, the application of artificial intelligence (AI) in the field of medicine is impeded by various obstacles such as the limited availability of clinical and genomic data, the diversity of datasets, ethical implications, and the inconclusive interpretation of AI models' results. In this review, a comprehensive evaluation of multiple machine learning algorithms utilized in the fields of clinical and genomic medicine is conducted. Furthermore, we present an overview of the implementation of artificial intelligence (AI) in the fields of clinical medicine, drug discovery, and genomic medicine. Finally, a number of constraints pertaining to the implementation of artificial intelligence within the healthcare industry are examined.

Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review

Healthcare-associated infections (HAIs) are the most common adverse events in healthcare and constitute a major global public health concern. Surveillance represents the foundation for the effective prevention and control of HAIs, yet conventional surveillance is costly and labor intensive. Artificial intelligence (AI) and machine learning (ML) have the potential to support the development of HAI surveillance algorithms for the understanding of HAI risk factors, the improvement of patient risk stratification as well as the prediction and timely detection and prevention of infections. AI-supported systems have so far been explored for clinical laboratory testing and imaging diagnosis, antimicrobial resistance profiling, antibiotic discovery and prediction-based clinical decision support tools in terms of HAIs. This review aims to provide a comprehensive summary of the current literature on AI applications in the field of HAIs and discuss the future potentials of this emerging technology in infection practice. Following the PRISMA guidelines, this study examined the articles in databases including PubMed and Scopus until November 2023, which were screened based on the inclusion and exclusion criteria, resulting in 162 included articles. By elucidating the advancements in the field, we aim to highlight the potential applications of AI in the field, report related issues and shortcomings and discuss the future directions.

Using machine learning to predict the bleeding risk for patients with cardiac valve replacement treated with warfarin in hospitalized

BACKGROUND

Distinguishing warfarin-related bleeding risk at the bedside remains challenging. Studies indicate that warfarin therapy should be suspended when international normalized ratio (INR) ≥ 4.5, or it may sharply increase the risk of bleeding. We aim to develop and validate a model to predict the high bleeding risk in valve replacement patients during hospitalization.

METHOD

Cardiac valve replacement patients from January 2016 to December 2021 across Nanjing First Hospital were collected. Five different machine-learning (ML) models were used to establish the prediction model. High bleeding risk was an INR ≥4.5. The area under the receiver operating characteristic curve (AUC) was used for evaluating the prediction performance of different models. The SHapley Additive exPlanations (SHAP) was used for interpreting the model. We also compared ML with ATRIA score and ORBIT score.

RESULTS

A total of 2376 patients were finally enrolled in this model, 131 (5.5%) of whom experienced the high bleeding risk after anticoagulation therapy of warfarin during hospitalization. The extreme gradient boosting (XGBoost) exhibited the best overall prediction performance (AUC: 0.882, confidence interval [CI] 0.817-0.946, Brier score, 0.158) compared to other prediction models. It also shows superior performance compared with ATRIA score and ORBIT score. The top 5 most influential features in XGBoost model were platelet, thyroid stimulation hormone, body surface area, serum creatinine and white blood cell.

CONCLUSION

A model for predicting high bleeding risk in valve replacement patients who treated with warfarin during hospitalization was successfully developed by using machine learning, which may well assist clinicians to identify patients at high risk of bleeding and allow timely adjust therapeutic strategies in evaluating individual patient.

Chinese experts' consensus on the application of intensive care big data

The development of intensive care medicine is inseparable from the diversified monitoring data. Intensive care medicine has been closely integrated with data since its birth. Critical care research requires an integrative approach that embraces the complexity of critical illness and the computational technology and algorithms that can make it possible. Considering the need of standardization of application of big data in intensive care, Intensive Care Medicine Branch of China Health Information and Health Care Big Data Society, Standard Committee has convened expert group, secretary group and the external audit expert group to formulate Chinese Experts' Consensus on the Application of Intensive Care Big Data (2022). This consensus makes 29 recommendations on the following five parts: Concept of intensive care big data, Important scientific issues, Standards and principles of database, Methodology in solving big data problems, Clinical application and safety consideration of intensive care big data. The consensus group believes this consensus is the starting step of application big data in the field of intensive care. More explorations and big data based retrospective research should be carried out in order to enhance safety and reliability of big data based models of critical care field.

Development and validation of machine learning models for nonalcoholic fatty liver disease

BACKGROUND

Nonalcoholic fatty liver disease (NAFLD) had become the most prevalent liver disease worldwide. Early diagnosis could effectively reduce NAFLD-related morbidity and mortality. This study aimed to combine the risk factors to develop and validate a novel model for predicting NAFLD.

METHODS

We enrolled 578 participants completing abdominal ultrasound into the training set. The least absolute shrinkage and selection operator (LASSO) regression combined with random forest (RF) was conducted to screen significant predictors for NAFLD risk. Five machine learning models including logistic regression (LR), RF, extreme gradient boosting (XGBoost), gradient boosting machine (GBM), and support vector machine (SVM) were developed. To further improve model performance, we conducted hyperparameter tuning with train function in Python package 'sklearn'. We included 131 participants completing magnetic resonance imaging into the testing set for external validation.

RESULTS

There were 329 participants with NAFLD and 249 without in the training set, while 96 with NAFLD and 35 without were in the testing set. Visceral adiposity index, abdominal circumference, body mass index, alanine aminotransferase (ALT), ALT/AST (aspartate aminotransferase), age, high-density lipoprotein cholesterol (HDL-C) and elevated triglyceride (TG) were important predictors for NAFLD risk. The area under curve (AUC) of LR, RF, XGBoost, GBM, SVM were 0.915 [95% confidence interval (CI): 0.886-0.937], 0.907 (95% CI: 0.856-0.938), 0.928 (95% CI: 0.873-0.944), 0.924 (95% CI: 0.875-0.939), and 0.900 (95% CI: 0.883-0.913), respectively. XGBoost model presented the best predictive performance, and its AUC was enhanced to 0.938 (95% CI: 0.870-0.950) with further parameter tuning.

CONCLUSIONS

This study developed and validated five novel machine learning models for NAFLD prediction, among which XGBoost presented the best performance and was considered a reliable reference for early identification of high-risk patients with NAFLD in clinical practice.

Disease Diagnosis Based on Improved Gray Wolf Optimization (IGWO) and Ensemble Classification

This paper introduces a simple strategy for diagnosing disease, which is called improved gray wolf optimization (IGWO) and ensemble classification. The proposed strategy consists of two sequential phases, which are; (i) Feature Selection Phase (FSP) and (ii) Ensemble Classification Phase (ECP). During the former, the most effective features for diagnosing disease are selected, while during the latter, the actual diagnosis takes place depending on voting of five different classifiers. The main contribution of this paper is a suggested modification for the traditional Gray Wolf Optimization (GWO), which is called Improved Gray Wolf Optimization (IGWO). As an optimization technique, the proposed IGWO is employed in the FSP for selecting the effective features. For evaluating, IGWO has been implemented using recent feature selection techniques as well as the proposed method. To accomplish the classification phase; ensemble classification has been used which uses several classification techniques such as; Naïve Bayes (NB), Support Vector Machines (SVM), Deep Neural Network (DNN), Decision Tree (DT), and K-Nearest Neighbors (KNN). Ensemble classification integrate several classifiers for improving prediction performance. Experimental results have shown that employing IGWO promotes the performance of the diagnosing strategy of different diseases in terms of precision, recall, and accuracy.

Aging-related genes revealed Neuroinflammatory mechanisms in ischemic stroke by bioinformatics

Ischemic stroke (IS) is a leading cause of disability, morbidity, and mortality globally. Aging affects immune function and contributes to poor outcomes of IS in elderly individuals. However, little is known about how aging-related genes (ARGs) are involved in IS. In this study, the relationship between ARGs and IS immune microenvironment biomarkers was explored by bioinformatics. Two IS microarray datasets (GSE22255, GSE16561) from human blood samples were analyzed and 502 ARGs were identified, from which 29 differentially expressed ARGs were selected. Functional analysis revealed that 7 of these ARGs (IL1B, FOS, JUN, CXCL5, PTGS2, TNFAIP3 and TLR4) were involved in five top enriched pathways (IL-17 signaling pathway, TNF signaling pathway, Rheumatoid arthritis, NF-kappa B signaling pathway and Pertussis) related to immune responses and inflammation. Five hub DE-ARGs (IL2RB, FOS, IL7R, ALDH2 and BIRC2) were identified using machine learning algorithms, and their association with immune-related characteristics was confirmed by additional tests. Single-cell sequencing dataset GSE129788 was retrieved to analyze aging molecular-related features, which was in accordance with microarray datasets. Clustering analysis revealed two subtypes of IS, which were distinguished by their differential expression of genes related to the NF-kappa B signaling pathway. These findings highlight the importance of ARGs in regulating immune responses in IS and suggest potential prevention and treatment strategies as well as guidelines for future research.

Predicting sepsis onset in ICU using machine learning models: a systematic review and meta-analysis

BACKGROUND

Sepsis is a life-threatening condition caused by an abnormal response of the body to infection and imposes a significant health and economic burden worldwide due to its high mortality rate. Early recognition of sepsis is crucial for effective treatment. This study aimed to systematically evaluate the performance of various machine learning models in predicting the onset of sepsis.

METHODS

We conducted a comprehensive search of the Cochrane Library, PubMed, Embase, and Web of Science databases, covering studies from database inception to November 14, 2022. We used the PROBAST tool to assess the risk of bias. We calculated the predictive performance for sepsis onset using the C-index and accuracy. We followed the PRISMA guidelines for this study.

RESULTS

We included 23 eligible studies with a total of 4,314,145 patients and 26 different machine learning models. The most frequently used models in the studies were random forest (n = 9), extreme gradient boost (n = 7), and logistic regression (n = 6) models. The random forest (test set n = 9, acc = 0.911) and extreme gradient boost (test set n = 7, acc = 0.957) models were the most accurate based on our analysis of the predictive performance. In terms of the C-index outcome, the random forest (n = 6, acc = 0.79) and extreme gradient boost (n = 7, acc = 0.83) models showed the highest performance.

CONCLUSION

Machine learning has proven to be an effective tool for predicting sepsis at an early stage. However, to obtain more accurate results, additional machine learning methods are needed. In our research, we discovered that the XGBoost and random forest models exhibited the best predictive performance and were most frequently utilized for predicting the onset of sepsis.

TRIAL REGISTRATION

CRD42022384015.

Integration of eQTL and machine learning to dissect causal genes with pleiotropic effects in genetic regulation networks of seed cotton yield

The dissection of a gene regulatory network (GRN) that complements the genome-wide association study (GWAS) locus and the crosstalk underlying multiple agronomical traits remains a major challenge. In this study, we generate 558 transcriptional profiles of lint-bearing ovules at one day post-anthesis from a selective core cotton germplasm, from which 12,207 expression quantitative trait loci (eQTLs) are identified. Sixty-six known phenotypic GWAS loci are colocalized with 1,090 eQTLs, forming 38 functional GRNs associated predominantly with seed yield. Of the eGenes, 34 exhibit pleiotropic effects. Combining the eQTLs within the seed yield GRNs significantly increases the portion of narrow-sense heritability. The extreme gradient boosting (XGBoost) machine learning approach is applied to predict seed cotton yield phenotypes on the basis of gene expression. Top-ranking eGenes (NF-YB3, FLA2, and GRDP1) derived with pleiotropic effects on yield traits are validated, along with their potential roles by correlation analysis, domestication selection analysis, and transgenic plants.

A Retrospective Cohort Study: Predicting 90-Day Mortality for ICU Trauma Patients with a Machine Learning Algorithm Using XGBoost Using MIMIC-III Database

Objective

The aim of this study was to develop and validate a machine learning-based predictive model that predicts 90-day mortality in ICU trauma patients.

Methods

Data of patients with severe trauma were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. The performances of mortality prediction models generated using nine machine learning extreme gradient boosting (XGBoost), logistic regression, random forest, AdaBoost, multilayer perceptron (MLP) neural networks, support vector machine (SVM), light gradient boosting machine (GBM), k nearest neighbors (KNN) and gaussian naive bayes (GNB). The performance of the model was evaluated in terms of discrimination, calibration and clinical application.

Results

We found that the accuracy, sensitivity, specificity, PPV, NPV and F1 score of our proposed XGBoost model were 82.8%, 79.7%, 77.6%, 51.2%, 91.5% and 0.624, respectively. Among the nine models, the XGBoost model performed best. Compared with traditional logistic regression, the calibration curves of the XGBoost model and decision curve analysis (DCA) performed well.

Conclusion

Our study shows that the XGBoost model outperforms other machine learning models in predicting 90-day mortality in trauma patients. It can be used to assist clinicians in the early identification of mortality risk factors and early intervention to reduce mortality.

Machine Learning-Based Early Prediction of Sepsis Using Electronic Health Records: A Systematic Review

BACKGROUND

Sepsis, a life-threatening infection-induced inflammatory condition, has significant global health impacts. Timely detection is crucial for improving patient outcomes as sepsis can rapidly progress to severe forms. The application of machine learning (ML) and deep learning (DL) to predict sepsis using electronic health records (EHRs) has gained considerable attention for timely intervention.

METHODS

PubMed, IEEE Xplore, Google Scholar, and Scopus were searched for relevant studies. All studies that used ML/DL to detect or early-predict the onset of sepsis in the adult population using EHRs were considered. Data were extracted and analyzed from all studies that met the criteria and were also evaluated for their quality.

RESULTS

This systematic review examined 1942 articles, selecting 42 studies while adhering to strict criteria. The chosen studies were predominantly retrospective (n = 38) and spanned diverse geographic settings, with a focus on the United States. Different datasets, sepsis definitions, and prevalence rates were employed, necessitating data augmentation. Heterogeneous parameter utilization, diverse model distribution, and varying quality assessments were observed. Longitudinal data enabled early sepsis prediction, and quality criteria fulfillment varied, with inconsistent funding-article quality correlation.

CONCLUSIONS

This systematic review underscores the significance of ML/DL methods for sepsis detection and early prediction through EHR data.

Prediction of nonsentinel lymph node metastasis in breast cancer patients based on machine learning

BACKGROUND

Develop the best machine learning (ML) model to predict nonsentinel lymph node metastases (NSLNM) in breast cancer patients.

METHODS

From June 2016 to August 2022, 1005 breast cancer patients were included in this retrospective study. Univariate and multivariate analyses were performed using logistic regression. Six ML models were introduced, and their performance was compared.

RESULTS

NSLNM occurred in 338 (33.6%) of 1005 patients. The best ML model was XGBoost, whose average area under the curve (AUC) based on 10-fold cross-verification was 0.722. It performed better than the nomogram, which was based on logistic regression (AUC: 0.764 vs. 0.706).

CONCLUSIONS

The ML model XGBoost can well predict NSLNM in breast cancer patients.

XGBoost-based machine learning test improves the accuracy of hemorrhage prediction among geriatric patients with long-term administration of rivaroxaban

BACKGROUND

Hemorrhage is a potential and serious adverse drug reaction, especially for geriatric patients with long-term administration of rivaroxaban. It is essential to establish an effective model for predicting bleeding events, which could improve the safety of rivaroxaban use in clinical practice.

METHODS

The hemorrhage information of 798 geriatric patients (over the age of 70 years) who needed long-term administration of rivaroxaban for anticoagulation therapy was constantly tracked and recorded through a well-established clinical follow-up system. Relying on the 27 collected clinical indicators of these patients, conventional logistic regression analysis, random forest and XGBoost-based machine learning approaches were applied to analyze the hemorrhagic risk factors and establish the corresponding prediction models. Furthermore, the performance of the models was tested and compared by the area under curve (AUC) of the receiver operating characteristic (ROC) curve.

RESULTS

A total of 112 patients (14.0%) had bleeding adverse events after treatment with rivaroxaban for more than 3 months. Among them, 96 patients had gastrointestinal and intracranial hemorrhage during treatment, which accounted for 83.18% of the total hemorrhagic events. The logistic regression, random forest and XGBoost models were established with AUCs of 0.679, 0.672 and 0.776, respectively. The XGBoost model showed the best predictive performance in terms of discrimination, accuracy and calibration among all the models.

CONCLUSION

An XGBoost-based model with good discrimination and accuracy was built to predict the hemorrhage risk of rivaroxaban, which will facilitate individualized treatment for geriatric patients.

Novel models by machine learning to predict prognosis of breast cancer brain metastases

BACKGROUND

Breast cancer brain metastases (BCBM) are the most fatal, with limited survival in all breast cancer distant metastases. These patients are deemed to be incurable. Thus, survival time is their foremost concern. However, there is a lack of accurate prediction models in the clinic. What's more, primary surgery for BCBM patients is still controversial.

METHODS

The data used for analysis in this study was obtained from the SEER database (2010-2019). We made a COX regression analysis to identify prognostic factors of BCBM patients. Through cross-validation, we constructed XGBoost models to predict survival in patients with BCBM. Meanwhile, a BCBM cohort from our hospital was used to validate our models. We also investigated the prognosis of patients treated with surgery or not, using propensity score matching and K-M survival analysis. Our results were further validated by subgroup COX analysis in patients with different molecular subtypes.

RESULTS

The XGBoost models we created had high precision and correctness, and they were the most accurate models to predict the survival of BCBM patients (6-month AUC = 0.824, 1-year AUC = 0.813, 2-year AUC = 0.800 and 3-year survival AUC = 0.803). Moreover, the models still exhibited good performance in an externally independent dataset (6-month: AUC = 0.820; 1-year: AUC = 0.732; 2-year: AUC = 0.795; 3-year: AUC = 0.936). Then we used Shiny-Web tool to make our models be easily used from website. Interestingly, we found that the BCBM patients with an annual income of over USD$70,000 had better BCSS (HR = 0.523, 95%CI 0.273-0.999, P < 0.05) than those with less than USD$40,000. The results showed that in all distant metastasis sites, only lung metastasis was an independent poor prognostic factor for patients with BCBM (OS: HR = 1.606, 95%CI 1.157-2.230, P < 0.01; BCSS: HR = 1.698, 95%CI 1.219-2.365, P < 0.01), while bone, liver, distant lymph nodes and other metastases were not. We also found that surgical treatment significantly improved both OS and BCSS in BCBM patients with the HER2 + molecular subtypes and was beneficial to OS of the HR-/HER2- subtype. In contrast, surgery could not help BCBM patients with HR + /HER2- subtype improve their prognosis (OS: HR = 0.887, 95%CI 0.608-1.293, P = 0.510; BCSS: HR = 0.909, 95%CI 0.604-1.368, P = 0.630).

CONCLUSION

We analyzed the clinical features of BCBM patients and constructed 4 machine-learning prognostic models to predict their survival. Our validation results indicate that these models should be highly reproducible in patients with BCBM. We also identified potential prognostic factors for BCBM patients and suggested that primary surgery might improve the survival of BCBM patients with HER2 + and triple-negative subtypes.

Prognostic and recurrent significance of SII in patients with pancreatic head cancer undergoing pancreaticoduodenectomy

Background

To investigate the clinical significance of preoperative inflammatory status in patients with pancreatic head carcinoma (PHC), we performed a single-center study to assess it.

Method

We studied a total of 164 patients with PHC undergoing PD surgery (with or without allogeneic venous replacement) from January 2018 to April 2022. Systemic immune-inflammation index (SII) was the most important peripheral immune index in predicting the prognosis according to XGBoost analysis. The optimal cutoff value of SII for OS was calculated according to Youden index based on the receiver operating characteristic (ROC) curve and the cohort was divided into Low SII group and High SII group. Demographic, clinical data, laboratory data, follow-up data variables were obtained and compared between the two groups. Kaplan-Meier curves, univariable and multivariable Cox regression models were used to determine the association between preoperative inflammation index, nutritional index and TNM staging system with OS and DFS respectively.

Results

The median follow-up time was 16 months (IQR 23), and 41.4% of recurrences occurred within 1 year. The cutoff value of SII was 563, with a sensitivity of 70.3%, and a specificity of 60.7%. Peripheral immune status was different between the two groups. Patients in High SII group had higher PAR, NLR than those in Low SII group (P <0.01, <0.01, respectively), and lower PNI (P <0.01). Kaplan-Meier analysis showed significantly poorer OS and DFS (P < 0.001, <0.001, respectively) in patients with high SII. By using the multivariable Cox regression model, high SII (HR, 2.056; 95% CI, 1.082-3.905, P=0.028) was significant predictor of OS. Of these 68 high-risk patients who recurrence within one year, patients with widespread metastasis had lower SII and worse prognosis (P <0.01).

Conclusion

High SII was significantly associated with poor prognosis in patients with PHC. However, in patients who recurrence within one year, SII was lower in patients at TNM stage III. Thus, care needs to be taken to differentiate those high-risk patients.

Explainable ensemble machine learning model for prediction of 28-day mortality risk in patients with sepsis-associated acute kidney injury

Background

Sepsis-associated acute kidney injury (S-AKI) is a major contributor to mortality in intensive care units (ICU). Early prediction of mortality risk is crucial to enhance prognosis and optimize clinical decisions. This study aims to develop a 28-day mortality risk prediction model for S-AKI utilizing an explainable ensemble machine learning (ML) algorithm.

Methods

This study utilized data from the Medical Information Mart for Intensive Care IV (MIMIC-IV 2.0) database to gather information on patients with S-AKI. Univariate regression, correlation analysis and Boruta were combined for feature selection. To construct the four ML models, hyperparameters were tuned via random search and five-fold cross-validation. To evaluate the performance of all models, ROC, K-S, and LIFT curves were used. The discrimination of ML models and traditional scoring systems was compared using area under the receiver operating characteristic curve (AUC). Additionally, the SHapley Additive exPlanation (SHAP) was utilized to interpret the ML model and identify essential variables. To investigate the relationship between the top nine continuous variables and the risk of 28-day mortality. COX regression-restricted cubic splines were utilized while controlling for age and comorbidities.

Results

The study analyzed data from 9,158 patients with S-AKI, dividing them into a 28-day mortality group of 1,940 and a survival group of 7,578. The results showed that XGBoost was the best performing model of the four ML models with AUC of 0.873. All models outperformed APS-III 0.713 and SAPS-II 0.681. The K-S and LIFT curves indicated XGBoost as the most effective predictor for 28-day mortality risk. The model's performance was evaluated using ROCpr curves, calibration curves, accuracy, precision, and F1 scores. SHAP force plots were utilized to interpret and visualize the personalized predictive power of the 28-day mortality risk model. Additionally, COX regression restricted cubic splines revealed an interesting non-linear relationship between the top nine variables and 28-day mortality.

Conclusion

The use of ensemble ML models has shown to be more effective than the LR model and conventional scoring systems in predicting 28-day mortality risk in S-AKI patients. By visualizing the XGBoost model with the best predictive performance, clinicians are able to identify high-risk patients early on and improve prognosis.

Evaluation of machine learning-based models for prediction of clinical deterioration: A systematic literature review

BACKGROUND AND OBJECTIVE

Early identification of patients at risk of deterioration can prevent life-threatening adverse events and shorten length of stay. Although there are numerous models applied to predict patient clinical deterioration, most are based on vital signs and have methodological shortcomings that are not able to provide accurate estimates of deterioration risk. The aim of this systematic review is to examine the effectiveness, challenges, and limitations of using machine learning (ML) techniques to predict patient clinical deterioration in hospital settings.

METHODS

A systematic review was performed in accordance with the Preferred Reporting Items for Systematic Reviews and meta-Analyses (PRISMA) guidelines using EMBASE, MEDLINE Complete, CINAHL Complete, and IEEExplore databases. Citation searching was carried out for studies that met inclusion criteria. Two reviewers used the inclusion/exclusion criteria to independently screen studies and extract data. To address any discrepancies in the screening process, the two reviewers discussed their findings and a third reviewer was consulted as needed to reach a consensus. Studies focusing on use of ML in predicting patient clinical deterioration that were published from inception to July 2022 were included.

RESULTS

A total of 29 primary studies that evaluated ML models to predict patient clinical deterioration were identified. After reviewing these studies, we found that 15 types of ML techniques have been employed to predict patient clinical deterioration. While six studies used a single technique exclusively, several others utilised a combination of classical techniques, unsupervised and supervised learning, as well as other novel techniques. Depending on which ML model was applied and the type of input features, ML models predicted outcomes with an area under the curve from 0.55 to 0.99.

CONCLUSIONS

Numerous ML methods have been employed to automate the identification of patient deterioration. Despite these advancements, there is still a need for further investigation to examine the application and effectiveness of these methods in real-world situations.

A Comparative Study on Predication of Appropriate Mechanical Ventilation Mode through Machine Learning Approach

Ventilation mode is one of the most crucial ventilator settings, selected and set by knowledgeable critical care therapists in a critical care unit. The application of a particular ventilation mode must be patient-specific and patient-interactive. The main aim of this study is to provide a detailed outline regarding ventilation mode settings and determine the best machine learning method to create a deployable model for the appropriate selection of ventilation mode on a per breath basis. Per-breath patient data is utilized, preprocessed and finally a data frame is created consisting of five feature columns (inspiratory and expiratory tidal volume, minimum pressure, positive end-expiratory pressure, and previous positive end-expiratory pressure) and one output column (output column consisted of modes to be predicted). The data frame has been split into training and testing datasets with a test size of 30%. Six machine learning algorithms were trained and compared for performance, based on the accuracy, F1 score, sensitivity, and precision. The output shows that the Random-Forest Algorithm was the most precise and accurate in predicting all ventilation modes correctly, out of the all the machine learning algorithms trained. Thus, the Random-Forest machine learning technique can be utilized for predicting optimal ventilation mode setting, if it is properly trained with the help of the most relevant data. Aside from ventilation mode, control parameter settings, alarm settings and other settings may also be adjusted for the mechanical ventilation process utilizing appropriate machine learning, particularly deep learning approaches.

Chemical rules for optimization of chemical mutagenicity via matched molecular pairs analysis and machine learning methods

Chemical mutagenicity is a serious issue that needs to be addressed in early drug discovery. Over a long period of time, medicinal chemists have manually summarized a series of empirical rules for the optimization of chemical mutagenicity. However, given the rising amount of data, it is getting more difficult for medicinal chemists to identify more comprehensive chemical rules behind the biochemical data. Herein, we integrated a large Ames mutagenicity data set with 8576 compounds to derive mutagenicity transformation rules for reversing Ames mutagenicity via matched molecular pairs analysis. A well-trained consensus model with a reasonable applicability domain was constructed, which showed favorable performance in the external validation set with an accuracy of 0.815. The model was used to assess the generalizability and validity of these mutagenicity transformation rules. The results demonstrated that these rules were of great value and could provide inspiration for the structural modifications of compounds with potential mutagenic effects. We also found that the local chemical environment of the attachment points of rules was critical for successful transformation. To facilitate the use of these mutagenicity transformation rules, we integrated them into ADMETopt2 ( http://lmmd.ecust.edu.cn/admetsar2/admetopt2/ ), a free web server for optimization of chemical ADMET properties. The above-mentioned approach would be extended to the optimization of other toxicity endpoints.

Using machine learning models to predict acute pancreatitis in children with pancreaticobiliary maljunction

PURPOSE

To develop a model to identify risk factors and predictors of acute pancreatitis in children with pancreaticobiliary maljunction (PBM).

METHODS

We screened consecutive PBM patients treated at two centers between January, 2015 and July, 2021. For machine learning, the cohort was divided randomly at a 6:4 ratio to a training dataset and a validation dataset. Three parallel models were developed using logistic regression (LR), a support vector machine (SVM), and extreme gradient boosting (XGBoost), respectively. Model performance was judged primarily based on the area under the receiver operating curves (AUC).

RESULTS

A total of 99 patients were included in the analysis, 17 of whom suffered acute pancreatitis and 82 did not. The XGBoost (AUC = 0.814) and SVM (AUC = 0.813) models produced similar performance in the validation dataset; both outperformed the LR model (AUC = 0.805). Based on the SHapley Additive exPlanation values, the most important variable in both the XGBoost and SVM models were age, protein plugs, and white blood cell count.

CONCLUSIONS

Machine learning models, especially XGBoost and SVM, could be used to predict acute pancreatitis in children with PBM. The most important contributing factor to the models were age, protein plugs, and white blood cell count.

Prediction models for chronic postsurgical pain in patients with breast cancer based on machine learning approaches

Purpose

This study aimed to develop prediction models for chronic postsurgical pain (CPSP) after breast cancer surgery using machine learning approaches and evaluate their performance.

Methods

The study was a secondary analysis based on a high-quality dataset from a randomized controlled trial (NCT00418457), including patients with primary breast cancer undergoing mastectomy. The primary outcome was CPSP at 12 months after surgery, defined as modified Brief Pain Inventory > 0. The dataset was randomly split into a training dataset (90%) and a testing dataset (10%). Variables were selected using recursive feature elimination combined with clinical experience, and potential predictors were then incorporated into three machine learning models, including random forest, gradient boosting decision tree and extreme gradient boosting models for outcome prediction, as well as logistic regression. The performances of these four models were tested and compared.

Results

1152 patients were finally included, of which 22.1% developed CPSP at 12 months after breast cancer surgery. The 6 leading predictors were higher numerical rating scale within 2 days after surgery, post-menopausal status, urban medical insurance, history of at least one operation, under fentanyl with sevoflurane general anesthesia, and received axillary lymph node dissection. Compared with the multivariable logistic regression model, machine learning models showed better specificity, positive likelihood ratio and positive predictive value, helping to identify high-risk patients more accurately and create opportunities for early clinical intervention.

Conclusions

Our study developed prediction models for CPSP after breast cancer surgery based on machine learning approaches, which may help to identify high-risk patients and improve patients' management after breast cancer.

Blood Biomarkers Panels for Screening of Colorectal Cancer and Adenoma on a Machine Learning-Assisted Detection Platform

OBJECTIVE

A mini-invasive and good-compliance program is critical to broaden colorectal cancer (CRC) screening and reduce CRC-related mortality. Blood testing combined with imaging examination has been proved to be feasible on screen for multicancer and guide intervention. The study aims to construct a machine learning-assisted detection platform with available multi-targets for CRC and colorectal adenoma (CRA) screening.

METHODS

This was a retrospective study that the blood test data from 204 CRCs, 384 CRAs, and 229 healthy controls was extracted. The classified models were constructed with 4 machine learning (ML) algorithms including support vector machine (SVM), random forest (RF), decision tree (DT), and eXtreme Gradient Boosting (XGB) based on the candidate biomarkers. The importance index was used by SHapely Adaptive exPlanations (SHAP) analysis to identify the dominant characteristics. The performance of classified models was evaluated. The most dominating features from the proposed panel were developed by logistic regression (LR) for identification CRC from control.

RESULTS

The candidate biomarkers consisted of 26 multi-targets panel including CEA, AFP, and so on. Among the 4 models, the SVM classifier for CRA yields the best predictive performance (the area under the receiver operating curve, AUC: .925, sensitivity: .904, and specificity: .771). As for CRC classification, the RF model with 26 candidate biomarkers provided the best predictive parameters (AUC: .941, sensitivity: .902, and specificity: .912). Compared with CEA and CA199, the predictive performance was significantly improved. The streamlined model with 6 biomarkers for CRC also obtained a good performance (AUC: .946, sensitivity: .885, and specificity: .913).

CONCLUSIONS

The predictive models consisting of 26 multi-targets panel would be used as a non-invasive, economical, and effective risk stratification platform, which was expected to be applied for auxiliary screening of CRA and CRC in clinical practice.

Association of Pulmonary Artery Pressure Change With Post-Lung Transplantation Survival: Retrospective Analysis of China Registry

Background

Extracorporeal membrane oxygenation (ECMO) has been used as intraoperative hemodynamic support in patients with end-stage lung diseases and pulmonary hypertension undergoing lung transplantation (LT).

Objectives

The aim of this study was to identify the association of pulmonary artery pressure change during ECMO and post-LT survival.

Methods

The study investigators collected and analyzed the data from Chinese Lung Transplantation Registry. Patients who have severe pulmonary hypertension with intraoperative ECMO support were enrolled. Post-LT mortality and morbidity were further collected and compared.

Results

A total of 208 recipients were included in the study, during which 53 deaths occurred post-LT. All the patients had severe pulmonary hypertension and were supported by intraoperative ECMO. Using eXtreme Gradient Boosting, or XGboost, model method, 20 variables were selected and ranked. Changes of mean pulmonary artery pressure at the time of ECMO support and ECMO wean-off (ΔmPAP) were related to post-LT survival, after adjusting for potential confounders (recipient age, New York Heart Association functional class status before LT, body mass index, pre-LT hypertension, pre-LT steroids, and pre-LT ECMO bridging). A nonlinear relationship was detected between ΔmPAP and post-LT survival, which had an inflection point of 35 mm Hg. Recipients with ΔmPAP ≦35 mm Hg had higher mortality rate calculated through the Kaplan-Meier estimator (P = 0.041). Interaction analysis showed that recipients admitted in LT center with high case volume (≥50 cases/year) and ΔmPAP >35 mm Hg had better long-term survival. The trend was reversed in recipients who were admitted in LT center with low case volume (<50 cases/year).

Conclusions

The relationship between ΔmPAP and post-LT survival was nonlinear. Optimal perioperative ECMO management strategy with experienced team is further warranted.

Performance effectiveness of vital parameter combinations for early warning of sepsis-an exhaustive study using machine learning

Objective

To carry out exhaustive data-driven computations for the performance of noninvasive vital signs heart rate (HR), respiratory rate (RR), peripheral oxygen saturation (SpO2), and temperature (Temp), considered both independently and in all possible combinations, for early detection of sepsis.

Materials and methods

By extracting features interpretable by clinicians, we applied Gradient Boosted Decision Tree machine learning on a dataset of 2630 patients to build 240 models. Validation was performed on a geographically distinct dataset. Relative to onset, predictions were clocked as per 16 pairs of monitoring intervals and prediction times, and the outcomes were ranked.

Results

The combination of HR and Temp was found to be a minimal feature set yielding maximal predictability with area under receiver operating curve 0.94, sensitivity of 0.85, and specificity of 0.90. Whereas HR and RR each directly enhance prediction, the effects of SpO2 and Temp are significant only when combined with HR or RR. In benchmarking relative to standard methods Systemic Inflammatory Response Syndrome (SIRS), National Early Warning Score (NEWS), and quick-Sequential Organ Failure Assessment (qSOFA), Vital-SEP outperformed all 3 of them.

Conclusion

It can be concluded that using intensive care unit data even 2 vital signs are adequate to predict sepsis upto 6 h in advance with promising accuracy comparable to standard scoring methods and other sepsis predictive tools reported in literature. Vital-SEP can be used for fast-track prediction especially in limited resource hospital settings where laboratory based hematologic or biochemical assays may be unavailable, inaccurate, or entail clinically inordinate delays. A prospective study is essential to determine the clinical impact of the proposed sepsis prediction model and evaluate other outcomes such as mortality and duration of hospital stay.

Predicting recurrent cases of intussusception in children after air enema reduction with machine learning models

PURPOSE

To develop a model to identify risk factors and predict recurrent cases of intussusception in children.

METHODS

Consecutive cases and recurrent cases of intussusception in children from January 2016 to April 2022 were screened. The cohort was divided randomly at a 4:1 ratio to a training dataset and a validation dataset. Three parallel models were developed using extreme gradient boosting (XGBoost), logistic regression (LR), and support vector machine (SVM). Model performance was assessed by the area under the receiver operating characteristic curves (AUC).

RESULTS

A total of 2469 cases of intussusception were included, where 225 were recurrent cases. The XGBoost (AUC = 0.718) models showed the best performance in the validation dataset, followed by the LR model (AUC = 0.652), while the SVM model (AUC = 0.613) performed worst among the three models. Based on the Shapley Additive exPlanation values, the most important variables in the XGBoost models were air enema pressure, mass size, age, duration of symptoms, and absence of vomiting.

CONCLUSIONS

Machine learning models, especially XGBoost, could be used to predict recurrent cases of intussusception in children. The most important contributing factors to the models are air enema pressure, mass size, age, duration of symptoms and absence of vomiting.

Improving Machine Learning Diabetes Prediction Models for the Utmost Clinical Effectiveness

The early prediction of diabetes can facilitate interventions to prevent or delay it. This study proposes a diabetes prediction model based on machine learning (ML) to encourage individuals at risk of diabetes to employ healthy interventions. A total of 38,379 subjects were included. We trained the model on 80% of the subjects and verified its predictive performance on the remaining 20%. Furthermore, the performances of several algorithms were compared, including logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), Cox regression, and XGBoost Survival Embedding (XGBSE). The area under the receiver operating characteristic curve (AUROC) of the XGBoost model was the largest, followed by those of the decision tree, logistic regression, and random forest models. For the survival analysis, XGBSE yielded an AUROC exceeding 0.9 for the 2- to 9-year predictions and a C-index of 0.934, while the Cox regression achieved a C-index of 0.921. After lowering the threshold from 0.5 to 0.25, the sensitivity increased from 0.011 to 0.236 for the 2-year prediction model and from 0.607 to 0.994 for the 9-year prediction model, while the specificity showed negligible changes. We developed a high-performance diabetes prediction model that applied the XGBSE algorithm with threshold adjustment. We plan to use this prediction model in real clinical practice for diabetes prevention after simplifying and validating it externally.

A Machine Learning Approach Using XGBoost Predicts Lung Metastasis in Patients with Ovarian Cancer

Background

Liver metastasis (LM) is an independent risk factor that affects the prognosis of patients with ovarian cancer; however, there is still a lack of prediction. This study developed a limit gradient enhancement (XGBoost) to predict the risk of lung metastasis in newly diagnosed patients with ovarian cancer, thereby improving prediction efficiency. Patients and Methods. Data of patients diagnosed with ovarian cancer in the Surveillance, Epidemiology, and Final Results (SEER) database from 2010 to 2015 were retrospectively collected. The XGBoost algorithm was used to establish a lung metastasis model for patients with ovarian cancer. The performance of the predictive model was tested by the area under the curve (AUC) of the receiver operating characteristic curve (ROC).

Results

The results of the XGBoost algorithm showed that the top five important factors were age, laterality, histological type, grade, and marital status. XGBoost showed good discriminative ability, with an AUC of 0.843. Accuracy, sensitivity, and specificity were 0.982, 1.000, and 0.686, respectively.

Conclusion

This study is the first to develop a machine-learning-based prediction model for lung metastasis in patients with ovarian cancer. The prediction model based on the XGBoost algorithm has a higher accuracy rate than traditional logistic regression and can be used to predict the risk of lung metastasis in newly diagnosed patients with ovarian cancer.

Machine learning predicts the prognosis of breast cancer patients with initial bone metastases

Background

Bone is the most common metastatic site of patients with advanced breast cancer and the survival time is their primary concern; however, we lack accurate predictive models in clinical practice. In addition to this, primary surgery for breast cancer patients with bone metastases is still controversial.

Method

The data used for analysis in this study were obtained from the SEER database (2010-2019). We made a COX regression analysis to identify prognostic factors of patients with bone metastatic breast cancer (BMBC). Through cross-validation, we constructed an XGBoost model to predicting survival in patients with BMBC. We also investigated the prognosis of patients treated with neoadjuvant chemotherapy plus surgical and chemotherapy alone using propensity score matching and K-M survival analysis.

Results

Our validation results showed that the model has high sensitivity, specificity, and correctness, and it is the most accurate one to predict the survival of patients with BMBC (1-year AUC = 0.818, 3-year AUC = 0.798, and 5-year survival AUC = 0.791). The sensitivity of the 1-year model was higher (0.79), while the specificity of the 5-year model was higher (0.86). Interestingly, we found that if the time from diagnosis to therapy was ≥1 month, patients with BMBC had even better survival than those who started treatment immediately (HR = 0.920, 95%CI 0.869-0.974, P < 0.01). The BMBC patients with an income of more than USD$70,000 had better OS (HR = 0.814, 95%CI 0.745-0.890, P < 0.001) and BCSS (HR = 0.808 95%CI 0.735-0.889, P < 0.001) than who with income of < USD$50,000. We also found that compared with chemotherapy alone, neoadjuvant chemotherapy plus surgical treatment significantly improved OS and BCSS in all molecular subtypes of patients with BMBC, while only the patients with bone metastases only, bone and liver metastases, bone and lung metastases could benefit from neoadjuvant chemotherapy plus surgical treatment.

Conclusion

We constructed an AI model to provide a quantitative method to predict the survival of patients with BMBC, and our validation results indicate that this model should be highly reproducible in a similar patient population. We also identified potential prognostic factors for patients with BMBC and suggested that primary surgery followed by neoadjuvant chemotherapy might increase survival in a selected subgroup of patients.

Prediction of lymph node metastasis in patients with breast invasive micropapillary carcinoma based on machine learning and SHapley Additive exPlanations framework

Abstract

Background and purpose: Machine learning (ML) is applied for outcome prediction and treatment support. This study aims to develop different ML models to predict risk of axillary lymph node metastasis (LNM) in breast invasive micropapillary carcinoma (IMPC) and to explore the risk factors of LNM.

Methods

From the Surveillance, Epidemiology, and End Results (SEER) database and the records of our hospital, a total of 1547 patients diagnosed with breast IMPC were incorporated in this study. The ML model is built and the external validation is carried out. SHapley Additive exPlanations (SHAP) framework was applied to explain the optimal model; multivariable analysis was performed with logistic regression (LR); and nomograms were constructed according to the results of LR analysis.

Results

Age and tumor size were correlated with LNM in both cohorts. The luminal subtype is the most common in patients, with the tumor size <=20mm. Compared to other models, Xgboost was the best ML model with the biggest AUC of 0.813 (95% CI: 0.7994 - 0.8262) and the smallest Brier score of 0.186 (95% CI: 0.799-0.826). SHAP plots demonstrated that tumor size was the most vital risk factor for LNM. In both training and test sets, Xgboost had better AUC (0.761 vs 0.745; 0.813 vs 0.775; respectively), and it also achieved a smaller Brier score (0.202 vs 0.204; 0.186 vs 0.191; 0.220 vs 0.221; respectively) than the nomogram model based on LR in those three different sets. After adjusting for five most influential variables (tumor size, age, ER, HER-2, and PR), prediction score based on the Xgboost model was still correlated with LNM (adjusted OR:2.73, 95% CI: 1.30-5.71, P=0.008).

Conclusions

The Xgboost model outperforms the traditional LR-based nomogram model in predicting the LNM of IMPC patients. Combined with SHAP, it can more intuitively reflect the influence of different variables on the LNM. The tumor size was the most important risk factor of LNM for breast IMPC patients. The prediction score obtained by the Xgboost model could be a good indicator for LNM.

Thiamine May Be Beneficial for Patients With Ventilator-Associated Pneumonia in the Intensive Care Unit: A Retrospective Study Based on the MIMIC-IV Database

Background: Ventilator-associated pneumonia (VAP) is a common infection complication in intensive care units (ICU). It not only prolongs mechanical ventilation and ICU and hospital stays, but also increases medical costs and increases the mortality risk of patients. Although many studies have found that thiamine supplementation in critically ill patients may improve prognoses, there is still no research or evidence that thiamine supplementation is beneficial for patients with VAP. The purpose of this study was to determine the association between thiamine and the prognoses of patients with VAP.

Methods: This study retrospectively collected all patients with VAP in the ICU from the Medical Information Mart for Intensive Care-IV database. The outcomes were ICU and in-hospital mortality. Patients were divided into the no-thiamine and thiamine groups depending upon whether or not they had received supplementation. Associations between thiamine and the outcomes were tested using Kaplan-Meier (KM) survival curves and Cox proportional-hazards regression models. The statistical methods of propensity-score matching (PSM) and inverse probability weighting (IPW) based on the XGBoost model were also applied to ensure the robustness of our findings.

Results: The study finally included 1,654 patients with VAP, comprising 1,151 and 503 in the no-thiamine and thiamine groups, respectively. The KM survival curves indicated that the survival probability differed significantly between the two groups. After multivariate COX regression adjusted for confounding factors, the hazard ratio (95% confidence interval) values for ICU and in-hospital mortality in the thiamine group were 0.57 (0.37, 0.88) and 0.64 (0.45, 0.92), respectively. Moreover, the results of the PSM and IPW analyses were consistent with the original population.

Conclusion: Thiamine supplementation may reduce ICU and in-hospital mortality in patients with VAP in the ICU. Thiamine is an inexpensive and safe drug, and so further clinical trials should be conducted to provide more-solid evidence on whether it improves the prognosis of patients with VAP.

Predicting the Prognosis of Patients in the Coronary Care Unit: A Novel Multi-Category Machine Learning Model Using XGBoost

Background

Early prediction and classification of prognosis is essential for patients in the coronary care unit (CCU). We applied a machine learning (ML) model using the eXtreme Gradient Boosting (XGBoost) algorithm to prognosticate CCU patients and compared XGBoost with traditional classification models.

Methods

CCU patients' data were extracted from the MIMIC-III v1.4 clinical database, and divided into four groups based on the time to death: <30 days, 30 days−1 year, 1–5 years, and ≥5 years. Four classification models, including XGBoost, naïve Bayes (NB), logistic regression (LR), and support vector machine (SVM) were constructed using the Python software. These four models were tested and compared for accuracy, F1 score, Matthews correlation coefficient (MCC), and area under the curve (AUC) of the receiver operating characteristic curves. Subsequently, Local Interpretable Model-Agnostic Explanations method was performed to improve XGBoost model interpretability. We also constructed sub-models of each model based on the different categories of death time and compared the differences by decision curve analysis. The optimal model was further analyzed using a clinical impact curve. At last, feature ablation curves of the XGBoost model were conducted to obtain the simplified model.

Results

Overall, 5360 CCU patients were included. Compared to NB, LR, and SVM, the XGBoost model showed better accuracy (0.663, 0.605, 0.632, and 0.622), micro-AUCs (0.873, 0.811, 0.841, and 0.818), and MCC (0.337, 0.317, 0.250, and 0.182). In subgroup analysis, the XGBoost model had a better predictive performance in acute myocardial infarction subgroup. The decision curve and clinical impact curve analyses verified the clinical utility of the XGBoost model for different categories of patients. Finally, we obtained a simplified model with thirty features.

Conclusions

For CCU physicians, the ML technique by XGBoost is a potential predictive tool in patients with different conditions, and it may contribute to improvements in prognosis.

Blood Purification Studies in the ICU: What Endpoints Should We Use?

The potential for treatment of the critically ill using blood purification techniques has been discussed for several decades. However, since the first attempts at applying extracorporeal techniques to patients with sepsis were described, there has been considerable hesitancy towards the widespread adoption of such methods, given the lack of mortality benefit observed and indeed the paucity of randomized controlled studies. However, this is not unique so far as studies on the critically ill are concerned where there is a dearth of studies providing a positive finding to influence clinical practice. Consequently, as well as targeted patient selection, it is perhaps time to consider endpoints other than mortality in studies on the critically ill, particularly in blood purification studies where, to-date, such heterogeneous groups of patients have been studied.

Developing machine learning models for prediction of mortality in the medical intensive care unit

BACKGROUND AND OBJECTIVE

Alert of patient deterioration is essential for prompt medical intervention in the Medical Intensive Care Unit (MICU). Logistic Regression (LR) has been used for the development of most conventional severity-of-illness scoring systems to anticipate the risk of mortality in the MICU. Machine Learning (ML) models such as probabilistic graphical models and Extreme Gradient Boosting (XGB) have demonstrated improved prediction accuracy in patient outcomes compared to LR. The aim was to compare three ML models to the SAPS, SAPS II, SAPS III, SOFA, serial SOFA, LODS, and OASIS for prediction of MICU mortality.

METHODS

A Bayesian Network (BN), Naïve Bayes network (NB), and a XGB model were developed. 9893 adult MICU-stays from the MIMIC-III database were studied. The primary outcome was MICU mortality prediction and the secondary outcome was 1-year mortality prediction. Data analyzed consisted on routine physiological measurements collected during 5 hours in the MICU, demographic and diagnoses/procedure features. The performance was evaluated by accuracy statistics, discrimination and calibration measures. Limitations of the study were discussed.

RESULTS

The AUROC for MICU mortality prediction was 0.919 for XGB, 0.905 for BN, and 0.864 for NB, while the conventional systems displayed much lower values with the serial SOFA having the best value (0.814). The Diagnostic Odds Ratio was ≤7.099 for all the conventional systems, reaching values of 30.115 for XGB and 22.648 for BN. The XGB achieved a sensitivity of 0.831 and specificity of 0.86 assuring an acceptable precision (0.528), whose values were much lower for the conventional systems. The Brier score was better for the ML models, except for the NB (0.119), with 0.072 for XGB and 0.081 for BN.

CONCLUSIONS

The XGB and BN substantially outperformed the conventional systems for discrimination, calibration and the accuracy statistics assessed. The NB showed inferior performance to the XGB and BN but improved the discrimination and all accuracy statistics of the conventional systems except for an inferior calibration and 1-year mortality discrimination. The XGB showed the best performance among all models. These ML models have the potential to improve the monitoring of MICU patients, which must be evaluated in future studies.

A machine learning-based risk stratification tool for in-hospital mortality of intensive care unit patients with heart failure

BACKGROUND

Predicting hospital mortality risk is essential for the care of heart failure patients, especially for those in intensive care units.

METHODS

Using a novel machine learning algorithm, we constructed a risk stratification tool that correlated patients' clinical features and in-hospital mortality. We used the extreme gradient boosting algorithm to generate a model predicting the mortality risk of heart failure patients in the intensive care unit in the derivation dataset of 5676 patients from the Medical Information Mart for Intensive Care III database. The logistic regression model and a common risk score for mortality were used for comparison. The eICU Collaborative Research Database dataset was used for external validation.

RESULTS

The performance of the machine learning model was superior to that of conventional risk predictive methods, with the area under curve 0.831 (95% CI 0.820-0.843) and acceptable calibration. In external validation, the model had an area under the curve of 0.809 (95% CI 0.805-0.814). Risk stratification through the model was specific when the hospital mortality was very low, low, moderate, high, and very high (2.0%, 10.2%, 11.5%, 21.2% and 56.2%, respectively). The decision curve analysis verified that the machine learning model is the best clinically valuable in predicting mortality risk.

CONCLUSION

Using readily available clinical data in the intensive care unit, we built a machine learning-based mortality risk tool with prediction accuracy superior to that of linear regression model and common risk scores. The risk tool may support clinicians in assessing individual patients and making individualized treatment.

Quo Vadis Anesthesiologist? The Value Proposition of Future Anesthesiologists Lies in Preserving or Restoring Presurgical Health after Surgical Insult

This Special Issue of the Journal of Clinical Medicine is devoted to anesthesia and perioperative care [...].

Event Prediction Model Considering Time and Input Error Using Electronic Medical Records in the Intensive Care Unit: Retrospective Study

BACKGROUND

In the era of artificial intelligence, event prediction models are abundant. However, considering the limitation of the electronic medical record-based model, including the temporally skewed prediction and the record itself, these models could be delayed or could yield errors.

OBJECTIVE

In this study, we aim to develop multiple event prediction models in intensive care units to overcome their temporal skewness and evaluate their robustness against delayed and erroneous input.

METHODS

A total of 21,738 patients were included in the development cohort. Three events-death, sepsis, and acute kidney injury-were predicted. To overcome the temporal skewness, we developed three models for each event, which predicted the events in advance of three prespecified timepoints. Additionally, to evaluate the robustness against input error and delays, we added simulated errors and delayed input and calculated changes in the area under the receiver operating characteristic curve (AUROC) values.

RESULTS

Most of the AUROC and area under the precision-recall curve values of each model were higher than those of the conventional scores, as well as other machine learning models previously used. In the error input experiment, except for our proposed model, an increase in the noise added to the model lowered the resulting AUROC value. However, the delayed input did not show the performance decreased in this experiment.

CONCLUSIONS

For a prediction model that was applicable in the real world, we considered not only performance but also temporal skewness, delayed input, and input error.

Early Prediction of Sepsis Based on Machine Learning Algorithm

Sepsis is an organ failure disease caused by an infection resulting in extremely high mortality. Machine learning algorithms XGBoost and LightGBM are applied to construct two processing methods: mean processing method and feature generation method, aiming to predict early sepsis 6 hours in advance. The feature generation methods are constructed by combining different features, including statistical strength features, window features, and medical features. Miceforest multiple interpolation method is applied to tackle large missing data problems. Results show that the feature generation method outperforms the mean processing method. XGBoost and LightGBM algorithms are both excellent in prediction performance (AUC: 0.910∼0.979), among which LightGBM boasts a faster running speed and is stronger in generalization ability especially on multidimensional data, with AUC reaching 0.979 in the feature generation method. PTT, WBC, and platelets are the key risk factors to predict early sepsis.

The impact of recency and adequacy of historical information on sepsis predictions using machine learning

Sepsis is a major public and global health concern. Every hour of delay in detecting sepsis significantly increases the risk of death, highlighting the importance of accurately predicting sepsis in a timely manner. A growing body of literature has examined developing new or improving the existing machine learning (ML) approaches for timely and accurate predictions of sepsis. This study contributes to this literature by providing clear insights regarding the role of the recency and adequacy of historical information in predicting sepsis using ML. To this end, we implemented a deep learning model using a bidirectional long short-term memory (BiLSTM) algorithm and compared it with six other ML algorithms based on numerous combinations of the prediction horizons (to capture information recency) and observation windows (to capture information adequacy) using different measures of predictive performance. Our results indicated that the BiLSTM algorithm outperforms all other ML algorithms and provides a great separability of the predicted risk of sepsis among septic versus non-septic patients. Moreover, decreasing the prediction horizon (in favor of information recency) always boosts the predictive performance; however, the impact of expanding the observation window (in favor of information adequacy) depends on the prediction horizon and the purpose of prediction. More specifically, when the prediction is responsive to the positive label (i.e., Sepsis), increasing historical data improves the predictive performance when the prediction horizon is short-moderate.