Machine Learning for Early Warning of Septic Shock in Children With Hematological Malignancies Accompanied by Fever or Neutropenia: A Single Center Retrospective Study

Filling-well: An effective technique to handle incomplete well-log data for lithology classification using machine learning algorithms

Lithology classification is crucial for efficient and sustainable resource exploration in the oil and gas industry. Missing values in well-log data, such as Gamma Ray (GR), Neutron Porosity (NPHI), Bulk Density (RHOB), Deep Resistivity (RS), Delta Time Compressional (DTCO), Delta Time Shear (DTSM), and Resistivity Deep (RD), significantly affect machine learning classification accuracy. This study applied three algorithms, extreme gradient boosting (XGBoost), K-nearest neighbours (KNN), and the artificial neural network (ANN), to handle missing values in well-log datasets, particularly datasets with extreme missing data (30 %). Results indicated that XGBoost was the most efficient and accurate, especially for RHOB, NPHI, DTCO, and DTSM, with the lowest Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) values. The ANN also performed effectively, particularly on the GR, RS, and RD features, after the use of preprocessing techniques such as isolation forest and bias correction. However, the ANN can suffer from overfitting and requires large datasets for optimal performance. In contrast, KNN struggled with missing-not-at-random (MNAR) data due to its reliance on the k parameter and distance metric, making it less effective in mapping missing data relationships.•Missing values in well-log data can hinder lithology classification accuracy for efficient resource exploration in the oil and gas industry.•This research aims to address the problem of missing values in well-log datasets by applying machine learning algorithms such as XGBoost, ANN, and KNN to enhance classification performance.•XGBoost demonstrated superior performance in handling extreme missing data (30 %) in well-log datasets. ANN was effective but prone to overfitting for small datasets, while KNN struggled with missing-not-at-random (MNAR) data due to limitations in its distance-based approach.

Machine Learning-based Prediction of Blood Stream Infection in Pediatric Febrile Neutropenia

OBJECTIVES

This study aimed to develop machine learning (ML) prediction models for identifying bloodstream infection (BSI) and septic shock (SS) in pediatric patients with cancer who presenting febrile neutropenia (FN) at emergency department (ED) visit.

MATERIALS AND METHODS

A retrospective study was conducted on patients, younger than 18 years of age, who visited a tertiary university-affiliated hospital ED due to FN between January 2004 and August 2022. ML models, based on XGBoost, were developed for BSI and SS prediction.

RESULTS

After applying the exclusion criteria, we identified 4423 FN events during the study period. We identified 195 (4.4%) BSI and 107 (2.4%) SS events. The BSI and SS models demonstrated promising performance, with area under the receiver operating characteristic curve values of 0.87 and 0.88, respectively, which were superior to those of the logistic regression models. Clinical features, including body temperature, some laboratory results, vital signs, and diagnosis of acute myeloblastic leukemia were identified as significant predictors.

CONCLUSIONS

The ML-based prediction models, which use data obtainable at ED visits may be valuable tools for ED physicians to predict BSI or SS.

A scoping review on pediatric sepsis prediction technologies in healthcare

This scoping review evaluates recent advancements in data-driven technologies for predicting non-neonatal pediatric sepsis, including artificial intelligence, machine learning, and other methodologies. Of the 27 included studies, 23 (85%) were single-center investigations, and 16 (59%) used logistic regression. Notably, 20 (74%) studies used datasets with a low prevalence of sepsis-related outcomes, with area under the receiver operating characteristic scores ranging from 0.56 to 0.99. Prediction time points varied widely, and development characteristics, performance metrics, implementation outcomes, and considerations for human factors-especially workflow integration and clinical judgment-were inconsistently reported. The variations in endpoint definitions highlight the potential significance of the 2024 consensus criteria in future development. Future research should strengthen the involvement of clinical users to enhance the understanding and integration of human factors in designing and evaluating these technologies, ultimately aiming for safe and effective integration in pediatric healthcare.

Improving management of febrile neutropenia in oncology patients: the role of artificial intelligence and machine learning

INTRODUCTION

Artificial intelligence (AI) and machine learning (ML) have the potential to revolutionize the management of febrile neutropenia (FN) and drive progress toward personalized medicine.

AREAS COVERED

In this review, we detail how the collection of a large number of high-quality data can be used to conduct precise mathematical studies with ML and AI. We explain the foundations of these techniques, covering the fundamentals of supervised and unsupervised learning, as well as the most important challenges, e.g. data quality, 'black box' model interpretation and overfitting. To conclude, we provide detailed examples of how AI and ML have been used to enhance predictions of chemotherapy-induced FN, detection of bloodstream infections (BSIs) and multidrug-resistant (MDR) bacteria, and anticipation of severe complications and mortality.

EXPERT OPINION

There is promising potential of implementing accurate AI and ML models whilst managing FN. However, their integration as viable clinical tools poses challenges, including technical and implementation barriers. Improving global accessibility, fostering interdisciplinary collaboration, and addressing ethical and security considerations are essential. By overcoming these challenges, we could transform personalized care for patients with FN.

Dissecting bloodstream infections in febrile neutropenic patients with hematological malignancies, a decade-long single center retrospective observational study (2009-2019)

BACKGROUND

The use of ill-suited antibiotics is a significant risk factor behind the increase in the mortality, morbidity, and economic burden for patients who are under treatment for hematological malignancy (HM) and bloodstream infections (BSI). Such unfitting treatment choices intensify the evolution of resistant variants which is a public health concern due to possible healthcare-associated infection spread to the general population. Hence, this study aims to evaluate antibiograms of patients with BSI and risk factors associated with septicemia.

METHODS

A total of 1166 febrile neutropenia episodes (FNE) among 513 patients with HM from the National Center for Cancer Care and Research (NCCCR), Qatar, during 2009-2019 were used for this study. The socio-demographic, clinical, microbial, and anti-microbial data retrieved from the patient's health records were used.

RESULTS

We analyzed the sensitivity of gram-negative and gram-positive bacilli reported in HM-FN-BSI patients. Out of the total 512 microorganisms isolated, 416 (81%) were gram-negative bacteria (GNB), 76 (15%) were gram-positive bacteria (GPB) and 20 (4%) were fungi. Furthermore, in 416 GNB, 298 (71.6%) were Enterobacteriaceae sp. among which 121 (41%) were ESBL (Extended Spectrum Beta-Lactamase) resistant to Cephalosporine third generation and Piperacillin-Tazobactam, 54 (18%) were Carbapenem-resistant or multidrug-resistant organism (MDRO). It's noteworthy that the predominant infectious agents in our hospital include E. coli, Klebsiella species, and P. aeruginosa. Throughout the study period, the mortality rate due to BSI was 23%. Risk factors that show a significant correlation with death are age, disease status, mono or polymicrobial BSI and septic shock.

CONCLUSION

Decision pertaining to the usage of antimicrobials for HM-FN-BSI patients is a critical task that relies on the latest pattern of prevalence, treatment resistance, and clinical outcomes. Analysis of the antibiogram of HM-FN-BSI patients in Qatar calls for a reconsideration of currently followed empirical antibiotic therapy towards better infection control and antimicrobial stewardship.

Development of Machine Learning Models for Predicting Osteoporosis in Patients with Type 2 Diabetes Mellitus-A Preliminary Study

Purpose

Diagnosing osteoporosis in T2DM based on bone mineral density (BMD) remains challenging. We sought to develop prediction models employing machine learning algorithms for use as screening instruments for osteoporosis in T2DM patients.

Patients and Methods

Data were collected from 433 participants and analyzed using nine categorical machine learning algorithms to select features based on demographic and clinical variables. Multiple classification models were compared using the area under the receiver operating characteristic curve (ROC-AUC), accuracy, sensitivity, specificity, the average precision (AP), precision, F1 score, precision-recall curves, calibration plots, and decision curve analysis (DCA) to determine the best model. In addition, 5-fold cross-validation was utilized to optimize the model, followed by an evaluation of feature significance using Shapley Additive exPlanations (SHAP). Using latent class analysis (LCA), distinct subpopulations were identified by constructing several discrete clusters.

Results

In this study, nine feature variables were identified to construct predictive models for osteoporosis in individuals with T2DM. The machine learning algorithms achieved an AP range of 0.444-1.000. The XGBoost model was selected as the final prediction model with an AUROC of 0.940 in the training set, 0.772 in the validation set for 5-fold cross-validation, and 0.872 in the test set. Using SHAP methodology, 25(OH)D was identified as the most important risk factor. Additionally, a 3-Class model was constructed using LCA, which categorized individuals into high, medium, and low-risk groups.

Conclusion

Our study developed a predictive model with high accuracy and clinical validity for predicting osteoporosis in type 2 diabetes patients. We also identified three subpopulations with varying osteoporosis risk using clustering. However, limited sample size warrants cautious interpretation of results, and validation in larger cohorts is needed.

Prediction of Multiple Clinical Complications in Cancer Patients to Ensure Hospital Preparedness and Improved Cancer Care

Reliable and rapid medical diagnosis is the cornerstone for improving the survival rate and quality of life of cancer patients. The problem of clinical decision-making pertaining to the management of patients with hematologic cancer is multifaceted and intricate due to the risk of therapy-induced myelosuppression, multiple infections, and febrile neutropenia (FN). Myelosuppression due to treatment increases the risk of sepsis and mortality in hematological cancer patients with febrile neutropenia. A high prevalence of multidrug-resistant organisms is also noted in such patients, which implies that these patients are left with limited or no-treatment options amidst severe health complications. Hence, early screening of patients for such organisms in their bodies is vital to enable hospital preparedness, curtail the spread to other weak patients in hospitals, and limit community outbreaks. Even though predictive models for sepsis and mortality exist, no model has been suggested for the prediction of multidrug-resistant organisms in hematological cancer patients with febrile neutropenia. Hence, for predicting three critical clinical complications, such as sepsis, the presence of multidrug-resistant organisms, and mortality, from the data available from medical records, we used 1166 febrile neutropenia episodes reported in 513 patients. The XGboost algorithm is suggested from 10-fold cross-validation on 6 candidate models. Other highlights are (1) a novel set of easily available features for the prediction of the aforementioned clinical complications and (2) the use of data augmentation methods and model-scoring-based hyperparameter tuning to address the problem of class disproportionality, a common challenge in medical datasets and often the reason behind poor event prediction rate of various predictive models reported so far. The proposed model depicts improved recall and AUC (area under the curve) for sepsis (recall = 98%, AUC = 0.85), multidrug-resistant organism (recall = 96%, AUC = 0.91), and mortality (recall = 86%, AUC = 0.88) prediction. Our results encourage the need to popularize artificial intelligence-based devices to support clinical decision-making.

Artificial Intelligence Combined With Big Data to Predict Lymph Node Involvement in Prostate Cancer: A Population-Based Study

Background

A more accurate preoperative prediction of lymph node involvement (LNI) in prostate cancer (PCa) would improve clinical treatment and follow-up strategies of this disease. We developed a predictive model based on machine learning (ML) combined with big data to achieve this.

Methods

Clinicopathological characteristics of 2,884 PCa patients who underwent extended pelvic lymph node dissection (ePLND) were collected from the U.S. National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. Eight variables were included to establish an ML model. Model performance was evaluated by the receiver operating characteristic (ROC) curves and calibration plots for predictive accuracy. Decision curve analysis (DCA) and cutoff values were obtained to estimate its clinical utility.

Results

Three hundred and forty-four (11.9%) patients were identified with LNI. The five most important factors were the Gleason score, T stage of disease, percentage of positive cores, tumor size, and prostate-specific antigen levels with 158, 137, 128, 113, and 88 points, respectively. The XGBoost (XGB) model showed the best predictive performance and had the highest net benefit when compared with the other algorithms, achieving an area under the curve of 0.883. With a 5%~20% cutoff value, the XGB model performed best in reducing omissions and avoiding overtreatment of patients when dealing with LNI. This model also had a lower false-negative rate and a higher percentage of ePLND was avoided. In addition, DCA showed it has the highest net benefit across the whole range of threshold probabilities.

Conclusions

We established an ML model based on big data for predicting LNI in PCa, and it could lead to a reduction of approximately 50% of ePLND cases. In addition, only ≤3% of patients were misdiagnosed with a cutoff value ranging from 5% to 20%. This promising study warrants further validation by using a larger prospective dataset.

Using Machine Learning Algorithms to Predict Hospital Acquired Thrombocytopenia after Operation in the Intensive Care Unit: A Retrospective Cohort Study

Hospital acquired thrombocytopenia (HAT) is a common hematological complication after surgery. This research aimed to develop and compare the performance of seven machine learning (ML) algorithms for predicting patients that are at risk of HAT after surgery. We conducted a retrospective cohort study which enrolled adult patients transferred to the intensive care unit (ICU) after surgery in West China Hospital of Sichuan University from January 2016 to December 2018. All subjects were randomly divided into a derivation set (70%) and test set (30%). ten-fold cross-validation was used to estimate the hyperparameters of ML algorithms during the training process in the derivation set. After ML models were developed, the sensitivity, specificity, area under the curve (AUC), and net benefit (decision analysis curve, DCA) were calculated to evaluate the performances of ML models in the test set. A total of 10,369 patients were included and in 1354 (13.1%) HAT occurred. The AUC of all seven ML models exceeded 0.7, the two highest were Gradient Boosting (GB) (0.834, 0.814-0.853, p < 0.001) and Random Forest (RF) (0.828, 0.807-0.848, p < 0.001). There was no difference between GB and RF (0.834 vs. 0.828, p = 0.293); however, these two were better than the remaining five models (p < 0.001). The DCA revealed that all ML models had high net benefits with a threshold probability approximately less than 0.6. In conclusion, we found that ML models constructed by multiple preoperative variables can predict HAT in patients transferred to ICU after surgery, which can improve risk stratification and guide management in clinical practice.