Machine Learning-based Prediction Model Research Articles

Machine learning-based prediction models in medical decision-making in kidney disease: patient, caregiver, and clinician perspectives on trust and appropriate use.

This study aims to improve the ethical use of machine learning (ML)-based clinical prediction models (CPMs) in shared decision-making for patients with kidney failure on dialysis. We explore factors that inform acceptability, interpretability, and implementation of ML-based CPMs among multiple constituent groups. We collected and analyzed qualitative data from focus groups with varied end users, including: dialysis support providers (clinical providers and additional dialysis support providers such as dialysis clinic staff and social workers); patients; patients' caregivers (n = 52). Participants were broadly accepting of ML-based CPMs, but with concerns on data sources, factors included in the model, and accuracy. Use was desired in conjunction with providers' views and explanations. Differences among respondent types were minimal overall but most prevalent in discussions of CPM presentation and model use. Evidence of acceptability of ML-based CPM usage provides support for ethical use, but numerous specific considerations in acceptability, model construction, and model use for shared clinical decision-making must be considered. There are specific steps that could be taken by data scientists and health systems to engender use that is accepted by end users and facilitates trust, but there are also ongoing barriers or challenges in addressing desires for use. This study contributes to emerging literature on interpretability, mechanisms for sharing complexities, including uncertainty regarding the model results, and implications for decision-making. It examines numerous stakeholder groups including providers, patients, and caregivers to provide specific considerations that can influence health system use and provide a basis for future research.

Machine learning-based prediction of soil organic matter via smartphone

Color is an important property of soil that indicates soil composition and fertility. Soil organic matter (SOM) of darker color soils, which have rich humus and minerals, is higher than others such as red soils. The aim of this study is to construct models to predict SOM for a range of colors using a smartphone as an image-capturing device. Random forest of classification (RFC), random forest of logical regression (RFLR), convolutional neural network (CNN) and MobileNet models are compared, which is better for SOM prediction. Soil photos were collected by smartphone in a camera obscura with a steady light. After treatment by OpenCV, photos were separated by SOM content into five groups. The prediction accuracies of RFC, RFLR, CNN and MobileNet were 0.9743, 0.5614, 0.9600 and 0.7915, respectively. Based on these results, the RFC model for SOM has the best performance both in training and validation. The proposed combination of smartphone and machine learning-based prediction models provide a fast, economic, and robust approach to monitor, detect, and predict SOM contents in precision and intelligence agriculture.

Machine learning-based models for prediction of survival in medulloblastoma: a systematic review and meta-analysis.

Medulloblastoma (MB) is the pediatric population's most frequent malignant intracranial lesions. Prognostication plays a crucial role in optimizing treatment strategy in the MB setting. Several studies have developed ML-based models to predict survival outcomes in individuals with MB. In this systematic review and meta-analysis study, we aimed to evaluate the role of ML-based models in predicting survival in MB patients. Literature records were retrieved on May 14th, 2024, using the relevant key terms without filters in PubMed, Embase, Scopus, and Web of Science. Records were screened according to the eligibility criteria, and the data from the included studies were extracted. The quality assessment was performed using the QUADAS-2 tool. The meta-analysis and sensitivity analysis were conducted using R software. Six studies were included, with 2771 patients ranging from 46 to 1759 individuals. A total of 23 ML and DL models were developed, 20 of which were ML and three DL. Random forest (RF) was the most frequent classifier, as it was utilized in nine models, followed by support vector machine (SVM). Eight models were included in the meta-analysis. Our meta-analysis revealed a pooled AUC of 0.77 (95% CI: 0.75-0.80). In addition, the radionics-based and genomics-based models had a pooled AUC of 0.77 (95% CI: 0.76-079) and 0.76 (0.63-0.88), respectively (P = 0.77). Our results suggested that ML-based models, especially ML algorithms, could play a vital and efficient role in the prediction of survival of patients based on radiomics and genomics.

Machine learning-based predictive model for post-stroke dementia.

Post-stroke dementia (PSD), a common complication, diminishes rehabilitation efficacy and affects disease prognosis in stroke patients. Many factors may be related to PSD, including demographic, comorbidities, and examination characteristics. However, most existing methods are qualitative evaluations of independent factors, which ignore the interaction amongst various factors. Therefore, the purpose of this study is to explore the applicability of machine learning (ML) methods for predicting PSD. 9 acceptable features were screened out by the Spearman correlation analysis and Boruta algorithm. We developed and evaluated 8 ML models: logistic regression, elastic net, k-nearest neighbors, decision tree, extreme gradient boosting, support vector machine, random forest, and multilayer perceptron. A total of 539 stroke patients were included in this study. Among the 8 models used to predict PSD, extreme gradient boosting and random forest showed the highest area under the curve (AUC) of the receiver operating characteristic curve (ROC), with values of 0.7287 and 0.7285, respectively. The most important features for predicting PSD included age, high sensitivity C-reactive protein, stroke side and location, and the occurrence of cerebral hemorrhage. Our findings suggest that ML models, especially extreme gradient boosting, can best predict the risk of PSD.

A Forest Fire Prediction Model Based on Meteorological Factors and the Multi-Model Ensemble Method

More than half of South Korea’s land area is covered by forests, which significantly increases the potential for extensive damage in the event of a forest fire. The majority of forest fires in South Korea are caused by humans. Over the past decade, more than half of these types of fires occurred during the spring season. Although human activities are the primary cause of forest fires, the fact that they are concentrated in the spring underscores the strong association between forest fires and meteorological factors. When meteorological conditions favor the occurrence of forest fires, certain triggering factors can lead to their ignition more easily. The purpose of this study is to analyze the meteorological factors influencing forest fires and to develop a machine learning-based prediction model for forest fire occurrence, focusing on meteorological data. The study focuses on four regions within Gangwon province in South Korea, which have experienced substantial damage from forest fires. To construct the model, historical meteorological data were collected, surrogate variables were calculated, and a variable selection process was applied to identify relevant meteorological factors. Five machine learning models were then used to predict forest fire occurrence and ensemble techniques were employed to enhance the model’s performance. The performance of the developed forest fire prediction model was evaluated using evaluation metrics. The results indicate that the ensemble model outperformed the individual models, with a higher F1-score and a notable reduction in false positives compared to the individual models. This suggests that the model developed in this study, when combined with meteorological forecast data, can potentially predict forest fire occurrence and provide insights into the expected severity of fires. This information could support decision-making for forest fire management, aiding in the development of more effective fire response plans.

Accurate prediction of protein–ligand interactions by combining physical energy functions and graph-neural networks

We introduce an advanced model for predicting protein–ligand interactions. Our approach combines the strengths of graph neural networks with physics-based scoring methods. Existing structure-based machine-learning models for protein–ligand binding prediction often fall short in practical virtual screening scenarios, hindered by the intricacies of binding poses, the chemical diversity of drug-like molecules, and the scarcity of crystallographic data for protein–ligand complexes. To overcome the limitations of existing machine learning-based prediction models, we propose a novel approach that fuses three independent neural network models. One classification model is designed to perform binary prediction of a given protein–ligand complex pose. The other two regression models are trained to predict the binding affinity and root-mean-square deviation of a ligand conformation from an input complex structure. We trained the model to account for both deviations in experimental and predicted binding affinities and pose prediction uncertainties. By effectively integrating the outputs of the triplet neural networks with a physics-based scoring function, our model showed a significantly improved performance in hit identification. The benchmark results with three independent decoy sets demonstrate that our model outperformed existing models in forward screening. Our model achieved top 1% enrichment factors of 32.7 and 23.1 with the CASF2016 and DUD-E benchmark sets, respectively. The benchmark results using the LIT-PCBA set further confirmed its higher average enrichment factors, emphasizing the model’s efficiency and generalizability. The model’s efficiency was further validated by identifying 23 active compounds from 63 candidates in experimental screening for autotaxin inhibitors, demonstrating its practical applicability in hit discovery.Scientific contributionOur work introduces a novel training strategy for a protein–ligand binding affinity prediction model by integrating the outputs of three independent sub-models and utilizing expertly crafted decoy sets. The model showcases exceptional performance across multiple benchmarks. The high enrichment factors in the LIT-PCBA benchmark demonstrate its potential to accelerate hit discovery.

Machine learning-based predictive model for temperature and comfort parameters in indoor enviroment using experimantal data

The research introduces an artificial neural network model that predicts temperature and assesses thermal comfort metrics for a cooling room, demonstrating how machine learning advancements can enhance thermal efficiency and cost-effectiveness in building design. The study utilized the Levenberg-Marquardt (LM) artificial neural network (ANN) approach to derive the average temperature and thermal comfort metrics collected under actual operating settings. The Predicted Mean Vote (PMV) and Predicted Percentage Dissatisfied (PPD)values were measured at three distinct sites and then compared to the trial findings. The model uses a dataset of 205 observations, with 143 cases used for training and 31 examples for testing and validation. The ANN model demonstrated effective training, with negligible errors in estimated error values. The mean squared error values for average temperature and thermal comfort parameters were 0.0342, 0.0376, 0.0571, 0.0029, and 0.2296. The R values for temperature measurements are 0.9947 and 0.9923, 0.9847 and 0.9437, and 0.9737, demonstrating a highly effective engineering method. The ANN model provided precise predictions for temperature and thermal comfort metrics, such as PMV and PPD in a cooling chamber, with a tolerance of ± 15 %. The LM approach, a machine learning methodology, produced excellent outcomes, particularly at lower temperatures, with 15 % of the data exceeding this range.

A Novel Machine Learning-Based Predictive Model of Clinically Significant Prostate Cancer and Online Risk Calculator

ObjectivesTo create a machine learning predictive model combining PI-RADS score, PSA density, and clinical variables to predict clinically significant prostate cancer (csPCa). MethodsWe evaluated a cohort of patients who underwent prostate biopsy for suspected prostate cancer (PCa) in New Zealand, Australia, and Switzerland. We collected data on age, body mass index (BMI), PSA level, prostate volume, PSA density (PSAD), PI-RADS scores, previous biopsy, and corresponding histology results. The dataset was divided into derivation (training) and validation (test) sets using random splits. An independent dataset was obtained from the Harvard Dataverse for external validation. A cohort of 1272 patients was analyzed. We fitted a Lasso model, XGBoost, and LightGBM to the training set and assessed their accuracy. ResultsAll models demonstrated ROC AUC values ranging from 0.830 to 0.851. LightGBM was considered the superior model, with an ROC of 0.851 [95%CI: 0.804 – 0.897] in the test set and 0.818 [95% CI: 0.798 – 0.831] in the external dataset. The most important variable was PI-RADS, followed by PSA density, history of previous biopsy, age, and BMI. ConclusionsWe developed a predictive model for detecting csPCa that exhibited a high ROC-AUC value for internal and external validations. This suggests that the integration of the clinical parameters outperformed each individual predictor. Additionally, the model demonstrated good calibration metrics, indicative of a more balanced model than the existing models.

Group interaction effect on breaking wave forces on a vertical pile: Experimental tests and predictive models

Pile groups are extensively utilized as supports for many coastal structures, such as bridges, jetties, and oil production platforms. The problem of understanding the interaction effects within pile groups and predicting the breaking wave forces on them is considered in this paper, using experimental tests and machine learning-based predictive modeling. The restriction of previous studies on this important engineering problem is that the pile group arrangements considered are limited. Prediction methods are therefore developed only for specific pile group arrangements and do not incorporate the effect of the incident wave direction. In this study, to partially overcome this limitation, an extensive experimental investigation is conducted on 70 different pile group arrangements under six breaking wave conditions. Three pile group coefficients, characterized by the total, quasi-static, and dynamic forces, are introduced for a thorough assessment of the interaction effects within the pile group. First, the pile group coefficients for three basic arrangements (tandem, side-by-side, and staggered) are evaluated. The results reveal a sheltering effect in the tandem arrangement and an amplification effect in the side-by-side arrangement. However, the forces on the measured pile in the staggered arrangement resemble those on the isolated pile, with neither significant sheltering nor amplification effects observed. Then, the results for all arrangements highlight the significant effect of wave direction on the pile group coefficients for small inter-pile spacing. Finally, different machine learning algorithms are adopted to develop predictive models for the group coefficients. The XGBoost model demonstrates superior accuracy for predicting the total and quasi-static force coefficients, while the dynamic force coefficient remains challenging to predict accurately due to its stochastic nature.

Machine learning based prediction models for cardiovascular disease risk using electronic health records data: systematic review and meta-analysis

Abstract Background Cardiovascular disease (CVD) remains a major cause of mortality in the UK, prompting the need for improved risk predictive models for primary prevention. Machine learning (ML) models utilizing electronic health records (EHR) offer potential enhancements over traditional risk scores like QRISK3 and ASCVD. Objective To systematically evaluate and compare the efficacy of ML models against conventional CVD risk prediction algorithms using EHR data for medium to long-term (5-10 years) CVD risk prediction. Methods A systematic review and random-effect meta-analysis were conducted according to PRISMA guidelines, assessing studies from 2010 to 2024. We retrieved 32 ML models and 26 conventional statistical models from 20 selected studies, focusing on performance metrics such as Area Under the Curve (AUC) and heterogeneity across models. Results ML models, particularly random forest and deep learning, demonstrated superior performance, with the highest recorded pooled AUCs of 0.865 (95% CI: 0.812-0.917) and 0.847 (95% CI: 0.766-0.927), respectively. These significantly outperformed the conventional risk score of 0.765 (95% CI: 0.734-0.796). However, significant heterogeneity (I² > 99%) and potential publication bias were noted across the studies. Conclusion While ML models show enhanced calibration for CVD risk, substantial variability and methodological concerns limit their current clinical applicability. Future research should address these issues by enhancing methodological transparency and standardization to improve the reliability and utility of these models in clinical settings. Implications This study highlights the advanced capabilities of ML models in CVD risk prediction and emphasizes the need for rigorous validation to facilitate their integration into clinical practice. This review was registered with PROSPERO (CRD42021250058).

Meta-analysis of prediction models for predicting lymph node metastasis in thyroid cancer

BackgroundThe purpose of this systematic review and meta-analysis is to assess the efficacy of various machine learning (ML) techniques in predicting preoperative lymph node metastasis (LNM) in patients diagnosed with papillary thyroid carcinoma (PTC). Although prior studies have investigated the potential of ML in this context, the current evidence is not sufficiently strong. Hence, we undertook a thorough analysis to ascertain the predictive accuracy of different ML models and their practical relevance in predicting preoperative LNM in PTC patients.Materials and methodsIn our search, we thoroughly examined PubMed, Cochrane Library, Embase, and Web of Science, encompassing their complete database history until December 3rd, 2022. To evaluate the potential bias in the machine learning models documented in the included studies, we employed the Prediction Model Risk of Bias Assessment Tool (PROBAST).ResultsA total of 107 studies, involving 136,245 patients, were included. Among them, 21,231 patients showed central LNM (CLNM) and 4,637 had lateral LNM (LLNM). The meta-analysis results revealed that the c-index for predicting LNM, CLNM, and LLNM were 0.762 (95% CI: 0.747–0.777), 0.762 (95% CI: 0.747–0.777), and 0.803 (95% CI: 0.773–0.834) in the training set, and 0.773 (95% CI: 0.754–0.791), 0.762 (95% CI: 0.747–0.777), and 0.829 (95% CI: 0.779–0.879) in the validation set, respectively. A total of 134 machine learning-based prediction models were included, covering 10 different types. Logistic Regression (LR) was the most commonly used model, accounting for 81.34% (109/134) of the included models.ConclusionsMachine learning methods have shown a certain level of accuracy in predicting preoperative LNM in PTC patients, indicating their potential as a predictive tool. Their use in the clinical management of PTC holds great promise. Among the various ML models investigated, the performance of logistic regression-based nomograms was deemed satisfactory.

Medical diagnosis based on artificial intelligence and decision support system in the management of health development.

Medical diagnosis plays a critical role in our daily lives. Every day, over 10 billion cases of both mental and physical health disorders are diagnosed and reported worldwide. To diagnose these disorders, medical practitioners and health professionals employ various assessment tools. However, these tools often face scrutiny due to their complexity, prompting researchers to increase their experimental parameters to provide accurate justifications. Additionally, it is essential for professionals to properly justify, interpret, and analyse the results from these prediction tools. This research paper explores the use of artificial intelligence and advanced analytics in developing Clinical Decision Support Systems (CDSS). These systems are capable of diagnosing and detecting patterns of various medical disorders. Various machine learning algorithms contribute to building these assessment tools, with the Network Pattern Recognition (NEPAR) algorithm being the first to aid in developing CDSS. Over time, researchers have recognised the value of machine learning-based prediction models in successfully justifying medical diagnoses. The proposed CDSS models have demonstrated the ability to diagnose mental disorders with an accuracy of up to 89% using only 28 questions, without requiring human input. For physical health issues, additional parameters are used to enhance the accuracy of CDSS models. Consequently, medical professionals are increasingly relying on these machine learning-based CDSS models, utilising these tools to improve medical diagnosis and assist in decision-making. The different cross-validation values are considered to remove the data biasness.

Residual behaviour and damage assessment of UHPC-filled double-skin steel tubular columns after lateral impact

Ultra-high performance concrete (UHPC)-filled double-skin steel tubular (DST) column has great potential to be used in the protective structures. Although its lateral impact behaviour has been well understood, the residual behaviour after lateral impact remains unexplored. As a result, this study extensively investigated the residual behaviour and damage assessment of UHPC-filled DST columns after lateral impact. Firstly, a set of six DST columns were designed and tested under lateral impact, followed by static axial compression. In addition, two intact columns were subjected to static axial compression for comparative analysis. Secondly, the refined finite element models were developed and validated using the current test data, and the impact resistant mechanism of UHPC-filled DST columns with different impact locations was analysed. Thirdly, the suitability of different damage indexes for the damage assessment of impacted UHPC-filled DST columns was evaluated. Two damage indexes, the ratio of mid-height deflection to column height (R1), and the ratio of local deflection to the column diameter (R2), were proposed for the DST columns. Finally, two types of machine learning-based models were developed to predict the impact damage of UHPC-filled DST columns. The prediction models were interpreted locally and globally using the additive feature attribution method Shapley Additive Explanation (SHAP). The machine learning-based prediction models can rapidly evaluate the damage extent of impacted UHPC-filled DST column, which hold great significance for the selection of strengthening and retrofitting schemes.

Machine learning-based prediction models unleash the enhanced production of fucoxanthin in Isochrysis galbana.

The marine microalga Isochrysis galbana is prolific producer of fucoxanthin, which is a xanthophyll carotenoid with substantial global market value boasting extensive applications in the food, nutraceutical, pharmaceutical, and cosmetic industries. This study presented a novel integrated experimental approach coupled with machine learning (ML) models to predict the fucoxanthin content in I. galbana by altering the type and concentration of phytohormone supplementation, thus overcoming the multiple methodological limitations of conventional fucoxanthin quantification. A novel integrated experimental approach was developed, analyzing the effect of varying phytohormone types and concentrations on fucoxanthin production in I. galbana. Morphological analysis was conducted to assess changes in microalgal structure, while growth rate and fucoxanthin yield correlations were explored using statistical analysis and machine learning models. Several ML models were employed to predict fucoxanthin content, with and without hormone descriptors as variables. The findings revealed that the Random Forest (RF) model was highly significant with a high of 0.809 and of 0.776 when hormone descriptors were excluded, and the inclusion of hormone descriptors further improved prediction accuracy to of 0.839, making it a useful tool for predicting the fucoxanthin yield. The model that fitted the experimental data indicated methyl jasmonate (0.2 mg/L) as an effective phytohormone. The combined experimental and ML approach demonstrated rapid, reliable, and cost-efficient prediction of fucoxanthin yield. This study highlights the potential of machine learning models, particularly Random Forest, to optimize parameters influencing microalgal growth and fucoxanthin production. This approach offers a more efficient alternative to conventional methods, providing valuable insights into improving fucoxanthin production in microalgal cultivation. The findings suggest that leveraging diverse ML models can enhance the predictability and efficiency of fucoxanthin production, making it a promising tool for industrial applications.

A Machine Learning Approach to Determine Risk Factors for Respiratory Bacterial/Fungal Coinfection in Critically Ill Patients with Influenza and SARS-CoV-2 Infection: A Spanish Perspective.

Background: Bacterial/fungal coinfections (COIs) are associated with antibiotic overuse, poor outcomes such as prolonged ICU stay, and increased mortality. Our aim was to develop machine learning-based predictive models to identify respiratory bacterial or fungal coinfections upon ICU admission. Methods: We conducted a secondary analysis of two prospective multicenter cohort studies with confirmed influenza A (H1N1)pdm09 and COVID-19. Multiple logistic regression (MLR) and random forest (RF) were used to identify factors associated with BFC in the overall population and in each subgroup (influenza and COVID-19). The performance of these models was assessed by the area under the ROC curve (AUC) and out-of-bag (OOB) methods for MLR and RF, respectively. Results: Of the 8902 patients, 41.6% had influenza and 58.4% had SARS-CoV-2 infection. The median age was 60 years, 66% were male, and the crude ICU mortality was 25%. BFC was observed in 14.2% of patients. Overall, the predictive models showed modest performances, with an AUC of 0.68 (MLR) and OOB 36.9% (RF). Specific models did not show improved performance. However, age, procalcitonin, CRP, APACHE II, SOFA, and shock were factors associated with BFC in most models. Conclusions: Machine learning models do not adequately predict the presence of co-infection in critically ill patients with pandemic virus infection. However, the presence of factors such as advanced age, elevated procalcitonin or CPR, and high severity of illness should alert clinicians to the need to rule out this complication on admission to the ICU.

Comparison of Machine Learning-Based Predictive Models of the Nutrient Loads Delivered from the Mississippi/Atchafalaya River Basin to the Gulf of Mexico

Predicting nutrient loads is essential to understanding and managing one of the environmental issues faced by the northern Gulf of Mexico hypoxic zone, which poses a severe threat to the Gulf’s healthy ecosystem and economy. The development of hypoxia in the Gulf of Mexico is strongly associated with the eutrophication process initiated by excessive nutrient loads. Due to the complexities in the excessive nutrient loads to the Gulf of Mexico, it is challenging to understand and predict the underlying temporal variation of nutrient loads. The study was aimed at identifying an optimal predictive machine learning model to capture and predict nonlinear behavior of the nutrient loads delivered from the Mississippi/Atchafalaya River Basin (MARB) to the Gulf of Mexico. For this purpose, monthly nutrient loads (N and P) in tons were collected from US Geological Survey (USGS) monitoring station 07373420 from 1980 to 2020. Machine learning models—including autoregressive integrated moving average (ARIMA), gaussian process regression (GPR), single-layer multilayer perceptron (MLP), and a long short-term memory (LSTM) with the single hidden layer—were developed to predict the monthly nutrient loads, and model performances were evaluated by standard assessment metrics—Root Mean Square Error (RMSE) and Correlation Coefficient (R). The residuals of predictive models were examined by the Durbin–Watson statistic. The results showed that MLP and LSTM persistently achieved better accuracy in predicting monthly TN and TP loads compared to GPR and ARIMA. In addition, GPR models achieved slightly better test RMSE score than ARIMA models while their correlation coefficients are much lower than ARIMA models. Moreover, MLP performed slightly better than LSTM in predicting monthly TP loads while LSTM slightly outperformed for TN loads. Furthermore, it was found that the optimizer and number of inputs didn’t show effects on the LSTM performance while they exhibited impacts on MLP outcomes. This study explores the capability of machine learning models to accurately predict nonlinearly fluctuating nutrient loads delivered to the Gulf of Mexico. Further efforts focus on improving the accuracy of forecasting using hybrid models which combine several machine learning models with superior predictive performance for nutrient fluxes throughout the MARB.

Development of optimized ensemble machine learning-based prediction models for wire electrical discharge machining processes

This paper proposes development of optimized heterogeneous ensemble models for prediction of responses based on given sets of input parameters for wire electrical discharge machining (WEDM) processes, which have found immense applications in many of the present-day manufacturing industries because of their ability to generate complicated 2D and 3D profiles on hard-to-machine engineering materials. These ensembles are developed combining predictions of the three base models, i.e. random forest, support vector machine and ridge regression. These three base models are first framed utilizing the training datasets, providing predictions for all the responses under consideration. Based on these predictions, two optimization problems are formulated for each of the responses, while minimizing root mean squared error and mean absolute error, for subsequent development of two optimized ensembles whose predictions are the weighted sum of the predictions of the base models. The prediction performance of all the five models is ascertained through nine statistical metrics, after which a cumulative quality loss-based multi-response signal-to-noise (MRSN) ratio for each model is computed, for each of the responses, where a higher MRSN ratio indicates greater accuracy in prediction. This study is conducted using two experimental datasets of WEDM process. Overall, the optimized ensemble models having higher MRSN ratios than the base models are indicated to deliver better prediction accuracy.

Machine learning–based prediction model for hypofibrinogenemia after tigecycline therapy

BackgroundIn clinical practice, the incidence of hypofibrinogenemia (HF) after tigecycline (TGC) treatment significantly exceeds the probability claimed by drug manufacturers.ObjectiveWe aimed to identify the risk factors for TGC-associated HF and develop prediction and survival models for TGC-associated HF and the timing of TGC-associated HF.MethodsThis single-center retrospective cohort study included 222 patients who were prescribed TGC. First, we used binary logistic regression to screen the independent factors influencing TGC-associated HF, which were used as predictors to train the extreme gradient boosting (XGBoost) model. Receiver operating characteristic curve (ROC), calibration curve, decision curve analysis (DCA), and clinical impact curve analysis (CICA) were used to evaluate the performance of the model in the verification cohort. Subsequently, we conducted survival analysis using the random survival forest (RSF) algorithm. A consistency index (C-index) was used to evaluate the accuracy of the RSF model in the verification cohort.ResultsBinary logistic regression identified nine independent factors influencing TGC-associated HF, and the XGBoost model was constructed using these nine predictors. The ROC and calibration curves showed that the model had good discrimination (areas under the ROC curves (AUC) = 0.792 [95% confidence interval (CI), 0.668–0.915]) and calibration ability. In addition, DCA and CICA demonstrated good clinical practicability of this model. Notably, the RSF model showed good accuracy (C-index = 0.746 [95%CI, 0.652–0.820]) in the verification cohort. Stratifying patients treated with TGC based on the RSF model revealed a statistically significant difference in the mean survival time between the low- and high-risk groups.ConclusionsThe XGBoost model effectively predicts the risk of TGC-associated HF, whereas the RSF model has advantages in risk stratification. These two models have significant clinical practical value, with the potential to reduce the risk of TGC therapy.

A Machine Learning-Based Prediction Model for the Probability of Fall Risk Among Chinese Community-Dwelling Older Adults.

Fall is a common adverse event among older adults. This study aimed to identify essential fall factors and develop a machine learning-based prediction model to predict the fall risk category among community-dwelling older adults, leading to earlier intervention and better outcomes. Three prediction models (logistic regression, random forest, and naive Bayes) were constructed and evaluated. A total of 459 people were involved, including 156 participants (34.0%) with high fall risk. Seven independent predictors (frail status, age, smoking, heart attack, cerebrovascular disease, arthritis, and osteoporosis) were selected to develop the models. Among the three machine learning models, the logistic regression model had the best model fit, with the highest area under the curve (0.856) and accuracy (0.797) and sensitivity (0.735) in the test set. The logistic regression model had excellent discrimination, calibration, and clinical decision-making ability, which could aid in accurately identifying the high-risk groups and taking early intervention with the model.

A machine learning-based predictive model discriminates nonalcoholic steatohepatitis from nonalcoholic fatty liver disease

BackgroundNon-alcoholic fatty liver disease (NAFLD) is a leading cause of liver-related morbidity and mortality. The diagnosis of non-alcoholic steatohepatitis (NASH) plays a crucial role in the management of NAFLD patients. ObjectiveThe aim of our observational study was to build a machine learning model to identify NASH in NAFLD patients. MethodsThe clinical characteristics of 259 NAFLD patients and their initial laboratory data (Cohort 1) were collected to train the model and carry out internal validation. We compared the models built by five machine learning algorithms and screened out the best models. Receiver operating characteristic (ROC) curves, sensitivity, specificity, and accuracy were used to evaluate the performance of the model. In addition, the NAFLD patients in Cohort 2 (n = 181) were externally verified. ResultsWe finally identified six independent risk factors for predicting NASH, including neutrophil percentage (NEU%), aspartate aminotransferase/alanine aminotransferase (AST/ALT), hematocrit (HCT), creatinine (CREA), uric acid (UA), and prealbumin (PA). The NASH-XGB6 model built using the XGBoost algorithm showed sufficient prediction accuracy, with ROC values of 0.95 (95 % CI, 0.91–0.98) and 0.90 (95 % CI, 0.88–0.93) in Cohort 1 and Cohort 2, respectively. ConclusionsNASH-XGB6 can serve as an effective tool for distinguishing NASH patients from NAFLD patients.