Develop Machine Learning Algorithms Research Articles

Predictive modeling of COVID-19 mortality risk in chronic kidney disease patients using multiple machine learning algorithms

The coronavirus disease 2019 (COVID-19) has a significant impact on the global population, particularly on individuals with chronic kidney disease (CKD). COVID-19 patients with CKD will face a considerably higher risk of mortality than the general population. This study developed a predictive model for assessing mortality in COVID-19-affected CKD patients, providing personalized risk prediction to optimize clinical management and reduce mortality rates. We developed machine learning algorithms to analyze 219 patients’ clinical laboratory test data retrospectively. The performance of each model was assessed using a calibration curve, decision curve analysis, and receiver operating characteristic (ROC) curve. It was found that the LightGBM model showed the most satisfied performance, with an area under the ROC curve of 0.833, sensitivity of 0.952, and specificity of 0.714. Prealbumin, neutrophil percent, respiratory index in arterial blood, half-saturated pressure of oxygen, carbon dioxide in serum, glucose, neutrophil count, and uric acid were the top 8 significant variables in the prediction model. Validation by 46 patients demonstrated acceptable accuracy. This model can serve as a powerful tool for screening CKD patients at high risk of COVID-19-related mortality and providing decision support for clinical staff, enabling efficient allocation of resources, and facilitating timely and targeted management for those who need the relevant interference urgently.

Using machine learning to identify risk factors for short-term complications following thumb carpometacarpal arthroplasty

BackgroundThumb carpometacarpal (CMC) joint osteoarthritis is among the most common degenerative hand diseases. Thumb CMC arthroplasty, or trapeziectomy with or without tendon augmentation, is the most frequently performed surgical treatment and has a strong safety profile. Though adverse outcomes are infrequent, the ability to predict risk for complications has substantial clinical benefits. In the present study, we evaluated a well-known surgical database with machine learning (ML) techniques to predict short-term complications and reoperations after thumb CMC arthroplasty. MethodsA retrospective study was conducted using data from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) years 2005–2020. Outcomes were 30-day wound and medical complications and 30-day return to the operating room. We used three ML algorithms — a Random Forest (RF), Elastic-Net Regression (ENet), and Extreme Gradient Boosted Tree (XGBoost), and a deep learning Neural Network (NN). Feature importance analysis was performed in the highest performing model for each outcome to identify predictors with the greatest contributions. ResultsWe included a total of 7711 cases. The RF was the best performing algorithm for all outcomes, with an AUC score of 0.61±0.03 for reoperations, 0.55±0.04 for medical complications, and 0.59±0.03 for wound complications. On feature importance analysis, procedure duration was the highest weighted predictor for reoperations. In all outcomes, procedure duration, older age, and female sex were consistently among the top five predictors. ConclusionsWe successfully developed ML algorithms to predict reoperations, wound complications, and medical complications. RF models had the highest performance in all outcomes.

Using Machine Learning to Predict Outcomes Following Transfemoral Carotid Artery Stenting.

Transfemoral carotid artery stenting (TFCAS) carries important perioperative risks. Outcome prediction tools may help guide clinical decision-making but remain limited. We developed machine learning algorithms that predict 1-year stroke or death following TFCAS. The VQI (Vascular Quality Initiative) database was used to identify patients who underwent TFCAS for carotid artery stenosis between 2005 and 2024. We identified 112 features from the index hospitalization (82 preoperative [demographic/clinical], 13 intraoperative [procedural], and 17 postoperative [in-hospital course/complications]). The primary outcome was 1-year postprocedural stroke or death. The data were divided into training (70%) and test (30%) sets. Six machine learning models were trained using preoperative features with 10-fold cross-validation. The primary model evaluation metric was area under the receiver operating characteristic curve. The algorithm with the best performance was further trained using intra- and postoperative features. Model robustness was assessed using calibration plots and Brier scores. Overall, 35 214 patients underwent TFCAS during the study period and 3257 (9.2%) developed 1-year stroke or death. The best preoperative prediction model was extreme gradient boosting, achieving an area under the receiver operating characteristic curve of 0.94 (95% CI, 0.93-0.95). In comparison, logistic regression had an AUROC of 0.65 (95% CI, 0.63-0.67). The extreme gradient boosting model maintained excellent performance at the intra- and postoperative stages, with area under the receiver operating characteristic curve values of 0.94 (95% CI, 0.93-0.95) and 0.98 (95% CI, 0.97-0.99), respectively. Calibration plots showed good agreement between predicted/observed event probabilities with Brier scores of 0.11 (preoperative), 0.11 (intraoperative), and 0.09 (postoperative). Machine learning can accurately predict 1-year stroke or death following TFCAS, performing better than logistic regression.

Prediction of prognosis in lung cancer using machine learning with inter-institutional generalizability: A multicenter cohort study (WJOG15121L: REAL-WIND)

ObjectivesPredicting the prognosis of lung cancer is crucial for providing optimal medical care. However, a method to accurately predict the overall prognosis in patients with stage IV lung cancer, even with the use of machine learning, has not been established. Moreover, the inter-institutional generalizability of such algorithms remains unexplored. This study aimed to establish machine learning-based algorithms with inter-institutional generalizability to predict prognosis. Materials and MethodsThis multicenter, retrospective, hospital-based cohort study included consecutive patients with stage IV lung cancer who were randomly categorized into the training and independent test cohorts with a 2:1 ratio, respectively. The primary metric to assess algorithm performance was the area under the receiver operating characteristic curve in the independent test cohort. To assess the inter-institutional generalizability of the algorithms, we investigated their ability to predict patient outcomes in the remaining facility after being trained using data from 15 other facilities. ResultsOverall, 6,751 patients (median age, 70 years) were enrolled, and 1,515 (22 %) showed mutated epidermal growth factor receptor expression. The median overall survival was 16.6 (95 % confidence interval, 15.9–17.5) months. Algorithm performance metrics in the test cohort showed that the areas under the curves were 0.90 (95 % confidence interval, 0.88–0.91), 0.85 (0.84–0.87), 0.83 (0.81–0.85), and 0.85 (0.82–0.87) at 180, 360, 720, and 1,080 predicted survival days, respectively. The performance test of 16 algorithms for investigating inter-institutional generalizability showed median areas under the curves of 0.87 (range, 0.84–0.92), 0.84 (0.78–0.88), 0.84 (0.76–0.89), and 0.84 (0.75–0.90) at 180, 360, 720, and 1,080 days, respectively. ConclusionThis study developed machine learning algorithms that could accurately predict the prognosis in patients with stage IV lung cancer with high inter-institutional generalizability. This can enhance the accuracy of prognosis prediction and support informed and shared decision-making in clinical settings.

Machine learning approach as an early warning system to prevent foodborne Salmonella outbreaks in northwestern Italy

Salmonellosis, one of the most common foodborne infections in Europe, is monitored by food safety surveillance programmes, resulting in the generation of extensive databases. By leveraging tree-based machine learning (ML) algorithms, we exploited data from food safety audits to predict spatiotemporal patterns of salmonellosis in northwestern Italy. Data on human cases confirmed in 2015–2018 (n = 1969) and food surveillance data collected in 2014–2018 were used to develop ML algorithms. We integrated the monthly municipal human incidence with 27 potential predictors, including the observed prevalence of Salmonella in food. We applied the tree regression, random forest and gradient boosting algorithms considering different scenarios and evaluated their predictivity in terms of the mean absolute percentage error (MAPE) and R2. Using a similar dataset from the year 2019, spatiotemporal predictions and their relative sensitivities and specificities were obtained. Random forest and gradient boosting (R2 = 0.55, MAPE = 7.5%) outperformed the tree regression algorithm (R2 = 0.42, MAPE = 8.8%). Salmonella prevalence in food; spatial features; and monitoring efforts in ready-to-eat milk, fruits and vegetables, and pig meat products contributed the most to the models’ predictivity, reducing the variance by 90.5%. Conversely, the number of positive samples obtained for specific food matrices minimally influenced the predictions (2.9%). Spatiotemporal predictions for 2019 showed sensitivity and specificity levels of 46.5% (due to the lack of some infection hotspots) and 78.5%, respectively. This study demonstrates the added value of integrating data from human and veterinary health services to develop predictive models of human salmonellosis occurrence, providing early warnings useful for mitigating foodborne disease impacts on public health.

Predicting UK Housing Price using Machine Learning Algorithms

The development of reliable predictive algorithm for house price as the housing market is a stand-out among the most involved regarding valuing the price and continues to fluctuate, is constantly a need for socio-economic advancement and welfare of citizen. In this paper, we develop machine learning algorithms for forecasting UK housing Price, and find an optimal algorithm that forecasts housing price accurately on the premises of the presence of many features or covariates. After applying correlation analysis to remove correlated variables in order to avoid multicollinearity, thereby increasing the statistical power, a novel method of using regression analysis to first of all understand and select statistically significant features for the various regions in England based on North South divide is adopted. These features are then used in the machine learning algorithm to further increase the statistical power of the algorithm, increase the level of accuracy for each of them and ultimately increase the predictive values for the algorithms.The model construction involves 3 stages: 1- correlation analysis to identify and remove correlated variables thereby avoiding multicollinearity and increasing the statistical power of the linear regression, 2 - using linear regression to determine variables that are statistically significant and 3 - building the machine learning algorithms based on the variables that are statistically significant from the linear regression. A comprehensive dataset of UK Paid housing Price from 2010 to 2019 was linked to a number of other datasets to generate a total 21 variables or features used for the models. Catboost, Gradient Boosting, Bagging, Random Forest, Extra Tree all achieved the excellent model’s performance result in all the regions considered. The comparison of the seven models showed that Extra Tree algorithm consistently achieved the best performance in term of level of accuracy in all the regions. K-Nearest Neighbours (KNN) is the only algorithm with less than 50% level of accuracy. Noticeably, the regions considered had varying or differing insignificant variables, implying that although many variables are common (statistically significant) to all the regions, there are regional differences and impact when modelling or predicting housing prices. This study validates the practicability of developing a machine learning methodology for the prediction of housing price. This research offers a reference for future house price prediction based on machine learning.© 2024 The Author(s). Published by RITHA Publishing. This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.

-A machine learning model to predict surgical site infection after surgery of lower extremity fractures.

This study aimed to develop machine learning algorithms for identifying predictive factors associated with the risk of postoperative surgical site infection in patients with lower extremity fractures. A machine learning analysis was conducted on a dataset comprising 1,579 patients who underwent surgical fixation for lower extremity fractures to create a predictive model for risk stratification of postoperative surgical site infection. We evaluated different clinical and demographic variables to train four machine learning models (neural networks, boosted generalised linear model, naïve bayes, and penalised discriminant analysis). Performance was measured by the area under the curve score, Youdon's index and Brier score. A multivariate adaptive regression splines (MARS) was used to optimise predictor selection. The final model consisted of five predictors. (1) Operating room time, (2) ankle region, (3) open injury, (4) body mass index, and (5) age. The best-performing machine learning algorithm demonstrated a promising predictive performance, with an area under the ROC curve, Youdon's index, and Brier score of 77.8%, 62.5%, and 5.1%-5.6%, respectively. The proposed predictive model not only assists surgeons in determining high-risk factors for surgical site infections but also empowers patients to closely monitor these factors and take proactive measures to prevent complications. Furthermore, by considering the identified predictors, this model can serve as a reference for implementing preventive measures and reducing postoperative complications, ultimately enhancing patient outcomes. However, further investigations involving larger datasets and external validations are required to confirm the reliability and applicability of our model.

Machine learning for predicting Chagas disease infection in rural areas of Brazil.

Chagas disease is a severe parasitic illness that is prevalent in Latin America and often goes unaddressed. Early detection and treatment are critical in preventing the progression of the illness and its associated life-threatening complications. In recent years, machine learning algorithms have emerged as powerful tools for disease prediction and diagnosis. In this study, we developed machine learning algorithms to predict the risk of Chagas disease based on five general factors: age, gender, history of living in a mud or wooden house, history of being bitten by a triatomine bug, and family history of Chagas disease. We analyzed data from the Retrovirus Epidemiology Donor Study (REDS) to train five popular machine learning algorithms. The sample comprised 2,006 patients, divided into 75% for training and 25% for testing algorithm performance. We evaluated the model performance using precision, recall, and AUC-ROC metrics. The Adaboost algorithm yielded an AUC-ROC of 0.772, a precision of 0.199, and a recall of 0.612. We simulated the decision boundary using various thresholds and observed that in this dataset a threshold of 0.45 resulted in a 100% recall. This finding suggests that employing such a threshold could potentially save 22.5% of the cost associated with mass testing of Chagas disease. Our findings highlight the potential of applying machine learning to improve the sensitivity and effectiveness of Chagas disease diagnosis and prevention. Furthermore, we emphasize the importance of integrating socio-demographic and environmental factors into neglected disease prediction models to enhance their performance.

Generalizability of machine learning models predicting 30-day unplanned readmission after primary total knee arthroplasty using a nationally representative database.

Unplanned readmission after primary total knee arthroplasty (TKA) costs an average of US $39,000 per episode and negatively impacts patient outcomes. Although predictive machine learning (ML) models show promise for risk stratification in specific populations, existing studies do not address model generalizability. This study aimed to establish the generalizability of previous institutionally developed ML models to predict 30-day readmission following primary TKA using a national database. Data from 424,354 patients from the ACS-NSQIP database was used to develop and validate four ML models to predict 30-day readmission risk after primary TKA. Individual model performance was assessed and compared based on discrimination, accuracy, calibration, and clinical utility. Length of stay (> 2.5days), body mass index (BMI) (> 33.21kg/m2), and operation time (> 93min) were important determinants of 30-day readmission. All ML models demonstrated equally good accuracy, calibration, and discriminatory ability (Brier score, ANN = RF = HGB = NEPLR = 0.03; ANN, slope = 0.90, intercept = - 0.11; RF, slope = 0.93, intercept = - 0.12; HGB, slope = 0.90, intercept = - 0.12; NEPLR, slope = 0.77, intercept = 0.01; AUCANN = AUCRF = AUCHGB = AUCNEPLR = 0.78). This study validates the generalizability of four previously developed ML algorithms in predicting readmission risk in patients undergoing TKA and offers surgeons an opportunity to reduce readmissions by optimizing discharge planning, BMI, and surgical efficiency.

Abstract 3547: The ISB Cancer Gateway in the Cloud (ISB-CGC): Access, explore and analyze large-scale cancer data through the Google Cloud

Abstract Rapid growth of cancer data in recent decades has made data discovery and wrangling difficult for the average cancer research lab. Our mission at the ISB Cancer Gateway in the Cloud (ISB-CGC), part of the NCI’s Cancer Research Data Commons ecosystem, is to democratize access to large cancer datasets. Funded by the NCI, we have performed ETL processes on data from GDC and PDC projects such as TCGA, TARGET, and CPTAC. We generated hundreds of BigQuery tables containing data such as mutations, gene expression, and protein abundance, which enable data analysis in the cloud via SQL. BigQuery analyses are inexpensive and rapid even when scaled to petabyte sized inputs, for example we ran 6.6 billion correlations in 2.5 hours with a total cost of about one dollar. These data can also be accessed affordably from Google Cloud VMs where researchers can develop analysis pipelines in Python, R, and workflow languages such as CWL. We present two recent collaborations: In one BigQuery was used to develop machine learning algorithms that calculated genetic risk scores from TCGA glioblastoma and ovarian cancer copy number variation. In another example researchers combined SQL queries of our BQ tables with data from the ISPY2 Trial initiative and generated an R shiny app that can dynamically create data visualizations for genes of interest in different TCGA cohorts. Citation Format: Fabian Seidl, Lauren Hagen, Jacob Wilson, Boris Aguilar, Deena Bleich, Lauren Wolfe, Poojitha Gundluru, Prema Venkatesan, Mi Tian, Suzanne Paquette, Elaine Lee, Danna Huffman, David Pot, William Longabaugh. The ISB Cancer Gateway in the Cloud (ISB-CGC): Access, explore and analyze large-scale cancer data through the Google Cloud [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3547.

Abstract 3493: Read-level methylation pattern extraction for high-multiplex large-scale targeted NGS assay

Abstract The abnormal changes in DNA methylation are linked to the early stages of carcinogenesis. Identifying these epigenetic changes in circulating tumor DNA (ctDNA) can reveal potential biomarkers for the early diagnosis of various cancers. However, analyzing such data poses bioinformatics challenges due to the lack of sensitivity in detecting the low abundance ctDNA signals in biopsy samples, which are often overwhelmed by the complexity of libraries containing hundreds of targeted regions. Read-level methylation analysis holds the promise of more in-depth DNA methylation detection due to the wide coverage and high sensitivity of rare signals. However, this approach is hindered by the absence of a standardized workflow capable of generating interpretable reports suitable for both bench scientists and professional bioinformaticians. Here, we present a bioinformatics workflow that examines next-generation sequencing (NGS) data and characterizes the read-level methylation patterns of amplicons. Compared to other currently available tools, our method is designed to work with high-multiplex, large-scale targeted assays. It effectively eliminates the undesired noise derived from sequencing byproducts such as false CpG calls, dimers, and off-target alignments. Additionally, to accommodate the substantial volume of data generated by state-of-the-art NGS platforms, the workflow enables parallel processing of samples compatible with both cloud-based and on-premises computing resources. This workflow provides a comprehensive per-sample visualization of DNA methylation patterns and reports read-level methylation results in a “pattern-as-a-feature” table. In this table, the occurrence of an amplicon epiallelic haplotype (pattern) for every sample is represented as a “feature column” and is aggregated with all patterns discovered in the experiment. These read-level patterns, along with other information, can be used to develop machine learning algorithms to reiteratively harvest true predictive features and penalize confounding signals in predicting cancer diagnosis. Citation Format: Mingda Jin, Masatomo Kaneko, Steven Cen, Hongtao Li, Wei Guo, Xinyi Zhou, Atsuko Fujihara, Tsuyoshi Iwata, Lorenzo Storino Ramacciotti, Divyangi Paralkar, Giovanni E Cacciamani, Manju Aron, Osamu Ukimura, Inderbir S. Gill, Gangning Liang, Andre L. Abreu, Jeffrey Bhasin, Xiaojing Yang, Xi-Yu Jia. Read-level methylation pattern extraction for high-multiplex large-scale targeted NGS assay [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3493.

Pulse shape discrimination in an organic scintillation phoswich detector using machine learning techniques

We developed machine learning algorithms for distinguishing scintillation signals from a plastic-liquid coupled detector known as a phoswich. The challenge lies in discriminating signals from organic scintillators with similar shapes and short decay times. Using a single-readout phoswich detector, we successfully identified γ radiation signals from two scintillating components. Our Boosted Decision Tree algorithm demonstrated a maximum discrimination power of 3.02 ± 0.85 standard deviation in the 950 keV region, providing an efficient solution for self-shielding and enhancing radiation detection capabilities.

Diagnostics and Assessment of the Severity of Alzheimer’s Disease: Machine Learning Algorithms Based on Markers of Inflammation

Background: as the most common form of dementia, Alzheimer’s disease (AD) is characterized by cognitive deterioration and usually begins with loss of memory of recent events. It is important to search for biological, sensitive and affordable methods that could be used for early diagnostics of AD and determine the severity of the disease.Objective: to develop machine learning algorithms based on such inflammatory markers as the enzymatic activity of leukocyte elastase (LE) and the functional activity of the α1-proteinase inhibitor (α1-PI) for diagnosing and assessing the severity of AD.Patients and methods: the study included128 people aged 55 to 94 years (73.7 ± 7.9 years), of which 91 patients were diagnosed with Alzheimer’s disease and 37 apparently healthy people (control). The indicators of LE and α1-PI in blood plasma were used as classifying features for building models. The following algorithms were used to build a machine learning model: Optimal Valid Partition (OVP), logistic regression (LR), support vector machine (SVM), random forest (RF), gradient boosting (GB) and statistically weighted syndromes (WSWS). The predictive performance of the constructed classiers was evaluated by the overall accuracy (accuracy), sensitivity (sensitivity), specificity (specificity), F-measure and ROC-analysis.Results: the developed machine learning algorithms made it possible to reliably divide the general group of subjects (patients + conditionally healthy), as well as patients with different AD severity, into 4 quadrants of a two-dimensional diagram in the LE and α1-PI coordinates and showed close and fairly high predictive efficiency.Conclusion: the developed machine learning algorithms have proven close and sufficiently high prognostic efficacy for assessing the severity of AD based on inflammatory markers (enzymatic activity of LE and functional activity of α1-PI) and, probably, can be useful for early diagnostics of the disease and timely administration of therapy.

Prediction of intraoperative red blood cell transfusion in valve replacement surgery: machine learning algorithm development based on non-anemic cohort.

Our study aimed to develop machine learning algorithms capable of predicting red blood cell (RBC) transfusion during valve replacement surgery based on a preoperative dataset of the non-anemic cohort. A total of 423 patients who underwent valvular replacement surgery from January 2015 to December 2020 were enrolled. A comprehensive database that incorporated demographic characteristics, clinical conditions, and results of preoperative biochemistry tests was used for establishing the models. A range of machine learning algorithms were employed, including decision tree, random forest, extreme gradient boosting (XGBoost), categorical boosting (CatBoost), support vector classifier and logistic regression (LR). Subsequently, the area under the receiver operating characteristic curve (AUC), accuracy, recall, precision, and F1 score were used to determine the predictive capability of the algorithms. Furthermore, we utilized SHapley Additive exPlanation (SHAP) values to explain the optimal prediction model. The enrolled patients were randomly divided into training set and testing set according to the 8:2 ratio. There were 16 important features identified by Sequential Backward Selection for model establishment. The top 5 most influential features in the RF importance matrix plot were hematocrit, hemoglobin, ALT, fibrinogen, and ferritin. The optimal prediction model was CatBoost algorithm, exhibiting the highest AUC (0.752, 95% CI: 0.662-0.780), which also got relatively high F1 score (0.695). The CatBoost algorithm also showed superior performance over the LR model with the AUC (0.666, 95% CI: 0.534-0.697). The SHAP summary plot and the SHAP dependence plot were used to visually illustrate the positive or negative effects of the selected features attributed to the CatBoost model. This study established a series of prediction models to enhance risk assessment of intraoperative RBC transfusion during valve replacement in no-anemic patients. The identified important predictors may provide effective preoperative interventions.

Experimental Investigations on the Thermal Characteristics of Domestic Convectors

Better understanding of local thermal characteristics of domestic convectors could play a crucial role in reducing energy consumption for space heating and decarbonizing the economy. The current study evaluates the impact of varying water inlet temperature and flowrate on the local surface temperature of domestic convectors through extensive empirical investigations. Experiments are performed using a custom-made test-rig featuring a 400 mm × 600 mm Type 11 convector within a large and well-ventilated environment, minimizing the thermal influence of the surrounding space on the thermal behavior of the convector. Infrared thermography (IR) is used to acquire local surface temperature data for further analysis. Based on the results obtained, it has been observed that the inlet water temperature has a negligible effect on thermal characteristics of the convector while increasing the flowrate substantially decreases the time required for the convector to reach maximum surface temperature. Based on the numerical data, an analytical model for average surface temperature has been developed using multiple variable regression analysis, demonstrating a prediction accuracy of >90% compared with the experimental data. A detailed understanding of the heating behavior exhibited by domestic convectors has led to a better understanding of the local thermal characteristics, while the prediction model can be used to develop machine learning algorithms to install better flow control techniques for efficient space heating.

AI-assisted image analysis and physiological validation for progressive drought detection in a diverse panel of Gossypium hirsutum L.

Drought detection, spanning from early stress to severe conditions, plays a crucial role in maintaining productivity, facilitating recovery, and preventing plant mortality. While handheld thermal cameras have been widely employed to track changes in leaf water content and stomatal conductance, research on thermal image classification remains limited due mainly to low resolution and blurry images produced by handheld cameras. In this study, we introduce a computer vision pipeline to enhance the significance of leaf-level thermal images across 27 distinct cotton genotypes cultivated in a greenhouse under progressive drought conditions. Our approach involved employing a customized software pipeline to process raw thermal images, generating leaf masks, and extracting a range of statistically relevant thermal features (e.g., min and max temperature, median value, quartiles, etc.). These features were then utilized to develop machine learning algorithms capable of assessing leaf hydration status and distinguishing between well-watered (WW) and dry-down (DD) conditions. Two different classifiers were trained to predict the plant treatment-random forest and multilayer perceptron neural networks-finding 75% and 78% accuracy in the treatment prediction, respectively. Furthermore, we evaluated the predicted versus true labels based on classic physiological indicators of drought in plants, including volumetric soil water content, leaf water potential, and chlorophyll a fluorescence, to provide more insights and possible explanations about the classification outputs. Interestingly, mislabeled leaves mostly exhibited notable responses in fluorescence, water uptake from the soil, and/or leaf hydration status. Our findings emphasize the potential of AI-assisted thermal image analysis in enhancing the informative value of common heterogeneous datasets for drought detection. This application suggests widening the experimental settings to be used with deep learning models, designing future investigations into the genotypic variation in plant drought response and potential optimization of water management in agricultural settings.

PATIENT-SPECIFIC ADAPTIVE DYNAMIC CYCLING IS ASSOCIATED WITH IMPROVED PD SYMPTOMS

https://youtu.be/b2S2VPwRthE BACKGROUND High-cadence dynamic cycling is an effective therapy for improving motor symptoms in individuals with Parkinson’s Disease (PD), as measured by the Unified Disease Parkinson’s Rating Scale-Motor III (UPDRS-III). However, there is significant variability in individual responses to this therapy. Our lab developed a patient-specific adaptive dynamic cycling (PSADC) paradigm that manipulates entropy of cadence to optimize exercise prescriptions for individuals at various stages of disease progression. The purpose of this study is to evaluate the effectiveness of 12 sessions of PSADC on motor symptom improvement (UPDRS-III score) in individuals with PD. METHODS Twenty-three individuals with idiopathic PD (were randomized into two groups: PSADC (n=13) or active control (n=10). All individuals completed 12 sessions (3 sessions per week for 4 weeks) of dynamic cycling on a SMART (Speed Manipulated Adaptive Rehabilitation Therapy) bicycle. Each session consisted of a 5-minute warm-up at 60 revolutions per minute (rpm), 30-minute exercise session (80 rpm), and 5-minute cool-down (60 rpm). Individuals in the PSADC group followed an adaptive exercise prescription in which resistance level was optimized on a weekly basis, based on the individual’s entropy of cadence, and cycling effort. Individuals in the active control group remained at a constant resistance level for the entirety of the intervention. UPDRS-III was assessed in all participants prior to and following the 12-session intervention. Two-way analysis of variance (ANOVA) and paired samples t-tests were performed to detect statistical differences in UPDRS-III score between the groups. RESULTS There was a significant group by time interaction (F= 18.746, p <0.001). The PSADC group showed a significant reduction (improvement) in UPDRS-III score (Pre: 32.8, Post: 27.5; p<0.001), while the active control group showed no significant change in UPDRS-III score (Pre: 28.2, Post: 32.4, p=0.08). CONCLUSIONS 12 sessions of PSADC significantly improved UPDRS-III score, compared to non-adaptive high-cadence dynamic cycling. These results suggest that optimizing entropy of cadence is valuable for motor symptom improvement. Future studies will develop machine learning algorithms designed to predict appropriate clinical exercise prescriptions for individuals with PD.

Use of novel cancer staging model to analyze healthcare costs of commercially insured patients with colorectal cancer.

35 Background: Earlier colorectal cancer diagnosis can improve patient outcomes, but the payer cost impact of earlier stage at cancer diagnosis is not well understood because stage information is not directly available in administrative claims data. This data gap impairs researchers’ ability to fully evaluate the financial impact of earlier neoplasia detection resulting from colorectal cancer screening. In prior work, we used SEER-Medicare data to develop machine learning algorithms to assign AJCC stages to colon (CC) and rectal (RC) cancer patients using treatment flags derived from claims data. These algorithms were 83% (CC) and 69% (RC) accurate at assigning incident cancer patients to their SEER-recorded AJCC stage. This new work applied those validated algorithms to commercial claims data to study the healthcare costs by stage of patients identified with incident CC or RC. Methods: Newly diagnosed CC and RC patients were identified in Milliman’s 2019-2020 Consolidated Health Cost Guidelines Sources Data for commercially insured patients. Patients were assigned a cancer stage using a claims-based predictive multinomial logistic regression model (R Statistical Software - v4.1.2 R Core Team 2021; nnet package - Venables and Ripley 2002). Allowed medical and pharmacy costs were summarized by month, starting from each patient’s initial claim with a cancer diagnosis through the end of the patient’s enrollment or through September 2022. Patients with each cancer type were then grouped by stage, and mean cumulative costs at 12-, 24-, and 36-months were calculated. Results: CC patients with stage 4 disease incur costs over 4x greater than those with stages 2B or lower and 1.7x greater than patients with stages 2B/3 during the first year after diagnosis. Cost differentials by stage are smaller among patients with RC. RC patients with stage 4 disease incur costs 2.2x greater than those with stages 2B or lower and 1.3x greater than patients with stages 2B/3 over the first year after diagnosis. For both CC and RC, substantial differences in cost persist over a 3-year period post-diagnosis. Conclusions: We applied a novel cancer staging model to commercial claims to estimate patients’ healthcare costs by stage at diagnosis. CC and RC patients’ costs increase substantially as their stages at diagnosis increase. In addition to improving patient outcomes, diagnosis of CC and RC at earlier stages among commercial patients could reduce treatment costs. [Table: see text]

Machine learning models using symptoms and clinical variables to predict coronary artery disease on coronary angiography.

Coronary angiography (CAG) is invasive and expensive, while numbers of patients suspected of coronary artery disease (CAD) undergoing CAG results have no coronary lesions. To develop machine learning algorithms using symptoms and clinical variables to predict CAD. This study was conducted as a cross-sectional study of patients undergoing CAG. We randomly chose 2082 patients from 2602 patients suspected of CAD as the training set, and 520 patients as the test set. We utilized LASSO regression to do feature selection. The area under the receiver operating characteristic curve (AUC), confusion matrix of different thresholds, positive predictive value (PPV) and negative predictive value (NPV) were shown. Support vector machine algorithm performances in 10 folds were conducted in the training set for detecting severe CAD, while XGBoost algorithm performances were conducted in the test set for detecting severe CAD. The algorithm of logistic regression achieved an average AUC of 0.77 in the training set during 10-fold validation and an AUC of 0.75 in the test set. When probability predicted by the model was less than 0.1, 11 patients in the test set (520 patients) were screened out, and NPV reached 90.9%. When probability predicted by the model was less than 0.2, 110 patients in the test set were screened out, and reached 83.6%. Meanwhile, when threshold was set to 0.9, PPV reached 97.4%. When the threshold was set to 0.8, PPV reached 91.5%. Machine learning algorithm using data from hospital information systems could assist in severe CAD exclusion and confirmation, and thus help patients avoid unnecessary CAG.

Machine learning assessment of dental age classification based on cone-beam CT images: a different approach.

Machine learning (ML) algorithms are a portion of artificial intelligence that may be used to create more accurate algorithmic procedures for estimating an individual's dental age or defining an age classification. This study aims to use ML algorithms to evaluate the efficacy of pulp/tooth area ratio (PTR) in cone-beam CT (CBCT) images to predict dental age classification in adults. CBCT images of 236 Turkish individuals (121 males and 115 females) from 18 to 70 years of age were included. PTRs were calculated for six teeth in each individual, and a total of 1416 PTRs encompassed the study dataset. Support vector machine, classification and regression tree, and random forest (RF) models for dental age classification were employed. The accuracy of these techniques was compared. To facilitate this evaluation process, the available data were partitioned into training and test datasets, maintaining a proportion of 70% for training and 30% for testing across the spectrum of ML algorithms employed. The correct classification performances of the trained models were evaluated. The models' performances were found to be low. The models' highest accuracy and confidence intervals were found to belong to the RF algorithm. According to our results, models were found to be low in performance but were considered as a different approach. We suggest examining the different parameters derived from different measuring techniques in the data obtained from CBCT images in order to develop ML algorithms for age classification in forensic situations.