Modeling and validation of drug release kinetics using hybrid method for prediction of drug efficiency and novel formulations.

This paper presents a thorough examination for drug release from a polymeric matrix to improve understanding of drug release behavior for tissue regeneration. A comprehensive model was developed utilizing mass transfer and machine learning (ML). In the machine learning section, three distinct regression models, namely, Decision Tree Regression (DTR), Passive Aggressive Regression (PAR), and Quadratic Polynomial Regression (QPR) applied to a comprehensive dataset of drug release. The dataset includes r(m) and z(m) inputs, with corresponding concentration of solute in the matrix (C) as response. The primary objective is to assess and compare the predictive performance of these models in finding the correlation between input parameters and chemical concentrations. The hyper-parameter optimization process is executed using Sequential Model-Based Optimization (SMBO), ensuring the robustness of the models in handling the complexity of the controlled drug release. The Decision Tree Regression model exhibits outstanding predictive accuracy, with an R2 score of 0.99887, RMSE of 9.0092E-06, MAE of 3.51486E-06, and a Max Error of 6.87000E-05. This exceptional performance underscores the model's capability to discern intricate patterns within the drug release dataset. The Passive Aggressive Regression model, while displaying a slightly lower R2 score of 0.94652, demonstrates commendable predictive capabilities with an RMSE of 6.0438E-05, MAE of 4.82782E-05, and a Max Error of 2.36600E-04. The model's effectiveness in capturing non-linear relationships within the dataset is evident. The Quadratic Polynomial Regression model, designed to accommodate quadratic relationships, yields a noteworthy R2 score of 0.95382, along with an RMSE of 5.6655E-05, MAE of 4.49198E-05, and a Max Error of 1.86375E-04. These results affirm the model's proficiency in capturing the inherent complexities of the drug release system.

A hybrid model for the detection of retinal disorders using artificial intelligence techniques.

The prevalence of vision impairment is increasing at an alarming rate. The goal of the study was to create an automated method that uses optical coherence tomography (OCT) to classify retinal disorders into four categories: choroidal neovascularization, diabetic macular edema, drusen, and normal cases. This study proposed a new framework that combines machine learning and deep learning-based techniques. The utilized classifiers were support vector machine (SVM), K-nearest neighbor (K-NN), decision tree (DT), and ensemble model (EM). A feature extractor, the InceptionV3 convolutional neural network, was also employed. The performance of the models was evaluated against nine criteria using a dataset of 18000 OCT images. For the SVM, K-NN, DT, and EM classifiers, the analysis exhibited state-of-the-art performance, with classification accuracies of 99.43%, 99.54%, 97.98%, and 99.31%, respectively. A promising methodology has been introduced for the automatic identification and classification of retinal disorders, leading to reduced human error and saved time.

The most important variables associated with death due to COVID-19 disease, based on three data mining models Decision Tree, AdaBoost, and Support Vector Machine: A cross-sectional study.

Introduction: Death due to covid-19 is one of the biggest health challenges in the world. There are many models that can predict death due to COVID-19. This study aimed to fit and compare Decision Tree (DT), Support Vector Machine (SVM), and AdaBoost models to predict death due to COVID-19. Methods: To describe the variables, mean (SD) and frequency (%) were reported. To determine the relationship between the variables and the death caused by COVID-19, chi-square test was performed with a significance level of 0.05. To compare DT, SVM and AdaBoost models for predicting death due to COVID-19 from sensitivity, specificity, accuracy and the area under the rock curve under R software using psych, caTools, random over-sampling examples, rpart, rpartplot packages was done. Results: Out of the total of 23,054 patients studied, 10,935 cases (46.5%) were women, and 12,569 cases (53.5%) were men. Additionally, the mean age of the patients was 54.9 ± 21.0 years. There is a statistically significant relationship between gender, fever, cough, muscle pain, smell and taste, abdominal pain, nausea and vomiting, diarrhea, anorexia, dizziness, chest pain, intubation, cancer, diabetes, chronic blood disease, Violation of immunity, pregnancy, Dialysis, chronic lung disease with the death of covid-19 patients showed (p < 0.05). The results showed that the sensitivity, specificity, accuracy and the area under the receiver operating characteristic curve were respectively 0.60, 0.68, 0.71, and 0.75 in the DT model, 0.54, 0.62, 0.63, and 0.71 in the SVM model, and 0.59, 0.65, 0.69 and 0.74 in the AdaBoost model. Conclusion: The results showed that DT had a high predictive power compared to other data mining models. Therefore, it is suggested to researchers in different fields to use DT to predict the studied variables. Also, it is suggested to use other approaches such as random forest or XGBoost to improve the accuracy in future studies.

Investigating the molecular mechanisms underlying the co-occurrence of Parkinson's disease and inflammatory bowel disease through the integration of multiple datasets.

Parkinson's disease (PD) and inflammatory bowel disease (IBD) are chronic diseases affecting the central nervous system and gastrointestinal tract, respectively. Recent research suggests a bidirectional relationship between neurodegeneration in PD and intestinal inflammation in IBD. PD patients may experience gastrointestinal dysfunction over a decade before motor symptom onset, and IBD may increase the risk of developing PD. Despite the "gut-brain axis" concept, the underlying pathophysiological mechanisms of this potential association remain unclear. This study aimed to investigate the biological mechanisms of differentially expressed genes in PD and IBD using bioinformatics tools, providing novel insights into the co-diagnosis and treatment of these diseases. We constructed a gene marker for disease diagnosis and identified five important genes (BTK, NCF2, CRH, FCGR3A and SERPINA3). Through nomogram and decision tree analyses, we found that both the IBD and PD required only the expression levels of BTK and NCF2 for accurate discrimination. Additionally, small molecule drugs RO-90-7501 and MST-312 may be useful for the treatment of both IBD and PD. These findings offer new perspectives on the co-diagnosis and treatment of PD and IBD, and suggest that targeting BTK may be a promising therapeutic strategy for both diseases.

Interpretable machine learning for dermatological disease detection: Bridging the gap between accuracy and explainability.

Research on disease detection by leveraging machine learning techniques has been under significant focus. The use of machine learning techniques is important to detect critical diseases promptly and provide the appropriate treatment. Disease detection is a vital and sensitive task and while machine learning models may provide a robust solution, they can come across as complex and unintuitive. Therefore, it is important to gauge a better understanding of the predictions and trust the results. This paper takes up the crucial task of skin disease detection and introduces a hybrid machine learning model combining SVM and XGBoost for the detection task. The proposed model outperformed the existing machine learning models - Support Vector Machine (SVM), decision tree, and XGBoost with an accuracy of 99.26%. The increased accuracy is essential for detecting skin disease due to the similarity in the symptoms which make it challenging to differentiate between the different conditions. In order to foster trust and gain insights into the results we turn to the promising field of Explainable Artificial Intelligence (XAI). We explore two such frameworks for local as well as global explanations for these machine learning models namely, SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME).

NodeFlow: Towards End-to-End Flexible Probabilistic Regression on Tabular Data.

We introduce NodeFlow, a flexible framework for probabilistic regression on tabular data that combines Neural Oblivious Decision Ensembles (NODEs) and Conditional Continuous Normalizing Flows (CNFs). It offers improved modeling capabilities for arbitrary probabilistic distributions, addressing the limitations of traditional parametric approaches. In NodeFlow, the NODE captures complex relationships in tabular data through a tree-like structure, while the conditional CNF utilizes the NODE's output space as a conditioning factor. The training process of NodeFlow employs standard gradient-based learning, facilitating the end-to-end optimization of the NODEs and CNF-based density estimation. This approach ensures outstanding performance, ease of implementation, and scalability, making NodeFlow an appealing choice for practitioners and researchers. Comprehensive assessments on benchmark datasets underscore NodeFlow's efficacy, revealing its achievement of state-of-the-art outcomes in multivariate probabilistic regression setup and its strong performance in univariate regression tasks. Furthermore, ablation studies are conducted to justify the design choices of NodeFlow. In conclusion, NodeFlow's end-to-end training process and strong performance make it a compelling solution for practitioners and researchers. Additionally, it opens new avenues for research and application in the field of probabilistic regression on tabular data.

Predicting the immunomodulatory activity of probiotic lactic acid bacteria using supervised machine learning in a Cornu aspersum snail model.

In the process of screening for probiotic strains, there are no clearly established bacterial phenotypic markers which could be used for the prediction of their in vivo mechanism of action. In this work, we demonstrate for the first time that Machine Learning (ML) methods can be used for accurately predicting the in vivo immunomodulatory activity of probiotic strains based on their cell surface phenotypic features using a snail host-microbe interaction model. A broad range of snail gut presumptive probiotics, including 240 new lactic acid bacterial strains (Lactobacillus, Leuconostoc, Lactococcus, and Enterococcus), were isolated and characterized based on their capacity to withstand snails' gastrointestinal defense barriers, such as the pedal mucus, gastric mucus, gastric juices, and acidic pH, in association with their cell surface hydrophobicity, autoaggregation, and biofilm formation ability. The implemented ML pipeline predicted with high accuracy (88 %) strains with a strong capacity to enhance chemotaxis and phagocytic activity of snails' hemolymph cells, while also revealed bacterial autoaggregation and cell surface hydrophobicity as the most important parameters that significantly affect host immune responses. The results show that ML approaches may be useful to derive a predictive understanding of host-probiotic interactions, while also highlighted the use of snails as an efficient animal model for screening presumptive probiotic strains in the light of their interaction with cellular innate immune responses.

Age-appropriate BMI cut-offs for malnutrition among older adults in India.

With the increasing prevalence of obesity in India, body mass index (BMI) has garnered importance as a disease predictor. The current World Health Organization (WHO) body mass index (BMI) cut-offs may not accurately portray these health risks in older adults aged 60 years and above. This study aims to define age-appropriate cut-offs for older adults (60-74 years and 75 years and above) and compare the performance of these cut-offs with the WHO BMI cut-offs using cardio-metabolic conditions as outcomes. Using baseline data from the Longitudinal Ageing Study in India (LASI), classification and regression tree (CART) cross-sectional analysis was conducted to obtain age-appropriate BMI cut-offs based on cardio-metabolic conditions as outcomes. Logistic regression models were estimated to compare the association of the two sets of cut-offs with cardio-metabolic outcomes. The area under the receiver operating characteristic curve (AUC), sensitivity and specificity were estimated. Agreement with waist circumference, an alternate measure of adiposity, was conducted. For older adults aged 60-74 years and 75 years and above, the cut-off for underweight reduced from < 18.5 to < 17.4 and < 13.3 respectively. The thresholds for overweight and obese increased for older adults aged 60-74 years old from > = 25 to > 28.8 and > = 30 to > 33.7 respectively. For older adults aged 75 years and above, the thresholds decreased for both categories. The largest improvement in AUC was observed in older adults aged 75 years and above. The newly derived cut-offs also demonstrated higher sensitivity and specificity among all age-sex stratifications. There is a need to adopt greater rigidity in defining overweight/obesity among older adults aged 75 years and above, as opposed to older adults aged 60-74 years old among whom the thresholds need to be less conservative. Further stratification in the low risk category could also improve BMI classification among older adults. These age-specific thresholds may act as improved alternatives of the current WHO BMI thresholds and improve classification among older adults in India.

Mapping and assessment of groundwater pollution risks in the main aquifer of the Mostaganem plateau (Northwest Algeria): utilizing the novel vulnerability index and decision tree model.

Water plays a pivotal role in socio-economic development in Algeria. However, the overexploitations of groundwater resources, water scarcity, and the proliferation of pollution sources (including industrial and urban effluents, untreated landfills, and chemical fertilizers, etc.) have resulted in substantial groundwater contamination. Preserving water irrigation quality has thus become a primary priority, capturing the attention of both scientists and local authorities. The current study introduces an innovative method to mapping contamination risks, integrating vulnerability assessments, land use patterns (as a sources of pollution), and groundwater overexploitation (represented by the waterhole density) through the implementation of a decision tree model. The resulting risk map illustrates the probability of contamination occurrence in the substantial aquifer on the plateau of Mostaganem. An agricultural region characterized by the intensive nutrients and pesticides use, the significant presence of septic tanks, widespread illegal dumping, and a technical landfill not compliant with environmental standards. The critical situation in the region is exacerbated by excessive groundwater pumping surpassing the aquifer's natural replenishment capacity (with 115 boreholes and 6345 operational wells), especially in a semi-arid climate featuring limited water resources and frequent drought. Vulnerability was evaluated using the DRFTID method, a derivative of the DRASTIC model, considering parameters such as depth to groundwater, recharge, fracture density, slope, nature of the unsaturated zone, and the drainage density. All these parameters are combined with analyses of inter-parameter relationship effects. The results show a spatial distribution into three risk levels (low, medium, and high), with 31.5% designated as high risk, and 56% as medium risk. The validation of this mapping relies on the assessment of physicochemical analyses in samples collected between 2010 and 2020. The results indicate elevated groundwater contamination levels in samples. Chloride exceeded acceptable levels by 100%, nitrate by 71%, calcium by 50%, and sodium by 42%. These elevated concentrations impact electrical conductivity, resulting in highly mineralized water attributed to anthropogenic agricultural pollution and septic tank discharges. High-risk zones align with areas exhibiting elevated nitrate and chloride concentrations. This model, deemed satisfactory, significantly enhances the sustainable management of water resources and irrigated land across various areas. In the long term, it would be beneficial to refine "vulnerability and risk" models by integrating detailed data on land use, groundwater exploitation, and hydrogeological and hydrochemical characteristics. This approach could improve vulnerability accuracy and pollution risk maps, particularly through detailed local data availability. It is also crucial that public authorities support these initiatives by adapting them to local geographical and climatic specificities on a regional and national scale. Finally, these studies have the potential to foster sustainable development at different geographical levels.

KeratoEL: Detection of keratoconus using corneal parameters with ensemble learning.

Background and Aims: Keratoconus is a progressive eye condition in which the normally round cornea thins and bulges outwards into a cone shape. This irregular shape causes light to scatter in multiple directions as it enters the eye, leading to distorted vision, increased sensitivity to light and frequent changes in the prescription of glasses or contact lenses. Detecting keratoconus at an early stage is not only difficult but also challenging. Methods: The study has proposed an ensemble-based machine learning (ML) technique named KeratoEL to detect keratoconus at an early stage. The proposed KeratoEL model combines the basic machine learning algorithms, namely support vector machine (SVM), decision tree (DT), random forest (RF) and artificial neural network (ANN). Before employing the ML model for keratoconus detection, the data set is first preprocessed manually by eliminating some features that don't contribute any significant value to predict the exact class. Moreover, the output features are labelled into three different classes and Extra Trees Classifier is used to find out the important features. Then, the features are sorted in descending order and top 45, 30, and 15 features are taken as input datasets against the output. Finally, different machine learning models are tested using the input datasets and performance metrics are measured. Results: The proposed model obtains 98.0%, 98.9% and 99.8% accuracy for top 45, 30, and 15 number of features respectively. Overall experimental results show that the proposed ensemble model outperforms the existing machine learning models. Conclusion: The proposed KeratoEL model effectively detects keratoconus at an early stage by combining SVM, DT, RF, and ANN algorithms, demonstrating superior performance over existing models. These results underscore the potential of the KeratoEL ensemble approach in enhancing early detection and treatment of keratoconus.

Factors associated with a better treatment efficacy among psoriasis patients: a study based on decision tree model and logistic regression in Shanghai, China.

BACKGROUND: Many effective therapies for psoriasis are being applied in clinical practice in recent years, however, some patients still can't achieve satisfied effect even with biologics. Therefore, it is crucial to identify factors associated with the treatment efficacy among psoriasis patients. This study aims to explore factors influencing the treatment efficacy of psoriasis patients based on decision tree model and logistic regression. METHODS: We implemented an observational study and recruited 512 psoriasis patients in Shanghai Skin Diseases Hospital from 2021 to 2022. We used face-to-face questionnaire interview and physical examination to collect data. Influencing factors of treatment efficacy were analyzed by using logistic regression, and decision tree model based on the CART algorithm. The receiver operator curve (ROC) was plotted for model evaluation and the statistical significance was set at P < 0.05. RESULTS: The 512 patients were predominately males (72.1%), with a median age of 47.5 years. In this study, 245 patients achieved ≥ 75% improvement in psoriasis area and severity index (PASI) score in week 8 and was identified as treatment success (47.9%). Logistic regression analysis showed that patients with senior high school and above, without psoriasis family history, without tobacco smoking and alcohol drinking had higher percentage of treatment success in patients with psoriasis. The final decision tree model contained four layers with a total of seventeen nodes. Nine classification rules were extracted and five factors associated with treatment efficacy were screened, which indicated tobacco smoking was the most critical variable for treatment efficacy prediction. Model evaluation by ROC showed that the area under curve (AUC) was 0.79 (95%CI: 0.75 ~ 0.83) both for logistic regression model (0.80 sensitivity and 0.69 specificity) and decision tree model (0.77 sensitivity and 0.73 specificity). CONCLUSION: Psoriasis patients with higher education, without tobacco smoking, alcohol drinking and psoriasis family history had better treatment efficacy. Decision tree model had similar predicting effect with the logistic regression model, but with higher feasibility due to the nature of simple, intuitive, and easy to understand.

Characterizing the post-traumatic growth trajectory in gastric cancer survivors: a population-based longitudinal study.

OBJECTIVES: Post-traumatic growth can improve the quality of life of cancer survivors. The objective of this study was to investigate post-traumatic growth heterogeneity trajectory in perioperative gastric cancer survivors, and to identify characteristics that predict membership for each trajectory. METHODS: Gastric cancer survivors (n = 403) were recruited before surgery, their baseline assessment (including post-traumatic growth and related characteristics) was completed, and post-traumatic growth levels were followed up on the day they left the intensive care unit, at discharge, and 1 month after discharge. Latent growth mixture mode was used to identify the heterogeneous trajectory of post-traumatic growth, and the core predictors of trajectory subtypes were explored using a decision tree model. RESULTS: Three post-traumatic growth development trajectories were identified among gastric cancer survivors: stable high of PTG group (20.6%), fluctuation of PTG group (44.4%), persistent low of PTG group (35.0%). The decision tree model showed anxiety, coping style, and psychological resilience-which was the primary predictor-might be used to predict the PTG trajectory subtypes of gastric cancer survivors. CONCLUSIONS: There was considerable variability in the experience of post-traumatic growth among gastric cancer survivors. Recognition of high-risk gastric cancer survivors who fall into the fluctuation or persistent low of PTG group and provision of psychological resilience-centered support might allow medical professionals to improve patients' post-traumatic growth and mitigate the impact of negative outcomes.

Comparison between the sodium chloride content and labeled salt value of industrially prepared Japanese single dishes.

Salt intake reduction is a global concern. In particular, Japanese consume higher amounts of salt than those of other ethnicities. The sodium content is mentioned on the label of industrially prepared dishes with an intention of reducing salt intake. This study aimed to evaluate the difference between the actual sodium content and labeled salt value of industrially prepared Japanese single dishes. Samples labeled "estimated" were collected and classified as Japanese, Western, and Chinese cuisines. The sodium content ranged from 180 to 1011 mg/100 g. The sodium content was higher than their reported values in other countries. Specifically, Chinese dishes contained high amounts of sodium, although the chloride content was similar across cuisine styles. Further, the molar ratio (i.e., sodium/chloride) had no significant effect on the difference between the actual content and labeled value. The measured salt contents were 20% higher than the labeled values. The results of decision tree analysis indicated that if the labeled salt value of stir-fried foods is determined by calculation, the actual sodium content is much higher than the labeled salt value. These findings are crucial for customers, dietitian, and researchers as they refer to the labeled salt value to determine the sodium content of industrially prepared foods.

Treatment gap and barriers to access mental healthcare among women with postpartum depression symptoms in Pakistan.

Background and Objectives: Postpartum depression (PPD) is prevalent among women after childbirth, but accessing mental healthcare for PPD is challenging. This study aimed to assess the treatment gap and barriers to mental healthcare access for women with PPD symptoms living in Punjab, Pakistan. Methods: A multicenter cross-sectional study was conducted in five populous cities of Punjab from January to June 2023 by administering the questionnaire to the women using stratified random sampling. A total of 3,220 women in first 6 months postpartum were screened using the Edinburgh Postnatal Depression Scale. Of them, 1,503 women scored thirteen or above, indicating potential depressive disorder. Interviews were conducted to explore help-seeking behavior and barriers to accessing mental healthcare. Descriptive statistics along with nonparametric tests (e.g., Kruskal-Wallis, Mann-Whitney U) were used and group differences were examined. Scatter plot matrices with fitted lines were used to explore associations between variables. Classification and regression tree methods were used to classify the importance and contribution of different variables for the intensity of PPD. Results: Only 2% of women (n = 33) with high PPD symptoms sought mental healthcare, and merely 5% of women (n = 75) had been in contact with a health service since the onset of their symptoms. 92.80% of women with PPD symptoms did not seek any medical attention. The majority of women, 1,215 (81%), perceived the need for mental health treatment; however, 91.23% of them did not seek treatment from healthcare services. Women who recently gave birth to a female child had higher mean depression scores compared to those who gave birth to a male child. Age, education, and birth location of newborn were significantly associated (p  <  0.005) with mean barrier scores, mean social support scores, mean depression scores and treatment gap. The results of classification and regression decision tree model showed that instrumental barrier scores are the most important in predicting mean PPD scores. Conclusion: Women with PPD symptoms encountered considerable treatment gap and barriers to access mental health care. Integration of mental health services into obstetric care as well as PPD screening in public and private hospitals of Punjab, Pakistan is critically needed to overcome the treatment gap and barriers.

Driver Fatigue Detection Using Heart Rate Variability Features from 2-Minute Electrocardiogram Signals While Accounting for Sex Differences.

Traffic accidents due to fatigue account for a large proportion of road fatalities. Based on simulated driving experiments with drivers recruited from college students, this paper investigates the use of heart rate variability (HRV) features to detect driver fatigue while considering sex differences. Sex-independent and sex-specific differences in HRV features between alert and fatigued states derived from 2 min electrocardiogram (ECG) signals were determined. Then, decision trees were used for driver fatigue detection using the HRV features of either all subjects or those of only males or females. Nineteen, eighteen, and thirteen HRV features were significantly different (Mann-Whitney U test, p < 0.01) between the two mental states for all subjects, males, and females, respectively. The fatigue detection models for all subjects, males, and females achieved classification accuracies of 86.3%, 94.8%, and 92.0%, respectively. In conclusion, sex differences in HRV features between drivers' mental states were found according to both the statistical analysis and classification results. By considering sex differences, precise HRV feature-based driver fatigue detection systems can be developed. Moreover, in contrast to conventional methods using HRV features from 5 min ECG signals, our method uses HRV features from 2 min ECG signals, thus enabling more rapid driver fatigue detection.

Cross-Combination Analyses of Random Forest Feature Selection and Decision Tree Model for Predicting Intraoperative Hypothermia in Total Joint Arthroplasty.

BACKGROUND: In total joint arthroplasty patients, intraoperative hypothermia (IOH) is associated with perioperative complications and an increased economic burden. Previous models have some limitations and mainly focus on regression modeling. Random forest (RF) algorithms and decision tree modeling are effective for eliminating irrelevant features and making predictions that aid in accelerating modeling and reducing application difficulty. METHODS: We conducted this prospective observational study using convenience sampling and collected data from 327 total joint arthroplasty patients in a tertiary hospital from March 4, 2023 to September 11, 2023. Of those, 229 patients were assigned to the training and 98 to the testing sets. The Chi-square, Mann-Whitney U, and t-tests were used for baseline analyses. The feature variables selection used the RF algorithms, and the decision tree model was trained on 299 examples and validated on 98. The sensitivity, specificity, recall, F1 score, and area under the curve (AUC) were used to test the model's performance. RESULTS: The RF algorithms identified the preheating time, the volume of flushing fluids, the intraoperative infusion volume, the anesthesia time, the surgical time, and the core temperature after intubation as risk factors for IOH. The decision tree was grown to five levels with nine terminal nodes. The overall incidence of IOH was 42.13%. The sensitivity, specificity, recall, F1 score, and AUC were 0.651, 0.907, 0.916, 0.761, and 0.810, respectively. The model indicated strong internal consistency and predictive ability. CONCLUSIONS: The preheating time, the volume of flushing fluids, the intraoperative infusion volume, the anesthesia time, the surgical time, and the core temperature after intubation could accurately predict IOH in total joint arthroplasty patients. By monitoring these factors, the clinical staff could achieve early detection and intervention of IOH in total joint arthroplasty patients.

Current Use and Discrepancies in the Adoption of Health-Related Internet of Things and Apps Among Working Women in Japan: Large-Scale, Internet-Based, Cross-Sectional Survey.

BACKGROUND: Demographic changes and a low birth rate have led to a workforce shortage in Japan. To address this issue, the government has promoted engagement of female employment. However, increased female employment can impact women's health. Using Internet of Things (IoT) and apps to manage women's health has gained attention, but few studies have focused on working women. OBJECTIVE: This study aimed to clarify the current situation of working women and their use of IoT or apps to manage their health. METHODS: A large-scale, nationwide internet survey was conducted among 10,000 female participants aged from 20 years to 64 years in Japan. Participants were recruited from a marketing research company's active survey panel of 5.24 million members. The survey included questions about health status, sociodemographic factors, psychological characteristics, and the use of IoT or apps for health management. We compared perceived health status and reasons for current IoT use using t tests and assessed participant characteristics that predicted IoT use using the C5.0 decision tree algorithm. Ethical approval was granted by St. Luke's International University. RESULTS: Among participants, 14.6% (1455/10,000) currently used IoT or apps, 7% (695/10,000) used them previously, and 78.5% (7850/10,000) had never used them. Current users (42.7 years old) were older than past users (39.7 years old). Discrepancies were observed between participants' perceived health problems and the purpose for using IoT or apps, with 21.3% (2130/10,000) of all women reporting they experienced menstrual symptoms or disorders but only 3.5% (347/10,000) used IoT or apps to manage the same symptom. On the other hand, current users were more likely to use IoT or apps to manage nutrition-related problems such as underweight or obesity (405/1455, 27.8%). Device use was highest among current users, with 87.3% (1270/1455) using smartphones, 19.7% (287/1455) using smartwatches, and 13.3% (194/1455) using PCs. Decision tree analysis identified 6 clusters, the largest consisting of 81.6% (5323/6523) of non-IoT users who did not exercise regularly, while pregnant women were more likely to use IoT or apps. CONCLUSIONS: Our findings highlight the idea that woman with particular health problems (ie, menstrual symptoms or disorders and premenstrual syndrome) have lower use of IoT or apps, suggesting an unmet need for IoT and apps in specific areas.

Modeling pediatric antibiotic use in an area of declining malaria prevalence.

In malaria-endemic areas of Sub-Saharan Africa, overlap of clinical symptoms between malarial and non-malarial febrile illnesses can lead to empiric use of antibiotics among children. Our study aimed to illustrate the potential impact of decreasing malaria prevalence from malaria control efforts on antibiotic use. We constructed a probabilistic decision tree model representing antibiotic prescription in febrile children < 5 years. This model was used to predict change in absolute antibiotic use compared to baseline under levels of decreasing malaria prevalence. Model parameters were based on data from a hospital study in Ghana and validated via literature review. The baseline prevalence of malaria diagnoses was 52% among all hospitalized children. For our main results, we reported outcomes for a scenario representing a 50% decrease in malaria prevalence. Compared to baseline, absolute antibiotic prescription decreased from a baseline of 639 doses (95% CI 574-694) to 575 (95% CI 502-638). This reflected a 10% (95% CI 7%-13%) decrease in absolute antibiotic use. Our findings demonstrate that effective malaria control can reduce pediatric antibiotic use. However, until substantial progress is made in developing accurate diagnostics for non-malarial febrile illnesses, further reductions in antibiotic use will remain a challenge.

Development and validation of diagnosis model for inflammatory bowel diseases based on a serologic biomarker panel: A decision tree model study.

BACKGROUND AND STUDY AIMS: Currently, an increasing amount of experimental data is available on newly discovered biomarkers in inflammatory bowel diseases (IBD), but the role of these biomarkers is often questionable due to their limited sensitivity. Therefore, this study aimed to build a diagnostic tool incorporating a panel of serum biomarkers into a computational algorithm to identify patients with IBD and differentiate those with Crohn's disease (CD) from those with ulcerative colitis (UC). PATIENTS AND METHODS: We studied sera from 192 CD patients, 118 UC patients, 60 non-IBD controls and 60 healthy controls. Indirect immunofluorescence (IIF) assays were utilized to determine several serum biomarkers previously associated with IBD, and the decision tree algorithm was used to construct the diagnosis model. Performances of models were evaluated by prediction accuracy, precision, AUC and Matthews's correlation coefficient (MCC). The "Inflammatory Bowel Disease Multi-omics Database (IBDMDB)" cohorts were used to validate the model as external validation set. RESULTS: The prediction rates were determined and compared for decision tree models after each data was developed using C5.0, C&RT, QUEST and CHAID. The C5.0 and CHAID algorithms, which ranked top for the prediction rate in the IBD vs. non-IBD model and the CD vs. UC model, respectively, were utilized for final pattern analysis. The final decision tree model achieved higher classification accuracy than the approach based on conservative marker combinations (sensitivity 75.0% vs. 79.5%, specificity 93.8% vs. 78.3% for differentiating IBD from non-IBD; and sensitivity 84.3% vs. 73.4%, specificity 92.5% vs. 54.9% for differentiating CD from UC, respectively). The model prediction consistency was 93% (28/30) in the external validation set. CONCLUSION: The decision-tree-based approach used in this study, based on serum biomarkers, has shown to be a valid and useful approach to identifying IBD and differentiating CD from UC.

Prediction of Major Adverse Cardiac Events After Transcatheter Aortic Valve Implantation: A Machine Learning Approach with GRACE Score.

Objectives: Predictive risk scores have a significant impact on patient selection and assessing the likelihood of complications following interventions in patients with severe aortic stenosis (AS). This study aims to explore the utility of machine learning (ML) techniques in predicting 30-day major adverse cardiac events (MACE) by analyzing parameters, including the Global Registry of Acute Coronary Events (GRACE) score. Methods: This retrospective, multi-center, observational study enrolled 453 consecutive patients diagnosed with severe AS who underwent transcatheter aortic valve implantation (TAVI) from April 2020 to January 2023. The primary outcome was defined as a composition of MACE comprising periprocedural myocardial infarction (MI), cerebrovascular events (CVE), and all-cause mortality during the 1-month follow-up period after the procedure. Conventional binomial logistic regression and ML models were utilized and compared for prediction purposes. Results: The study population had a mean age of 76.1, with 40.8% being male. The primary endpoint was observed in 7.5% of cases. Among the individual components of the primary endpoint, the rates of all-cause mortality, MI, and CVE were reported as 4.2%, 2.4%, and 1.9%, respectively. The ML-based Extreme Gradient Boosting (XGBoost) model with the GRACE score demonstrated superior discriminative performance in predicting the primary endpoint, compared to both the ML model without the GRACE score and the conventional regression model [Area Under the Curve (AUC)= 0.98 (0.91-0.99), AUC= 0,87 (0.80-0.98), AUC= 0.84 (0.79-0.96)]. Conclusion: ML techniques hold the potential to enhance outcomes in clinical practice, especially when utilized alongside established clinical tools such as the GRACE score.