UNASSIGNED: Readmission to the coronary care unit (CCU) has significant implications for patient outcomes and healthcare expenditure, emphasizing the urgency to accurately identify patients at high readmission risk. This study aims to construct and externally validate a predictive model for CCU readmission using machine learning (ML) algorithms across multiple hospitals.
UNASSIGNED: Patient information, including demographics, medical history, and laboratory test results were collected from electronic health record system and contributed to a total of 40 features. Five ML models: logistic regression, random forest, support vector machine, gradient boosting, and multilayer perceptron were employed to estimate the readmission risk.
UNASSIGNED: The gradient boosting model was selected demonstrated superior performance with an area under the receiver operating characteristic curve (AUC) of 0.887 in the internal validation set. Further external validation in hold-out test set and three other medical centers upheld the model\'s robustness with consistent high AUCs, ranging from 0.852 to 0.879.
UNASSIGNED: The results endorse the integration of ML algorithms in healthcare to enhance patient risk stratification, potentially optimizing clinical interventions, and diminishing the burden of CCU readmissions.

Cervical cancer is one of the most dangerous malignancies in women. Prolonged survival times are made possible by breakthroughs in early recognition and efficient treatment of a disease.The existing methods are lagging on finding the important attributes to predict the survival outcome. The main objective of this study is to find individuals with cervical cancer who are at greater risk of death from recurrence by predicting the survival.A novel approach in a proposed technique is Triangulating feature importance to find the important risk factors through which the treatment may vary to improve the survival outcome.Five algorithms Support vector machine, Naive Bayes, supervised logistic regression, decision tree algorithm, Gradient boosting, and random forest are used to build the concept. Conventional attribute selection methods like information gain (IG), FCBF, and ReliefFare employed. The recommended classifier is evaluated for Precision, Recall, F1, Mathews Correlation Coefficient (MCC), Classification Accuracy (CA), and Area under curve (AUC) using various methods. Gradient boosting algorithm (CAT BOOST) attains the highest accuracy value of 0.99 to predict survival outcome of recurrence cervical cancer patients. The proposed outcome of the research is to identify the important risk factors through which the survival outcome of the patients improved.

This study focuses on developing machine learning models to detect subtle alterations in hepatocyte chromatin organization due to Iron (II, III) oxide nanoparticle exposure, hypothesizing that exposure will significantly alter chromatin texture. A total of 2000 hepatocyte nuclear regions of interest (ROIs) from mouse liver tissue were analyzed, and for each ROI, 5 different parameters were calculated: Long Run Emphasis, Short Run Emphasis, Run Length Nonuniformity, and 2 wavelet coefficient energies obtained after the discrete wavelet transform. These parameters served as input for supervised machine learning models, specifically random forest and gradient boosting classifiers. The models demonstrated relatively robust performance in distinguishing hepatocyte chromatin structures belonging to the group exposed to IONPs from the controls. The study\'s findings suggest that iron oxide nanoparticles induce substantial changes in hepatocyte chromatin distribution and underscore the potential of AI techniques in advancing hepatocyte evaluation in physiological and pathological conditions.

Significant developments are currently underway in the field of cancer research, particularly in Germany, regarding cancer registration and the use of medical information systems. The use of such systems contributes significantly to quality assurance and increased efficiency in data evaluation. The growing importance of artificial intelligence (AI) in cancer research is evident as these systems integrate AI for various purposes, i.e. to assist users in data analysis. This paper uses ensemble learning to classify the graphical user interface state of the medical information system CARESS. The results show that all ensemble learning models utilized achieved good performance. In particular, the gradient boosting algorithm performed the best with an accuracy of 97%. The results represent a starting point for further development of ensemble learning in medical data analysis, with the potential for integration into various applications such as recommender systems.

Applying machine-learning techniques for imbalanced data sets presents a significant challenge in materials science since the underrepresented characteristics of minority classes are often buried by the abundance of unrelated characteristics in majority of classes. Existing approaches to address this focus on balancing the counts of each class using oversampling or synthetic data generation techniques. However, these methods can lead to loss of valuable information or overfitting. Here, we introduce a deep learning framework to predict minority-class materials, specifically within the realm of metal-insulator transition (MIT) materials. The proposed approach, termed boosting-CGCNN, combines the crystal graph convolutional neural network (CGCNN) model with a gradient-boosting algorithm. The model effectively handled extreme class imbalances in MIT material data by sequentially building a deeper neural network. The comparative evaluations demonstrated the superior performance of the proposed model compared to other approaches. Our approach is a promising solution for handling imbalanced data sets in materials science.

UNASSIGNED: Many individuals with hypertension remain undiagnosed. We aimed to develop a predictive model for hypertension using diagnostic codes from prevailing electronic medical records in Swedish primary care.
UNASSIGNED: This sex- and age-matched case-control (1:5) study included patients aged 30-65 years living in the Stockholm Region, Sweden, with a newly recorded diagnosis of hypertension during 2010-19 (cases) and individuals without a recorded hypertension diagnosis during 2010-19 (controls), in total 507,618 individuals. Patients with diagnoses of cardiovascular diseases or diabetes were excluded. A stochastic gradient boosting machine learning model was constructed using the 1,309 most registered ICD-10 codes from primary care for three years prior the hypertension diagnosis.
UNASSIGNED: The model showed an area under the curve (95 % confidence interval) of 0.748 (0.742-0.753) for females and 0.745 (0.740-0.751) for males for predicting diagnosis of hypertension within three years. The sensitivity was 63 % and 68 %, and the specificity 76 % and 73 %, for females and males, respectively. The 25 diagnoses that contributed the most to the model for females and males all exhibited a normalized relative influence >1 %. The codes contributing most to the model, all with an odds ratio of marginal effects >1 for both sexes, were dyslipidaemia, obesity, and encountering health services in other circumstances.
UNASSIGNED: This machine learning model, using prevailing recorded diagnoses within primary health care, may contribute to the identification of patients at risk of unrecognized hypertension. The added value of this predictive model beyond information of blood pressure warrants further study.

OBJECTIVE: To develop and validate machine learning models for predicting coronary artery disease (CAD) within a Taiwanese cohort, with an emphasis on identifying significant predictors and comparing the performance of various models.
METHODS: This study involved a comprehensive analysis of clinical, demographic, and laboratory data from 8,495 subjects in Taiwan Biobank (TWB) after propensity score matching to address potential confounding factors. Key variables included age, gender, lipid profiles (T-CHO, HDL_C, LDL_C, TG), smoking and alcohol consumption habits, and renal and liver function markers. The performance of multiple machine learning models was evaluated.
RESULTS: The cohort comprised 1,699 individuals with CAD identified through self-reported questionnaires. Significant differences were observed between CAD and non-CAD individuals regarding demographics and clinical features. Notably, the Gradient Boosting model emerged as the most accurate, achieving an AUC of 0.846 (95% confidence interval [CI] 0.819-0.873), sensitivity of 0.776 (95% CI, 0.732-0.820), and specificity of 0.759 (95% CI, 0.736-0.782), respectively. The accuracy was 0.762 (95% CI, 0.742-0.782). Age was identified as the most influential predictor of CAD risk within the studied dataset.
CONCLUSIONS: The Gradient Boosting machine learning model demonstrated superior performance in predicting CAD within the Taiwanese cohort, with age being a critical predictor. These findings underscore the potential of machine learning models in enhancing the prediction accuracy of CAD, thereby supporting early detection and targeted intervention strategies.
BACKGROUND: Not applicable.

BACKGROUND: Takotsubo syndrome (TTS) is an acute heart failure syndrome with symptoms similar to acute myocardial infarction. TTS is often triggered by acute emotional or physical stress and is a significant cause of morbidity and mortality. Predictors of mortality in patients with TS are not well understood, and there is a need to identify high-risk patients and tailor treatment accordingly. This study aimed to assess the importance of various clinical factors in predicting 30-day mortality in TTS patients using a machine learning algorithm.
METHODS: We analyzed data from the nationwide Swedish Coronary Angiography and Angioplasty Registry (SCAAR) for all patients with TTS in Sweden between 2015 and 2022. Gradient boosting was used to assess the relative importance of variables in predicting 30-day mortality in TTS patients.
RESULTS: Of 3,180 patients hospitalized with TTS, 76.0% were women. The median age was 71.0 years (interquartile range 62-77). The crude all-cause mortality rate was 3.2% at 30 days. Machine learning algorithms by gradient boosting identified treating hospitals as the most important predictor of 30-day mortality. This factor was followed in significance by the clinical indication for angiography, creatinine level, Killip class, and age. Other less important factors included weight, height, and certain medical conditions such as hyperlipidemia and smoking status.
CONCLUSIONS: Using machine learning with gradient boosting, we analyzed all Swedish patients diagnosed with TTS over seven years and found that the treating hospital was the most significant predictor of 30-day mortality.

OBJECTIVE: It is crucial to identify a diabetes diagnosis early. Create a predictive model utilizing machine learning (ML) to identify new cases of diabetes in primary health care (PHC).
METHODS: A case-control study utilizing data on PHC visits for sex-, age, and PHC-matched controls. Stochastic gradient boosting was used to construct a model for predicting cases of diabetes based on diagnostic codes from PHC consultations during the year before index (diagnosis) date and number of consultations. Variable importance was estimated using the normalized relative influence (NRI) score. Risks of having diabetes were calculated using odds ratios of marginal effects (ORME). Four groups by age and sex were studied, age-groups 35-64 years and ≥ 65 years in men and women, respectively.
RESULTS: The most important predictive factors were hypertension with NRI 21.4-29.7 %, and obesity 4.8-15.2 %. The NRI for other top ten diagnoses and administrative codes generally ranged 1.0-4.2 %.
CONCLUSIONS: Our data confirm the known risk patterns for predicting a new diagnosis of diabetes, and the need to test blood glucose frequently. To assess the full potential of ML for risk prediction purposes in clinical practice, future studies could include clinical data on life-style patterns, laboratory tests and prescribed medication.

Most algorithms that are used to predict the effects of variants rely on evolutionary conservation. However, a majority of such techniques compute evolutionary conservation by solely using the alignment of multiple sequences while overlooking the evolutionary context of substitution events. We had introduced PHACT, a scoring-based pathogenicity predictor for missense mutations that can leverage phylogenetic trees, in our previous study. By building on this foundation, we now propose PHACTboost, a gradient boosting tree-based classifier that combines PHACT scores with information from multiple sequence alignments, phylogenetic trees, and ancestral reconstruction. By learning from data, PHACTboost outperforms PHACT. Furthermore, the results of comprehensive experiments on carefully constructed sets of variants demonstrated that PHACTboost can outperform 40 prevalent pathogenicity predictors reported in the dbNSFP, including conventional tools, metapredictors, and deep learning-based approaches as well as more recent tools such as AlphaMissense, EVE, and CPT-1. The superiority of PHACTboost over these methods was particularly evident in case of hard variants for which different pathogenicity predictors offered conflicting results. We provide predictions of 215 million amino acid alterations over 20,191 proteins. PHACTboost is available at https://github.com/CompGenomeLab/PHACTboost. PHACTboost can improve our understanding of genetic diseases and facilitate more accurate diagnoses.