AI-driven bone mineral density prediction from chest x-rays and its association with obstructive sleep apnea.

With an increasing aging population, the prevalence of chronic comorbidities is on the rise. The potential relationship between obstructive sleep apnea (OSA) and osteoporosis has garnered significant attention. Most studies examining the association between these two conditions have relied on dual-energy X-ray absorptiometry (DXA) to evaluate bone mineral density (BMD).

Machine learning for diabetic foot care: accuracy trends and emerging directions in healthcare AI.

Background: Diabetic foot is a common and debilitating complication of diabetes that significantly impacts patients' quality of life and frequently leads to amputation. In parallel, artificial intelligence (AI), particularly machine learning (ML), has emerged as a powerful tool in healthcare, offering novel solutions for disease prediction, monitoring, and management. Despite growing interest, a systematic overview of machine learning applications in diabetic foot research is still lacking.

Enhancing healthcare AI stability with edge computing and machine learning for extubation prediction.

The advancement of the Internet of Medical Things (IoMT) has revolutionized data acquisition and processing in critical care settings. Given the pivotal role of ventilators, accurately predicting extubation outcomes is essential to optimize patient care. This study presents an edge computing-based framework that incorporates machine learning algorithms to predict ventilator extubation success using real-time data collected directly from ventilators.

Post-surgical fall risk prediction: a machine learning approach for spine and lower extremity procedures.

In Taiwan, two key indicators of clinical care quality are pressure injuries and falls. Falls can have significant physical impacts, ranging from minor injuries like bruises to major injuries such as fractures, sprains, and severe head trauma. To assess fall risk early and implement preventive measures, this study analyzed 2,948 medical records of patients who underwent spinal and lower limb surgeries at the Veterans General Hospital in Taichung, Taiwan.

Optimizing extubation success: a comparative analysis of time series algorithms and activation functions.

Background: The success and failure of extubation of patients with acute respiratory failure is a very important issue for clinicians, and the failure of the ventilator often leads to possible complications, which in turn leads to a lot of doubts about the medical treatment in the minds of the people, so in order to increase the success of extubation success of the doctors to prevent the possible complications, the present study compared different time series algorithms and different activation functions for the training and prediction of extubation success or failure models.

Methods: This study compared different time series algorithms and different activation functions for training and predicting the success or failure of the extubation model.

Results: The results of this study using four validation methods show that the GRU model and Tanh's model have a better predictive model for predicting the success or failure of the extubation and better predictive result of 94.

Developing a machine-learning model for real-time prediction of successful extubation in mechanically ventilated patients using time-series ventilator-derived parameters.

Background: Successful weaning from mechanical ventilation is important for patients admitted to intensive care units. However, models for predicting real-time weaning outcomes remain inadequate. Therefore, this study aimed to develop a machine-learning model for predicting successful extubation only using time-series ventilator-derived parameters with good accuracy.

The role of extracranial carotid duplex in predicting functional outcome in first time lacunae strokes.

Background: Stroke is the third largest cause of death both worldwide and in Taiwan. Among the various stroke subtypes, lacunae strokes account for 20 to 30% of the total stroke population. Through vigorous risk control factors, the effective prevention and the long-term functional outcome remains are yet to be investigated.

Functional outcome prediction of ischemic stroke patients with atrial fibrillation accepting post-acute care training.

Background: Ischemic stroke poses a major threat to human health and represents the third leading cause of death worldwide and in Taiwan. Post-acute care (PAC) training has been reported to be beneficial for post-index stroke events. However, knowledge is still lacking on the outcome of stroke events with cardiac origin.

Long-Term Functionality Prediction for First Time Ischemic Middle Cerebral Artery Stroke Patients Receiving Conventional Medical Treatment.

Background: Middle cerebral artery (MCA) ischemic stroke poses a major threat to human beings and prompts intravenous thrombolytic and/or thrombectomy management remains the gold standard treatment. However, not all MCA stroke patients fit in the inclusion and exclusion criteria that many patients only receive conventional medical therapy. We attempt to seek the baseline parameters that can effectively predict patients' long-term functionality, as well as hypothesizing that the carotid duplex derived resistance/pulsatility index might be capable of fulfilling this purpose.

Analysis of agricultural exports based on deep learning and text mining.

Agricultural exports are an important source of economic profit for many countries. Accurate predictions of a country's agricultural exports month on month are key to understanding a country's domestic use and export figures and facilitate advance planning of export, import, and domestic use figures and the resulting necessary adjustments of production and marketing. This study proposes a novel method for predicting the rise and fall of agricultural exports, called agricultural exports time series-long short-term memory (AETS-LSTM).

Sparse principal component analysis in cancer research.

A critical challenging component in analyzing high-dimensional data in cancer research is how to reduce the dimension of data and how to extract relevant features. Sparse principal component analysis (PCA) is a powerful statistical tool that could help reduce data dimension and select important variables simultaneously. In this paper, we review several approaches for sparse PCA, including variance maximization (VM), reconstruction error minimization (REM), singular value decomposition (SVD), and probabilistic modeling (PM) approaches.

Prognostic and predictive value of a malignancy-risk gene signature in early-stage non-small cell lung cancer.

Background: The malignancy-risk gene signature is composed of numerous proliferative genes and has been applied to predict breast cancer risk. We hypothesized that the malignancy-risk gene signature has prognostic and predictive value for early-stage non-small cell lung cancer (NSCLC) patients.

Methods: The ability of the malignancy-risk gene signature to predict overall survival (OS) of early-stage NSCLC patients was tested using a large NSCLC microarray dataset from the Director's Challenge Consortium (n = 442) and two independent NSCLC microarray datasets (n = 117 and 133, for the GSE13213 and GSE14814 datasets, respectively).