Detection of pulmonary embolism severity using clinical characteristics, hematological indices, and machine learning techniques

Machine Learning-Based Predictive Models for Patients with Venous Thromboembolism: A Systematic Review

BACKGROUND

 Venous thromboembolism (VTE) is a chronic disorder with a significant health and economic burden. Several VTE-specific clinical prediction models (CPMs) have been used to assist physicians in decision-making but have several limitations. This systematic review explores if machine learning (ML) can enhance CPMs by analyzing extensive patient data derived from electronic health records. We aimed to explore ML-CPMs' applications in VTE for risk stratification, outcome prediction, diagnosis, and treatment.

METHODS

 Three databases were searched: PubMed, Google Scholar, and IEEE electronic library. Inclusion criteria focused on studies using structured data, excluding non-English publications, studies on non-humans, and certain data types such as natural language processing and image processing. Studies involving pregnant women, cancer patients, and children were also excluded. After excluding irrelevant studies, a total of 77 studies were included.

RESULTS

 Most studies report that ML-CPMs outperformed traditional CPMs in terms of receiver operating area under the curve in the four clinical domains that were explored. However, the majority of the studies were retrospective, monocentric, and lacked detailed model architecture description and external validation, which are essential for quality audit. This review identified research gaps and highlighted challenges related to standardized reporting, reproducibility, and model comparison.

CONCLUSION

 ML-CPMs show promise in improving risk assessment and individualized treatment recommendations in VTE. Apparently, there is an urgent need for standardized reporting and methodology for ML models, external validation, prospective and real-world data studies, as well as interventional studies to evaluate the impact of artificial intelligence in VTE.

Enhanced Harris hawks optimization-based fuzzy k-nearest neighbor algorithm for diagnosis of Alzheimer's disease

In order to stop deterioration and give patients with Alzheimer's disease (AD) early therapy, it is crucial to correctly diagnose AD and its early stage, mild cognitive impairment (MCI). A framework for diagnosing AD is presented in this paper, which includes magnetic resonance imaging (MRI) image preprocessing, feature extraction, and the Fuzzy k-nearest neighbor algorithm (FKNN) model. In particular, the framework's novelty lies in the use of an improved Harris Hawks Optimization (HHO) algorithm named SSFSHHO, which integrates the Sobol sequence and Stochastic Fractal Search (SFS) mechanisms for optimizing the parameters of FKNN. The HHO method improves the quality of the initial population overall by incorporating the Sobol sequence, and the SFS mechanism increases the algorithm's capacity to get out of the local optimum solution. Comparisons with other classical meta-heuristic algorithms, state-of-the-art HHO variants in low and high dimensions, and enhanced meta-heuristic algorithms on 30 typical IEEE CEC2014 benchmark test problems show that the overall performance of SSFSHHO is significantly better than other comparative algorithms. Moreover, the created framework based on the SSFSHHO-FKNN model is employed to classify AD and MCI using MRI scans from the ADNI dataset, achieving high classification performance for 6 representative cases. Experimental findings indicate that the proposed algorithm performs better than a number of high-performance optimization algorithms and classical machine learning algorithms, thus offering a promising approach for AD classification. Additionally, the proposed strategy can successfully identify relevant features and enhance classification performance for AD diagnosis.

Developing a nomogram-based scoring model to estimate the risk of pulmonary embolism in respiratory department patients suspected of pulmonary embolisms

Objective

Pulmonary embolisms (PE) are clinically challenging because of their high morbidity and mortality. This study aimed to create a nomogram to accurately predict the risk of PE in respiratory department patients in order to enhance their medical treatment and management.

Methods

This study utilized a retrospective method to collect information on medical history, complications, specific clinical characteristics, and laboratory biomarker results of suspected PE patients who were admitted to the respiratory department at Affiliated Dongyang Hospital of Wenzhou Medical University between January 2012 and December 2021. This study involved a total of 3,511 patients who were randomly divided into a training group (six parts) and a validation group (four parts) based on a 6:4 ratio. The LASSO regression and multivariate logistic regression were used to develop a scoring model using a nomogram. The performance of the model was evaluated using receiver operating characteristic curve (AUC), calibration curve, and clinical decision curve.

Results

Our research included more than 50 features from 3,511 patients. The nomogram-based scoring model was established using six predictive features including age, smoke, temperature, systolic pressure, D-dimer, and fibrinogen, which achieved AUC values of 0.746 in the training cohort (95% CI 0.720-0.765) and 0.724 in the validation cohort (95% CI 0.695-0.753). The results of the calibration curve revealed a strong consistency between probability predicted by the nomogram and actual probability. The decision curve analysis (DCA) also demonstrated that the nomogram-based scoring model produced a favorable net clinical benefit.

Conclusion

In this study, we successfully developed a novel numerical model that can predict the risk of PE in respiratory department patients suspected of PE, which can not only appropriately select PE prevention strategies but also decrease unnecessary computed tomographic pulmonary angiography (CTPA) scans and their adverse effects.