Machine learning models for early prediction of mortality risk in patients with burns: A single center experience.

Mortality rate is considered as the most important outcome measure for assessing the severity of burn injury. A scale or model that accurately predicts burn mortality can be useful to determine the clinical course of burn injuries, discuss treatment options and rehabilitation with patients and their families, and evaluate novel, innovative interventions for the injuries. This study aimed to use machine learning models to predict the mortality risk of patients with burns after their first admission to the center and to compare the performances of these models. Overall, 1064 patients hospitalized in burn intensive care and burn service units between 2016 and 2022 were included in the study. In total, 40 parameters, including demographic characteristics and biochemical parameters of all patients, were analyzed in the study. Furthermore, the dataset was randomly divided into two clusters with 70% of the data used for artificial neural networks (ANNs) training and 30% for model success testing. The ANN model proposed in this study showed high success across all machine learning methods tried in different variants, with an accuracy of 95.92% in the test set. Machine learning models can be used to predict the mortality risk of patients with burns. This study may help validate the use of machine learning models for applications in clinical practice. Conducting multicenter studies will further contribute to the literature.

Machine learning approaches in the interpretation of endobronchial ultrasound images: a comparative analysis.

BACKGROUND: This study explores the application of machine learning (ML) in analyzing endobronchial ultrasound (EBUS) images for the detection of lymph node (LN) malignancy, aiming to augment diagnostic accuracy and efficiency. We investigated whether ML could outperform conventional classification systems in identifying malignant involvement of LNs, based on eight established sonographic features. METHODS: Retrospective data from two tertiary care hospital bronchoscopy units were utilized, encompassing healthcare reports of patients who had undergone EBUS between January 2017 and March 2023. The ML model was trained and tested using MATLAB, with 80% of the data allocated for training/validation, and 20% for testing. Performance was evaluated based on validation and testing accuracy, and receiver operating characteristic curves with comparing trained models and existing classification rules. RESULTS: The study analyzed 992 LNs, with 42.3% malignancy prevalence. Malignant LNs showed characteristic features such as larger size and distinct margins. The fine tuned models achieved testing accuracies of 95.9% and 96.4% for fine Gaussian SVM and KNN, respectively. Corresponding AUROC's were 0.955 and 0.963, outperforming other similar studies and conventional analyses. CONCLUSION: Fine tuned ML applications like SVM and KNN, can significantly enhance the analysis of EBUS images, improving diagnostic accuracy.

Automatic Segmentation of Meniscus in Multispectral MRI Using Regions with Convolutional Neural Network (R-CNN).

The meniscus has a significant function in human anatomy, and Magnetic Resonance Imaging (MRI) has an essential role in meniscus examination. Due to a variety of MRI data, it is excessively difficult to segment the meniscus with image processing methods. An MRI data sequence contains multiple images, and the region features we are looking for may vary from each image in the sequence. Therefore, feature extraction becomes more difficult, and hence, explicitly programming for segmentation becomes more difficult. Convolutional Neural Network (CNN) extracts features directly from images and thus eliminates the need for manual feature extraction. Regions with Convolutional Neural Network (R-CNN) allow us to use CNN features in object detection problems by combining CNN features with Region Proposals. In this study, we designed and trained an R-CNN for detecting meniscus region in MRI data sequence. We used transfer learning for training R-CNN with a small amount of meniscus data. After detection of the meniscus region by R-CNN, we segmented meniscus by morphological image analysis using two different MRI sequences. Automatic detection of the meniscus region with R-CNN made the meniscus segmentation process easier, and the use of different contrast features of two different image sequences allowed us to differentiate the meniscus from its surroundings.

Metagenome Profiling Identifies Potential Biocontrol Agents for Selaginella kraussiana in New Zealand.

Metagenomics can be used to identify potential biocontrol agents for invasive species and was used here to identify candidate species for biocontrol of an invasive club moss in New Zealand. Profiles were obtained for Selaginella kraussiana collected from nine geographically disjunct locations in Northern New Zealand. These profiles were distinct from those obtained for the exotic club moss Selaginella moellendorffii and the native club mosses Lycopodium deuterodensum and Lycopodium volubile also collected in Northern New Zealand. Fungi and bacteria implicated elsewhere in causing plant disease were identified on plants of Selaginella that exhibited signs of necrosis. Most notably, high densities of sequence reads from Xanthomonas translucens and Pseudomonas syringae were associated with some populations of Selaginella but not Lycopodium. Since these bacteria are already in use as biocontrol agents elsewhere, further investigation into their potential as biocontrol of Selaginella in New Zealand is suggested.

Distribution and community composition of lichens on mature mangroves (Avicennia marina subsp. australasica (Walp.) J.Everett) in New Zealand.

Mangrove forests of a single trees species, Avicennia marina subsp. australasica are widespread in the upper North Island of New Zealand, but there is little available information on the diversity of epiphytes such as lichens within them. A survey of 200 trees from 20 mangrove sites recorded a total of 106 lichen species from 45 genera. Two of these species are considered to be 'Threatened', five 'At Risk' and 27 'Data Deficient'. Multiple regression indicated that tree diameter (DBH) and mean annual rain days positively influenced site species richness. Multidimensional scaling showed that sites from the same geographical region generally formed distinct clusters. Redundancy analysis indicated that mean annual wet days, latitude and DBH measurably influenced species composition.

A secure communication using cascade chaotic computing systems on clinical decision support.

Clinical decision support systems (C-DSS) provide supportive tools to the expert for the determination of the disease. Today, many of the support systems, which have been developed for a better and more accurate diagnosis, have reached a dynamic structure due to artificial intelligence techniques. However, in cases when important diagnosis studies should be performed in secret, a secure communication system is required. In this study, secure communication of a DSS is examined through a developed double layer chaotic communication system. The developed communication system consists of four main parts: random number generator, cascade chaotic calculation layer, PCM, and logical mixer layers. Thanks to this system, important patient data created by DSS will be conveyed to the center through a secure communication line.

Tuberculosis disease diagnosis using artificial neural networks.

Tuberculosis is an infectious disease, caused in most cases by microorganisms called Mycobacterium tuberculosis. Tuberculosis is a great problem in most low income countries; it is the single most frequent cause of death in individuals aged fifteen to forty-nine years. Tuberculosis is important health problem in Turkey also. In this study, a study on tuberculosis diagnosis was realized by using multilayer neural networks (MLNN). For this purpose, two different MLNN structures were used. One of the structures was the MLNN with one hidden layer and the other was the MLNN with two hidden layers. A general regression neural network (GRNN) was also performed to realize tuberculosis diagnosis for the comparison. Levenberg-Marquardt algorithms were used for the training of the multilayer neural networks. The results of the study were compared with the results of the pervious similar studies reported focusing on tuberculosis diseases diagnosis. The tuberculosis dataset were taken from a state hospital's database using patient's epicrisis reports.

A comparative study on chronic obstructive pulmonary and pneumonia diseases diagnosis using neural networks and artificial immune system.

Millions of people are diagnosed every year with a chest disease in the world. Chronic obstructive pulmonary and pneumonia diseases are two of the most important chest diseases. And these are very common illnesses in Turkey. In this paper, a comparative study on chronic obstructive pulmonary and pneumonia diseases diagnosis was realized by using neural networks and artificial immune systems. For this purpose, three different neural networks structures and one artificial immune system were used. Used neural network structures in this study were multilayer, probabilistic, and learning vector quantization neural networks. The results of the study were compared with the results of the pervious similar studies reported focusing on chronic obstructive pulmonary and pneumonia diseases diagnosis. The chronic obstructive pulmonary and pneumonia diseases dataset were prepared from a chest diseases hospital's database using patient's epicrisis reports.

A study on chronic obstructive pulmonary disease diagnosis using multilayer neural networks.

Chronic Obstructive Pulmonary Disease (COPD) is a disease state characterized by airflow limitation that is not fully reversible. The airflow limitation is usually both progressive and associated with an abnormal inflammatory response of the lungs to noxious particles or gases. COPD is important health problem and one of the most common illnesses in Turkey. It is generally accepted that cigarette smoking is the most important risk factor and genetic factors are believed to play a role in the individual susceptibility. In this study, a study on COPD diagnosis was realized by using multilayer neural networks (MLNN). For this purpose, two different MLNN structures were used. One of the structures was the MLNN with one hidden layer and the other was the MLNN with two hidden layers. Back propagation with momentum and Levenberg-Marquardt algorithms were used for the training of the neural networks. The COPD dataset were prepared from a chest diseases hospital's database using patient's epicrisis reports.