Intelligence-based medicine最新文献

Humanity faces various types of viral infections, such as COVID-19, annually. In this paper, we propose a Geospatial SEIR(D) model based on a multi-agent approach with continuous-discrete states. This model accounts for key parameters of viral infections, daily human activities, and geodata. Our developed algorithms enable the simulation of statistical parameters such as the number of infected, recovered, deceased, and susceptible individuals, along with the spatial distribution of the pandemic on a geographical map. The model was validated by simulating the COVID-19 spread in Lviv, Ukraine. Several preventive strategies were analyzed: implementing a 50 % reduction in infection probability through mask mandates delayed the peak to 150 days with a 25 % reduction in the maximum number of patients, while a 75 % reduction delayed the peak to 240 days with a 60 % reduction in the maximum number of patients. Prohibiting public transport and public places resulted in the epidemic peaking on day 165 with 2854 patients, significantly reducing the spread rate compared to the base model. Simulating 50 %, 75 %, and 100 % vaccination rates showed a reduction in the peak number of infections by 34 %, 57 %, and 94 %, respectively, also extending the duration of the epidemic. Enforcing weekend quarantine delayed the epidemic onset by one month but had minimal impact on the overall number of infections and duration. Combining mask mandates, transport restrictions, and vaccination led to the most effective mitigation, with the average number of sick agents around 8 and never exceeding 15 over four years. This comprehensive approach highlights the effectiveness of combining various preventive measures to control the spread of viral infections. The proposed model provides a valuable tool for policymakers to evaluate and implement effective strategies against pandemics.

Objective

The study of the epidemiology of delirium in hospitalized patients is challenging. We aimed to identify the presence or absence of delirium from clinical text notes using natural language processing (NLP) techniques and machine learning (ML) models.

Materials and methods

We developed a delirium predictive model using 942 clinical notes from hospitalized patients with an ICD-10 delirium hospital discharge code. Moreover, we implemented ML models using a) delirium-suggestive words from an expert-defined dictionary or b) free text in clinical notes. Both strategies considered positive and negative delirium-associated words.

Results

At the note level, for the dictionary method, the logistic regression model achieved an area under the receiver-operating curve (AUROC) of 0.917 for positive words and 0.914 for combined positive and negative words. The areas under the precision-recall curve (AUPR) were 0.893 and 0.897, respectively. For the free-text method, the model achieved an AUROC of 0.826 and 0.830 and AUPR of 0.852 and 0.856, respectively.

Discussion

NLP-based ML models accurately identified the presence of delirium in clinical notes. The dictionary-based method was superior to the free-text method. The use of negative features improved performance in both methods.

Conclusion

Our proposed NLP-based ML model identified delirium in clinical notes. This model could automatically screen millions of notes and facilitate the study of the epidemiology of in-hospital delirium.

Objectives

Predict the 25 dihydroxy 20 epi vitamin d3 level (low, medium, or high) in spondyloarthritis patients.

Methods

Observational, descriptive, and cross-sectional study. We collected information from 115 patients. From a total of 32 variables, we selected the most relevant using mutual information tests, and, finally, we estimated two classification models using machine learning.

Result

We obtain an interpretable decision tree and an ensemble maximizing the expected accuracy using Bayesian optimization and 10-fold cross-validation over a preprocessed dataset.

Conclusion

We identify relevant variables not considered in previous research, such as age and post-treatment. We also estimate more flexible and high-capacity models using advanced data science techniques.

The performance of an image classification depends on the efficiency of the feature learning process. This process is a challenging task that traditionally requires prior knowledge from domain experts. Recently, representation learning was introduced to extract features directly from the raw images without any prior knowledge. Deep learning using a Convolutional Neural Network (CNN) has gained massive attention for performing image classification, as it achieves remarkable accuracy that sometimes exceeds human performance. But this type of network learns features by using a back-propagation approach. This approach requires a huge amount of training data and suffers from the vanishing gradient problem that deteriorates the feature learning. The forward-propagation approach uses predefined filters or filters learned outside the model and applied in a feed-forward manner. This approach is proven to achieve good results with small size labeled datasets. In this work, we investigate the suitability of using two feed-forward methods such as Convolutional Logistic Regression Network (CLR), and Convolutional Support Vector Machine Network for Histopathology Images (CSVM-H). The experiments we have conducted on two small breast cancer datasets (Sultan Qaboos University Hospital (SQUH) and BreaKHis dataset) demonstrate the advantage of using feed-forward approaches over the traditional back-propagation ones. On those datasets, the proposed models CLR and CSVM-H were faster to train and achieved better classification performance than the traditional back-propagation methods (VggNet-16 and ResNet-50) on the SQUH dataset. Importantly, our proposed approach CLR and CSVM-H efficiently learn representations from small amounts of breast cancer whole-slide images and achieve an AUC of 0.83 and 0.84, respectively, on the SQUH dataset. Moreover, the proposed models reduce memory footprint in the classification of Whole-Slide histopathology images since their training time is significantly reduced compared to the traditional CNN on the SQUH and BreaKHis datasets.

Chronic Low Back Pain (LBP) is one of the most prevalent musculoskeletal conditions and is the leading cause of disability worldwide. The morphology and composition of lumbar paraspinal muscles, in terms of infiltrated adipose tissue, constitute important guidelines for diagnosis and treatment choice but still require manual procedures to be assessed. We developed a fully automated artificial intelligence based algorithm both to segment paraspinal muscles from MRI scans through a U-Net architecture and to estimate the amount of fatty infiltrations by a home-made intensity- and region-based processing; we further validated our results by statistical assessment of the accuracy and agreement between our automated measures and the clinically reported values, achieving dice scores greater than 95 % on the preliminary segmentation task, as well as an excellent degree of agreement on the following area estimates (ICC_2,1 = 0.89). Furthermore, we employed an external public dataset to validate our model generalization abilities, reaching dice scores greater than 94 % with an average processing time of 21.92s(±3.38s) per subject. Hence, a deterministic and reliable measuring tool is proposed, without any manual confounding effect, to efficiently support daily clinical practice in LBP management.

The use of cough sounds as a diagnostic tool for various respiratory illnesses, including COVID-19, has gained significant attention in recent years. Artificial intelligence (AI) has been employed in cough sound analysis to provide a quick and convenient pre-screening tool for COVID-19 detection. However, few works have employed segmentation to standardize cough sounds, and most models are trained datasets from a single source. In this paper, a deep learning framework is proposed that uses the Mini VGGNet model and segmentation methods for COVID-19 detection using cough sounds. In addition, data augmentation was studied to investigate the effects on model performance when applied to individual cough sounds. The framework includes both single and cross-dataset model training and testing, using data from the University of Cambridge, Coswara project, and National Institute of Health (NIH) Malaysia. Results demonstrate that the use of segmented cough sounds significantly improves the performance of trained models. In addition, findings suggest that using data augmentation on individual cough sounds does not show any improvement towards the performance of the model. The proposed framework achieved an optimum test accuracy of 0.921, 0.973 AUC, 0.910 precision, and 0.910 recall, for a model trained on a combination of the three datasets using non-augmented data. The findings of this study highlight the importance of segmentation and the use of diverse datasets for AI-based COVID-19 detection through cough sounds. Furthermore, the proposed framework provides a foundation for extending the use of deep learning in detecting other pulmonary diseases and studying the signal properties of cough sounds from various respiratory illnesses.

Introduction

In the Netherlands, pathology reports are annotated using a nationwide pathology network (PALGA) thesaurus. Annotations must address topography, procedure, and diagnosis.

The Pathology Report Annotation Module (PRAM) can be used to annotate the report conclusion with PALGA-compliant code series. The equivalence of these generated annotations to manual annotations is unknown. We assess the equivalence of annotations by authoring pathologists, pathologists participating in this study, and PRAM.

Methods

New annotations were created for one thousand histopathology reports by the PRAM and a pathologist panel. We calculated dissimilarity of annotations using a semantic distance measure, Minimal Transition Cost (MTC). In absence of a gold standard, we compared dissimilarity scores having one common annotator. The resulting comparisons yielded a measure for the coding dissimilarity between PRAM, the pathologist panel and the authoring pathologist. To compare the comprehensiveness of the coding methods, we assessed number and length of the annotations.

Results

Eight of the twelve comparisons of dissimilarity scores were significantly equivalent. Non-equivalent score pairs involved dissimilarity between the code series by the original pathologist and the panel pathologists.

Coding dissimilarity was lowest for procedures, highest for diagnoses: MTC overall = 0.30, topographies = 0.22, procedures = 0.13, diagnoses = 0.33.

Both number and length of annotations per report increased with report conclusion length, mostly in PRAM-annotated conclusions: conclusion length ranging from 2 to 373 words, number of annotations ranged from 1 to 10 for pathologists, 1–19 for PRAM, annotation length ranged from 3 to 43 codes for pathologists, 4–123 for PRAM.

Conclusions

We measured annotation similarity among PRAM, authoring pathologists and panel pathologists. Annotating by PRAM, the panel pathologists and to a lesser extent by the authoring pathologist was equivalent. Therefore, the use of annotations by PRAM in a practical setting is justified. PRAM annotations are equivalent to study-setting annotations, and more comprehensive than routine coding. Further research on annotation quality is needed.

Background

Triggers of organ dysfunction have been associated with the worsening of microcirculatory dysfunction in sepsis, and because microcirculatory changes occur before macro-hemodynamic abnormalities, they can potentially detect disease progression early on. The difficulty in distinguishing altered microcirculatory characteristics corresponding to varying stages of sepsis severity has been a limiting factor for the use of microcirculatory imaging as a diagnostic and prognostic tool in sepsis. The aim of this study was to develop a convolutional neural network (CNN) based on progressive sublingual microcirculatory dysfunction images in sepsis, and test its diagnostic accuracy for these progressive stages.

Methods

Sepsis was induced in Wistar rats (2 mL of E. coli 10⁸ CFU/mL inoculation into the jugular vein), and 2 mL saline injection in sham animals was the control. Sublingual microvessels of all animals with surrounding tissue images were captured by Sidestream dark field imaging (SDF) at T0 (basal) and T2, T4, and T6 h after sepsis induction. From a total of 137 videos, 37.930 frames were extracted; a part (29.341) was used for the training of Resnet-50 (CNN-construct), and the remaining (8.589) was used for validation of accuracy.

Results

The CNN-construct successfully classified the various stages of sepsis with a high accuracy (97.07%). The average AUC value of the ROC curve was 0.9833, and the sensitivity and specificity ranged from 94.57% to 99.91%, respectively, at all time points.

Conclusions

By blind testing with new sublingual microscopy images captured at different periods of the acute phase of sepsis, the CNN-construct was able to accurately diagnose the four stages of sepsis severity. Thus, this new method presents the diagnostic potential for different stages of microcirculatory dysfunction and enables the prediction of clinical evolution and therapeutic efficacy. Automated simultaneous assessment of multiple characteristics, both microvessels and adjacent tissues, may account for this diagnostic skill. As such a task cannot be analyzed with human visual criteria only, CNN is a novel method to identify the different stages of sepsis by assessing the distinct features of each stage.