scholarly journals Development of a Machine-Learning Model of Short-Term Prognostic Prediction for Spinal Stenosis Surgery in Korean Patients

2020 ◽  
Vol 10 (11) ◽  
pp. 764
Author(s):  
Kyeong-Rae Kim ◽  
Hyeun Sung Kim ◽  
Jae-Eun Park ◽  
Seung-Yeon Kang ◽  
So-Young Lim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Spinal Stenosis ◽  
Area Under The Curve ◽  
Individual Characteristics ◽  
Learning Technology ◽  
Short Term ◽  
Korean Patients ◽  
Patient Prognosis ◽  
Prognostic Prediction ◽  
The Individual

Background: In this study, based on machine-learning technology, we aim to develop a predictive model of the short-term prognosis of Korean patients who received spinal stenosis surgery. Methods: Using the data obtained from 112 patients with spinal stenosis admitted at N hospital from February to November, 2019, a predictive analysis was conducted for the pain index, reoperation, and surgery time. Results: Results show that the predicted area under the curve was 0.803, 0.887, and 0.896 for the pain index, reoperation, and surgery time, respectively, thereby indicating the accuracy of the model. Conclusion: This study verified that the individual characteristics of the patient and treatment characteristics during surgery enable a prediction of the patient prognosis and validate the accuracy of the approach. Further studies should be conducted to extend the scope of this research by incorporating a larger and more accurate dataset.

scholarly journals Predicting short-term survival after total resection in glioblastomas by machine learning-based radiomic analysis of preoperative MRI

2021 ◽  
Author(s):  
Santiago Cepeda ◽  
Angel Perez-Nuñez ◽  
Sergio Garcia-Garcia ◽  
Daniel Garcia-Perez ◽  
Ignacio Arrese ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tumor Resection ◽  
Area Under The Curve ◽  
Patient Characteristics ◽  
Risk Groups ◽  
Feature Reduction ◽  
Short Term ◽  
Term Survival ◽  
The Individual ◽  
Short Term Survival

Abstract Background Radiomics, in combination with artificial intelligence, emerges as a powerful tool for the development of predictive models in neuro-oncology. Our study aims to find an answer to a clinically relevant question: is there a radiomic profile that can identify glioblastoma (GBM) patients with short-term survival after complete tumor resection?Methods A retrospective study of GBM patients who underwent surgery was conducted in two institutions between January 2019 and January 2020, along with cases from public databases. Cases with gross total or near-total tumor resection were included. Preoperative structural multiparametric magnetic resonance imaging (mpMRI) sequences were pre-processed, and a total of 15720 radiomic features were extracted. After feature reduction, machine learning-based classifiers were used to predict early mortality (< 6 months). Also, a survival analysis was performed using the Random Survival Forest (RSF) algorithm. Results A total of 203 patients were enrolled in this study. In the classification task, the Naive Bayes classifier obtained the best results in the testing cohort, with an area under the curve (AUC) of 0.769 and classification accuracy of 80%. RSF model allowed the stratification of patients into low and high-risk groups. In the validation set, this model obtained values of C-Index = 0.61, IBS = 0.123 and integrated AUC at six months of 0.761.Conclusion In this study, we have developed a reliable predictive model of short-term survival in GBM applying open-source and user-friendly computational means. These new tools will assist clinicians in adapting our therapeutic approach considering the individual patient characteristics.

Clinical Score and Machine Learning-Based Model to Predict Diagnosis of Primary Aldosteronism in Arterial Hypertension

Hypertension ◽  
2021 ◽  
Vol 78 (5) ◽  
pp. 1595-1604
Author(s):  
Fabrizio Buffolo ◽  
Jacopo Burrello ◽  
Alessio Burrello ◽  
Daniel Heinrich ◽  
Christian Adolf ◽  
...  
Keyword(s):  
Machine Learning ◽  
Arterial Hypertension ◽  
Primary Aldosteronism ◽  
Learning Algorithm ◽  
Area Under The Curve ◽  
Clinical Score ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Individual Risk ◽  
The Individual

Primary aldosteronism (PA) is the cause of arterial hypertension in 4% to 6% of patients, and 30% of patients with PA are affected by unilateral and surgically curable forms. Current guidelines recommend screening for PA ≈50% of patients with hypertension on the basis of individual factors, while some experts suggest screening all patients with hypertension. To define the risk of PA and tailor the diagnostic workup to the individual risk of each patient, we developed a conventional scoring system and supervised machine learning algorithms using a retrospective cohort of 4059 patients with hypertension. On the basis of 6 widely available parameters, we developed a numerical score and 308 machine learning-based models, selecting the one with the highest diagnostic performance. After validation, we obtained high predictive performance with our score (optimized sensitivity of 90.7% for PA and 92.3% for unilateral PA [UPA]). The machine learning-based model provided the highest performance, with an area under the curve of 0.834 for PA and 0.905 for diagnosis of UPA, with optimized sensitivity of 96.6% for PA, and 100.0% for UPA, at validation. The application of the predicting tools allowed the identification of a subgroup of patients with very low risk of PA (0.6% for both models) and null probability of having UPA. In conclusion, this score and the machine learning algorithm can accurately predict the individual pretest probability of PA in patients with hypertension and circumvent screening in up to 32.7% of patients using a machine learning-based model, without omitting patients with surgically curable UPA.

scholarly journals Predicting Short-Term Survival after Gross Total or Near Total Resection in Glioblastomas by Machine Learning-Based Radiomic Analysis of Preoperative MRI

Cancers ◽  
2021 ◽  
Vol 13 (20) ◽  
pp. 5047
Author(s):  
Santiago Cepeda ◽  
Angel Pérez-Nuñez ◽  
Sergio García-García ◽  
Daniel García-Pérez ◽  
Ignacio Arrese ◽  
...  
Keyword(s):  
Machine Learning ◽  
Test Data ◽  
Tumor Resection ◽  
Area Under The Curve ◽  
Risk Groups ◽  
Feature Reduction ◽  
Short Term ◽  
Data Set ◽  
Term Survival ◽  
Short Term Survival

Radiomics, in combination with artificial intelligence, has emerged as a powerful tool for the development of predictive models in neuro-oncology. Our study aims to find an answer to a clinically relevant question: is there a radiomic profile that can identify glioblastoma (GBM) patients with short-term survival after complete tumor resection? A retrospective study of GBM patients who underwent surgery was conducted in two institutions between January 2019 and January 2020, along with cases from public databases. Cases with gross total or near total tumor resection were included. Preoperative structural multiparametric magnetic resonance imaging (mpMRI) sequences were pre-processed, and a total of 15,720 radiomic features were extracted. After feature reduction, machine learning-based classifiers were used to predict early mortality (<6 months). Additionally, a survival analysis was performed using the random survival forest (RSF) algorithm. A total of 203 patients were enrolled in this study. In the classification task, the naive Bayes classifier obtained the best results in the test data set, with an area under the curve (AUC) of 0.769 and classification accuracy of 80%. The RSF model allowed the stratification of patients into low- and high-risk groups. In the test data set, this model obtained values of C-Index = 0.61, IBS = 0.123 and integrated AUC at six months of 0.761. In this study, we developed a reliable predictive model of short-term survival in GBM by applying open-source and user-friendly computational means. These new tools will assist clinicians in adapting our therapeutic approach considering individual patient characteristics.

scholarly journals A multi-layer model for the early detection of COVID-19

2021 ◽  
Vol 18 (181) ◽  
pp. 20210284
Author(s):  
Erez Shmueli ◽  
Ronen Mansuri ◽  
Matan Porcilan ◽  
Tamar Amir ◽  
Lior Yosha ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
General Health ◽  
Medical Condition ◽  
Area Under The Curve ◽  
Sociodemographic Characteristics ◽  
Layer Model ◽  
Machine Learning Model ◽  
Spatio Temporal ◽  
The Individual

Current COVID-19 screening efforts mainly rely on reported symptoms and the potential exposure to infected individuals. Here, we developed a machine-learning model for COVID-19 detection that uses four layers of information: (i) sociodemographic characteristics of the individual, (ii) spatio-temporal patterns of the disease, (iii) medical condition and general health consumption of the individual and (iv) information reported by the individual during the testing episode. We evaluated our model on 140 682 members of Maccabi Health Services who were tested for COVID-19 at least once between February and October 2020. These individuals underwent, in total, 264 516 COVID-19 PCR tests, out of which 16 512 were positive. Our multi-layer model obtained an area under the curve (AUC) of 81.6% when evaluated over all the individuals in the dataset, and an AUC of 72.8% when only individuals who did not report any symptom were included. Furthermore, considering only information collected before the testing episode—i.e. before the individual had the chance to report on any symptom—our model could reach a considerably high AUC of 79.5%. Our ability to predict early on the outcomes of COVID-19 tests is pivotal for breaking transmission chains, and can be used for a more efficient testing policy.

scholarly journals Fertility Intentions in Times of Rising Economic Uncertainty: Evidence from Italy from a Gender Perspective

2020 ◽  
Author(s):  
Marco Novelli ◽  
Alberto Cazzola ◽  
Aurora Angeli ◽  
Lucia Pasquini
Keyword(s):  
Great Recession ◽  
Multilevel Models ◽  
Fertility Intentions ◽  
Individual Characteristics ◽  
Economic Uncertainty ◽  
Short Term ◽  
Economic Determinants ◽  
The Great Recession ◽  
Italian Women ◽  
The Individual

AbstractThis study analyses the socio-economic determinants of the short-term fertility plans of Italian women and men living as couples, before and shortly after the onset of the 2007/2008 Great Recession, which may have affected their reproductive plans through a climate of rising economic uncertainty. Using multilevel models, we investigate how fertility intentions are related to the individual characteristics of the respondents and their partners as well as to changes in the economic context. The findings confirm that the Great Recession modified the determinants of short-term fertility intentions differently for women and men. Among the most relevant issues, we outline the importance of couples’ working conditions and the contextual labour market indicators.

scholarly journals A Multilayer Model for Early Detection of COVID-19

2021 ◽  
Author(s):  
Erez Shmueli ◽  
Ronen Mansuri ◽  
Matan Porcilan ◽  
Tamar Amir ◽  
Lior Yosha ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Medical Condition ◽  
Area Under The Curve ◽  
Spatiotemporal Patterns ◽  
Sociodemographic Characteristics ◽  
Multilayer Model ◽  
The Past ◽  
Machine Learning Model ◽  
The Individual

ABSTRACTCurrent efforts for COVID-19 screening mainly rely on reported symptoms and potential exposure to infected individuals. Here, we developed a machine-learning model for COVID-19 detection that utilizes four layers of information: 1) sociodemographic characteristics of the tested individual, 2) spatiotemporal patterns of the disease observed near the testing episode, 3) medical condition and general health consumption of the tested individual over the past five years, and 4) information reported by the tested individual during the testing episode. We evaluated our model on 140,682 members of Maccabi Health Services, tested for COVID-19 at least once between February and October 2020. These individuals had 264,516 COVID-19 PCR-tests, out of which 16,512 were found positive. Our multilayer model obtained an area under the curve (AUC) of 81.6% when tested over all individuals, and of 72.8% when tested over individuals who did not report any symptom. Furthermore, considering only information collected before the testing episode – that is, before the individual may had the chance to report on any symptom – our model could reach a considerably high AUC of 79.5%. Namely, most of the value contributed by the testing episode can be gained by earlier information. Our ability to predict early the outcomes of COVID-19 tests is pivotal for breaking transmission chains, and can be utilized for a more efficient testing policy.

scholarly journals Predicting short-term survival after total resection in glioblastomas by machine learning-based radiomic analysis of preoperative MRI

2021 ◽  
Author(s):  
Santiago Cepeda ◽  
Angel Perez-Nuñez ◽  
Sergio Garcia-Garcia ◽  
Daniel Garcia-Perez ◽  
Ignacio Arrese ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tumor Resection ◽  
Area Under The Curve ◽  
Patient Characteristics ◽  
Risk Groups ◽  
Feature Reduction ◽  
Short Term ◽  
Term Survival ◽  
Reduction Machine ◽  
Short Term Survival

Abstract Background Radiomics, in combination with artificial intelligence, has emerged as a powerful tool for the development of predictive models in neuro-oncology. Our study aims to find an answer to a clinically relevant question: is there a radiomic profile that can identify glioblastoma (GBM) patients with short-term survival after complete tumor resection?Methods A retrospective study of GBM patients who underwent surgery was conducted in two institutions between January 2019 and January 2020, along with cases from public databases. Cases with gross total or near-total tumor resection were included. Preoperative structural multiparametric magnetic resonance imaging (mpMRI) sequences were preprocessed, and a total of 15720 radiomic features were extracted. After feature reduction, machine learning-based classifiers were used to predict early mortality (< 6 months). Additionally, a survival analysis was performed using the random survival forest (RSF) algorithm.Results A total of 203 patients were enrolled in this study. In the classification task, the naive Bayes classifier obtained the best results in the testing cohort, with an area under the curve (AUC) of 0.769 and classification accuracy of 80%. The RSF model allowed the stratification of patients into low- and high-risk groups. In the validation set, this model obtained values of C-Index = 0.61, IBS = 0.123 and integrated AUC at six months of 0.761.Conclusion In this study, we developed a reliable predictive model of short-term survival in GBM by applying open-source and user-friendly computational means. These new tools will assist clinicians in adapting our therapeutic approach considering individual patient characteristics.

Machine Learning Approach to Short-Term Traffic Congestion Prediction in a Connected Environment

2018 ◽  
Vol 2672 (45) ◽  
pp. 185-195 ◽  
Author(s):  
Amr Elfar ◽  
Alireza Talebpour ◽  
Hani S. Mahmassani
Keyword(s):  
Machine Learning ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Historical Data ◽  
Machine Learning Techniques ◽  
Short Term ◽  
Congested Traffic ◽  
Vehicle Trajectories ◽  
Congestion Prediction ◽  
The Individual

Traffic congestion is a complex phenomenon triggered by a combination of multiple interacting factors. One of the main factors is the disturbances caused by individual vehicles, which cannot be identified in aggregate traffic data. Advances in vehicle wireless communications present new opportunities to measure traffic perturbations at the individual vehicle level. The key question is whether it is possible to find the relationship between these perturbations and shockwave formation and utilize this knowledge to improve the identification and prediction of congestion formation. Accordingly, this paper explores the use of three machine learning techniques, logistic regression, random forests, and neural networks, for short-term traffic congestion prediction using vehicle trajectories available through connected vehicles technology. Vehicle trajectories provided by the Next Generation SIMulation (NGSIM) program were utilized in this study. Two types of predictive models were developed in this study: (1) offline models which are calibrated based on historical data and are updated (re-trained) whenever significant changes occur in the system, such as changes/updates to the infrastructure, and (2) online models which are calibrated using historical data and updated regularly using real-time information on prevailing traffic conditions obtained through V2V/V2I communications. Results show that the accuracy of the models built in this study to predict the congested traffic state can reach 97%. The models presented can be used in various potential applications including improving road safety by warning drivers of upcoming traffic slowdowns and improving mobility through integration with traffic control systems.

scholarly journals Early Stage Identification of COVID-19 Patients in Mexico Using Machine Learning: A Case Study for the Tijuana General Hospital

Information ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 490
Author(s):  
Cristián Castillo-Olea ◽  
Roberto Conte-Galván ◽  
Clemente Zuñiga ◽  
Alexandra Siono ◽  
Angelica Huerta ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mathematical Model ◽  
Logistic Regression ◽  
General Hospital ◽  
Early Stage ◽  
Individual Characteristics ◽  
Supervised Learning Algorithms ◽  
Neural Network Algorithm ◽  
The Individual ◽  
Logistic Regression Algorithm

Background: The current pandemic caused by SARS-CoV-2 is an acute illness of global concern. SARS-CoV-2 is an infectious disease caused by a recently discovered coronavirus. Most people who get sick from COVID-19 experience either mild, moderate, or severe symptoms. In order to help make quick decisions regarding treatment and isolation needs, it is useful to determine which significant variables indicate infection cases in the population served by the Tijuana General Hospital (Hospital General de Tijuana). An Artificial Intelligence (Machine Learning) mathematical model was developed in order to identify early-stage significant variables in COVID-19 patients. Methods: The individual characteristics of the study subjects included age, gender, age group, symptoms, comorbidities, diagnosis, and outcomes. A mathematical model that uses supervised learning algorithms, allowing the identification of the significant variables that predict the diagnosis of COVID-19 with high precision, was developed. Results: Automatic algorithms were used to analyze the data: for Systolic Arterial Hypertension (SAH), the Logistic Regression algorithm showed results of 91.0% in area under ROC (AUC), 80% accuracy (CA), 80% F1 and 80% Recall, and 80.1% precision for the selected variables, while for Diabetes Mellitus (DM) with the Logistic Regression algorithm it obtained 91.2% AUC, 89.2% accuracy, 88.8% F1, 89.7% precision, and 89.2% recall for the selected variables. The neural network algorithm showed better results for patients with Obesity, obtaining 83.4% AUC, 91.4% accuracy, 89.9% F1, 90.6% precision, and 91.4% recall. Conclusions: Statistical analyses revealed that the significant predictive symptoms in patients with SAH, DM, and Obesity were more substantial in fatigue and myalgias/arthralgias. In contrast, the third dominant symptom in people with SAH and DM was odynophagia.

scholarly journals Accuracy of deep learning, a machine learning technology, using ultra-wide-field fundus ophthalmoscopy for detecting idiopathic macular holes

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5696 ◽  
Author(s):  
Toshihiko Nagasawa ◽  
Hitoshi Tabuchi ◽  
Hiroki Masumoto ◽  
Hiroki Enno ◽  
Masanori Niki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Area Under The Curve ◽  
High Sensitivity ◽  
High Specificity ◽  
Wide Field ◽  
Learning Technology ◽  
Macular Holes ◽  
Idiopathic Macular Holes ◽  
Wide Angle Camera

We aimed to investigate the detection of idiopathic macular holes (MHs) using ultra-wide-field fundus images (Optos) with deep learning, which is a machine learning technology. The study included 910 Optos color images (715 normal images, 195 MH images). Of these 910 images, 637 were learning images (501 normal images, 136 MH images) and 273 were test images (214 normal images and 59 MH images). We conducted training with a deep convolutional neural network (CNN) using the images and constructed a deep-learning model. The CNN exhibited high sensitivity of 100% (95% confidence interval CI [93.5–100%]) and high specificity of 99.5% (95% CI [97.1–99.9%]). The area under the curve was 0.9993 (95% CI [0.9993–0.9994]). Our findings suggest that MHs could be diagnosed using an approach involving wide angle camera images and deep learning.

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