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General General

Machine Learning for Predicting the 3-Year Risk of Incident Diabetes in Chinese Adults.

In Frontiers in public health

Purpose: We aimed to establish and validate a risk assessment system that combines demographic and clinical variables to predict the 3-year risk of incident diabetes in Chinese adults. Methods: A 3-year cohort study was performed on 15,928 Chinese adults without diabetes at baseline. All participants were randomly divided into a training set (n = 7,940) and a validation set (n = 7,988). XGBoost method is an effective machine learning technique used to select the most important variables from candidate variables. And we further established a stepwise model based on the predictors chosen by the XGBoost model. The area under the receiver operating characteristic curve (AUC), decision curve and calibration analysis were used to assess discrimination, clinical use and calibration of the model, respectively. The external validation was performed on a cohort of 11,113 Japanese participants. Result: In the training and validation sets, 148 and 145 incident diabetes cases occurred. XGBoost methods selected the 10 most important variables from 15 candidate variables. Fasting plasma glucose (FPG), body mass index (BMI) and age were the top 3 important variables. And we further established a stepwise model and a prediction nomogram. The AUCs of the stepwise model were 0.933 and 0.910 in the training and validation sets, respectively. The Hosmer-Lemeshow test showed a perfect fit between the predicted diabetes risk and the observed diabetes risk (p = 0.068 for the training set, p = 0.165 for the validation set). Decision curve analysis presented the clinical use of the stepwise model and there was a wide range of alternative threshold probability spectrum. And there were almost no the interactions between these predictors (most P-values for interaction >0.05). Furthermore, the AUC for the external validation set was 0.830, and the Hosmer-Lemeshow test for the external validation set showed no statistically significant difference between the predicted diabetes risk and observed diabetes risk (P = 0.824). Conclusion: We established and validated a risk assessment system for characterizing the 3-year risk of incident diabetes.

Wu Yang, Hu Haofei, Cai Jinlin, Chen Runtian, Zuo Xin, Cheng Heng, Yan Dewen

2021

Incident diabetes, extreme gradient boosting, machine learning, risk, simple stepwise model

General General

Developing a clinical decision support system based on the fuzzy logic and decision tree to predict colorectal cancer.

In Medical journal of the Islamic Republic of Iran

Background: Colorectal Cancer (CRC) is the most prevalent digestive system- related cancer and has become one of the deadliest diseases worldwide. Given the poor prognosis of CRC, it is of great importance to make a more accurate prediction of this disease. Early CRC detection using computational technologies can significantly improve the overall survival possibility of patients. Hence this study was aimed to develop a fuzzy logic-based clinical decision support system (FL-based CDSS) for the detection of CRC patients. Methods: This study was conducted in 2020 using the data related to CRC and non-CRC patients, which included the 1162 cases in the Masoud internal clinic, Tehran, Iran. The chi-square method was used to determine the most important risk factors in predicting CRC. Furthermore, the C4.5 decision tree was used to extract the rules. Finally, the FL-based CDSS was designed in a MATLAB environment and its performance was evaluated by a confusion matrix. Results: Eleven features were selected as the most important factors. After fuzzification of the qualitative variables and evaluation of the decision support system (DSS) using the confusion matrix, the accuracy, specificity, and sensitivity of the system was yielded 0.96, 0.97, and 0.96, respectively. Conclusion: We concluded that developing the CDSS in this field can provide an earlier diagnosis of CRC, leading to a timely treatment, which could decrease the CRC mortality rate in the community.

Nopour Raoof, Shanbehzadeh Mostafa, Kazemi-Arpanahi Hadi

2021

Artificial intelligence, CRC, Colorectal cancer, Fuzzy logic, Risk analysis, Screening

Public Health Public Health

Cervical Cancer Prediction by Merging Features of Different Colposcopic Images and Using Ensemble Classifier.

In Journal of medical signals and sensors

Background : Cervical cancer is a significant cause of cancer mortality in women, particularly in low-income countries. In regular cervical screening methods, such as colposcopy, an image is taken from the cervix of a patient. The particular image can be used by computer-aided diagnosis (CAD) systems that are trained using artificial intelligence algorithms to predict the possibility of cervical cancer. Artificial intelligence models had been highlighted in a number of cervical cancer studies. However, there are a limited number of studies that investigate the simultaneous use of three colposcopic screening modalities including Greenlight, Hinselmann, and Schiller.

Methods : We propose a cervical cancer predictor model which incorporates the result of different classification algorithms and ensemble classifiers. Our approach merges features of different colposcopic images of a patient. The feature vector of each image includes semantic medical features, subjective judgments, and a consensus. The class label of each sample is calculated using an aggregation function on expert judgments and consensuses.

Results : We investigated different aggregation strategies to find the best formula for aggregation function and then we evaluated our method using the quality assessment of digital colposcopies dataset, and our approach performance with 96% of sensitivity and 94% of specificity values yields a significant improvement in the field.

Conclusion : Our model can be used as a supportive clinical decision-making strategy by giving more reliable information to the clinical decision makers. Our proposed model also is more applicable in cervical cancer CAD systems compared to the available methods.

Nikookar Elham, Naderi Ebrahim, Rahnavard Ali

Aggregation strategy, artificial intelligence, cervical cancer, ensemble classifier, machine learning

Internal Medicine Internal Medicine

Utilizing Artificial Intelligence in Critical Care: Adding A Handy Tool to Our Armamentarium.

In Cureus

We have witnessed rapid advancement in technology over the last few decades. With the advent of artificial intelligence (AI), newer avenues have opened for researchers. AI has added an entirely new dimension to this technological boom. Researchers in medical science have been excited about the tantalizing prospect of utilizing AI for the benefit of patient care. Lately, we have come across studies trying to test and validate various models based on AI to improve patient care strategies in critical care medicine as well. Thus, in this review, we will attempt to succinctly review current literature discussing AI in critical care medicine and analyze its future utility based on prevailing evidence.

Sharma Munish, Taweesedt Pahnwat T, Surani Salim

2021-Jun

ai and machine learning, artificial intelligence in medicine, critical care medicine, mechanical ventilation, sepsis

Radiology Radiology

Constructing high-order functional connectivity network based on central moment features for diagnosis of autism spectrum disorder.

In PeerJ

The sliding-window-based dynamic functional connectivity network (D-FCN) has been becoming an increasingly useful tool for understanding the changes of brain connectivity patterns and the association of neurological diseases with these dynamic variations. However, conventional D-FCN is essentially low-order network, which only reflects the pairwise interaction pattern between brain regions and thus overlooking the high-order interactions among multiple brain regions. In addition, D-FCN is innate with temporal sensitivity issue, i.e., D-FCN is sensitive to the chronological order of its subnetworks. To deal with the above issues, we propose a novel high-order functional connectivity network framework based on the central moment feature of D-FCN. Specifically, we firstly adopt a central moment approach to extract multiple central moment feature matrices from D-FCN. Furthermore, we regard the matrices as the profiles to build multiple high-order functional connectivity networks which further capture the higher level and more complex interaction relationships among multiple brain regions. Finally, we use the voting strategy to combine the high-order networks with D-FCN for autism spectrum disorder diagnosis. Experimental results show that the combination of multiple functional connectivity networks achieves accuracy of 88.06%, and the best single network achieves accuracy of 79.5%.

Xie Qingsong, Zhang Xiangfei, Rekik Islem, Chen Xiaobo, Mao Ning, Shen Dinggang, Zhao Feng

2021

Autism spectrum disorder, Central moment feature, Cross validation, Dynamic functional connectivity network, Feature extraction, Feature selection, Functional connectivity, Functional magnetic resonance imaging, High functional connectivity network, Low functional connectivity network

General General

Artificial Intelligence Deconstructs Drug Targeting In Vivo by Leveraging a Transformer Platform.

In ACS medicinal chemistry letters ; h5-index 37.0

Lead optimization in structure-based drug design ultimately requires that the therapeutic agent be evaluated in the cellular context. However, the in vivo control of the target structure remains unyielding to computational modeling. This situation may change as transformer technologies enable a deconstruction of in vivo cooperativity steering drug-induced protein folding.

Fernández Ariel

2021-Jul-08