P213 Can artificial intelligence help differentiate inflammatory intestinal diseases in China? Models applying random forest and convolutional neuron network

Abstract Background Differentiating between ulcerative colitis (UC), Crohn’s disease (CD) and intestinal tuberculosis (ITB) is challenging under endoscopy. We aimed to realise automatic differential diagnosis among these diseases through machine learning algorithms. Methods A total of 6399 consecutive patients (5128 UC, 875 CD and 396 ITB) who had taken colonoscopy examinations in Peking Union Medical College Hospital from January 2008 to November 2018 was enrolled. The input was the description of the endoscopic image in the form of free-text. Word segmentation and key word infiltration were conducted as data pre-processing. Random forest (RF) and convolutional neural network (CNN) were applied to different disease entities. Three two-class classifiers (UC and CD, UC and ITB, CD and ITB) and a three-class classifier (UC, CD and ITB) were built. Sensitivity/specificity and precision/recall were applied to evaluate the performance of two-class classifiers and the three-class classifier, respectively. Results The classifiers built in this research were well-performed and the CNN had a better performance in general. The RF sensitivities/specificities of UC-CD, UC-ITB and CD-ITB were 0.89/0.84, 0.83/0.82 and 0.72/0.77, while the CNN of CD-ITB was 0.90/0.77. The precision/recall of UC-CD-ITB was 0.97/0.97, 0.65/0.53 and 0.68/0.76 by RF, respectively, and 0.99/0.97,0.87/0.83 and 0.52/0.81 by CNN, respectively. Conclusion Classifiers built by RF and CNN had an excellent performance when classifying UC with CD or ITB. For the differentiation of CD and ITB, high specificity and sensitivity were reached as well. Artificial intelligence through machine learning is very promising in helping unexperienced endoscopists differentiate inflammatory intestinal diseases.

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Can natural language processing help differentiate inflammatory intestinal diseases in China? Models applying random forest and convolutional neural network approaches

10.21203/rs.3.rs-39653/v3 ◽

2020 ◽

Author(s):

Yuanren Tong ◽

Keming Lu ◽

Yingyun Yang ◽

Ji Li ◽

Yucong Lin ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Random Forest ◽

Convolutional Neural Network ◽

Language Processing ◽

Intestinal Tuberculosis ◽

Machine Learning Algorithms ◽

Free Text ◽

Intestinal Diseases ◽

Specificity And Sensitivity

Abstract Background: Differentiating between ulcerative colitis (UC), Crohn’s disease (CD) and intestinal tuberculosis (ITB) using endoscopy is challenging. We aimed to realize automatic differential diagnosis among these diseases through machine learning algorithms. Methods: A total of 6399 consecutive patients (5128 UC, 875 CD and 396 ITB) who had undergone colonoscopy examinations in the Peking Union Medical College Hospital from January 2008 to November 2018 were enrolled. The input was the description of the endoscopic image in the form of free text. Word segmentation and key word filtering were conducted as data preprocessing. Random forest (RF) and convolutional neural network (CNN) approaches were applied to different disease entities. Three two-class classifiers (UC and CD, UC and ITB, and CD and ITB) and a three-class classifier (UC, CD and ITB) were built. Results: The classifiers built in this research performed well, and the CNN had better performance in general. The RF sensitivities/specificities of UC-CD, UC-ITB, and CD-ITB were 0.89/0.84, 0.83/0.82, and 0.72/0.77, respectively, while the values for the CNN of CD-ITB were 0.90/0.77. The precisions/recalls of UC-CD-ITB when employing RF were 0.97/0.97, 0.65/0.53, and 0.68/0.76, respectively, and when employing the CNN were 0.99/0.97, 0.87/0.83, and 0.52/0.81, respectively.Conclusions: Classifiers built by RF and CNN approaches had excellent performance when classifying UC with CD or ITB. For the differentiation of CD and ITB, high specificity and sensitivity were achieved as well. Artificial intelligence through machine learning is very promising in helping unexperienced endoscopists differentiate inflammatory intestinal diseases.

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Can Natural Language Processing Help Differentiate Inflammatory Intestinal Diseases in China? Models Applying Random Forest and Convolutional Neural Network Approaches

10.21203/rs.3.rs-39653/v1 ◽

2020 ◽

Author(s):

Yuanren Tong ◽

Keming Lu ◽

Yingyun Yang ◽

Ji Li ◽

Yucong Lin ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Random Forest ◽

Convolutional Neural Network ◽

Language Processing ◽

Intestinal Tuberculosis ◽

Machine Learning Algorithms ◽

Free Text ◽

Intestinal Diseases ◽

Specificity And Sensitivity

Abstract Background: Differentiating between ulcerative colitis (UC), Crohn’s disease (CD) and intestinal tuberculosis (ITB) using endoscopy is challenging. We aimed to realize automatic differential diagnosis among these diseases through machine learning algorithms. Methods: A total of 6399 consecutive patients (5128 UC, 875 CD and 396 ITB) who had undergone colonoscopy examinations in the Peking Union Medical College Hospital from January 2008 to November 2018 were enrolled. The input was the description of the endoscopic image in the form of free text. Word segmentation and key word filtering were conducted as data preprocessing. Random forest (RF) and convolutional neural network (CNN) approaches were applied to different disease entities. Three two-class classifiers (UC and CD, UC and ITB, and CD and ITB) and a three-class classifier (UC, CD and ITB) were built.Results: The classifiers built in this research performed well, and the CNN had better performance in general. The RF sensitivities/specificities of UC-CD, UC-ITB, and CD-ITB were 0.89/0.84, 0.83/0.82, and 0.72/0.77, respectively, while the values for the CNN of CD-ITB were 0.90/0.77. The precisions/recalls of UC-CD-ITB when employing RF were 0.97/0.97, 0.65/0.53, and 0.68/0.76, respectively, and when employing the CNN were 0.99/0.97, 0.87/0.83, and 0.52/0.81, respectively.Conclusions: Classifiers built by RF and CNN approaches had excellent performance when classifying UC with CD or ITB. For the differentiation of CD and ITB, high specificity and sensitivity were achieved as well. Artificial intelligence through machine learning is very promising in helping unexperienced endoscopists differentiate inflammatory intestinal diseases.

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Can natural language processing help differentiate inflammatory intestinal diseases in China? Models applying random forest and convolutional neural network approaches

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-020-01277-w ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Yuanren Tong ◽

Keming Lu ◽

Yingyun Yang ◽

Ji Li ◽

Yucong Lin ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Random Forest ◽

Convolutional Neural Network ◽

Language Processing ◽

Intestinal Tuberculosis ◽

Machine Learning Algorithms ◽

Free Text ◽

Intestinal Diseases ◽

Specificity And Sensitivity

Abstract Background Differentiating between ulcerative colitis (UC), Crohn’s disease (CD) and intestinal tuberculosis (ITB) using endoscopy is challenging. We aimed to realize automatic differential diagnosis among these diseases through machine learning algorithms. Methods A total of 6399 consecutive patients (5128 UC, 875 CD and 396 ITB) who had undergone colonoscopy examinations in the Peking Union Medical College Hospital from January 2008 to November 2018 were enrolled. The input was the description of the endoscopic image in the form of free text. Word segmentation and key word filtering were conducted as data preprocessing. Random forest (RF) and convolutional neural network (CNN) approaches were applied to different disease entities. Three two-class classifiers (UC and CD, UC and ITB, and CD and ITB) and a three-class classifier (UC, CD and ITB) were built. Results The classifiers built in this research performed well, and the CNN had better performance in general. The RF sensitivities/specificities of UC-CD, UC-ITB, and CD-ITB were 0.89/0.84, 0.83/0.82, and 0.72/0.77, respectively, while the values for the CNN of CD-ITB were 0.90/0.77. The precisions/recalls of UC-CD-ITB when employing RF were 0.97/0.97, 0.65/0.53, and 0.68/0.76, respectively, and when employing the CNN were 0.99/0.97, 0.87/0.83, and 0.52/0.81, respectively. Conclusions Classifiers built by RF and CNN approaches had excellent performance when classifying UC with CD or ITB. For the differentiation of CD and ITB, high specificity and sensitivity were achieved as well. Artificial intelligence through machine learning is very promising in helping unexperienced endoscopists differentiate inflammatory intestinal diseases. Conference The abstract of this article has won the first prize of the Young Investigator Award during the Asian Pacific Digestive Week (APDW) 2019 held in Kolkata, India.

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Can natural language processing help differentiate inflammatory intestinal diseases in China? Models applying random forest and convolutional neural network approaches

10.21203/rs.3.rs-39653/v2 ◽

2020 ◽

Author(s):

Yuanren Tong ◽

Keming Lu ◽

Yingyun Yang ◽

Ji Li ◽

Yucong Lin ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Random Forest ◽

Convolutional Neural Network ◽

Language Processing ◽

Intestinal Tuberculosis ◽

Machine Learning Algorithms ◽

Free Text ◽

Intestinal Diseases ◽

Specificity And Sensitivity

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Reviewing the relationship between machines and radiology: the application of artificial intelligence

Acta Radiologica Open ◽

10.1177/2058460121990296 ◽

2021 ◽

Vol 10 (2) ◽

pp. 205846012199029

Author(s):

Rani Ahmad

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Deep Learning ◽

Health Care Professionals ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Health Science ◽

Computer Algorithms ◽

Learning Models ◽

Specificity And Sensitivity

Background The scope and productivity of artificial intelligence applications in health science and medicine, particularly in medical imaging, are rapidly progressing, with relatively recent developments in big data and deep learning and increasingly powerful computer algorithms. Accordingly, there are a number of opportunities and challenges for the radiological community. Purpose To provide review on the challenges and barriers experienced in diagnostic radiology on the basis of the key clinical applications of machine learning techniques. Material and Methods Studies published in 2010–2019 were selected that report on the efficacy of machine learning models. A single contingency table was selected for each study to report the highest accuracy of radiology professionals and machine learning algorithms, and a meta-analysis of studies was conducted based on contingency tables. Results The specificity for all the deep learning models ranged from 39% to 100%, whereas sensitivity ranged from 85% to 100%. The pooled sensitivity and specificity were 89% and 85% for the deep learning algorithms for detecting abnormalities compared to 75% and 91% for radiology experts, respectively. The pooled specificity and sensitivity for comparison between radiology professionals and deep learning algorithms were 91% and 81% for deep learning models and 85% and 73% for radiology professionals (p < 0.000), respectively. The pooled sensitivity detection was 82% for health-care professionals and 83% for deep learning algorithms (p < 0.005). Conclusion Radiomic information extracted through machine learning programs form images that may not be discernible through visual examination, thus may improve the prognostic and diagnostic value of data sets.

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EP11 Using machine learning to recover unrecorded prehospital data

Emergency Medicine Journal ◽

10.1136/emermed-2021-999.11 ◽

2021 ◽

Vol 38 (9) ◽

pp. A5.3-A6

Author(s):

Thilo Reich ◽

Adam Bancroft ◽

Marcin Budka

Keyword(s):

Machine Learning ◽

Random Forest ◽

Vital Signs ◽

Machine Learning Algorithms ◽

Free Text ◽

Patient Records ◽

Electronic Patient Records ◽

Minority Class ◽

South Central ◽

Tree Classifier

BackgroundThe recording practices, of electronic patient records for ambulance crews, are continuously developing. South Central Ambulance Service (SCAS) adapted the common AVPU-scale (Alert, Voice, Pain, Unresponsive) in 2019 to include an option for ‘New Confusion’. Progressing to this new AVCPU-scale made comparisons with older data impossible. We demonstrate a method to retrospectively classify patients into the alertness levels most influenced by this update.MethodsSCAS provided ~1.6 million Electronic Patient Records, including vital signs, demographics, and presenting complaint free-text, these were split into training, validation, and testing datasets (80%, 10%, 10% respectively), and under sampled to the minority class. These data were used to train and validate predictions of the classes most affected by the modification of the scale (Alert, New Confusion, Voice).A transfer-learning natural language processing (NLP) classifier was used, using a language model described by Smerity et al. (2017) to classify the presenting complaint free-text.A second approach used vital signs, demographics, conveyance, and assessments (30 metrics) for classification. Categorical data were binary encoded and continuous variables were normalised. 20 machine learning algorithms were empirically tested and the best 3 combined into a voting ensemble combining three vital-sign based algorithms (Random Forest, Extra Tree Classifier, Decision Tree) with the NLP classifier using a Random Forest output layer.ResultsThe ensemble method resulted in a weighted F1 of 0.78 for the test set. The sensitivities/specificities for each of the classes are: 84%/ 90% (Alert), 73%/ 89% (Newly Confused) and 68%/ 93% (Voice).ConclusionsThe ensemble combining free text and vital signs resulted in high sensitivity and specificity when reclassifying the alertness levels of prehospital patients. This study demonstrates the capabilities of machine learning classifiers to recover missing data, allowing the comparison of data collected with different recording standards.

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Neuro-Based Prognosticative Analytics for Parkinson Disease using Random Forest Approach

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.j7434.0991120 ◽

2020 ◽

Vol 9 (11) ◽

pp. 11-15

Keyword(s):

Machine Learning ◽

Random Forest ◽

Parkinson Disease ◽

Neurodegenerative Disorder ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Single Test ◽

Specificity And Sensitivity ◽

The World ◽

The Given

Parkinson’s malady is the most current neurodegenerative disorder poignant quite ten million folks across the world. There's no single test at which may be administered for diagnosis Parkinson’s malady. Our aim is to analyze machine learning based mostly techniques for Parkinson malady identification in patients. Our machine learning-based technique is employed to accurately predict the malady by speech and handwriting patterns of humans and by predicting leads to the shape of best accuracy and in addition compare the performance of assorted machine learning algorithms from the given hospital dataset with analysis and classification report and additionally determine the result and prove against with best accuracy and exactness, Recall ,F1 Score specificity and sensitivity.

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Development of innovative hygienic technologies for preservation of the workers’ reproductive health

Russian Journal of Occupational Health and Industrial Ecology ◽

10.31089/1026-9428-2019-59-9-792-793 ◽

2020 ◽

pp. 792-792

Author(s):

M. A. Fesenko ◽

G. V. Golovaneva ◽

A. V. Miskevich

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Reproductive Health ◽

Reproductive System ◽

Preventive Measures ◽

Learning Algorithms ◽

Reproductive Function ◽

Machine Learning Algorithms ◽

New Model

The new model «Prognosis of men’ reproductive function disorders» was developed. The machine learning algorithms (artificial intelligence) was used for this purpose, the model has high prognosis accuracy. The aim of the model applying is prioritize diagnostic and preventive measures to minimize reproductive system diseases complications and preserve workers’ health and efficiency.

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Development of Prediction Models Using Machine Learning Algorithms for Girls with Suspected Central Precocious Puberty: Retrospective Study (Preprint)

10.2196/preprints.11728 ◽

2018 ◽

Author(s):

Liyan Pan ◽

Guangjian Liu ◽

Xiaojian Mao ◽

Huixian Li ◽

Jiexin Zhang ◽

...

Keyword(s):

Machine Learning ◽

Retrospective Study ◽

Random Forest ◽

Precocious Puberty ◽

Prediction Models ◽

Central Precocious Puberty ◽

Machine Learning Algorithms ◽

Stimulation Test ◽

Gnrh Analogue ◽

Prediction Probability

BACKGROUND Central precocious puberty (CPP) in girls seriously affects their physical and mental development in childhood. The method of diagnosis—gonadotropin-releasing hormone (GnRH)–stimulation test or GnRH analogue (GnRHa)–stimulation test—is expensive and makes patients uncomfortable due to the need for repeated blood sampling. OBJECTIVE We aimed to combine multiple CPP–related features and construct machine learning models to predict response to the GnRHa-stimulation test. METHODS In this retrospective study, we analyzed clinical and laboratory data of 1757 girls who underwent a GnRHa test in order to develop XGBoost and random forest classifiers for prediction of response to the GnRHa test. The local interpretable model-agnostic explanations (LIME) algorithm was used with the black-box classifiers to increase their interpretability. We measured sensitivity, specificity, and area under receiver operating characteristic (AUC) of the models. RESULTS Both the XGBoost and random forest models achieved good performance in distinguishing between positive and negative responses, with the AUC ranging from 0.88 to 0.90, sensitivity ranging from 77.91% to 77.94%, and specificity ranging from 84.32% to 87.66%. Basal serum luteinizing hormone, follicle-stimulating hormone, and insulin-like growth factor-I levels were found to be the three most important factors. In the interpretable models of LIME, the abovementioned variables made high contributions to the prediction probability. CONCLUSIONS The prediction models we developed can help diagnose CPP and may be used as a prescreening tool before the GnRHa-stimulation test.

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Exploratory Analysis of Driving Force of Wildfires in Australia: An Application of Machine Learning within Google Earth Engine

Remote Sensing ◽

10.3390/rs13010010 ◽

2020 ◽

Vol 13 (1) ◽

pp. 10

Author(s):

Andrea Sulova ◽

Jamal Jokar Arsanjani

Keyword(s):

Climate Change ◽

Machine Learning ◽

Random Forest ◽

Google Earth ◽

Summer Season ◽

Driving Factors ◽

Machine Learning Algorithms ◽

Classification And Regression Tree ◽

Training Dataset ◽

Google Earth Engine

Recent studies have suggested that due to climate change, the number of wildfires across the globe have been increasing and continue to grow even more. The recent massive wildfires, which hit Australia during the 2019–2020 summer season, raised questions to what extent the risk of wildfires can be linked to various climate, environmental, topographical, and social factors and how to predict fire occurrences to take preventive measures. Hence, the main objective of this study was to develop an automatized and cloud-based workflow for generating a training dataset of fire events at a continental level using freely available remote sensing data with a reasonable computational expense for injecting into machine learning models. As a result, a data-driven model was set up in Google Earth Engine platform, which is publicly accessible and open for further adjustments. The training dataset was applied to different machine learning algorithms, i.e., Random Forest, Naïve Bayes, and Classification and Regression Tree. The findings show that Random Forest outperformed other algorithms and hence it was used further to explore the driving factors using variable importance analysis. The study indicates the probability of fire occurrences across Australia as well as identifies the potential driving factors of Australian wildfires for the 2019–2020 summer season. The methodical approach and achieved results and drawn conclusions can be of great importance to policymakers, environmentalists, and climate change researchers, among others.

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