GP.6 Improving Triaging of EEG Referrals for Rule out Infantile Spasms
                        (ITERIS)

Background: Infantile spasms (IS) is a devastating pediatric seizure disorder for which EEG referrals are prioritized at the Hospital for Sick Children, representing a resource challenge. The goal of this study was to improve the triaging system for these referrals. Methods: Part 1: descriptive analysis was performed retrospectively on EEG referrals. Part 2: prospective questionnaires were used to determine relative risk of various predictive factors. Part 3: electronic referral form was amended to include 5 positive predictive factors. A triage point system was tested by assigning EEGs as high risk (3 days), standard risk (1 week), or low risk (2 weeks). A machine learning model was developed. Results: Most EEG referrals were from community pediatricians with a low yield of IS diagnoses. Using the 5 predictive factors, the proposed triage system accurately diagnosed all IS within 3 days. No abnormal EEGs were missed in the low-risk category. The machine learning model had over 90% predictive accuracy and will be prospectively tested. Conclusions: Improving EEG triaging for IS may be possible to prioritize higher risk patients. Machine Learning techniques can potentially be applied to help with predictions. We hope that our findings will ultimately improve resource utilization and patient care.

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Machine learning techniques to predict daily rainfall amount

Journal Of Big Data ◽

10.1186/s40537-021-00545-4 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Chalachew Muluken Liyew ◽

Haileyesus Amsaya Melese

Keyword(s):

Machine Learning ◽

Pearson Correlation ◽

Daily Rainfall ◽

Learning Model ◽

Machine Learning Techniques ◽

Gradient Boosting ◽

Correlation Technique ◽

Learning Techniques ◽

Machine Learning Model ◽

Extreme Gradient Boosting

AbstractPredicting the amount of daily rainfall improves agricultural productivity and secures food and water supply to keep citizens healthy. To predict rainfall, several types of research have been conducted using data mining and machine learning techniques of different countries’ environmental datasets. An erratic rainfall distribution in the country affects the agriculture on which the economy of the country depends on. Wise use of rainfall water should be planned and practiced in the country to minimize the problem of the drought and flood occurred in the country. The main objective of this study is to identify the relevant atmospheric features that cause rainfall and predict the intensity of daily rainfall using machine learning techniques. The Pearson correlation technique was used to select relevant environmental variables which were used as an input for the machine learning model. The dataset was collected from the local meteorological office at Bahir Dar City, Ethiopia to measure the performance of three machine learning techniques (Multivariate Linear Regression, Random Forest, and Extreme Gradient Boost). Root mean squared error and Mean absolute Error methods were used to measure the performance of the machine learning model. The result of the study revealed that the Extreme Gradient Boosting machine learning algorithm performed better than others.

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Technical note: how to rationally compare the performances of different machine learning models?

10.7287/peerj.preprints.26714 ◽

2018 ◽

Cited By ~ 1

Author(s):

Terazima Maeda

Keyword(s):

Machine Learning ◽

Predictive Accuracy ◽

Learning Model ◽

Technical Note ◽

Learning Models ◽

Specific Data ◽

Machine Learning Model ◽

Specific Prediction ◽

Data Group ◽

Machine Learning Models

Nowadays, there is a large number of machine learning models that could be used for various areas. However, different research targets are usually sensitive to the type of models. For a specific prediction target, the predictive accuracy of a machine learning model is always dependent to the data feature, data size and the intrinsic relationship between inputs and outputs. Therefore, for a specific data group and a fixed prediction mission, how to rationally compare the predictive accuracy of different machine learning model is a big question. In this brief note, we show how should we compare the performances of different machine models by raising some typical examples.

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Supervised machine learning model to predict oncotype DX risk category in patients over age 50

Breast Cancer Research and Treatment ◽

10.1007/s10549-021-06443-w ◽

2021 ◽

Author(s):

Kate R. Pawloski ◽

Mithat Gonen ◽

Hannah Y. Wen ◽

Audree B. Tadros ◽

Donna Thompson ◽

...

Keyword(s):

Machine Learning ◽

Learning Model ◽

Oncotype Dx ◽

Supervised Machine Learning ◽

Risk Category ◽

Machine Learning Model

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Abstract P391: An-adcs 2 : A Novel Machine-Learning Model to Predict the Risk of Stroke-Associated Pneumonia

Stroke ◽

10.1161/str.52.suppl_1.p391 ◽

2021 ◽

Vol 52 (Suppl_1) ◽

Author(s):

Lingling Ding ◽

Zixiao Li ◽

Yongjun Wang

Keyword(s):

Machine Learning ◽

High Risk ◽

Predictive Value ◽

External Validation ◽

Learning Model ◽

Low Risk ◽

Stroke Recurrence ◽

Clinical Prognosis ◽

Machine Learning Model ◽

Validation Set

Objective: We aimed to develop and validate a machine learning-based prediction model that could assess the risk of stroke-associated pneumonia (SAP) for individual patients with acute ischemic stroke (AIS). Methods: A machine-learning model incorporating A 2 DS 2 scores and clinical features (AN-ADCS 2 ) was developed to predict the risk of SAP in patients with AIS. Two independent datasets were used for model derivation and external validation. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were estimated. The further analysis evaluated thresholds from the training set that identified patients as low-risk, intermediate-risk and high-risk, and performance at these thresholds was compared in the external validation set. Results: The AN-ADCS 2 model achieved favorable performance with a high AUC of 0.892 (95% confidence interval [CI] 0.885-0.898) in the test set and similar performance in the external validation set (AUC 0.813 [95% CI 0.812-0.814]). The AN-ADCS 2 threshold identifying low-risk was 0.03, with a NPV of 97.6% (97.2-97.9%) and sensitivity of 93.5% (92.5-94.5%). The AN-ADCS 2 threshold identifying high-risk was 0.65, with a PPV of 94.7% (93.9-95.6%) and specificity of 99.5% (99.5-99.6%). The AN-ADCS 2 model performed better than the A 2 DS 2 score (AUC 0.739, 95%CI [0.720-0.754]). Having a high risk of SAP classified by the AN-ADCS 2 was associated with unfavorable outcomes of mortality and in-hospital stroke recurrence. Conclusions: Using machine learning, the AN-ADCS 2 model provides an individualized risk prediction of SAP, which can be used as an indicator of clinical prognosis for patients with AIS.

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PCirc: random forest-based plant circRNA identification software

BMC Bioinformatics ◽

10.1186/s12859-020-03944-1 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Shuwei Yin ◽

Xiao Tian ◽

Jingjing Zhang ◽

Peisen Sun ◽

Guanglin Li

Keyword(s):

Machine Learning ◽

Random Forest ◽

Circular Rna ◽

Learning Model ◽

Open Reading Frames ◽

Machine Learning Techniques ◽

Rna Seq ◽

Random Forest Algorithm ◽

Machine Learning Model ◽

Identification Software

Abstract Background Circular RNA (circRNA) is a novel type of RNA with a closed-loop structure. Increasing numbers of circRNAs are being identified in plants and animals, and recent studies have shown that circRNAs play an important role in gene regulation. Therefore, identifying circRNAs from increasing amounts of RNA-seq data is very important. However, traditional circRNA recognition methods have limitations. In recent years, emerging machine learning techniques have provided a good approach for the identification of circRNAs in animals. However, using these features to identify plant circRNAs is infeasible because the characteristics of plant circRNA sequences are different from those of animal circRNAs. For example, plants are extremely rich in splicing signals and transposable elements, and their sequence conservation in rice, for example is far less than that in mammals. To solve these problems and better identify circRNAs in plants, it is urgent to develop circRNA recognition software using machine learning based on the characteristics of plant circRNAs. Results In this study, we built a software program named PCirc using a machine learning method to predict plant circRNAs from RNA-seq data. First, we extracted different features, including open reading frames, numbers of k-mers, and splicing junction sequence coding, from rice circRNA and lncRNA data. Second, we trained a machine learning model by the random forest algorithm with tenfold cross-validation in the training set. Third, we evaluated our classification according to accuracy, precision, and F1 score, and all scores on the model test data were above 0.99. Fourth, we tested our model by other plant tests, and obtained good results, with accuracy scores above 0.8. Finally, we packaged the machine learning model built and the programming script used into a locally run circular RNA prediction software, Pcirc (https://github.com/Lilab-SNNU/Pcirc). Conclusion Based on rice circRNA and lncRNA data, a machine learning model for plant circRNA recognition was constructed in this study using random forest algorithm, and the model can also be applied to plant circRNA recognition such as Arabidopsis thaliana and maize. At the same time, after the completion of model construction, the machine learning model constructed and the programming scripts used in this study are packaged into a localized circRNA prediction software Pcirc, which is convenient for plant circRNA researchers to use.

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CT imaging-based machine learning model: a potential modality for predicting low-risk and high-risk groups of thymoma: “Impact of surgical modality choice”

World Journal of Surgical Oncology ◽

10.1186/s12957-021-02259-6 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Ayten Kayi Cangir ◽

Kaan Orhan ◽

Yusuf Kahya ◽

Hilal Özakıncı ◽

Betül Bahar Kazak ◽

...

Keyword(s):

Machine Learning ◽

High Risk ◽

Risk Groups ◽

Learning Model ◽

Low Risk ◽

Pathology Report ◽

Histopathological Diagnosis ◽

Gray Level ◽

Long Run ◽

Machine Learning Model

Abstract Introduction Radiomics methods are used to analyze various medical images, including computed tomography (CT), magnetic resonance, and positron emission tomography to provide information regarding the diagnosis, patient outcome, tumor phenotype, and the gene-protein signatures of various diseases. In low-risk group, complete surgical resection is typically sufficient, whereas in high-risk thymoma, adjuvant therapy is usually required. Therefore, it is important to distinguish between both. This study evaluated the CT radiomics features of thymomas to discriminate between low- and high-risk thymoma groups. Materials and methods In total, 83 patients with thymoma were included in this study between 2004 and 2019. We used the Radcloud platform (Huiying Medical Technology Co., Ltd.) to manage the imaging and clinical data and perform the radiomics statistical analysis. The training and validation datasets were separated by a random method with a ratio of 2:8 and 502 random seeds. The histopathological diagnosis was noted from the pathology report. Results Four machine-learning radiomics features were identified to differentiate a low-risk thymoma group from a high-risk thymoma group. The radiomics feature names were Energy, Zone Entropy, Long Run Low Gray Level Emphasis, and Large Dependence Low Gray Level Emphasis. Conclusions The results demonstrated that a machine-learning model and a multilayer perceptron classifier analysis can be used on CT images to predict low- and high-risk thymomas. This combination could be a useful preoperative method to determine the surgical approach for thymoma.

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CT Imaging-based Machine Learning Model: A Potential Modality for Predicting Low-Risk and High-Risk Groups of Thymoma “Impact of surgical modality choice”

10.21203/rs.3.rs-139108/v1 ◽

2021 ◽

Author(s):

Ayten KAYICANGIR ◽

Kaan ORHAN ◽

Yusuf KAHYA ◽

Hilal ÖZAKINCI ◽

Betül Bahar KAZAK ◽

...

Keyword(s):

Machine Learning ◽

High Risk ◽

Risk Groups ◽

Learning Model ◽

Low Risk ◽

Pathology Report ◽

Histopathological Diagnosis ◽

Gray Level ◽

Long Run ◽

Machine Learning Model

Abstract IntroductionRadiomics has become a hot issue in the medical imaging field, particularly in cancer imaging. Radiomics methods are used to analyze various medical images, including computed tomography (CT), magnetic resonance, and positron emission tomography to provide information regarding the diagnosis, patient outcome, tumor phenotype, and the gene-protein signatures of various diseases.This study evaluated the CT radiomics features of thymomas to discriminate between low- and high-risk thymoma groups.Materials and MethodsIn total, 83 patients with thymoma were included in this study between 2004 and 2019. We used the Radcloud platform (Huiying Medical Technology Co., Ltd.) to manage the imaging and clinical data and perform the radiomics statistical analysis. The training and validation datasets were separated by a random method with a ratio of 2:8 and 502 random seeds. The histopathological diagnosis was noted from the pathology report.ResultsFour machine learning radiomics features were identified to differentiate a low-risk thymoma group from a high-risk thymoma group. The radiomics feature names were Energy, Zone Entropy, Long Run Low Gray Level Emphasis, and Large Dependence Low Gray Level Emphasis.ConclusionsThe results demonstrated that a machine-learning model and a multilayer perceptron classifier analysis can be used on CT images to predict low- and high-risk thymomas. This combination could be a useful preoperative method to determine the surgical approach for thymoma.

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Simulating the Ghost: Quantum Dynamics of the Solvated Electron

10.26434/chemrxiv.12551234.v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Jinggang Lan ◽

Venkat Kapil ◽

Piero Gasparotto ◽

Michele Ceriotti ◽

Marcella Iannuzzi ◽

...

Keyword(s):

Machine Learning ◽

Quantum Dynamics ◽

Predictive Accuracy ◽

Accurate Determination ◽

Learning Model ◽

Structure Theory ◽

Solvated Electron ◽

Surrounding Water ◽

Machine Learning Model ◽

Cavity Structure

The nature of bulk hydrated electron has been a challenge for both experiment and theory due to its short life time and high reactivity, and the need for a high-level of electronic structure theory to achieve predictive accuracy. The lack of a classical atomistic structural formula makes it exceedingly difficult to model the solvated electron using conventional empirical force fields, which describe the system in terms of interactions between point particles associated with atomic nuclei. Here we overcome this problem using a machine-learning model, that is sufficiently flexible to describe the effect of the excess electron on the structure of the surrounding water, without including the electron in the model explicitly. The resulting potential is not only able to reproduce the stable cavity structure, but also recovers the correct localization dynamics that follows the injection of an electron in neat water. The machine learning model achieves the accuracy of the state-of-the-art correlated wave function method it is trained on. It is sufficiently inexpensive to afford a full quantum statistical and dynamical description, and allows us to achieve a highly accurate determination of the structure, diffusion mechanisms and vibrational spectroscopy of the solvated electron

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Statistical machine learning model to predict Oncotype DX risk category in women over age 50.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.524 ◽

2020 ◽

Vol 38 (15_suppl) ◽

pp. 524-524

Author(s):

Kate R. Pawloski ◽

Mithat Gonen ◽

Hannah Yong Wen ◽

Audree B. Tadros ◽

Kelly Abbate ◽

...

Keyword(s):

Machine Learning ◽

High Risk ◽

Tumor Size ◽

Low Risk ◽

Risk Category ◽

Statistical Machine Learning ◽

Node Negative ◽

Machine Learning Model ◽

Er Positive ◽

Validation Set

524 Background: The 21-gene Oncotype DX Breast Recurrence Score multigene assay (RS) identifies women with ER positive, HER negative, axillary node-negative breast cancer (BC) for whom chemotherapy provides no invasive disease-free survival benefit compared to endocrine therapy alone. International adoption of RS testing is limited by cost and resource availability. We created a supervised statistical machine learning model using standard clinicopathologic data to predict RS risk category in women > 50 years old. Methods: From 2012 to 2018, women with ER positive, HER2 negative, pathologically node-negative BC of all ages were retrospectively identified from a prospective institutional database. Standard clinicopathologic data and RS were collected. Per institutional protocol, RS are ordered for all early-stage, ER positive tumors > 5 mm. Data were randomly split into training (n=3755) and validation sets (n=1609). A random forest model with 500 trees was developed on the training set, then evaluated on the validation set. Model predictors included age, tumor size, histologic subtype, hormone receptor status, lymphovascular invasion, and overall grade. The model was used to predict RS category (low risk: RS ≤ 25, high risk: RS > 25) in women > 50 years old. Results: 5364 unique tumors in 5189 women were identified. 3731 (70%) of tumors were identified in women > 50 years; median age was 63 years (IQR 57-69). In women > 50, median tumor size was 12 mm (IQR 9-17). Most tumors were invasive ductal (79%), low or intermediate grade (79%), and LVI was absent in 82% of tumors. Median ER staining by IHC was 95%; 28% of tumors had negative or weakly positive PR staining (1-20%). The model correctly classified 96.8% of patients as low risk (95% CI: 95.7-97.7). Negative predictive value for identifying low risk category was also high (92.3%, 90.7-93.6). Sensitivity for identifying high-risk women was 44.7% (37.4-52.1) and positive predictive value was 67.2% (58.2-75.3). A classification table on the validation set includes tumors with complete data available, including predictors and RS. Conclusions: Our model was highly specific (96.8%) for identifying women > 50 with RS ≤ 25 who do not benefit from adjuvant chemotherapy. This model may be utilized in lieu of RS testing if cost and availability are prohibitive. True RS > 25 was not as well predicted. The model will be refined following pathologic review of discordant cases to reduce false negatives. [Table: see text]

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Simulating the ghost: quantum dynamics of the solvated electron

Nature Communications ◽

10.1038/s41467-021-20914-0 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Jinggang Lan ◽

Venkat Kapil ◽

Piero Gasparotto ◽

Michele Ceriotti ◽

Marcella Iannuzzi ◽

...

Keyword(s):

Machine Learning ◽

Quantum Dynamics ◽

Predictive Accuracy ◽

Accurate Determination ◽

Learning Model ◽

Structure Theory ◽

Solvated Electron ◽

Surrounding Water ◽

Machine Learning Model ◽

Cavity Structure

AbstractThe nature of the bulk hydrated electron has been a challenge for both experiment and theory due to its short lifetime and high reactivity, and the need for a high-level of electronic structure theory to achieve predictive accuracy. The lack of a classical atomistic structural formula makes it exceedingly difficult to model the solvated electron using conventional empirical force fields, which describe the system in terms of interactions between point particles associated with atomic nuclei. Here we overcome this problem using a machine-learning model, that is sufficiently flexible to describe the effect of the excess electron on the structure of the surrounding water, without including the electron in the model explicitly. The resulting potential is not only able to reproduce the stable cavity structure but also recovers the correct localization dynamics that follow the injection of an electron in neat water. The machine learning model achieves the accuracy of the state-of-the-art correlated wave function method it is trained on. It is sufficiently inexpensive to afford a full quantum statistical and dynamical description and allows us to achieve accurate determination of the structure, diffusion mechanisms, and vibrational spectroscopy of the solvated electron.

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