scholarly journals Predicting Dynamic Clinical Outcomes of the Chemotherapy for Canine Lymphoma Patients Using a Machine Learning Model

2021 ◽  
Vol 8 (12) ◽  
pp. 301
Author(s):  
Jamin Koo ◽  
Kyucheol Choi ◽  
Peter Lee ◽  
Amanda Polley ◽  
Raghavendra Sumanth Pudupakam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Accuracy ◽  
Ex Vivo ◽  
Progression Free Survival ◽  
Superior Performance ◽  
Canine Lymphoma ◽  
Hazards Model ◽  
Machine Learning Model ◽  
Stratification Method ◽  
Unequal Rates

First-line treatments of cancer do not always work, and even when they do, they cure the disease at unequal rates mostly owing to biological and clinical heterogeneity across patients. Accurate prediction of clinical outcome and survival following the treatment can support and expedite the process of comparing alternative treatments. We describe the methodology to dynamically determine remission probabilities for individual patients, as well as their prospects of progression-free survival (PFS). The proposed methodology utilizes the ex vivo drug sensitivity of cancer cells, their immunophenotyping results, and patient information, such as age and breed, in training machine learning (ML) models, as well as the Cox hazards model to predict the probability of clinical remission (CR) or relapse across time for a given patient. We applied the methodology using the three types of data obtained from 242 canine lymphoma patients treated by (L)-CHOP chemotherapy. The results demonstrate substantial enhancement in the predictive accuracy of the ML models by utilizing features from all the three types of data. They also highlight superior performance and utility in predicting survival compared to the conventional stratification method. We believe that the proposed methodology can contribute to improving and personalizing the care of cancer patients.

scholarly journals Blue-collared Workers’ Travel Behavior Modeling using “exPlainable” Machine Learning Model: The Case of Qatar

2021 ◽  
Author(s):  
Aya Alkhereibi ◽  
Ali AbuZaid ◽  
Tadesse Wakjira
Keyword(s):  
Machine Learning ◽  
Travel Behavior ◽  
Total Population ◽  
Performance Metrics ◽  
Predictive Accuracy ◽  
Mode Choice ◽  
Behavior Modeling ◽  
Significant Feature ◽  
Machine Learning Model ◽  
Occupation Level

This paper presents a novel study on the examination of explainable machine learning (ML) technique to predict the mode choice for communities with a majority of blue-collared workers. A total of 4875 trip records for 1050 blue-collared workers have been used to predict their travel mode choices based on 11 trips and socio-economic attributes. The data used in this paper are obtained from the Ministry of Transportation and Communication (MoTC), which targeted blue-collared workers as they represent 89% of the total population in the State of Qatar. A total of four ML models are evaluated to propose the best predictive model. The four models were examined using different performance metrics. The models’ prediction results showed that the random forest (RF) model had the highest accuracy with a predictive accuracy of 0.97. Moreover, SHapley Additive exPlanation (SHAP) approach is used to investigate the significance of the input features and explain the output of the RF model. The results of SHAP analysis revealed that occupation level is the most significant feature that influences the mode choice followed by occupation section, arrival time, and arrival municipality.

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

2021 ◽  
Vol 48 (s3) ◽  
pp. S13-S13
Author(s):  
D Djordjevic ◽  
J Tracey ◽  
M Alqahtani ◽  
J Boyd ◽  
C Go
Keyword(s):  
Machine Learning ◽  
Predictive Factors ◽  
Predictive Accuracy ◽  
Learning Model ◽  
Infantile Spasms ◽  
Low Risk ◽  
Machine Learning Techniques ◽  
Risk Category ◽  
Point System ◽  
Machine Learning Model

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.

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

2018 ◽  
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.

scholarly journals Predicting COVID-19 mortality risk in Toronto, Canada: a comparison of tree-based and regression-based machine learning methods

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Cindy Feng ◽  
George Kephart ◽  
Elizabeth Juarez-Colunga
Keyword(s):  
Machine Learning ◽  
Mortality Risk ◽  
Predictive Accuracy ◽  
Classification Tree ◽  
Superior Performance ◽  
Gradient Boosting ◽  
Learning Methods ◽  
Linear Discriminant ◽  
Machine Learning Methods ◽  
Extreme Gradient Boosting

Abstract Background Coronavirus disease (COVID-19) presents an unprecedented threat to global health worldwide. Accurately predicting the mortality risk among the infected individuals is crucial for prioritizing medical care and mitigating the healthcare system’s burden. The present study aimed to assess the predictive accuracy of machine learning methods to predict the COVID-19 mortality risk. Methods We compared the performance of classification tree, random forest (RF), extreme gradient boosting (XGBoost), logistic regression, generalized additive model (GAM) and linear discriminant analysis (LDA) to predict the mortality risk among 49,216 COVID-19 positive cases in Toronto, Canada, reported from March 1 to December 10, 2020. We used repeated split-sample validation and k-steps-ahead forecasting validation. Predictive models were estimated using training samples, and predictive accuracy of the methods for the testing samples was assessed using the area under the receiver operating characteristic curve, Brier’s score, calibration intercept and calibration slope. Results We found XGBoost is highly discriminative, with an AUC of 0.9669 and has superior performance over conventional tree-based methods, i.e., classification tree or RF methods for predicting COVID-19 mortality risk. Regression-based methods (logistic, GAM and LASSO) had comparable performance to the XGBoost with slightly lower AUCs and higher Brier’s scores. Conclusions XGBoost offers superior performance over conventional tree-based methods and minor improvement over regression-based methods for predicting COVID-19 mortality risk in the study population.

scholarly journals A machine learning approach for Colles’ fracture treatment diagnosis

2020 ◽  
Author(s):  
Kwun Ho Ngan ◽  
Artur d’Avila Garcez ◽  
Karen M. Knapp ◽  
Andy Appelboam ◽  
Constantino Carlos Reyes-Aldasoro
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Superior Performance ◽  
Fracture Treatment ◽  
Post Intervention ◽  
X Ray ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Colles Fracture ◽  
Manipulation Under Anaesthesia

AbstractWrist fractures (e.g. Colles’ fracture) are the most common injuries in the upper extremity treated in Emergency Departments. Most patients are treated with a procedure called Manipulation under Anaesthesia. Surgical treatment may still be needed in complex fractures or if the wrist stability is not restored. This can lead to inefficiency in constrained medical resources and patients’ inconvenience. Previous geometric measurements in X-ray images were found to provide statistical differences between healthy controls and fractured cases as well as pre- and post-intervention images. The most discriminating measurements were associated with the texture analysis of the radial bone.This work presents further analysis of these measurements and applying them as features to identify the best machine learning model for Colles’ fracture treatment diagnosis. Random forest was evaluated to be the best model based on validation accuracy. The non-linearity of the measurement features has attributed to the superior performance of an ensembled tree-based model. It is also interesting that the most important features (i.e. texture and swelling) required in the optimised random forest model are consistent with previous findings.

scholarly journals The trauma severity model: An ensemble machine learning approach to risk prediction

2017 ◽  
Author(s):  
Michael T. Gorczyca ◽  
Nicole C. Toscano ◽  
Julius D. Cheng
Keyword(s):  
Machine Learning ◽  
Risk Prediction ◽  
Generalized Linear Models ◽  
Linear Models ◽  
Learning Model ◽  
Superior Performance ◽  
List Type ◽  
Trauma Severity ◽  
Ensemble Machine Learning ◽  
Machine Learning Model

AbstractStatistical theory indicates that a flexible model can attain a lower generalization error than an inflexible model, provided that the setting is appropriate. This is highly relevant in the context of mortality risk prediction for trauma patients, as researchers have focused exclusively on the use of generalized linear models for risk prediction, and generalized linear models may be too inflexible to capture the potentially complex relationships in trauma data. Due to this, we propose a machine learning model, the Trauma Severity Model (TSM), for risk prediction. In order to validate TSM’s performance, this study compares TSM to three established risk prediction models: the Bayesian Logistic Injury Severity Score, the Harborview Assessment for Risk of Mortality, and the Trauma Mortality Prediction Model. Our results indicate that TSM has superior performance, and thereby provides improved risk prediction.Highlights:We propose an ensemble machine learning model for trauma risk prediction.A hyper-parameter search scheme is proposed for model development.We compare our model to established models for trauma risk prediction.Our model improves over established models for each performance metric considered.

scholarly journals Simulating the Ghost: Quantum Dynamics of the Solvated Electron

2020 ◽  
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

scholarly journals Simulating the ghost: quantum dynamics of the solvated electron

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.

scholarly journals Identifying main and interaction effects of risk factors to predict intensive care admission in patients hospitalized with COVID-19: a retrospective cohort study in Hong Kong

2020 ◽  
Author(s):  
Jiandong Zhou ◽  
Gary Tse ◽  
Sharen Lee ◽  
Tong Liu ◽  
William KK Wu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Hong Kong ◽  
Intensive Care ◽  
Cell Count ◽  
Learning Model ◽  
Older Age ◽  
Superior Performance ◽  
Icu Admission ◽  
Machine Learning Model

ABSTRACTBackgroundThe coronavirus disease 2019 (COVID-19) has become a pandemic, placing significant burdens on the healthcare systems. In this study, we tested the hypothesis that a machine learning approach incorporating hidden nonlinear interactions can improve prediction for Intensive care unit (ICU) admission.MethodsConsecutive patients admitted to public hospitals between 1st January and 24th May 2020 in Hong Kong with COVID-19 diagnosed by RT-PCR were included. The primary endpoint was ICU admission.ResultsThis study included 1043 patients (median age 35 (IQR: 32-37; 54% male). Nineteen patients were admitted to ICU (median hospital length of stay (LOS): 30 days, median ICU LOS: 16 days). ICU patients were more likely to be prescribed angiotensin converting enzyme inhibitors/angiotensin receptor blockers, anti-retroviral drugs lopinavir/ritonavir and remdesivir, ribavirin, steroids, interferon-beta and hydroxychloroquine. Significant predictors of ICU admission were older age, male sex, prior coronary artery disease, respiratory diseases, diabetes, hypertension and chronic kidney disease, and activated partial thromboplastin time, red cell count, white cell count, albumin and serum sodium. A tree-based machine learning model identified most informative characteristics and hidden interactions that can predict ICU admission. These were: low red cells with 1) male, 2) older age, 3) low albumin, 4) low sodium or 5) prolonged APTT. A five-fold cross validation confirms superior performance of this model over baseline models including XGBoost, LightGBM, random forests, and multivariate logistic regression.ConclusionsA machine learning model including baseline risk factors and their hidden interactions can accurately predict ICU admission in COVID-19.

Sign in / Sign up

Export Citation Format

Share Document