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 A Neural Network Based Algorithm for Dynamically Adjusting Activity Targets to Sustain Exercise Engagement Among People Using Activity Trackers

2019 ◽  
Author(s):  
Ramin Mohammadi ◽  
Amanda Jayne Centi ◽  
Mursal Atif ◽  
Stephen Agboola ◽  
Kamal Jethwani ◽  
...  
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Absolute Error ◽  
Learning Model ◽  
Step Count ◽  
Entropy Measure ◽  
Daily Step ◽  
Activity Trackers ◽  
Machine Learning Model ◽  
Daily Step Count

AbstractIt is well established that lack of physical activity is detrimental to overall health of an individual. Modern day activity trackers enable individuals to monitor their daily activity to meet and maintain targets and to promote activity encouraging behavior. However, the benefits of activity trackers are attenuated over time due to waning adherence. One of the key methods to improve adherence to goals is to motivate individuals to improve on their historic performance metrics. In this work we developed a machine learning model to dynamically adjust the activity target for the forthcoming week that can be realistically achieved by the activity-tracker users. This model prescribes activity target for the forthcoming week. We considered individual user-specific personal, social, and environmental factors, daily step count through the current week (7 days). In addition, we computed an entropy measure that characterizes the pattern of daily step count for the current week. Data for training the machine learning model was collected from 30 participants over a duration of 9 weeks. The model predicted target daily count with mean absolute error of 1545 steps. The proposed work can be used to set personalized goals in accordance with the individual’s level of activity and thereby improving adherence to fitness tracker.

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 Machine Learning Outperforms Logistic Regression Analysis to Predict Next-Season NHL Player Injury: An Analysis of 2322 Players From 2007 to 2017

2020 ◽  
Vol 8 (9) ◽  
pp. 232596712095340
Author(s):  
Bryan C. Luu ◽  
Audrey L. Wright ◽  
Heather S. Haeberle ◽  
Jaret M. Karnuta ◽  
Mark S. Schickendantz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Injury Risk ◽  
Operating Characteristic ◽  
Performance Metrics ◽  
Historical Data ◽  
Characteristic Curve ◽  
National Hockey League ◽  
K Nearest Neighbors ◽  
Machine Learning Model

Background: The opportunity to quantitatively predict next-season injury risk in the National Hockey League (NHL) has become a reality with the advent of advanced computational processors and machine learning (ML) architecture. Unlike static regression analyses that provide a momentary prediction, ML algorithms are dynamic in that they are readily capable of imbibing historical data to build a framework that improves with additive data. Purpose: To (1) characterize the epidemiology of publicly reported NHL injuries from 2007 to 2017, (2) determine the validity of a machine learning model in predicting next-season injury risk for both goalies and position players, and (3) compare the performance of modern ML algorithms versus logistic regression (LR) analyses. Study Design: Descriptive epidemiology study. Methods: Professional NHL player data were compiled for the years 2007 to 2017 from 2 publicly reported databases in the absence of an official NHL-approved database. Attributes acquired from each NHL player from each professional year included age, 85 performance metrics, and injury history. A total of 5 ML algorithms were created for both position player and goalie data: random forest, K Nearest Neighbors, Naïve Bayes, XGBoost, and Top 3 Ensemble. LR was also performed for both position player and goalie data. Area under the receiver operating characteristic curve (AUC) primarily determined validation. Results: Player data were generated from 2109 position players and 213 goalies. For models predicting next-season injury risk for position players, XGBoost performed the best with an AUC of 0.948, compared with an AUC of 0.937 for LR ( P < .0001). For models predicting next-season injury risk for goalies, XGBoost had the highest AUC with 0.956, compared with an AUC of 0.947 for LR ( P < .0001). Conclusion: Advanced ML models such as XGBoost outperformed LR and demonstrated good to excellent capability of predicting whether a publicly reportable injury is likely to occur the next season.

scholarly journals Machine Learning Predicts Drug Metabolism and Bioaccumulation by Intestinal Microbiota

Pharmaceutics ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2001
Author(s):  
Laura E. McCoubrey ◽  
Stavriani Thomaidou ◽  
Moe Elbadawi ◽  
Simon Gaisford ◽  
Mine Orlu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gut Microbiota ◽  
Performance Metrics ◽  
Literature Mining ◽  
Learning Models ◽  
Dosage Regimens ◽  
Machine Learning Model ◽  
True Number ◽  
Unique Composition ◽  
Industry Professionals

Over 150 drugs are currently recognised as being susceptible to metabolism or bioaccumulation (together described as depletion) by gastrointestinal microorganisms; however, the true number is likely higher. Microbial drug depletion is often variable between and within individuals, depending on their unique composition of gut microbiota. Such variability can lead to significant differences in pharmacokinetics, which may be associated with dosing difficulties and lack of medication response. In this study, literature mining and unsupervised learning were used to curate a dataset of 455 drug–microbiota interactions. From this, 11 supervised learning models were developed that could predict drugs’ susceptibility to depletion by gut microbiota. The best model, a tuned extremely randomised trees classifier, achieved performance metrics of AUROC: 75.1% ± 6.8; weighted recall: 79.2% ± 3.9; balanced accuracy: 69.0% ± 4.6; and weighted precision: 80.2% ± 3.7 when validated on 91 drugs. This machine learning model is the first of its kind and provides a rapid, reliable, and resource-friendly tool for researchers and industry professionals to screen drugs for susceptibility to depletion by gut microbiota. The recognition of drug–microbiome interactions can support successful drug development and promote better formulations and dosage regimens for patients.

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 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 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 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

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