The Experts below are selected from a list of 291 Experts worldwide ranked by ideXlab platform
Ritankar Das - One of the best experts on this subject based on the ideXlab platform.
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Supervised Machine Learning for the Early Prediction of Acute Respiratory Distress Syndrome (ARDS)
2020Co-Authors: Emily Pellegrini, Abigail Green-saxena, Charlotte Summers, Jana Hoffman, Jacob Calvert, Ritankar DasAbstract:Purpose: Acute respiratory distress syndrome (ARDS) is a serious respiratory condition with high mortality and associated morbidity. The objective of this study is to develop and evaluate a novel application of gradient Boosted Tree models trained on patient health record data for the early prediction of ARDS. Materials and Methods: 9919 patient encounters were retrospectively analyzed from the Medical Information Mart for Intensive Care III (MIMIC-III) data base. XGBoost gradient Boosted Tree models for early ARDS prediction were created using routinely collected clinical variables and numerical representations of radiology reports as inputs. XGBoost models were iteratively trained and validated using 10-fold cross validation. Results: On a hold-out test set, algorithm classifiers attained area under the receiver operating characteristic curve (AUROC) values of 0.905, 0.827, 0.810, and 0.790 when tested for the prediction of ARDS at 0-, 12-, 24-, and 48-hour windows prior to onset, respectively. Conclusion: Supervised machine learning predictions may help predict patients with ARDS up to 48 hours prior to onset.
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Supervised machine learning for the early prediction of acute respiratory distress syndrome (ARDS).
Journal of Critical Care, 1Co-Authors: Emily Pellegrini, Abigail Green-saxena, Charlotte Summers, Jana Hoffman, Jacob Calvert, Ritankar DasAbstract:Abstract Purpose Acute respiratory distress syndrome (ARDS) is a serious respiratory condition with high mortality and associated morbidity. The objective of this study is to develop and evaluate a novel application of gradient Boosted Tree models trained on patient health record data for the early prediction of ARDS. Materials and methods 9919 patient encounters were retrospectively analyzed from the Medical Information Mart for Intensive Care III (MIMIC-III) data base. XGBoost gradient Boosted Tree models for early ARDS prediction were created using routinely collected clinical variables and numerical representations of radiology reports as inputs. XGBoost models were iteratively trained and validated using 10-fold cross validation. Results On a hold-out test set, algorithm classifiers attained area under the receiver operating characteristic curve (AUROC) values of 0.905 when tested for the detection of ARDS at onset and 0.827, 0.810, and 0.790 for the prediction of ARDS at 12-, 24-, and 48-h windows prior to onset, respectively. Conclusion Supervised machine learning predictions may help predict patients with ARDS up to 48 h prior to onset.
Gilad Ravid - One of the best experts on this subject based on the ideXlab platform.
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Applying machine learning on sensor data for irrigation recommendations: revealing the agronomist’s tacit knowledge
Precision Agriculture, 2018Co-Authors: Anat Goldstein, Amit Meitin, Shiran Bohadana, Oscar Lutenberg, Lior Fink, Gilad RavidAbstract:Jojoba Israel is a world-leading producer of Jojoba products, whose orchards are covered with sensors that collect soil moisture data for monitoring plant needs at real-time. Based on these data, the company’s agronomist defines a weekly irrigation plan. In addition, data on weather, irrigation, and yield are recorded from other sources (e.g. meteorological station and irrigation-plan records). However, so far, there has been no attempt to use the entire set of collected data to reveal insights and interesting relationships between different variables, such as soil, weather, irrigation characteristics, and resulting yield. By integrating and utilizing data from different sources, our research aims at using the collected data not only for monitoring and controlling the crop, but also for predicting irrigation recommendations. In particular, a dataset was constructed by integrating data collected over almost two years from 22 soil-sensors spread in four major plots (which are divided into 28 subplots and eight irrigation groups), from a meteorological station, and from actual irrigation records. Different regression and classification algorithms were applied on this dataset to develop models that were able to predict the weekly irrigation plan as recommended by the agronomist. The models were developed using eight different subsets of variables to determine which variables consistently contributed to prediction accuracy. By comparing the resulting models, it was shown that the best regression model was Gradient Boosted Regression Trees, with 93% accuracy, and the best classification model was the Boosted Tree Classifier, with 95% accuracy (on the test-set). Data that were not contributing to the model prediction success rate were identified as well. The resulting model can significantly facilitate the agronomist’s irrigation planning process. In addition, the potential of applying machine learning on the company data for yield and disease prediction is discussed.
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applying machine learning on sensor data for irrigation recommendations revealing the agronomist s tacit knowledge
Precision Agriculture, 2018Co-Authors: Anat Goldstein, Amit Meitin, Shiran Bohadana, Oscar Lutenberg, Lior Fink, Gilad RavidAbstract:Jojoba Israel is a world-leading producer of Jojoba products, whose orchards are covered with sensors that collect soil moisture data for monitoring plant needs at real-time. Based on these data, the company’s agronomist defines a weekly irrigation plan. In addition, data on weather, irrigation, and yield are recorded from other sources (e.g. meteorological station and irrigation-plan records). However, so far, there has been no attempt to use the entire set of collected data to reveal insights and interesting relationships between different variables, such as soil, weather, irrigation characteristics, and resulting yield. By integrating and utilizing data from different sources, our research aims at using the collected data not only for monitoring and controlling the crop, but also for predicting irrigation recommendations. In particular, a dataset was constructed by integrating data collected over almost two years from 22 soil-sensors spread in four major plots (which are divided into 28 subplots and eight irrigation groups), from a meteorological station, and from actual irrigation records. Different regression and classification algorithms were applied on this dataset to develop models that were able to predict the weekly irrigation plan as recommended by the agronomist. The models were developed using eight different subsets of variables to determine which variables consistently contributed to prediction accuracy. By comparing the resulting models, it was shown that the best regression model was Gradient Boosted Regression Trees, with 93% accuracy, and the best classification model was the Boosted Tree Classifier, with 95% accuracy (on the test-set). Data that were not contributing to the model prediction success rate were identified as well. The resulting model can significantly facilitate the agronomist’s irrigation planning process. In addition, the potential of applying machine learning on the company data for yield and disease prediction is discussed.
Emily Pellegrini - One of the best experts on this subject based on the ideXlab platform.
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Supervised Machine Learning for the Early Prediction of Acute Respiratory Distress Syndrome (ARDS)
2020Co-Authors: Emily Pellegrini, Abigail Green-saxena, Charlotte Summers, Jana Hoffman, Jacob Calvert, Ritankar DasAbstract:Purpose: Acute respiratory distress syndrome (ARDS) is a serious respiratory condition with high mortality and associated morbidity. The objective of this study is to develop and evaluate a novel application of gradient Boosted Tree models trained on patient health record data for the early prediction of ARDS. Materials and Methods: 9919 patient encounters were retrospectively analyzed from the Medical Information Mart for Intensive Care III (MIMIC-III) data base. XGBoost gradient Boosted Tree models for early ARDS prediction were created using routinely collected clinical variables and numerical representations of radiology reports as inputs. XGBoost models were iteratively trained and validated using 10-fold cross validation. Results: On a hold-out test set, algorithm classifiers attained area under the receiver operating characteristic curve (AUROC) values of 0.905, 0.827, 0.810, and 0.790 when tested for the prediction of ARDS at 0-, 12-, 24-, and 48-hour windows prior to onset, respectively. Conclusion: Supervised machine learning predictions may help predict patients with ARDS up to 48 hours prior to onset.
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Supervised machine learning for the early prediction of acute respiratory distress syndrome (ARDS).
Journal of Critical Care, 1Co-Authors: Emily Pellegrini, Abigail Green-saxena, Charlotte Summers, Jana Hoffman, Jacob Calvert, Ritankar DasAbstract:Abstract Purpose Acute respiratory distress syndrome (ARDS) is a serious respiratory condition with high mortality and associated morbidity. The objective of this study is to develop and evaluate a novel application of gradient Boosted Tree models trained on patient health record data for the early prediction of ARDS. Materials and methods 9919 patient encounters were retrospectively analyzed from the Medical Information Mart for Intensive Care III (MIMIC-III) data base. XGBoost gradient Boosted Tree models for early ARDS prediction were created using routinely collected clinical variables and numerical representations of radiology reports as inputs. XGBoost models were iteratively trained and validated using 10-fold cross validation. Results On a hold-out test set, algorithm classifiers attained area under the receiver operating characteristic curve (AUROC) values of 0.905 when tested for the detection of ARDS at onset and 0.827, 0.810, and 0.790 for the prediction of ARDS at 12-, 24-, and 48-h windows prior to onset, respectively. Conclusion Supervised machine learning predictions may help predict patients with ARDS up to 48 h prior to onset.
Raymond S. Dean - One of the best experts on this subject based on the ideXlab platform.
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Identification of individuals with ADHD using the dean–woodcock sensory motor battery and a Boosted Tree algorithm
Behavior Research Methods, 2015Co-Authors: Holmes W. Finch, Andrew Davis, Raymond S. DeanAbstract:The accurate and early identification of individuals with pervasive conditions such as attention deficit hyperactivity disorder (ADHD) is crucial to ensuring that they receive appropriate and timely assistance and treatment. Heretofore, identification of such individuals has proven somewhat difficult, typically involving clinical decision making based on descriptions and observations of behavior, in conjunction with the administration of cognitive assessments. The present study reports on the use of a sensory motor battery in conjunction with a recursive partitioning computer algorithm, Boosted Trees, to develop a prediction heuristic for identifying individuals with ADHD. Results of the study demonstrate that this method is able to do so with accuracy rates of over 95 %, much higher than the popular logistic regression model against which it was compared. Implications of these results for practice are provided.
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identification of individuals with adhd using the dean woodcock sensory motor battery and a Boosted Tree algorithm
Behavior Research Methods, 2015Co-Authors: Holmes Finch, Andrew S Davis, Raymond S. DeanAbstract:The accurate and early identification of individuals with pervasive conditions such as attention deficit hyperactivity disorder (ADHD) is crucial to ensuring that they receive appropriate and timely assistance and treatment. Heretofore, identification of such individuals has proven somewhat difficult, typically involving clinical decision making based on descriptions and observations of behavior, in conjunction with the administration of cognitive assessments. The present study reports on the use of a sensory motor battery in conjunction with a recursive partitioning computer algorithm, Boosted Trees, to develop a prediction heuristic for identifying individuals with ADHD. Results of the study demonstrate that this method is able to do so with accuracy rates of over 95 %, much higher than the popular logistic regression model against which it was compared. Implications of these results for practice are provided.
Anat Goldstein - One of the best experts on this subject based on the ideXlab platform.
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Applying machine learning on sensor data for irrigation recommendations: revealing the agronomist’s tacit knowledge
Precision Agriculture, 2018Co-Authors: Anat Goldstein, Amit Meitin, Shiran Bohadana, Oscar Lutenberg, Lior Fink, Gilad RavidAbstract:Jojoba Israel is a world-leading producer of Jojoba products, whose orchards are covered with sensors that collect soil moisture data for monitoring plant needs at real-time. Based on these data, the company’s agronomist defines a weekly irrigation plan. In addition, data on weather, irrigation, and yield are recorded from other sources (e.g. meteorological station and irrigation-plan records). However, so far, there has been no attempt to use the entire set of collected data to reveal insights and interesting relationships between different variables, such as soil, weather, irrigation characteristics, and resulting yield. By integrating and utilizing data from different sources, our research aims at using the collected data not only for monitoring and controlling the crop, but also for predicting irrigation recommendations. In particular, a dataset was constructed by integrating data collected over almost two years from 22 soil-sensors spread in four major plots (which are divided into 28 subplots and eight irrigation groups), from a meteorological station, and from actual irrigation records. Different regression and classification algorithms were applied on this dataset to develop models that were able to predict the weekly irrigation plan as recommended by the agronomist. The models were developed using eight different subsets of variables to determine which variables consistently contributed to prediction accuracy. By comparing the resulting models, it was shown that the best regression model was Gradient Boosted Regression Trees, with 93% accuracy, and the best classification model was the Boosted Tree Classifier, with 95% accuracy (on the test-set). Data that were not contributing to the model prediction success rate were identified as well. The resulting model can significantly facilitate the agronomist’s irrigation planning process. In addition, the potential of applying machine learning on the company data for yield and disease prediction is discussed.
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applying machine learning on sensor data for irrigation recommendations revealing the agronomist s tacit knowledge
Precision Agriculture, 2018Co-Authors: Anat Goldstein, Amit Meitin, Shiran Bohadana, Oscar Lutenberg, Lior Fink, Gilad RavidAbstract:Jojoba Israel is a world-leading producer of Jojoba products, whose orchards are covered with sensors that collect soil moisture data for monitoring plant needs at real-time. Based on these data, the company’s agronomist defines a weekly irrigation plan. In addition, data on weather, irrigation, and yield are recorded from other sources (e.g. meteorological station and irrigation-plan records). However, so far, there has been no attempt to use the entire set of collected data to reveal insights and interesting relationships between different variables, such as soil, weather, irrigation characteristics, and resulting yield. By integrating and utilizing data from different sources, our research aims at using the collected data not only for monitoring and controlling the crop, but also for predicting irrigation recommendations. In particular, a dataset was constructed by integrating data collected over almost two years from 22 soil-sensors spread in four major plots (which are divided into 28 subplots and eight irrigation groups), from a meteorological station, and from actual irrigation records. Different regression and classification algorithms were applied on this dataset to develop models that were able to predict the weekly irrigation plan as recommended by the agronomist. The models were developed using eight different subsets of variables to determine which variables consistently contributed to prediction accuracy. By comparing the resulting models, it was shown that the best regression model was Gradient Boosted Regression Trees, with 93% accuracy, and the best classification model was the Boosted Tree Classifier, with 95% accuracy (on the test-set). Data that were not contributing to the model prediction success rate were identified as well. The resulting model can significantly facilitate the agronomist’s irrigation planning process. In addition, the potential of applying machine learning on the company data for yield and disease prediction is discussed.