scholarly journals A Machine Learning Approach: Enhancing the Predictive Performance of Pharmaceutical Stock Price Movement during COVID

2022 ◽  
Vol 10 (01) ◽  
pp. 1-21
Author(s):  
Beilei He ◽  
Weiyi Han ◽  
Suet Ying Isabelle Hon
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Predictive Performance ◽  
Learning Approach ◽  
Price Movement ◽  
Machine Learning Approach

scholarly journals Predicting patient outcomes in psychiatric hospitals with routine data: a machine learning approach

2020 ◽  
Author(s):  
Jan Wolff ◽  
Alexander Gary ◽  
Daniela Jung ◽  
Claus Normann ◽  
Klaus Kaier ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Psychiatric Hospital ◽  
Hospital Care ◽  
Routine Data ◽  
Psychiatric Hospitals ◽  
Predictive Performance ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Better Than

Abstract Background: A common problem in machine learning applications is availability of data at the point of decision making. The aim of the present study was to use routine data readily available at admission to predict aspects relevant to the organization of psychiatric hospital care. A further aim was to compare the results of a machine learning approach with those obtained through a traditional method and those obtained through a naive baseline classifier. Methods: The study included consecutively discharged patients between 1 st of January 2017 and 31 st of December 2018 from nine psychiatric hospitals in Hesse, Germany. We compared the predictive performance achieved by stochastic gradient boosting (GBM) with multiple logistic regression and a naive baseline classifier. We tested the performance of our final models on unseen patients from another calendar year and from different hospitals. Results: The study included 45,388 inpatient episodes. The models’ performance, as measured by the area under the Receiver Operating Characteristic curve, varied strongly between the predicted outcomes, with relatively high performance in the prediction of coercive treatment (area under the curve: 0.83) and 1:1 observations (0.80) and relatively poor performance in the prediction of short length of stay (0.69) and non-response to treatment (0.65). The GBM performed slightly better than logistic regression. Both approaches were substantially better than a naive prediction based solely on basic diagnostic grouping. Conclusion: The present study has shown that administrative routine data can be used to predict aspects relevant to the organisation of psychiatric hospital care. Future research should investigate the predictive performance that is necessary to provide effective assistance in clinical practice for the benefit of both staff and patients.

scholarly journals Predicting patient outcomes in psychiatric hospitals with routine data: a machine learning approach

2020 ◽  
Author(s):  
Jan Wolff ◽  
Alexander Gary ◽  
Daniela Jung ◽  
Claus Normann ◽  
Klaus Kaier ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Psychiatric Hospital ◽  
Hospital Care ◽  
Routine Data ◽  
Psychiatric Hospitals ◽  
Predictive Performance ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Better Than

Abstract Background: A common problem in machine learning applications is availability of data at the point of decision making. The aim of the present study was to use routine data readily available at admission to predict aspects relevant to the organization of psychiatric hospital care. A further aim was to compare the results of a machine learning approach with those obtained through a traditional method and those obtained through a naive baseline classifier.Methods: The study included consecutively discharged patients between 1st of January 2017 and 31st of December 2018 from nine psychiatric hospitals in Hesse, Germany. We compared the predictive performance achieved by stochastic gradient boosting (GBM) with multiple logistic regression and a naive baseline classifier. We tested the performance of our final models on unseen patients from another calendar year and from different hospitals. Results: The study included 45,388 inpatient episodes. The models’ performance, as measured by the area under the Receiver Operating Characteristic curve, varied strongly between the predicted outcomes, with relatively high performance in the prediction of coercive treatment (area under the curve: 0.83) and 1:1 observations (0.80) and relatively poor performance in the prediction of short length of stay (0.69) and non-response to treatment (0.65). The GBM performed slightly better than logistic regression. Both approaches were substantially better than a naive prediction based solely on basic diagnostic grouping. Conclusion: The present study has shown that administrative routine data can be used to predict aspects relevant to the organisation of psychiatric hospital care. Future research should investigate the predictive performance that is necessary to provide effective assistance in clinical practice for the benefit of both staff and patients.

scholarly journals Predicting patient outcomes in psychiatric hospitals with routine data: a machine learning approach

2019 ◽  
Author(s):  
Jan Wolff ◽  
Alexander Gary ◽  
Daniela Jung ◽  
Claus Normann ◽  
Klaus Kaier ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Psychiatric Hospital ◽  
Hospital Care ◽  
Routine Data ◽  
Psychiatric Hospitals ◽  
Predictive Performance ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Better Than

Abstract Background:A common problem in machine learning applications is availability of data at the point of decision making. The aim of the present study was to use routine data readily available at admission to predict aspects relevant to the organization of psychiatric hospital care. A further aim was to compare the results of a machine learning approach with those obtained through a traditional method and those obtained through a naive baseline classifier. Methods:The study included consecutively discharged patients between 1stof January 2017 and 31stof December 2018 from nine psychiatric hospitals in Hesse, Germany. We compared the predictive performance achieved by stochastic gradient boosting (GBM) with multiple logistic regression and a naive baseline classifier. We tested the performance of our final models on unseen patients from another calendar year and from different hospitals. Results: The study included 45,388 inpatient episodes. The models’ performance, as measured by the area under the Receiver Operating Characteristic curve, varied strongly between the predicted outcomes, with relatively high performance in the prediction of coercive treatment (area under the curve: 0.83) and 1:1 observations (0.80) and relatively poor performance in the prediction of short length of stay (0.69) and non-response to treatment (0.65). The GBM performed slightly better than logistic regression. Both approaches were substantially better than a naive prediction based solely on basic diagnostic grouping. Conclusion:The present study has shown that administrative routine data can be used to predict aspects relevant to the organisation of psychiatric hospital care. Future research should investigate the predictive performance that is necessary to provide effective assistance in clinical practice for the benefit of both staff and patients.

scholarly journals Direct Comparison of the Prediction of the Unbound Brain-to-Plasma Partitioning Utilizing Machine Learning Approach and Mechanistic Neuropharmacokinetic Model

2021 ◽  
Vol 23 (4) ◽  
Author(s):  
Yohei Kosugi ◽  
Kunihiko Mizuno ◽  
Cipriano Santos ◽  
Sho Sato ◽  
Natalie Hosea ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multiple Drug Resistance ◽  
Predictive Performance ◽  
Training Dataset ◽  
Multiple Drug ◽  
Learning Approach ◽  
Cancer Resistance ◽  
Learning Models ◽  
Machine Learning Approach ◽  
Machine Learning Models

AbstractThe mechanistic neuropharmacokinetic (neuroPK) model was established to predict unbound brain-to-plasma partitioning (Kp,uu,brain) by considering in vitro efflux activities of multiple drug resistance 1 (MDR1) and breast cancer resistance protein (BCRP). Herein, we directly compare this model to a computational machine learning approach utilizing physicochemical descriptors and efflux ratios of MDR1 and BCRP-expressing cells for predicting Kp,uu,brain in rats. Two different types of machine learning techniques, Gaussian processes (GP) and random forest regression (RF), were assessed by the time and cluster-split validation methods using 640 internal compounds. The predictivity of machine learning models based on only molecular descriptors in the time-split dataset performed worse than the cluster-split dataset, whereas the models incorporating MDR1 and BCRP efflux ratios showed similar predictivity between time and cluster-split datasets. The GP incorporating MDR1 and BCRP in the time-split dataset achieved the highest correlation (R2 = 0.602). These results suggested that incorporation of MDR1 and BCRP in machine learning is beneficial for robust and accurate prediction. Kp,uu,brain prediction utilizing the neuroPK model was significantly worse compared to machine learning approaches for the same dataset. We also investigated the predictivity of Kp,uu,brain using an external independent test set of 34 marketed drugs. Compared to machine learning models, the neuroPK model showed better predictive performance with R2 of 0.577. This work demonstrates that the machine learning model for Kp,uu,brain achieves maximum predictive performance within the chemical applicability domain, whereas the neuroPK model is applicable more widely beyond the chemical space covered in the training dataset.

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