Risk factor assessment of homelessness at psychiatric hospital admission: a machine learning approach

Age-related macular degeneration (AMD) is the leading cause of visual dysfunction and irreversible blindness in developed countries and a rising cause in underdeveloped countries. There is a current debate on whether or not cataracts are significant risk factors for AMD development. In particular, research regarding this association is so far inconclusive. For this reason, we aimed to employ here a machine-learning approach to analyze the relevance and importance of cataracts as a risk factor for AMD in a large cohort of Hispanics from Mexico. We conducted a nested case control study of 119 cataract cases and 137 healthy unmatched controls focusing on clinical data from electronic medical records. Additionally, we studied two single nucleotide polymorphisms in the CFH gene previously associated with the disease in various populations as positive control for our method. We next determined the most relevant variables and found the bivariate association between cataracts and AMD. Later, we used supervised machine-learning methods to replicate these findings without bias. To improve the interpretability, we detected the five most relevant features and displayed them using a bar graph and a rule-based tree. Our findings suggest that bilateral cataracts are not a significant risk factor for AMD development among Hispanics from Mexico.

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

10.21203/rs.2.15371/v4 ◽

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.

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A machine learning approach to college drinking prediction and risk factor identification

ACM Transactions on Intelligent Systems and Technology ◽

10.1145/2508037.2508053 ◽

2013 ◽

Vol 4 (4) ◽

pp. 1-24 ◽

Cited By ~ 7

Author(s):

Jinbo Bi ◽

Jiangwen Sun ◽

Yu Wu ◽

Howard Tennen ◽

Stephen Armeli

Keyword(s):

Machine Learning ◽

Risk Factor ◽

College Drinking ◽

Learning Approach ◽

Machine Learning Approach

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Predicting need for hospital admission in patients with traumatic brain injury or skull fractures identified on CT imaging: a machine learning approach

Emergency Medicine Journal ◽

10.1136/emermed-2020-210776 ◽

2021 ◽

pp. emermed-2020-210776

Author(s):

Carl Marincowitz ◽

Lewis Paton ◽

Fiona Lecky ◽

Paul Tiffin

Keyword(s):

Machine Learning ◽

Traumatic Brain Injury ◽

Brain Injury ◽

Hospital Admission ◽

Decision Trees ◽

Decision Rule ◽

Predictive Value ◽

Learning Approach ◽

Skull Fractures ◽

Machine Learning Approach

BackgroundPatients with mild traumatic brain injury on CT scan are routinely admitted for inpatient observation. Only a small proportion of patients require clinical intervention. We recently developed a decision rule using traditional statistical techniques that found neurologically intact patients with isolated simple skull fractures or single bleeds <5 mm with no preinjury antiplatelet or anticoagulant use may be safely discharged from the emergency department. The decision rule achieved a sensitivity of 99.5% (95% CI 98.1% to 99.9%) and specificity of 7.4% (95% CI 6.0% to 9.1%) to clinical deterioration. We aimed to transparently report a machine learning approach to assess if predictive accuracy could be improved.MethodsWe used data from the same retrospective cohort of 1699 initial Glasgow Coma Scale (GCS) 13–15 patients with injuries identified by CT who presented to three English Major Trauma Centres between 2010 and 2017 as in our original study. We assessed the ability of machine learning to predict the same composite outcome measure of deterioration (indicating need for hospital admission). Predictive models were built using gradient boosted decision trees which consisted of an ensemble of decision trees to optimise model performance.ResultsThe final algorithm reported a mean positive predictive value of 29%, mean negative predictive value of 94%, mean area under the curve (C-statistic) of 0.75, mean sensitivity of 99% and mean specificity of 7%. As with logistic regression, GCS, severity and number of brain injuries were found to be important predictors of deterioration.ConclusionWe found no clear advantages over the traditional prediction methods, although the models were, effectively, developed using a smaller data set, due to the need to divide it into training, calibration and validation sets. Future research should focus on developing models that provide clear advantages over existing classical techniques in predicting outcomes in this population.

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

10.21203/rs.2.15371/v3 ◽

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.

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

10.21203/rs.2.15371/v2 ◽

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.

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