Predicting hospital readmission in patients with mental or substance use disorders: A machine learning approach

Morel, Didier; Liu-Ferrara, Ann; Caceres-Suriel, Ambiorix J.; Kurtz, Stephan G.; Tabak, Ying P.

doi:10.1016/j.ijmedinf.2020.104136

Cited by 32 publications

(36 citation statements)

References 12 publications

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“…Many models have been used to predict a patient admission to a hospital, mortality and other health care applications based on comorbidities. Some examples include: predicting morbidity of patients with chronic obstructive pulmonary disease [ 7 ], febrile neutropenia [ 8 ], as well as classifying the hospitalization of patients with preconditions on diabetes [ 9 ], heart disease [ 10 , 11 ], and hospital readmission for patients with mental or substance use disorders [ 12 ]. Recent advances in the machine learning literature have suggested that sparse classifiers, those that use few variables (e.g.,

1-regularized Support Vector Machines), have stronger predictive power and generalize better on out-of-sample data points than very complex classifiers [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Personalized predictive models for symptomatic COVID-19 patients using basic preconditions: Hospitalizations, mortality, and the need for an ICU or ventilator

Wollenstein-Betech

Cassandras

Paschalidis

2020

International Journal of Medical Informatics

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Background The rapid global spread of the SARS-CoV-2 virus has provoked a spike in demand for hospital care. Hospital systems across the world have been over-extended, including in Northern Italy, Ecuador, and New York City, and many other systems face similar challenges. As a result, decisions on how to best allocate very limited medical resources and design targeted policies for vulnerable subgroups have come to the forefront. Specifically, under consideration are decisions on who to test, who to admit into hospitals, who to treat in an Intensive Care Unit (ICU), and who to support with a ventilator. Given today’s ability to gather, share, analyze and process data, personalized predictive models based on demographics and information regarding prior conditions can be used to (1) help decision-makers allocate limited resources, when needed, (2) advise individuals how to better protect themselves given their risk profile, (3) differentiate social distancing guidelines based on risk, and (4) prioritize vaccinations once a vaccine becomes available. Objective To develop personalized models that predict the following events: (1) hospitalization, (2) mortality, (3) need for ICU, and (4) need for a ventilator. To predict hospitalization, it is assumed that one has access to a patient’s basic preconditions, which can be easily gathered without the need to be at a hospital and hence serve citizens and policy makers to assess individual risk during a pandemic. For the remaining models, different versions developed include different sets of a patient’s features, with some including information on how the disease is progressing (e.g., diagnosis of pneumonia). Materials and Methods National data from a publicly available repository, updated daily, containing information from approximately 91,000 patients in Mexico were used. The data for each patient include demographics, prior medical conditions, SARS-CoV-2 test results, hospitalization, mortality and whether a patient has developed pneumonia or not. Several classification methods were applied and compared, including robust versions of logistic regression, and support vector machines, as well as random forests and gradient boosted decision trees. Results Interpretable methods (logistic regression and support vector machines) perform just as well as more complex models in terms of accuracy and detection rates, with the additional benefit of elucidating variables on which the predictions are based. Classification accuracies reached 72 %, 79 %, 89 %, and 90 % for predicting hospitalization, mortality, need for ICU and need for a ventilator, respectively. The analysis reveals the most important preconditions for making the predictions. For the four models derived, these are: (1) for hospitalization: age, pregnancy, diabetes, gender, chronic renal insufficiency, and immunosuppression ; (2) for mortality: age, immunosuppression, chronic...

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1-regularized Support Vector Machines), have stronger predictive power and generalize better on out-of-sample data points than very complex classifiers [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Personalized predictive models for symptomatic COVID-19 patients using basic preconditions: Hospitalizations, mortality, and the need for an ICU or ventilator

Wollenstein-Betech

Cassandras

Paschalidis

2020

International Journal of Medical Informatics

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show abstract

“…Many models have been used to predict a patient admission to a hospital, mortality and other health care applications based on comorbidities. Some examples include: predicting morbidity of patients with chronic obstructive pulmonary disease [6], febrile neutropenia [7], as well as classifying the hospitalization of patients with preconditions on diabetes [8], heart disease [9,10], and hospital readmission for patients with mental or substance use disorders [11]. Recent advances in the machine learning literature have suggested that sparse classifiers, those that use few variables (e.g., l1-regularized Support Vector Machines), have stronger predictive power and generalize better on out-of-sample data points than very complex classifiers [12].…”

Section: Introductionmentioning

confidence: 99%

Personalized Predictive Models for Symptomatic COVID-19 Patients Using Basic Preconditions:Hospitalizations, Mortality, and the Need for an ICU or Ventilator

Wollenstein-Betech

Cassandras

Paschalidis

2020

Preprint

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Background: The rapid global spread of the virus SARS-CoV-2 has provoked a spike in demand for hospital care. Hospital systems across the world have been over-extended, including in Northern Italy, Ecuador, and New York City, and many other systems face similar challenges. As a result, decisions on how to best allocate very limited medical resources have come to the forefront. Specifically, under consideration are decisions on who to test, who to admit into hospitals, who to treat in an Intensive Care Unit (ICU), and who to support with a ventilator. Given today's ability to gather, share, analyze and process data, personalized predictive models based on demographics and information regarding prior conditions can be used to (1) help decision-makers allocate limited resources, when needed, (2) advise individuals how to better protect themselves given their risk profile, (3) differentiate social distancing guidelines based on risk, and (4) prioritize vaccinations once a vaccine becomes available. Objective: To develop personalized models that predict the following events: (1) hospitalization, (2) mortality, (3) need for ICU, and (4) need for a ventilator. To predict hospitalization, it is assumed that one has access to a patient's basic preconditions, which can be easily gathered without the need to be at a hospital. For the remaining models, different versions developed include different sets of a patient's features, with some including information on how the disease is progressing (e.g., diagnosis of pneumonia). Materials and Methods: Data from a publicly available repository, updated daily, containing information from approximately 91,000 patients in Mexico were used. The data for each patient include demographics, prior medical conditions, SARS-CoV-2 test results, hospitalization, mortality and whether a patient has developed pneumonia or not. Several classification methods were applied, including robust versions of logistic regression, and support vector machines, as well as random forests and gradient boosted decision trees. Results: Interpretable methods (logistic regression and support vector machines) perform just as well as more complex models in terms of accuracy and detection rates, with the additional benefit of elucidating variables on which the predictions are based. Classification accuracies reached 61%, 76%, 83%, and 84% for predicting hospitalization, mortality, need for ICU and need for a ventilator, respectively. The analysis reveals the most important preconditions for making the predictions. For the four models derived, these are: (1) for hospitalization: age, gender, chronic renal insufficiency, diabetes, immunosuppression; (2) for mortality: age, SARS-CoV-2 test status, immunosuppression and pregnancy; (3) for ICU need: development of pneumonia (if available), cardiovascular disease, asthma, and SARS-CoV-2 test status; and (4) for ventilator need: ICU and pneumonia (if available), age, gender, cardiovascular disease, obesity, pregnancy, and SARS-CoV-2 test result.

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“…The specificity of DT was highest among the eight candidate modes, while its AUC and sensitivity were lower, indicating that DT, as a simple machine learning model, functioned in the majority samples of the majority class instead of its prediction effect. The AUC of XGB was best among the eight candidate modes, and it also performed well in other evaluation metrics, which inferred XGB might have outstanding performance in the prediction of readmission [ 43 , 44 ]. Compared with XGB, the AUC, accuracy, sensitivity and specificity of the proposed stacking model improved by 0.98, 0.52, 0.38 and 0.49%, respectively, suggesting that our model could further improve the overall predictive performance compared with the best individual model.…”

Section: Discussionmentioning

confidence: 99%

A stacking-based model for predicting 30-day all-cause hospital readmissions of patients with acute myocardial infarction

Zhang

Qiu

et al. 2020

BMC Med Inform Decis Mak

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Background Acute myocardial infarction (AMI) is a serious cardiovascular disease, followed by a high readmission rate within 30-days of discharge. Accurate prediction of AMI readmission is a crucial way to identify the high-risk group and optimize the distribution of medical resources. Methods In this study, we propose a stacking-based model to predict the risk of 30-day unplanned all-cause hospital readmissions for AMI patients based on clinical data. Firstly, we conducted an under-sampling method of neighborhood cleaning rule (NCR) to alleviate the class imbalance and then utilized a feature selection method of SelectFromModel (SFM) to select effective features. Secondly, we adopted a self-adaptive approach to select base classifiers from eight candidate models according to their performances in datasets. Finally, we constructed a three-layer stacking model in which layer 1 and layer 2 were base-layer and level 3 was meta-layer. The predictions of the base-layer were used to train the meta-layer in order to make the final forecast. Results The results show that the proposed model exhibits the highest AUC (0.720), which is higher than that of decision tree (0.681), support vector machine (0.707), random forest (0.701), extra trees (0.709), adaBoost (0.702), bootstrap aggregating (0.704), gradient boosting decision tree (0.710) and extreme gradient enhancement (0.713). Conclusion It is evident that our model could effectively predict the risk of 30-day all cause hospital readmissions for AMI patients and provide decision support for the administration.

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Predicting hospital readmission in patients with mental or substance use disorders: A machine learning approach

Cited by 32 publications

References 12 publications

Personalized predictive models for symptomatic COVID-19 patients using basic preconditions: Hospitalizations, mortality, and the need for an ICU or ventilator

Personalized predictive models for symptomatic COVID-19 patients using basic preconditions: Hospitalizations, mortality, and the need for an ICU or ventilator

Personalized Predictive Models for Symptomatic COVID-19 Patients Using Basic Preconditions:Hospitalizations, Mortality, and the Need for an ICU or Ventilator

A stacking-based model for predicting 30-day all-cause hospital readmissions of patients with acute myocardial infarction

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