Machine learning model to predict mental health crises from electronic health records

Calleja, Roger Garriga; Mas, Javier; Abraha, Semhar; Nolan, Jon; Harrison, Oliver; Tadros, George; Matic, Aleksandar

doi:10.1038/s41591-022-01811-5

Cited by 73 publications

(49 citation statements)

References 50 publications

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“…GBDTs are highly effective in modeling complex, nonlinear relationships and in handling high-dimensional data as in the case of EHRs. Both models were chosen because they are well-established in the medical literature [26,28,29]. Furthermore, this allows us to make direct comparisons between a linear model (logistic regression) and a more flexible model (GBDTs).…”

Section: Statistical Analysis Methodsmentioning

confidence: 99%

Machine Learning for Predicting Micro- and Macrovascular Complications in Individuals With Prediabetes or Diabetes: Retrospective Cohort Study

Schallmoser¹,

Zueger

Kraus³

et al. 2023

J Med Internet Res

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Background Micro- and macrovascular complications are a major burden for individuals with diabetes and can already arise in a prediabetic state. To allocate effective treatments and to possibly prevent these complications, identification of those at risk is essential. Objective This study aimed to build machine learning (ML) models that predict the risk of developing a micro- or macrovascular complication in individuals with prediabetes or diabetes. Methods In this study, we used electronic health records from Israel that contain information about demographics, biomarkers, medications, and disease codes; span from 2003 to 2013; and were queried to identify individuals with prediabetes or diabetes in 2008. Subsequently, we aimed to predict which of these individuals developed a micro- or macrovascular complication within the next 5 years. We included 3 microvascular complications: retinopathy, nephropathy, and neuropathy. In addition, we considered 3 macrovascular complications: peripheral vascular disease (PVD), cerebrovascular disease (CeVD), and cardiovascular disease (CVD). Complications were identified via disease codes, and, for nephropathy, the estimated glomerular filtration rate and albuminuria were considered additionally. Inclusion criteria were complete information on age and sex and on disease codes (or measurements of estimated glomerular filtration rate and albuminuria for nephropathy) until 2013 to account for patient dropout. Exclusion criteria for predicting a complication were diagnosis of this specific complication before or in 2008. In total, 105 predictors from demographics, biomarkers, medications, and disease codes were used to build the ML models. We compared 2 ML models: logistic regression and gradient-boosted decision trees (GBDTs). To explain the predictions of the GBDTs, we calculated Shapley additive explanations values. Results Overall, 13,904 and 4259 individuals with prediabetes and diabetes, respectively, were identified in our underlying data set. For individuals with prediabetes, the areas under the receiver operating characteristic curve for logistic regression and GBDTs were, respectively, 0.657 and 0.681 (retinopathy), 0.807 and 0.815 (nephropathy), 0.727 and 0.706 (neuropathy), 0.730 and 0.727 (PVD), 0.687 and 0.693 (CeVD), and 0.707 and 0.705 (CVD); for individuals with diabetes, the areas under the receiver operating characteristic curve were, respectively, 0.673 and 0.726 (retinopathy), 0.763 and 0.775 (nephropathy), 0.745 and 0.771 (neuropathy), 0.698 and 0.715 (PVD), 0.651 and 0.646 (CeVD), and 0.686 and 0.680 (CVD). Overall, the prediction performance is comparable for logistic regression and GBDTs. The Shapley additive explanations values showed that increased levels of blood glucose, glycated hemoglobin, and serum creatinine are risk factors for microvascular complications. Age and hypertension were associated with an elevated risk for macrovascular complications. Conclusions Our ML models allow for an identification of individuals with prediabetes or diabetes who are at increased risk of developing micro- or macrovascular complications. The prediction performance varied across complications and target populations but was in an acceptable range for most prediction tasks.

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Section: Statistical Analysis Methodsmentioning

confidence: 99%

Machine Learning for Predicting Micro- and Macrovascular Complications in Individuals With Prediabetes or Diabetes: Retrospective Cohort Study

Schallmoser¹,

Zueger

Kraus³

et al. 2023

J Med Internet Res

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“…15 In addition, we may be able to use behavioural data to identify patients with mental health crises before they present to the hospital. This can either be from electronic health records, 16 or from smartphones that are already tracking our location and social activities. For example, we can imagine that a patient with bipolar disorder who enters a manic phase may demonstrate atypical GPS location data or increased social activity at night.…”

Section: Monitoringmentioning

confidence: 99%

On the role of artificial intelligence in psychiatry

Rocheteau

2022

Br J Psychiatry

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Recently, there has been growing interest in artificial intelligence (AI) to improve efficiency and personalisation of mental health services. So far, the progress has been slow, however, advancements in deep learning may change this. This paper discusses the role for AI in psychiatry, in particular (a) diagnosis tools, (b) monitoring of symptoms, and (c) delivering personalised treatment recommendations. Finally, I discuss ethical concerns and technological limitations.

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“…Depression is a mental disorder that negatively impacts your life in various ways and left untreated, can ruin relationships and cause problems at school and work. It's estimated that nearly of all cases of depression are left undiagnosed, leading to the depression getting worse and causing that person's lifestyle and work performance to deteriorate [1].I wanted to develop a machine learning model that can accurately predict if someone has depression, as timely intervention can give undiagnosed patients the chance to receive treatment and therefore improve their life [2].…”

Section: Introductionmentioning

confidence: 99%

Using Machine Learning Models to Predict Depression

Pininty¹

2022

Preprint

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<p>Depression is a mood disorder that negatively affects performance in work and school and also ruins your social life and leads to risk taking behaviors that endangers peoples’ lives. Timely intervention and diagnosis can help identify and mitigate the effects of depression. In this study, I used a dataset formed by a survey to create multiple machine learning models in order to create a model that would accurately predict if someone had depression. Out of 4 different machine learning models I used, I was able to create a machine learning model using the Random Forest Classifier to achieve an accuracy of 90.48%.</p>

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Machine learning model to predict mental health crises from electronic health records

Cited by 73 publications

References 50 publications

Machine Learning for Predicting Micro- and Macrovascular Complications in Individuals With Prediabetes or Diabetes: Retrospective Cohort Study

Machine Learning for Predicting Micro- and Macrovascular Complications in Individuals With Prediabetes or Diabetes: Retrospective Cohort Study

On the role of artificial intelligence in psychiatry

Using Machine Learning Models to Predict Depression

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