Machine learning and big data: Implications for disease modeling and therapeutic discovery in psychiatry

Tai, Andy Man Yeung; Albuquerque, Alcides; Carmona, Nicole E.; Subramanieapillai, Mehala; Danielle, S.; Sheko, Margarita; Lee, Yena; Mansur, Rodrigo B.; McIntyre, Roger S.

doi:10.1016/j.artmed.2019.101704

Cited by 121 publications

(68 citation statements)

References 36 publications

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“…Understanding the complex processes of mental healthcare and their interaction with individual-level factors is becoming an increasingly important domain of using large data sets and employing sophisticated statistical methods in mental healthcare research. It promises to provide more exact personalized risk and outcome models in mental healthcare research [ 57 ]. ML and decision tree analysis have recently been used to improve suicide prediction models [ 58 ] and decision trees have been used to develop prediction models for workplace sickness absence due to mental disorders [ 59 ].…”

Section: Discussionmentioning

confidence: 99%

Identification of risk factors for involuntary psychiatric hospitalization: using environmental socioeconomic data and methods of machine learning to improve prediction

et al. 2020

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Background The purpose of this study was to identify factors associated with a high risk of involuntary psychiatric in-patient hospitalization both on the individual level and on the level of mental health services and the socioeconomic environment that patients live in. Methods The present study expands on a previous analysis of the health records of 5764 cases admitted as in-patients in the four psychiatric hospitals of the Metropolitan City of Cologne, Germany, in the year 2011 (1773 cases treated under the Mental Health Act and 3991 cases treated voluntarily). Our previous analysis had included medical, sociodemographic and socioeconomic data of every case and used a machine learning-based prediction model employing chi-squared automatic interaction detection (CHAID). Our current analysis attempts to improve the previous one through (1) optimizing the machine learning procedures (use of a different type of decision-tree prediction model (Classification and Regression Trees (CART) and application of hyperparameter tuning (HT)), and (2) the addition of patients’ environmental socioeconomic data (ESED) to the data set. Results Compared to our previous analysis, model fit was improved. Main diagnoses of an organic mental or a psychotic disorder (ICD-10 groups F0 and F2), suicidal behavior upon admission, admission outside of regular service hours and absence of outpatient treatment prior to admission were confirmed as powerful predictors of detention. Particularly high risks were shown for (1) patients with an organic mental disorder, specifically if they were retired, admitted outside of regular service hours and lived in assisted housing, (2) patients with suicidal tendencies upon admission who did not suffer from an affective disorder, specifically if it was unclear whether there had been previous suicide attempts, or if the affected person lived in areas with high unemployment rates, and (3) patients with psychosis, specifically those who lived in densely built areas with a large proportion of small or one-person households. Conclusions Certain psychiatric diagnoses and suicidal tendencies are major risk factors for involuntary psychiatric hospitalization. In addition, service-related and environmental socioeconomic factors contribute to the risk for detention. Identifying modifiable risk factors and particularly vulnerable risk groups should help to develop suitable preventive measures.

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Section: Discussionmentioning

confidence: 99%

Identification of risk factors for involuntary psychiatric hospitalization: using environmental socioeconomic data and methods of machine learning to improve prediction

et al. 2020

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“…Machine learning provides an opportunity to parse the mechanisms and symptoms of mental disorders with multi-factorial and complex impact factors. 6 Support vector machine (SVM), a machine learning method with high prediction accuracy, was first proposed by Vapnik et al 7 Machine learning involves means the establishment of a data classification model by through analysing a known data set with multi-dimensional parameters to predict the classification of new unknown data sets classified in accordance with unknown laws. 8 Among various machine learning techniques, SVM is best suited to solve pattern recognition of small samples with high-dimensional parameters.…”

Section: Introductionmentioning

confidence: 99%

<p>A Predictive Model Offor Attention Deficit Hyperactivity Disorder Based on Clinical Assessment Tools</p>

Han¹,

Fang²,

Hong³

2020

NDT

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Background: At present, clinicians diagnose that the clinical diagnosis of attention deficit hyperactivity disorder (ADHD) in children is mainly on the basis of the information provided by their parents, the behaviour of children in clinical clinics and the assessments of clinical rating scales and neuropsychological tests. Notably, no unified standard exists currently for analysing the results of various measurement tools for diagnosing ADHD. Therefore, clinicians interpret the results of clinical rating scales and neuropsychological tests entirely based on their clinical experience. Methods and subjects:: To provide guidance for clinicians on how to analyse the results of various clinical assessment tools when diagnosing ADHD, this study assessed children with ADHD and children in the control group using two clinical assessment scales-parent rating scale (PSQ) and Child Behavior Checklist (CBCL)-and one neuropsychological test (Integrated Visual and Auditory Continuous Performance Testing). The two-sample t-test (FDR correction) screened the parameters of the three assessment tools with significant intergroup differences. LibSVM was used to establish a classification prediction model for analysing the accuracy of ADHD prediction using parameters of the three assessment tools and weight values of each parameter for classification prediction. Results: A total of 19 parameters (16 from clinical rating scales, 3 from neuropsychological tests) with significant inter-group differences were screened. The accuracy of classification modelling was higher for the clinical rating scales (61.635%) than for the neuropsychological test (59.784%), whereas the accuracy of classification modelling was higher for the clinical rating scales combined with the neuropsychological test (70.440%) than for the former two parameters alone. The three parameters with the highest weight values were learning problem (0.731), hyperactivity/impulsivity (0.676) and activity capacity (0.569). The three parameters with the lowest weight values are integrated control force (0.028), visual attention (0.028) and integrated attention (0.034). Conclusion: Our study findings indicate that the diagnosis of ADHD should be based on multidimensional assessment. For a more accurate diagnosis of ADHD, assessments and that more assessment parameters should be developed on the basis of different dimensions of physiology or psychology in the future to obtain a more accurate diagnosis of ADHD. Furthermore, the predictive model for ADHD may improve our understanding and help in optimisation of the treatment of such a condition.

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“…To classify an object from an input vector, each tree gives a classification. The forest selects the classification that has the most votes 27,28 . In this study, the Gini Index was applied as the optimization criterion, with 1000 estimators used in the calculation.…”

Section: Methodsmentioning

confidence: 99%

“…The forest selects the classification that has the most votes. 27,28 In this study, the Gini Index was applied as the optimization criterion, with 1000 estimators used in the calculation. The hyperparameters used in the current study were as follows: criterion="gini," bootstrap = True, max_parame-ter="auto," max_depth = 10, n_jobs = 2, min_samples_split = 2.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Machine learning models predict coagulopathy in spontaneous intracerebral hemorrhage patients in ER

et al. 2020

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Machine learning and big data: Implications for disease modeling and therapeutic discovery in psychiatry

Cited by 121 publications

References 36 publications

Identification of risk factors for involuntary psychiatric hospitalization: using environmental socioeconomic data and methods of machine learning to improve prediction

Identification of risk factors for involuntary psychiatric hospitalization: using environmental socioeconomic data and methods of machine learning to improve prediction

<p>A Predictive Model Offor Attention Deficit Hyperactivity Disorder Based on Clinical Assessment Tools</p>

Machine learning models predict coagulopathy in spontaneous intracerebral hemorrhage patients in ER

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