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Machine Learning and Big Data in Psychiatric and Sleep Disorders

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A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 32935

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Special Issue Editor

Prof. Dr. Seung-Gul Kang

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Guest Editor

Department of Psychiatry, Gil Medical Center and Gachon University College of Medicine, Incheon 21565, Korea
Interests: sleep; psychiatry

Special Issue Information

Dear Colleagues,

A variety of psychiatric and sleep disorders including, but not limited to, depression and insomnia are quite common and greatly impair the quality of life. The current diagnosis and the prediction of prognosis in psychiatric and sleep disorders largely depend on the clinician’s judgment, which is made based on the clinical characteristics, DSM system, and polysomnography, among other factors. Since the underlying neurobiology and etiology is diverse, however, the current diagnostic and prognostic schemes in psychiatry are in need of improvement. Although a wealth of relevant evidence has accumulated that biological, genetic, neuroimaging, and environmental factors now contribute to the development and prognosis of specific psychiatric and sleep disorders, the individual risk factors do not always correctly indicate, if at all, the presence and prognosis of specific disease.

The algorithms developed from machine learning and big data provide new hope to address these issues. Machine learning is based on individual-level analysis, which enables us to predict the development and prognosis of a disease, attracting substantial attention in the field of psychiatry and sleep medicine. Big data has also been providing opportunities for new insights that have not previously been achieved through research on individual datasets.

The forthcoming Special Issue focuses on machine learning and big data in psychiatric and sleep disorders. Below are examples of preferred topics:

Machine learning and big data studies for the discovery of markers that predict disease onset, recurrence, and treatment response of psychiatric and sleep disorders.
Machine learning and big data studies that can predict the occurrence of suicide.
Machine learning studies using biological data such as brain MRI, EEG, and genetic information in psychiatry and sleep medicine.
Machine learning studies that are predictive of sleep disorders, such as sleep apnea, using clinical characteristics prior to polysomnography.
Psychiatric and sleep research using big data from various sources, including electrical medical records, insurance data, climate data, and internet data.

Prof. Dr. Seung-Gul Kang
Guest Editor

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Keywords

machine learning
big data
psychiatry
sleep disorder
diagnosis
treatment response

Published Papers (14 papers)

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Research

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17 pages, 3927 KiB

Open AccessArticle

Exploration of Despair Eccentricities Based on Scale Metrics with Feature Sampling Using a Deep Learning Algorithm

by Tawfiq Hasanin, Pravin R. Kshirsagar, Hariprasath Manoharan, Sandeep Singh Sengar, Shitharth Selvarajan and Suresh Chandra Satapathy

Diagnostics 2022, 12(11), 2844; https://doi.org/10.3390/diagnostics12112844 - 17 Nov 2022

Cited by 4 | Viewed by 1255

Abstract

The majority of people in the modern biosphere struggle with depression as a result of the coronavirus pandemic’s impact, which has adversely impacted mental health without warning. Even though the majority of individuals are still protected, it is crucial to check for post-corona virus symptoms if someone is feeling a little lethargic. In order to identify the post-coronavirus symptoms and attacks that are present in the human body, the recommended approach is included. When a harmful virus spreads inside a human body, the post-diagnosis symptoms are considerably more dangerous, and if they are not recognised at an early stage, the risks will be increased. Additionally, if the post-symptoms are severe and go untreated, it might harm one’s mental health. In order to prevent someone from succumbing to depression, the technology of audio prediction is employed to recognise all the symptoms and potentially dangerous signs. Different choral characters are used to combine machine-learning algorithms to determine each person’s mental state. Design considerations are made for a separate device that detects audio attribute outputs in order to evaluate the effectiveness of the suggested technique; compared to the previous method, the performance metric is substantially better by roughly 67%. Full article

(This article belongs to the Special Issue Machine Learning and Big Data in Psychiatric and Sleep Disorders)

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12 pages, 1070 KiB

Open AccessArticle

Model Building in Forensic Psychiatry: A Machine Learning Approach to Screening Offender Patients with SSD

by Lena Machetanz, David Huber, Steffen Lau and Johannes Kirchebner

Diagnostics 2022, 12(10), 2509; https://doi.org/10.3390/diagnostics12102509 - 16 Oct 2022

Viewed by 1503

Abstract

Today’s extensive availability of medical data enables the development of predictive models, but this requires suitable statistical methods, such as machine learning (ML). Especially in forensic psychiatry, a complex and cost-intensive field with risk assessments and predictions of treatment outcomes as central tasks, there is a need for such predictive tools, for example, to anticipate complex treatment courses and to be able to offer appropriate therapy on an individualized basis. This study aimed to develop a first basic model for the anticipation of adverse treatment courses based on prior compulsory admission and/or conviction as simple and easily objectifiable parameters in offender patients with a schizophrenia spectrum disorder (SSD). With a balanced accuracy of 67% and an AUC of 0.72, gradient boosting proved to be the optimal ML algorithm. Antisocial behavior, physical violence against staff, rule breaking, hyperactivity, delusions of grandeur, fewer feelings of guilt, the need for compulsory isolation, cannabis abuse/dependence, a higher dose of antipsychotics (measured by the olanzapine half-life) and an unfavorable legal prognosis emerged as the ten most influential variables out of a dataset with 209 parameters. Our findings could demonstrate an example of the use of ML in the development of an easy-to-use predictive model based on few objectifiable factors. Full article

(This article belongs to the Special Issue Machine Learning and Big Data in Psychiatric and Sleep Disorders)

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17 pages, 1294 KiB

Open AccessArticle

Reading Wishes from the Lips: Cancer Patients’ Need for Psycho-Oncological Support during Inpatient and Outpatient Treatment

by Jan Ben Schulze, Marc Dörner, Hermanas Usas, Moritz Philipp Günther, Roland von Känel and Sebastian Euler

Diagnostics 2022, 12(10), 2440; https://doi.org/10.3390/diagnostics12102440 - 09 Oct 2022

Cited by 1 | Viewed by 1168

Abstract

Background: Psycho-oncological support (PO) is an effective measure to reduce distress and improve the quality of life in patients with cancer. Currently, there are only a few studies investigating the (expressed) wish for PO. The aim of this study was to evaluate the number of patients who request PO and to identify predictors for the wish for PO. Methods: Data from 3063 cancer patients who had been diagnosed and treated at a Comprehensive Cancer Center between 2011 and 2019 were analyzed retrospectively. Potential predictors for the wish for PO were identified using logistic regression. As a novelty, a Back Propagation Neural Network (BPNN) was applied to establish a prediction model for the wish for PO. Results: In total, 1752 patients (57.19%) had a distress score above the cut-off and 14.59% expressed the wish for PO. Patients’ requests for pastoral care (OR = 13.1) and social services support (OR = 5.4) were the strongest predictors of the wish for PO. Patients of the female sex or who had a current psychiatric diagnosis, opioid treatment and malignant neoplasms of the skin and the hematopoietic system also predicted the wish for PO, while malignant neoplasms of digestive organs and older age negatively predicted the wish for PO. These nine significant predictors were used as input variables for the BPNN model. BPNN computations indicated that a three-layer network with eight neurons in the hidden layer is the most precise prediction model. Discussion: Our results suggest that the identification of predictors for the wish for PO might foster PO referrals and help cancer patients reduce barriers to expressing their wish for PO. Furthermore, the final BPNN prediction model demonstrates a high level of discrimination and might be easily implemented in the hospital information system. Full article

(This article belongs to the Special Issue Machine Learning and Big Data in Psychiatric and Sleep Disorders)

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10 pages, 1443 KiB

Open AccessArticle

Deep Learning for Automatic Detection of Periodic Limb Movement Disorder Based on Electrocardiogram Signals

by Erdenebayar Urtnasan, Jong-Uk Park, Jung-Hun Lee, Sang-Baek Koh and Kyoung-Joung Lee

Diagnostics 2022, 12(9), 2149; https://doi.org/10.3390/diagnostics12092149 - 03 Sep 2022

Cited by 3 | Viewed by 1768

Abstract

In this study, a deep learning model (deepPLM) is shown to automatically detect periodic limb movement syndrome (PLMS) based on electrocardiogram (ECG) signals. The designed deepPLM model consists of four 1D convolutional layers, two long short-term memory units, and a fully connected layer. The Osteoporotic Fractures in Men sleep (MrOS) study dataset was used to construct the model, including training, validating, and testing the model. A single-lead ECG signal of the polysomnographic recording was used for each of the 52 subjects (26 controls and 26 patients) in the MrOS dataset. The ECG signal was normalized and segmented (10 s duration), and it was divided into a training set (66,560 episodes), a validation set (16,640 episodes), and a test set (20,800 episodes). The performance evaluation of the deepPLM model resulted in an F1-score of 92.0%, a precision score of 90.0%, and a recall score of 93.0% for the control set, and 92.0%, 93.0%, and 90.0%, respectively, for the patient set. The results demonstrate the possibility of automatic PLMS detection in patients by using the deepPLM model based on a single-lead ECG. This could be an alternative method for PLMS screening and a helpful tool for home healthcare services for the elderly population. Full article

(This article belongs to the Special Issue Machine Learning and Big Data in Psychiatric and Sleep Disorders)

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10 pages, 862 KiB

Open AccessArticle

Deep Convolutional Recurrent Model for Automatic Scoring Sleep Stages Based on Single-Lead ECG Signal

by Erdenebayar Urtnasan, Jong-Uk Park, Eun Yeon Joo and Kyoung-Joung Lee

Diagnostics 2022, 12(5), 1235; https://doi.org/10.3390/diagnostics12051235 - 15 May 2022

Cited by 13 | Viewed by 2528

Abstract

Background: Sleep stage scoring, which is an essential step in the quantitative analysis of sleep monitoring, relies on human experts and is therefore subjective and time-consuming; thus, an easy and accurate method is needed for the automatic scoring of sleep stages. Methods: In this study, we constructed a deep convolutional recurrent (DCR) model for the automatic scoring of sleep stages based on a raw single-lead electrocardiogram (ECG). The DCR model uses deep convolutional and recurrent neural networks to apply the complex and cyclic rhythms of human sleep. It consists of three convolutional and two recurrent layers and is optimized by dropout and batch normalization. The constructed DCR model was evaluated using multiclass classification, including five-class sleep stages (wake, N1, N2, N3, and rapid eye movement (REM)) and three-class sleep stages (wake, non-REM (NREM), and REM), using a raw single-lead ECG signal. The single-lead ECG signal was collected from 112 subjects in two groups: control (52 subjects) and sleep apnea (60 subjects). The single-lead ECG signal was preprocessed, segmented at a duration of 30 s, and divided into a training set of 89 subjects and test set of 23 subjects. Results: We achieved an overall accuracy of 74.2% for five classes and 86.4% for three classes. Conclusions: These results show the DCR model’s superior performance over those in the previous studies, highlighting that the model can be an alternative tool for sleep monitoring and sleep screening. Full article

(This article belongs to the Special Issue Machine Learning and Big Data in Psychiatric and Sleep Disorders)

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13 pages, 426 KiB

Open AccessArticle

Predicting the Severity of Lockdown-Induced Psychiatric Symptoms with Machine Learning

by Giordano D’Urso, Alfonso Magliacano, Sayna Rotbei, Felice Iasevoli, Andrea de Bartolomeis and Alessio Botta

Diagnostics 2022, 12(4), 957; https://doi.org/10.3390/diagnostics12040957 - 12 Apr 2022

Cited by 4 | Viewed by 1371

Abstract

During the COVID-19 pandemic, an increase in the incidence of psychiatric disorders in the general population and an increase in the severity of symptoms in psychiatric patients have been reported. Anxiety and depression symptoms are the most commonly observed during large-scale dramatic events such as pandemics and wars, especially when these implicate an extended lockdown. The early detection of higher risk clinical and non-clinical individuals would help prevent the new onset and/or deterioration of these symptoms. This in turn would lead to the implementation of public policies aimed at protecting vulnerable populations during these dramatic contingencies, therefore optimising the effectiveness of interventions and saving the resources of national healthcare systems. We used a supervised machine learning method to identify the predictors of the severity of psychiatric symptoms during the Italian lockdown due to the COVID-19 pandemic. Via a case study, we applied this methodology to a small sample of healthy individuals, obsessive-compulsive disorder patients, and adjustment disorder patients. Our preliminary results show that our models were able to predict depression, anxiety, and obsessive-compulsive symptoms during the lockdown with up to 92% accuracy based on demographic and clinical characteristics collected before the pandemic. The presented methodology may be used to predict the psychiatric prognosis of individuals under a large-scale lockdown and thus supporting the related clinical decisions. Full article

(This article belongs to the Special Issue Machine Learning and Big Data in Psychiatric and Sleep Disorders)

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14 pages, 249 KiB

Open AccessArticle

Depressive Symptoms and Suicidal Ideation in Individuals Living Alone in South Korea

by Kyoung Ae Kong, Young Eun Kim, Sunho Lim, Bo Young Kim, Ga Eun Kim and Soo In Kim

Diagnostics 2022, 12(3), 603; https://doi.org/10.3390/diagnostics12030603 - 27 Feb 2022

Cited by 2 | Viewed by 1790

Abstract

This study compared the prevalence of depressive symptoms and suicidal ideation in individuals living alone compared with those living with others and assessed the contribution of socio-demographic factors and physical health to these differences. We analyzed 2221 individuals living alone and 19,397 individuals living with others aged 20–80 years, drawn from the Korean National Health and Nutrition Examination Survey dataset in South Korea. The study group divided into three subgroups based on age to determine whether there were differences in mental health according to age. Depressive symptoms and suicidal ideation were evaluated by self-reported questionnaires. The sex- and age-adjusted prevalence rates of depressive symptoms and suicidal ideation were higher in those living alone than those living with others. The proportion of socio-economic status and physical health explaining the differences of depressive mood and suicidal ideation between the two groups was greater in the age group over 35 years old. Considering the difference in factors that explain depressive symptoms and suicidal ideation among individuals living alone in the age group over 35 years of age and younger groups under 34 years of age, policies should be developed that will address the mental health needs of each age group. Full article

(This article belongs to the Special Issue Machine Learning and Big Data in Psychiatric and Sleep Disorders)

21 pages, 3528 KiB

Open AccessArticle

Apnea Detection in Polysomnographic Recordings Using Machine Learning Techniques

by Marek Piorecky, Martin Bartoň, Vlastimil Koudelka, Jitka Buskova, Jana Koprivova, Martin Brunovsky and Vaclava Piorecka

Diagnostics 2021, 11(12), 2302; https://doi.org/10.3390/diagnostics11122302 - 08 Dec 2021

Cited by 9 | Viewed by 2724

Abstract

Sleep disorders are diagnosed in sleep laboratories by polysomnography, a multi-parameter examination that monitors biological signals during sleep. The subsequent evaluation of the obtained records is very time-consuming. The goal of this study was to create an automatic system for evaluation of the [...] Read more.

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channels of polysomnography records, through the use of machine learning techniques and a large database, for apnea and desaturation detection (which is unusual in other studies). To that end, a convolutional neural network (CNN) was designed using hyperparameter optimization. It was then trained and tested for apnea and desaturation. The proposed CNN was compared with the commonly used k-nearest neighbors (k-NN) method. The classifiers were designed based on nasal airflow and blood oxygen saturation signals. The final neural network accuracy for apnea detection reached 84%, and that for desaturation detection was 74%, while the k-NN classifier reached accuracies of 83% and 64% for apnea detection and desaturation detection, respectively. Full article

(This article belongs to the Special Issue Machine Learning and Big Data in Psychiatric and Sleep Disorders)

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9 pages, 1295 KiB

Open AccessArticle

A Prediction Model of Incident Cardiovascular Disease in Patients with Sleep-Disordered Breathing

by Jong-Uk Park, Erdenebayar Urtnasan, Sang-Ha Kim and Kyoung-Joung Lee

Diagnostics 2021, 11(12), 2212; https://doi.org/10.3390/diagnostics11122212 - 26 Nov 2021

Cited by 3 | Viewed by 1766

Abstract

(1) Purpose: this study proposes a method of prediction of cardiovascular diseases (CVDs) that can develop within ten years in patients with sleep-disordered breathing (SDB). (2) Methods: For the design and evaluation of the algorithm, the Sleep Heart Health Study (SHHS) data from the 3367 participants were divided into a training set, validation set, and test set in the ratio of 5:3:2. From the data during a baseline period when patients did not have any CVD, we extracted 18 features from electrography (ECG) based on signal processing methods, 30 ECG features based on artificial intelligence (AI), ten clinical risk factors for CVD. We trained the model and evaluated it by using CVD outcomes result, monitored in follow-ups. The optimal feature vectors were selected through statistical analysis and support vector machine recursive feature elimination (SVM-RFE) of the extracted feature vectors. Features based on AI, a novel proposal from this study, showed excellent performance out of all selected feature vectors. In addition, new parameters based on AI were possibly meaningful predictors for CVD, when used in addition to the predictors for CVD that are already known. The selected features were used as inputs to the prediction model based on SVM for CVD, determining the development of CVD-free, coronary heart disease (CHD), heart failure (HF), or stroke within ten years. (3) Results: As a result, the respective recall and precision values were 82.9% and 87.5% for CVD-free; 71.9% and 63.8% for CVD; 57.2% and 55.4% for CHD; 52.6% and 40.8% for HF; 52.4% and 44.6% for stroke. The F1-score between CVD and CVD-free was 76.5%, and it was 59.1% in class four. (4) Conclusion: In conclusion, our results confirm the excellence of the prediction model for CVD in patients with SDB and verify the possibility of prediction within ten years of the CVDs that may occur in patients with SDB. Full article

(This article belongs to the Special Issue Machine Learning and Big Data in Psychiatric and Sleep Disorders)

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10 pages, 8180 KiB

Open AccessArticle

AI-Enabled Algorithm for Automatic Classification of Sleep Disorders Based on Single-Lead Electrocardiogram

by Erdenebayar Urtnasan, Eun Yeon Joo and Kyu Hee Lee

Diagnostics 2021, 11(11), 2054; https://doi.org/10.3390/diagnostics11112054 - 05 Nov 2021

Cited by 14 | Viewed by 2799

Abstract

Healthy sleep is an essential physiological process for every individual to live a healthy life. Many sleep disorders both destroy the quality and decrease the duration of sleep. Thus, a convenient and accurate detection or classification method is important for screening and identifying sleep disorders. In this study, we proposed an AI-enabled algorithm for the automatic classification of sleep disorders based on a single-lead electrocardiogram (ECG). An AI-enabled algorithm—named a sleep disorder network (SDN)—was designed for automatic classification of four major sleep disorders, namely insomnia (INS), periodic leg movement (PLM), REM sleep behavior disorder (RBD), and nocturnal frontal-lobe epilepsy (NFE). The SDN was constructed using deep convolutional neural networks that can extract and analyze the complex and cyclic rhythm of sleep disorders that affect ECG patterns. The SDN consists of five layers, a 1D convolutional layer, and is optimized via dropout and batch normalization. The single-lead ECG signal was extracted from the 35 subjects with the control (CNT) and the four sleep disorder groups (seven subjects of each group) in the CAP Sleep Database. The ECG signal was pre-processed, segmented at 30 s intervals, and divided into the training, validation, and test sets consisting of 74,135, 18,534, and 23,168 segments, respectively. The constructed SDN was trained and evaluated using the CAP Sleep Database, which contains not only data on sleep disorders, but also data of the control group. The proposed SDN algorithm for the automatic classification of sleep disorders based on a single-lead ECG showed very high performances. We achieved F1 scores of 99.0%, 97.0%, 97.0%, 95.0%, and 98.0% for the CNT, INS, PLM, RBD, and NFE groups, respectively. We proposed an AI-enabled method for the automatic classification of sleep disorders based on a single-lead ECG signal. In addition, it represents the possibility of the sleep disorder classification using ECG only. The SDN can be a useful tool or an alternative screening method based on single-lead ECGs for sleep monitoring and screening. Full article

(This article belongs to the Special Issue Machine Learning and Big Data in Psychiatric and Sleep Disorders)

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12 pages, 776 KiB

Open AccessArticle

Predicting Depression in Community Dwellers Using a Machine Learning Algorithm

by Seo-Eun Cho, Zong Woo Geem and Kyoung-Sae Na

Diagnostics 2021, 11(8), 1429; https://doi.org/10.3390/diagnostics11081429 - 07 Aug 2021

Cited by 15 | Viewed by 2523

Abstract

Depression is one of the leading causes of disability worldwide. Given the socioeconomic burden of depression, appropriate depression screening for community dwellers is necessary. We used data from the 2014 and 2016 Korea National Health and Nutrition Examination Surveys. The 2014 dataset was used as a training set, whereas the 2016 dataset was used as the hold-out test set. The synthetic minority oversampling technique (SMOTE) was used to control for class imbalances between the depression and non-depression groups in the 2014 dataset. The least absolute shrinkage and selection operator (LASSO) was used for feature reduction and classifiers in the final model. Data obtained from 9488 participants were used for the machine learning process. The depression group had poorer socioeconomic, health, functional, and biological measures than the non-depression group. From the initial 37 variables, 13 were selected using LASSO. All performance measures were calculated based on the raw 2016 dataset without the SMOTE. The area under the receiver operating characteristic curve and overall accuracy in the hold-out test set were 0.903 and 0.828, respectively. Perceived stress had the strongest influence on the classifying model for depression. LASSO can be practically applied for depression screening of community dwellers with a few variables. Future studies are needed to develop a more efficient and accurate classification model for depression. Full article

(This article belongs to the Special Issue Machine Learning and Big Data in Psychiatric and Sleep Disorders)

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19 pages, 495 KiB

Open AccessArticle

Automated Characterization of Cyclic Alternating Pattern Using Wavelet-Based Features and Ensemble Learning Techniques with EEG Signals

by Manish Sharma, Virendra Patel, Jainendra Tiwari and U. Rajendra Acharya

Diagnostics 2021, 11(8), 1380; https://doi.org/10.3390/diagnostics11081380 - 30 Jul 2021

Cited by 32 | Viewed by 3628

Abstract

Sleep is highly essential for maintaining metabolism of the body and mental balance for increased productivity and concentration. Often, sleep is analyzed using macrostructure sleep stages which alone cannot provide information about the functional structure and stability of sleep. The cyclic alternating pattern (CAP) is a physiological recurring electroencephalogram (EEG) activity occurring in the brain during sleep and captures microstructure of the sleep and can be used to identify sleep instability. The CAP can also be associated with various sleep-related pathologies, and can be useful in identifying various sleep disorders. Conventionally, sleep is analyzed using polysomnogram (PSG) in various sleep laboratories by trained physicians and medical practitioners. However, PSG-based manual sleep analysis by trained medical practitioners is onerous, tedious and unfavourable for patients. Hence, a computerized, simple and patient convenient system is highly desirable for monitoring and analysis of sleep. In this study, we have proposed a system for automated identification of CAP phase-A and phase-B. To accomplish the task, we have utilized the openly accessible CAP sleep database. The study is performed using two single-channel EEG modalities and their combination. The model is developed using EEG signals of healthy subjects as well as patients suffering from six different sleep disorders namely nocturnal frontal lobe epilepsy (NFLE), sleep-disordered breathing (SDB), narcolepsy, periodic leg movement disorder (PLM), insomnia and rapid eye movement behavior disorder (RBD) subjects. An optimal orthogonal wavelet filter bank is used to perform the wavelet decomposition and subsequently, entropy and Hjorth parameters are extracted from the decomposed coefficients. The extracted features have been applied to different machine learning algorithms. The best performance is obtained using ensemble of bagged tress (EBagT) classifier. The proposed method has obtained the average classification accuracy of 84%, 83%, 81%, 78%, 77%, 76% and 72% for NFLE, healthy, SDB, narcolepsy, PLM, insomnia and RBD subjects, respectively in discriminating phases A and B using a balanced database. Our developed model yielded an average accuracy of 78% when all 77 subjects including healthy and sleep disordered patients are considered. Our proposed system can assist the sleep specialists in an automated and efficient analysis of sleep using sleep microstructure. Full article

(This article belongs to the Special Issue Machine Learning and Big Data in Psychiatric and Sleep Disorders)

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12 pages, 5777 KiB

Open AccessArticle

Prediction Models for Obstructive Sleep Apnea in Korean Adults Using Machine Learning Techniques

by Young Jae Kim, Ji Soo Jeon, Seo-Eun Cho, Kwang Gi Kim and Seung-Gul Kang

Diagnostics 2021, 11(4), 612; https://doi.org/10.3390/diagnostics11040612 - 30 Mar 2021

Cited by 16 | Viewed by 2924

Abstract

This study aimed to investigate the applicability of machine learning to predict obstructive sleep apnea (OSA) among individuals with suspected OSA in South Korea. A total of 92 clinical variables for OSA were collected from 279 South Koreans (OSA, n = 213; no OSA, n = 66), from which seven major clinical indices were selected. The data were randomly divided into training data (OSA, n = 149; no OSA, n = 46) and test data (OSA, n = 64; no OSA, n = 20). Using the seven clinical indices, the OSA prediction models were trained using four types of machine learning models—logistic regression, support vector machine (SVM), random forest, and XGBoost (XGB)—and each model was validated using the test data. In the validation, the SVM showed the best OSA prediction result with a sensitivity, specificity, and area under curve (AUC) of 80.33%, 86.96%, and 0.87, respectively, while the XGB showed the lowest OSA prediction performance with a sensitivity, specificity, and AUC of 78.69%, 73.91%, and 0.80, respectively. The machine learning algorithms showed high OSA prediction performance using data from South Koreans with suspected OSA. Hence, machine learning will be helpful in clinical applications for OSA prediction in the Korean population. Full article

(This article belongs to the Special Issue Machine Learning and Big Data in Psychiatric and Sleep Disorders)

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Review

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14 pages, 266 KiB

Open AccessReview

Machine Learning-Based Definition of Symptom Clusters and Selection of Antidepressants for Depressive Syndrome

by Il Bin Kim and Seon-Cheol Park

Diagnostics 2021, 11(9), 1631; https://doi.org/10.3390/diagnostics11091631 - 07 Sep 2021

Cited by 8 | Viewed by 2713

Abstract

The current polythetic and operational criteria for major depression inevitably contribute to the heterogeneity of depressive syndromes. The heterogeneity of depressive syndrome has been criticized using the concept of language game in Wittgensteinian philosophy. Moreover, “a symptom- or endophenotype-based approach, rather than a diagnosis-based approach, has been proposed” as the “next-generation treatment for mental disorders” by Thomas Insel. Understanding the heterogeneity renders promise for personalized medicine to treat cases of depressive syndrome, in terms of both defining symptom clusters and selecting antidepressants. Machine learning algorithms have emerged as a tool for personalized medicine by handling clinical big data that can be used as predictors for subtype classification and treatment outcome prediction. The large clinical cohort data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D), Combining Medications to Enhance Depression Outcome (CO-MED), and the German Research Network on Depression (GRND) have recently began to be acknowledged as useful sources for machine learning-based depression research with regard to cost effectiveness and generalizability. In addition, noninvasive biological tools such as functional and resting state magnetic resonance imaging techniques are widely combined with machine learning methods to detect intrinsic endophenotypes of depression. This review highlights recent studies that have used clinical cohort or brain imaging data and have addressed machine learning-based approaches to defining symptom clusters and selecting antidepressants. Potentially applicable suggestions to realize machine learning-based personalized medicine for depressive syndrome are also provided herein. Full article

(This article belongs to the Special Issue Machine Learning and Big Data in Psychiatric and Sleep Disorders)

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