A Machine Learning-Based Method to Identify Bipolar Disorder Patients

Mateo-Sotos, Jorge; Torres, Ana; Santos, José Luís; Quevedo, Óscar Erades De; Basar, C.

doi:10.1007/s00034-021-01889-1

Cited by 15 publications

(5 citation statements)

References 60 publications

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“…Moreover, the similarity between the training and test phases shown in the radar plot indicates the absence of overfitting and overtraining. This implies high generalizability, so that when introducing new data, the results obtained are consistent with those obtained at the current time [63]. The method also exhibits high scalability and execution speed, allowing its usefulness in daily clinical practice to assist in decision making.…”

Section: Discussionsupporting

confidence: 58%

Machine Learning-Based Assessment of Survival and Risk Factors in Non-Alcoholic Fatty Liver Disease-Related Hepatocellular Carcinoma for Optimized Patient Management

Suárez,

Gil-Rojas,

Martínez-Blanco

et al. 2024

Cancers

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Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide, with an incidence that is exponentially increasing. Hepatocellular carcinoma (HCC) is the most frequent primary tumor. There is an increasing relationship between these entities due to the potential risk of developing NAFLD-related HCC and the prevalence of NAFLD. There is limited evidence regarding prognostic factors at the diagnosis of HCC. This study compares the prognosis of HCC in patients with NAFLD against other etiologies. It also evaluates the prognostic factors at the diagnosis of these patients. For this purpose, a multicenter retrospective study was conducted involving a total of 191 patients. Out of the total, 29 presented NAFLD-related HCC. The extreme gradient boosting (XGB) method was employed to develop the reference predictive model. Patients with NAFLD-related HCC showed a worse prognosis compared to other potential etiologies of HCC. Among the variables with the worst prognosis, alcohol consumption in NAFLD patients had the greatest weight within the developed predictive model. In comparison with other studied methods, XGB obtained the highest values for the analyzed metrics. In conclusion, patients with NAFLD-related HCC and alcohol consumption, obesity, cirrhosis, and clinically significant portal hypertension (CSPH) exhibited a worse prognosis than other patients. XGB developed a highly efficient predictive model for the assessment of these patients.

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Section: Discussionsupporting

confidence: 58%

Machine Learning-Based Assessment of Survival and Risk Factors in Non-Alcoholic Fatty Liver Disease-Related Hepatocellular Carcinoma for Optimized Patient Management

Suárez,

Gil-Rojas,

Martínez-Blanco

et al. 2024

Cancers

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“…In a study on BD, a machine learning approach based on EEG signals and XGB demonstrated remarkable performance, achieving a high prediction accuracy of 94%, precision exceeding 94%, and recall surpassing 94% [ 34 ]. In the realm of epilepsy, EEG-based methodologies have shown promise in seizure detection [ 50 ]. One study proposed a real-time EEG-based approach utilizing discrete wavelet transform, attaining an accuracy of 97% and a sensitivity of 96.67% in the UB dataset.…”

Section: Discussionmentioning

confidence: 99%

“…EEG-based diagnoses. Numerous studies have delved into the classification and diagnosis of mental disorders and neurological conditions through the utilization of EEG [34,[50][51][52][53][54][55]. In a study on BD, a machine learning approach based on EEG signals and XGB demonstrated remarkable performance, achieving a high prediction accuracy of 94%, precision exceeding 94%, and recall surpassing 94% [34].…”

Section: Literature Review and Comparison With Previous Studiesmentioning

confidence: 99%

A machine learning approach for differentiating bipolar disorder type II and borderline personality disorder using electroencephalography and cognitive abnormalities

Nazari,

Shalbafan,

Eissazade

et al. 2024

PLoS ONE

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This study addresses the challenge of differentiating between bipolar disorder II (BD II) and borderline personality disorder (BPD), which is complicated by overlapping symptoms. To overcome this, a multimodal machine learning approach was employed, incorporating both electroencephalography (EEG) patterns and cognitive abnormalities for enhanced classification. Data were collected from 45 participants, including 20 with BD II and 25 with BPD. Analysis involved utilizing EEG signals and cognitive tests, specifically the Wisconsin Card Sorting Test and Integrated Cognitive Assessment. The k-nearest neighbors (KNN) algorithm achieved a balanced accuracy of 93%, with EEG features proving to be crucial, while cognitive features had a lesser impact. Despite the strengths, such as diverse model usage, it’s important to note limitations, including a small sample size and reliance on DSM diagnoses. The study suggests that future research should explore multimodal data integration and employ advanced techniques to improve classification accuracy and gain a better understanding of the neurobiological distinctions between BD II and BPD.

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“…Previous studies have shown that small sample sizes lead to falsely increased accuracies reported in machine learning models ( 55 ). In most studies, it is not clear whether patients with BD are in the depressive phase, manic/hypomanic phase (type I or type II), or out of the episode, or whether they are euthymic patients ( 28 - 30 , 33 , 35 , 37 , 39 - 41 , 43 , 48 , 50 , 51 , 53 ). Meanwhile, previous studies have shown that there are different patterns of brain abnormalities between different episodes of this disorder ( 10 ).…”

Section: Discussionmentioning

confidence: 99%

An Overview of Bipolar Disorder Diagnosis Using Machine Learning Approaches: Clinical Opportunities and Challenges

Campos-Ugaz

Garay

Rivera‐Lozada

et al. 2023

IJPS

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Objective: Automatic diagnosis of psychiatric disorders such as bipolar disorder (BD) through machine learning techniques has attracted substantial attention from psychiatric and artificial intelligence communities. These approaches mostly rely on various biomarkers extracted from electroencephalogram (EEG) or magnetic resonance imaging (MRI)/functional MRI (fMRI) data. In this paper, we provide an updated overview of existing machine learning-based methods for bipolar disorder (BD) diagnosis using MRI and EEG data. Method: This study is a short non-systematic review with the aim of describing the current situation in automatic diagnosis of BD using machine learning methods. Therefore, an appropriate literature search was conducted via relevant keywords for original EEG/MRI studies on distinguishing BD from other conditions, particularly from healthy peers, in PubMed, Web of Science, and Google Scholar databases. Results: We reviewed 26 studies, including 10 EEG studies and 16 MRI studies (including structural and functional MRI), that used traditional machine learning methods and deep learning algorithms to automatically detect BD. The reported accuracies for EEG studies is about 90%, while the reported accuracies for MRI studies remains below the minimum level for clinical relevance, i.e. about 80% of the classification outcome for traditional machine learning methods. However, deep learning techniques have generally achieved accuracies higher than 95%. Conclusion: Research utilizing machine learning applied to EEG signals and brain images has provided proof of concept for how this innovative technique can help psychiatrists distinguish BD patients from healthy people. However, the results have been somewhat contradictory and we must keep away from excessive optimistic interpretations of the findings. Much progress is still needed to reach the level of clinical practice in this field.

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A Machine Learning-Based Method to Identify Bipolar Disorder Patients

Cited by 15 publications

References 60 publications

Machine Learning-Based Assessment of Survival and Risk Factors in Non-Alcoholic Fatty Liver Disease-Related Hepatocellular Carcinoma for Optimized Patient Management

Machine Learning-Based Assessment of Survival and Risk Factors in Non-Alcoholic Fatty Liver Disease-Related Hepatocellular Carcinoma for Optimized Patient Management

A machine learning approach for differentiating bipolar disorder type II and borderline personality disorder using electroencephalography and cognitive abnormalities

An Overview of Bipolar Disorder Diagnosis Using Machine Learning Approaches: Clinical Opportunities and Challenges

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