Assessing the severity of positive valence symptoms in initial psychiatric evaluation records: Should we use convolutional neural networks?

Dai, Hong-Jie; Jonnagaddala, Jitendra

doi:10.1371/journal.pone.0204493

Cited by 16 publications

(7 citation statements)

References 28 publications

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“…One of the first attempts to apply CNNs to classify textual data was proposed by Kim ( 13 ) and recently by Yang et al ( 14 ) for the automatic diagnosis of six diseases. Dai and Jonnagaddala ( 15 ) used a CNN to determine positive valence symptom severity in psychiatric evaluation records. We implemented the text-CNN model by applying convolutions to the represented sequences from both sections.…”

Section: Methodsmentioning

confidence: 99%

Deep Learning-Based Natural Language Processing for Screening Psychiatric Patients

Dai

Lee

et al. 2021

Front. Psychiatry

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The introduction of pre-trained language models in natural language processing (NLP) based on deep learning and the availability of electronic health records (EHRs) presents a great opportunity to transfer the “knowledge” learned from data in the general domain to enable the analysis of unstructured textual data in clinical domains. This study explored the feasibility of applying NLP to a small EHR dataset to investigate the power of transfer learning to facilitate the process of patient screening in psychiatry. A total of 500 patients were randomly selected from a medical center database. Three annotators with clinical experience reviewed the notes to make diagnoses for major/minor depression, bipolar disorder, schizophrenia, and dementia to form a small and highly imbalanced corpus. Several state-of-the-art NLP methods based on deep learning along with pre-trained models based on shallow or deep transfer learning were adapted to develop models to classify the aforementioned diseases. We hypothesized that the models that rely on transferred knowledge would be expected to outperform the models learned from scratch. The experimental results demonstrated that the models with the pre-trained techniques outperformed the models without transferred knowledge by micro-avg. and macro-avg. F-scores of 0.11 and 0.28, respectively. Our results also suggested that the use of the feature dependency strategy to build multi-labeling models instead of problem transformation is superior considering its higher performance and simplicity in the training process.

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Section: Methodsmentioning

confidence: 99%

Deep Learning-Based Natural Language Processing for Screening Psychiatric Patients

Dai

Lee

et al. 2021

Front. Psychiatry

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“…In this context, CNN 54 and RNN 55 have shown superiority in modeling syntax for text-based prediction. In particular, CNN has been used to mine the neuropsychiatric notes for predicting psychiatric symptom severity 56,57 . Tran and Kavuluru 58 used an RNN to analyze the history of present illness in neuropsychiatric notes for predicting mental health conditions.…”

Section: Electronic Health Recordsmentioning

confidence: 99%

“…Another potential way is to predict other clinical outcomes instead of the diagnostic labels. For example, several selected studies proposed to predict symptom severity scores 56,57,77,82,84,87,89 . In addition, Du et al 108 attempted to identify suicide-related psychiatric stressors from users' posts on Twitter, which plays an important role in the early prevention of suicidal behaviors.…”

Section: Diagnosis and Prediction Issuesmentioning

confidence: 99%

Deep learning in mental health outcome research: a scoping review

et al. 2020

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Mental illnesses, such as depression, are highly prevalent and have been shown to impact an individual's physical health. Recently, artificial intelligence (AI) methods have been introduced to assist mental health providers, including psychiatrists and psychologists, for decision-making based on patients' historical data (e.g., medical records, behavioral data, social media usage, etc.). Deep learning (DL), as one of the most recent generation of AI technologies, has demonstrated superior performance in many real-world applications ranging from computer vision to healthcare. The goal of this study is to review existing research on applications of DL algorithms in mental health outcome research. Specifically, we first briefly overview the state-of-the-art DL techniques. Then we review the literature relevant to DL applications in mental health outcomes. According to the application scenarios, we categorize these relevant articles into four groups: diagnosis and prognosis based on clinical data, analysis of genetics and genomics data for understanding mental health conditions, vocal and visual expression data analysis for disease detection, and estimation of risk of mental illness using social media data. Finally, we discuss challenges in using DL algorithms to improve our understanding of mental health conditions and suggest several promising directions for their applications in improving mental health diagnosis and treatment.

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“…These instruments are all self-report, with limited validity due to retrospective memory difficulties especially with respect to mood instability seen in patients with BoPD 10 . An alternative method of screening for mental illness is through examination of provider data in medical records, including historical information from progress notes 11 , and details from initial presentations 12 . However, these efforts have not yet been extended to individuals with BoPD.…”

Section: Introductionmentioning

confidence: 99%

Development of a screening algorithm for borderline personality disorder using electronic health records

Zang

Goodman

Zhu

et al. 2022

Sci Rep

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Borderline personality disorder (BoPD or BPD) is highly prevalent and characterized by reactive moods, impulsivity, behavioral dysregulation, and distorted self-image. Yet the BoPD diagnosis is underutilized and patients with BoPD are frequently misdiagnosed resulting in lost opportunities for appropriate treatment. Automated screening of electronic health records (EHRs) is one potential strategy to help identify possible BoPD patients who are otherwise undiagnosed. We present the development and analytical validation of a BoPD screening algorithm based on routinely collected and structured EHRs. This algorithm integrates rule-based selection and machine learning (ML) in a two-step framework by first selecting potential patients based on the presence of comorbidities and characteristics commonly associated with BoPD, and then predicting whether the patients most likely have BoPD. Leveraging a large-scale US-based de-identified EHR database and our clinical expert’s rating of two random samples of patient EHRs, results show that our screening algorithm has a high consistency with our clinical expert’s ratings, with area under the receiver operating characteristic (AUROC) 0.837 [95% confidence interval (CI) 0.778–0.892], positive predictive value 0.717 (95% CI 0.583–0.836), accuracy 0.820 (95% CI 0.768–0.873), sensitivity 0.541 (95% CI 0.417–0.667) and specificity 0.922 (95% CI 0.880–0.960). Our aim is, to provide an additional resource to facilitate clinical decision making and promote the development of digital medicine.

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Assessing the severity of positive valence symptoms in initial psychiatric evaluation records: Should we use convolutional neural networks?

Cited by 16 publications

References 28 publications

Deep Learning-Based Natural Language Processing for Screening Psychiatric Patients

Deep Learning-Based Natural Language Processing for Screening Psychiatric Patients

Deep learning in mental health outcome research: a scoping review

Development of a screening algorithm for borderline personality disorder using electronic health records

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