Depressive Symptoms and Functional Impairments Extraction From Electronic Health Records

Zhang, You-Chen; Lee, Chung-Hong; Liang, Tyng-Yeu; Chung, Wei-Che; Li, Kuei-Han; Huang, Cheng‐Chieh; Dai, Hong-Jie; Kuo, Cheng‐Deng; Su, Chu-Hsien; Yang, Horng-Chang

doi:10.1109/icmlc48188.2019.8949199

Cited by 1 publication

(2 citation statements)

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“…This suggests that it would be worthwhile comparing our method with the approach that identifies the major depressive cases by using the number of observed depressive symptoms only. Therefore, we used the depressive symptom recognizer developed in our previous studies ( 4 , 37 ) to recognize the nine depressive symptoms including depressed mood, loss of interest, fatigue, appetite, sleep, psychomotor, poor concentration, worthless, and suicidality from the BH and PME section of an EHR. We then applied the rule to determine whether a patient has major depression if at least five unique symptoms are recognized in their EHR.…”

Section: Discussionmentioning

confidence: 99%

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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: Discussionmentioning

confidence: 99%

“…The configuration “gold annotations” applied the same rule as the human annotators' depressive symptom annotations, which were annotated on the same dataset used in this study. Details of the symptom annotations can be found in our previous studies ( 4 , 37 ).…”

Section: Discussionmentioning

confidence: 99%