Natural Language Processing-Based Quantication of the Mental State of Psychiatric Patients

Abstract

Psychiatric practice routinely uses semistructured and/or unstructured free text to record the behavior and mental state of patients. Many of these data are unstructured, lack standardization, and are difficult to use for analysis. Thus, it is difficult to quantitatively analyze a patient's illness trajectory over time and his or her responsiveness to treatment, and it is also difficult to compare different patients quantitatively. In this article, experts in the field of psychiatry, along with machine learning models, have collaboratively transformed patient data available in status assessments generated by physicians into binary vector representations. Data from patients with mental health disorders collected within a real-world clinical setting from one of the largest behavioral electronic health record (EHR) systems in the United States have been used for generating these representations. The binary vector representation of these health records is shown to be useful in various clinical tasks, such as disease phenotyping, characterizing the suicidality of patients, and inferring diagnoses. To summarize, this approach can transform semistructured free-text summaries of patients' status assessments into a structured, quantifiable format, which enriches the data that reside within EHR systems. This allows for effective intra-and interpatient quantifications and comparisons, which are much needed in the field of mental health. With the aid of these binary representations, patients' mental states can be systematically tracked over time, as can their responses to medications at the individual and population levels.

NeuroBlu, an electronic health record (EHR) trusted research environment (TRE) to support mental healthcare analytics with real-world data

“…The MindLinc EHR system was developed at Duke University Medical Center to enable mental healthcare professionals to document clinical information while providing routine patient care 34. MindLinc includes structured fields to record sociodemographic data, diagnoses, medications and clinical outcome scales as well as semistructured free text fields to document the mental state examination (MSE) and treatment plan 33. A subset of deidentified MindLinc EHR data are generated (using the data pipeline described subsequently) to support secondary analyses in NeuroBlu.…”

“…NLP tools have been previously developed to extract clinical features from unstructured MindLinc MSE data. Details of the NLP pipeline and accuracy statistics have been previously published 33. In summary, a deep learning, long–short-term memory (LSTM) approach was used to develop NLP applications to extract 241 MSE features in 27 categories.…”

“…Furthermore, the availability of unstructured free text data has supported the development of NLP tools to automatically extract clinically meaningful information from the data set, as described previously 33 60…”

“…This paper presents a cohort profile of the NeuroBlu platform,32 a trusted research environment, which enables safe and secure analysis of deidentified EHR data from US mental healthcare providers, including state-of-the-art NLP software to characterise mental state information from semistructured text,33 and a graphical user interface to enable users from a wide range of backgrounds to perform statistical analysis to support mental health RWE generation.…”

Editorial: Methods and applications of natural language processing in psychiatry research

“…Nowadays, more and more NLP models have been used for text data mining to explore meaningful medical concepts from clinical notes and social media. For example, Wang et al ( 1 ) extracted drug-drug interactions from AERS reports, Pham et al ( 2 ) predicted health care trajectories from medical records, Mukherje et al ( 3 ) used quantification method to evaluate the psychiatric patients' mental status. In this Research Topic, all the accepted manuscripts have been rigorously peer-reviewed by researchers who have strong computational psychiatry research background.…”