Diagnosing post-traumatic stress disorder using electronic medical record data

Abstract

This study proposes a predictive model that uses structured data and unstructured narrative notes from Electronic Medical Records to accurately identify patients diagnosed with Post-Traumatic Stress Disorder (PTSD). We utilize data from primary care clinicians participating in the Manitoba Primary Care Research Network (MaPCReN) representing 154,118 patients. A reference sample of 195 patients that had their PTSD diagnosis confirmed using a manual chart review of structured data and narrative notes, and PTSD negative patients is used as the gold standard data for model training, validation and testing. We assess structured and unstructured data from eight tables in the MaPCReN namely, patient demographics, disease case, examinations, medication, billing records, health condition, risk factors, and encounter notes. Feature engineering is applied to convert data into proper representation for predictive modeling. We explore serial and parallel mixed data models that are trained on both structured and unstructured data to identify PTSD. Model performances were calculated based on a highly skewed hold-out test dataset. The serial model that uses both structured and text data as input, yielded the highest values in sensitivity (0.77), F-measure (0.76), and AUC (0.88) and the parallel model that uses both structured and text data as the input obtained the highest positive predicted value (PPV) (0.75). Diseases such as PTSD are difficult to diagnose. Information recorded in the chart note over multiple visits of the patients with the primary care physicians has higher predictive power than structured data and combining these two data types can increase the predictive capabilities of machine learning models in diagnosing PTSD. While the deep-learning model outperformed the traditional ensemble model in processing text data, the ensemble classifier obtained better results in ingesting a combination of features obtained from both data types in the serial mixed model. The study demonstrated that unstructured encounter notes enhance a model’s ability to identify patients diagnosed with PTSD. These findings can enhance quality improvement, research, and disease surveillance related to PTSD in primary care populations.

A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data

“…In 22 studies of this ICD-10 classifications addressing six health conditions [28,45,[89][90][91][92][93][94][95][96][97]119,81,120,121,[82][83][84][85][86][87][88], the involved population were from eight countries, mainly the US and the UK (n=14). These studies were published since 2013 with the highest number of studies in 2020 (44.4%).…”

Pan-Canadian Electronic Medical Record Diagnostic and Unstructured Text Data for Capturing PTSD: Retrospective Observational Study

“…Due to this limitation, previous studies have relied on small data sets or a small number of institutions, preventing evidence of transferability of the models [ 17 ]. Primary care EMR short diagnostic text fields, more widely available than free-text data, have been suggested as a method for supplementing diagnostic definitions when free-text is unavailable [ 15 , 19 , 20 ]. Supplementation of free-text data with short-text fields, matched with refined processes for annotation and classification can support the use of EMR data in research [ 17 ].…”

“…Posttraumatic stress disorder (PTSD) is a complex mental health disorder characterized by a constellation of distressing symptoms that occur after witnessing or experiencing a traumatic event [ 21 , 22 ]. PTSD involves intrusive thoughts, persistent avoidance, negative alterations in cognition and mood, and alterations in arousal and reactivity (eg, irritability, reduced concentration, and exaggerated startle response) due to trauma recollection, which occur for greater than 1 month and result in significant impairment for the individual [ 20 , 22 - 24 ]. PTSD is associated with an array of multimodal risk indicators suggesting no single factor can account for the large variance in PTSD symptoms [ 19 , 20 ].…”

“…PTSD involves intrusive thoughts, persistent avoidance, negative alterations in cognition and mood, and alterations in arousal and reactivity (eg, irritability, reduced concentration, and exaggerated startle response) due to trauma recollection, which occur for greater than 1 month and result in significant impairment for the individual [ 20 , 22 - 24 ]. PTSD is associated with an array of multimodal risk indicators suggesting no single factor can account for the large variance in PTSD symptoms [ 19 , 20 ]. When encountered in primary care, PTSD is associated with considerable functional impairment and health care utilization [ 24 ].…”

“…When encountered in primary care, PTSD is associated with considerable functional impairment and health care utilization [ 24 ]. This complex set of symptoms, combined with an individual’s possible reluctance to seek help, infrequent patient-clinician interaction, and overlapping symptoms with other mental health conditions, makes PTSD difficult to accurately diagnose in primary care [ 20 , 22 ]. Identifying PTSD requires both depth and breadth to detail the patients’ experience and capture associated factors [ 19 , 20 ].…”