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.
Highlights
Background and significancePost-Traumatic Stress Disorder (PTSD) is a mental health disorder resulting from having experienced or witnessed a traumatic event such as an accident or war.[1]
The data used in this study was extracted from the Electronic Medical Record (EMR) of primary care clinicians participating in the Manitoba Primary Care Research Network (MaPCReN), a subnetwork of the Canadian Primary Care Sentinel Surveillance Network (CPCSSN).[18]
For assessing free-text data, we developed two models, one based on the simple Bag of Words (BoW) model that serves as a baseline for text models, and a more sophisticated model that uses word embeddings and Convolutional Neural Networks (CNN)
Summary
Background and significancePost-Traumatic Stress Disorder (PTSD) is a mental health disorder resulting from having experienced or witnessed a traumatic event such as an accident or war.[1]. It requires that the symptoms persist for greater than 1 month; if a patient is reluctant to seek help, infrequent patient-clinician interactions can hinder diagnoses. Patients’ subjective and reporting biases, as well as variations in the symptoms of PTSD that can mimic other mental health conditions such as depression and anxiety, can prevent timely diagnoses. The forecasting method must account for missing risk indicators that might not be documented in some patients’ EMR and use prior knowledge to adjust the relative weights of putative predictors that are documented in the EMR
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