Using Natural Language Processing to Predict Fatal Drug Overdose From Autopsy Narrative Text: Algorithm Development and Validation Study

Abstract

Background Fatal drug overdose surveillance informs prevention but is often delayed because of autopsy report processing and death certificate coding. Autopsy reports contain narrative text describing scene evidence and medical history (similar to preliminary death scene investigation reports) and may serve as early data sources for identifying fatal drug overdoses. To facilitate timely fatal overdose reporting, natural language processing was applied to narrative texts from autopsies. Objective This study aimed to develop a natural language processing–based model that predicts the likelihood that an autopsy report narrative describes an accidental or undetermined fatal drug overdose. Methods Autopsy reports of all manners of death (2019-2021) were obtained from the Tennessee Office of the State Chief Medical Examiner. The text was extracted from autopsy reports (PDFs) using optical character recognition. Three common narrative text sections were identified, concatenated, and preprocessed (bag-of-words) using term frequency–inverse document frequency scoring. Logistic regression, support vector machine (SVM), random forest, and gradient boosted tree classifiers were developed and validated. Models were trained and calibrated using autopsies from 2019 to 2020 and tested using those from 2021. Model discrimination was evaluated using the area under the receiver operating characteristic, precision, recall, F1-score, and F2-score (prioritizes recall over precision). Calibration was performed using logistic regression (Platt scaling) and evaluated using the Spiegelhalter z test. Shapley additive explanations values were generated for models compatible with this method. In a post hoc subgroup analysis of the random forest classifier, model discrimination was evaluated by forensic center, race, age, sex, and education level. Results A total of 17,342 autopsies (n=5934, 34.22% cases) were used for model development and validation. The training set included 10,215 autopsies (n=3342, 32.72% cases), the calibration set included 538 autopsies (n=183, 34.01% cases), and the test set included 6589 autopsies (n=2409, 36.56% cases). The vocabulary set contained 4002 terms. All models showed excellent performance (area under the receiver operating characteristic ≥0.95, precision ≥0.94, recall ≥0.92, F1-score ≥0.94, and F2-score ≥0.92). The SVM and random forest classifiers achieved the highest F2-scores (0.948 and 0.947, respectively). The logistic regression and random forest were calibrated (P=.95 and P=.85, respectively), whereas the SVM and gradient boosted tree classifiers were miscalibrated (P=.03 and P<.001, respectively). “Fentanyl” and “accident” had the highest Shapley additive explanations values. Post hoc subgroup analyses revealed lower F2-scores for autopsies from forensic centers D and E. Lower F2-score were observed for the American Indian, Asian, ≤14 years, and ≥65 years subgroups, but larger sample sizes are needed to validate these findings. Conclusions The random forest classifier may be suitable for identifying potential accidental and undetermined fatal overdose autopsies. Further validation studies should be conducted to ensure early detection of accidental and undetermined fatal drug overdoses across all subgroups.

Advancing OCR Accuracy in Image-to-LaTeX Conversion—A Critical and Creative Exploration

“…Training OCR models with augmented data containing these semantic concepts enables the model to understand mathematical content better [39]. This understanding allows them to generate more meaningful LaTeX code that reflects the intended semantics of the original mathematical expressions.…”

“…Domain-specific rules can be included in the error correction process to foster the accuracy and consistency of the converted LaTeX representation. For instance, if the OCR output contains a mathematical symbol that is incompatible with the mathematical domain or context, the error correction mechanism can propose appropriate replacements based on the domainspecific rules [39]. Error correction techniques can improve the accuracy and reliability of the OCR system for image-to-LaTeX conversion by integrating external knowledge and domain-specific information.…”

“…It can also improve OCR model performance by allowing the model to learn from its mistakes and refine its understanding of symbol recognition and conversion. This iterative process allows the model to concentrate on critical areas for improvement, leading to more accurate and reliable image-to-LaTeX conversions [39]. The model becomes more robust and capable of handling various symbols, font styles, and mathematical structures encountered in real-world scenarios by actively targeting challenging samples for annotation [43].…”