Developing Artificial Intelligence Models for Extracting Oncologic Outcomes from Japanese Electronic Health Records.

INTRODUCTION: A framework that extracts oncological outcomes from large-scale databases using artificial intelligence (AI) is not well established. Thus, we aimed to develop AI models to extract outcomes in patients with lung cancer using unstructured text data from electronic health records of multiple hospitals. METHODS: We constructed AI models (Bidirectional Encoder Representations from Transformers [BERT], Naïve Bayes, and Longformer) for tumor evaluation using the University of Miyazaki Hospital (UMH) database. This data included both structured and unstructured data from progress notes, radiology reports, and discharge summaries. The BERT model was applied to the Life Data Initiative (LDI) data set of six hospitals. Study outcomes included the performance of AI models and time to progression of disease (TTP) for each line of treatment based on the treatment response extracted by AI models. RESULTS: For the UMH data set, the BERT model exhibited higher precision accuracy compared to the Naïve Bayes or the Longformer models, respectively (precision [0.42 vs. 0.47 or 0.22], recall [0.63 vs. 0.46 or 0.33] and F1 scores [0.50 vs. 0.46 or 0.27]). When this BERT model was applied to LDI data, prediction accuracy remained quite similar. The Kaplan-Meier plots of TTP (months) showed similar trends for the first (median 14.9 [95% confidence interval 11.5, 21.1] and 16.8 [12.6, 21.8]), the second (7.8 [6.7, 10.7] and 7.8 [6.7, 10.7]), and the later lines of treatment for the predicted data by the BERT model and the manually curated data. CONCLUSION: We developed AI models to extract treatment responses in patients with lung cancer using a large EHR database; however, the model requires further improvement.

The use of artificial intelligence (AI) to derive health outcomes from large electronic health records is not well established. Thus, we built three different AI models: Bidirectional Encoder Representations from Transformers (BERT), Naïve Bayes, and Longformer to serve this purpose. Initially, we developed these models based on data from the University of Miyazaki Hospital (UMH) and later improved them using the Life Data Initiative (LDI) data set of six hospitals. The performance of the BERT model was better than the other two, and it showed similar results when it was applied to the LDI data set. The Kaplan­Meier plots of time to progression of disease for the predicted data by the BERT model showed similar trends to those for the manually curated data. In summary, we developed an AI model to extract health outcomes using a large electronic health database in this study; however, the performance of the AI model could be improved using more training data.

Enantioseparation of hydroxyeicosatetraenoic acids by hydroxypropyl-γ-cyclodextrin-modified micellar electrokinetic chromatography.

Complete resolution of hydroxyeicosatetraenoic acid (HETE) enantiomers was achieved using hydroxypropyl-γ-cyclodextrin (HP-γ-CD)-modified MEKC. The optimum running conditions were determined to be utilizing a 30 mM phosphate-15 mM borate buffer (pH 9.0) containing 30 mM HP-γ-CD and 75 mM SDS as the BGE, application of +30 kV as the effective voltage, and carrying out the experiment at 15°C. The eluents were detected at 235 nm. The method was used successfully for the simultaneous separations of (S)- and (R)-enantiomers of regioisomeric 8-, 11-, 12-, and 15-HETEs. Subsequently, the optimized method was applied to evaluate the stereochemistry of 8- and 12-HETEs from the marine red algae, Gracilaria vermiculophylla and Gracilaria arcuata, respectively. The 8-HETE was found to be a mixture of 98% (R)-enantiomer and 2% (S)-enantiomer, while the 12-HETE was a mixture of 98% (S)-enantiomer and 2% (R)-enantiomer. The present study demonstrates that the HP-γ-CD-modified MEKC method is simple and sensitive and provides unambiguous information on the configuration of natural and synthetic HETEs.