A single-channel chip-based analytical microsystem that allows rapid flow injection measurements of the total content of organic explosive or nerve agent compounds, as well as detailed micellar chromatographic identification of the individual ones, is described. The protocol involves repetitive rapid flow injection (screening) assays--to provide a timely warning and alarm--and switching to the separation (fingerprint identification) mode only when harmful compounds are detected. While micellar electrokinetic chromatography, in the presence of sodium dodecyl sulfate (SDS), is used for separating the neutral nitroaromatic explosive and nerve agent compounds, an operation without SDS leads to high-speed measurements of the "total" explosives or nerve agent content. Switching between the "flow injection" and "separation" modes is accomplished by rapidly exchanging the SDS-free and SDS-containing buffers in the separation channel. Amperometric detection was used for monitoring the separation. Key factors influencing the sample throughput, resolution, and sensitivity have been assessed and optimized. Assays rates of about 360 and 30/h can thus be realized for the "total" screening and "individual" measurements, respectively. Ultimately, such development will lead to the creation of a field-deployable microanalyzer and will enable transporting the forensic laboratory to the sample source.
This study presents a multimodal machine learning model to predict ICD-10 diagnostic codes. We developed separate machine learning models that can handle data from different modalities, including unstructured text, semi-structured text and structured tabular data. We further employed an ensemble method to integrate all modality-specific models to generate ICD codes. Key evidence was also extracted to make our prediction more convincing and explainable.We used the Medical Information Mart for Intensive Care III (MIMIC-III) dataset to validate our approach. For ICD code prediction, our best-performing model (micro-F1 = 0.7633, micro-AUC = 0.9541) significantly outperforms other baseline models including TF-IDF (micro-F1 = 0.6721, micro-AUC = 0.7879) and Text-CNN model (micro-F1 = 0.6569, micro-AUC = 0.9235). For interpretability, our approach achieves a Jaccard Similarity Coefficient (JSC) of 0.1806 on text data and 0.3105 on tabular data, where well-trained physicians achieve 0.2780 and 0.5002 respectively.
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