In secondary analysis of electronic health records, a crucial task consists in correctly identifying the patient cohort under investigation. In many cases, the most valuable and relevant information for an accurate classification of medical conditions exist only in clinical narratives. Therefore, it is necessary to use natural language processing (NLP) techniques to extract and evaluate these narratives. The most commonly used approach to this problem relies on extracting a number of clinician-defined medical concepts from text and using machine learning techniques to identify whether a particular patient has a certain condition. However, recent advances in deep learning and NLP enable models to learn a rich representation of (medical) language. Convolutional neural networks (CNN) for text classification can augment the existing techniques by leveraging the representation of language to learn which phrases in a text are relevant for a given medical condition. In this work, we compare concept extraction based methods with CNNs and other commonly used models in NLP in ten phenotyping tasks using 1,610 discharge summaries from the MIMIC-III database. We show that CNNs outperform concept extraction based methods in almost all of the tasks, with an improvement in F1-score of up to 26 and up to 7 percentage points in area under the ROC curve (AUC). We additionally assess the interpretability of both approaches by presenting and evaluating methods that calculate and extract the most salient phrases for a prediction. The results indicate that CNNs are a valid alternative to existing approaches in patient phenotyping and cohort identification, and should be further investigated. Moreover, the deep learning approach presented in this paper can be used to assist clinicians during chart review or support the extraction of billing codes from text by identifying and highlighting relevant phrases for various medical conditions.

Key Points Question Is near-infrared low-level light therapy (LLLT) feasible and safe after moderate traumatic brain injury, and does LLLT affect the brain and exhibit neuroreactivity? Findings In this randomized clinical trial including 68 patients with moderate traumatic brain injury who were randomized to receive LLLT or sham therapy, 28 patients completed at least 1 LLLT session without any reported adverse events. In the late subacute stage, there were statistically significant differences in the magnetic resonance imaging–derived diffusion parameters of the white matter tracts between the sham- and light-treated groups, demonstrating neuroreactivity of LLLT. Meaning The results of this clinical trial show that transcranial LLLT is feasible, safe, and affects the brain in a measurable manner.

Severe peripheral nerve injuries often result in partial repair and lifelong disabilities in patients. New surgical techniques and better graft tissues are being studied to accelerate regeneration and improve functional recovery. Currently, limited tools are available to provide in vivo monitoring of changes in nerve physiology such as myelination and vascularization, and this has impeded the development of new therapeutic options. We have developed a wide-field and label-free functional microscopy platform based on angiographic and vectorial birefringence methods in optical coherence tomography (OCT). By incorporating the directionality of the birefringence, which was neglected in the previously reported polarization-sensitive OCT techniques for nerve imaging, vectorial birefringence contrast reveals internal nerve microanatomy and allows for quantification of local myelination with superior sensitivity. Advanced OCT angiography is applied in parallel to image the three-dimensional vascular networks within the nerve over wide-fields. Furthermore, by combining vectorial birefringence and angiography, intraneural vessels can be discriminated from those of the surrounding tissues. The technique is used to provide longitudinal imaging of myelination and revascularization in the rodent sciatic nerve model, i.e. imaged at certain sequential time-points during regeneration. The animals were exposed to either crush or transection injuries, and in the case of transection, were repaired using an autologous nerve graft or acellular nerve allograft. Such label-free functional imaging by the platform can provide new insights into the mechanisms that limit regeneration and functional recovery, and may ultimately provide intraoperative assessment in human subjects.

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