18 research outputs found
Microfabricated Probes for Studying Brain Chemistry: A Review
Probe techniques for monitoring in vivo chemistry (e.g., electrochemical sensors and microdialysis sampling probes) have significantly contributed to a better understanding of neurotransmission in correlation to behaviors and neurological disorders. Microfabrication allows construction of neural probes with high reproducibility, scalability, design flexibility, and multiplexed features. This technology has translated well into fabricating miniaturized neurochemical probes for electrochemical detection and sampling. Microfabricated electrochemical probes provide a better control of spatial resolution with multisite detection on a single compact platform. This development allows the observation of heterogeneity of neurochemical activity precisely within the brain region. Microfabricated sampling probes are starting to emerge that enable chemical measurements at high spatial resolution and potential for reducing tissue damage. Recent advancement in analytical methods also facilitates neurochemical monitoring at high temporal resolution. Furthermore, a positive feature of microfabricated probes is that they can be feasibly built with other sensing and stimulating platforms including optogenetics. Such integrated probes will empower researchers to precisely elucidate brain function and develop novel treatments for neurological disorders.Microfabricated neurochemical probes: Microfabrication technology emerges as an important tool for developing miniature, high precision probes for electrochemical detection and sampling from live brain tissues. This review describes advances and perspectives in adapting microfabrication to create the next generation of neurochemical probes.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/144231/1/cphc201701180_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/144231/2/cphc201701180.pd
How Natural Language Processing Can Aid With Pulmonary Oncology Tumor Node Metastasis Staging From Free-Text Radiology Reports:Algorithm Development and Validation
Anti-reflection subwavelength gratings for InP-based waveguide facets
We demonstrate the anti-reflection properties of lithographically defined subwavelength gratings applied to the facets of integrated waveguides realized in the InP membrane-on-silicon platform. The subwavelength gratings are based on the gradient index effect to create a smooth index transition between the core material and air, making it possible to obtain reflections below −30 dB at a wavelength of 1550 nm for both TE and TM polarized modes, as shown by 3D finite-difference time-domain simulations. Characterizations performed using Mach–Zehnder interferometers as test structures show relative reflections as low as −25 dB, confirming the effectiveness of the technique
Natural Language Processing in Dutch Free Text Radiology Reports:Challenges in a Small Language Area Staging Pulmonary Oncology
Reports are the standard way of communication between the radiologist and the referring clinician. Efforts are made to improve this communication by, for instance, introducing standardization and structured reporting. Natural Language Processing (NLP) is another promising tool which can improve and enhance the radiological report by processing free text. NLP as such adds structure to the report and exposes the information, which in turn can be used for further analysis. This paper describes pre-processing and processing steps and highlights important challenges to overcome in order to successfully implement a free text mining algorithm using NLP tools and machine learning in a small language area, like Dutch. A rule-based algorithm was constructed to classify T-stage of pulmonary oncology from the original free text radiological report, based on the items tumor size, presence and involvement according to the 8th TNM classification system. PyContextNLP, spaCy and regular expressions were used as tools to extract the correct information and process the free text. Overall accuracy of the algorithm for evaluating T-stage was 0,83 in the training set and 0,87 in the validation set, which shows that the approach in this pilot study is promising. Future research with larger datasets and external validation is needed to be able to introduce more machine learning approaches and perhaps to reduce required input efforts of domain-specific knowledge. However, a hybrid NLP approach will probably achieve the best results. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10278-020-00327-z) contains supplementary material, which is available to authorized users
Development and validation of deep learning and BERT models for classification of lung cancer radiology reports
T-staging pulmonary oncology from radiological reports using natural language processing: translating into a multi-language setting
Abstract Background In the era of datafication, it is important that medical data are accurate and structured for multiple applications. Especially data for oncological staging need to be accurate to stage and treat a patient, as well as population-level surveillance and outcome assessment. To support data extraction from free-text radiological reports, Dutch natural language processing (NLP) algorithm was built to quantify T-stage of pulmonary tumors according to the tumor node metastasis (TNM) classification. This structuring tool was translated and validated on English radiological free-text reports. A rule-based algorithm to classify T-stage was trained and validated on, respectively, 200 and 225 English free-text radiological reports from diagnostic computed tomography (CT) obtained for staging of patients with lung cancer. The automated T-stage extracted by the algorithm from the report was compared to manual staging. A graphical user interface was built for training purposes to visualize the results of the algorithm by highlighting the extracted concepts and its modifying context. Results Accuracy of the T-stage classifier was 0.89 in the validation set, 0.84 when considering the T-substages, and 0.76 when only considering tumor size. Results were comparable with the Dutch results (respectively, 0.88, 0.89 and 0.79). Most errors were made due to ambiguity issues that could not be solved by the rule-based nature of the algorithm. Conclusions NLP can be successfully applied for staging lung cancer from free-text radiological reports in different languages. Focused introduction of machine learning should be introduced in a hybrid approach to improve performance
Clinical Concept-Based Radiology Reports Classification Pipeline for Lung Carcinoma
Rising incidence and mortality of cancer have led to an incremental amount of research in the field. To learn from preexisting data, it has become important to capture maximum information related to disease type, stage, treatment, and outcomes. Medical imaging reports are rich in this kind of information but are only present as free text. The extraction of information from such unstructured text reports is labor-intensive. The use of Natural Language Processing (NLP) tools to extract information from radiology reports can make it less time-consuming as well as more effective. In this study, we have developed and compared different models for the classification of lung carcinoma reports using clinical concepts. This study was approved by the institutional ethics committee as a retrospective study with a waiver of informed consent. A clinical concept-based classification pipeline for lung carcinoma radiology reports was developed using rule-based as well as machine learning models and compared. The machine learning models used were XGBoost and two more deep learning model architectures with bidirectional long short-term neural networks. A corpus consisting of 1700 radiology reports including computed tomography (CT) and positron emission tomography/computed tomography (PET/CT) reports were used for development and testing. Five hundred one radiology reports from MIMIC-III Clinical Database version 1.4 was used for external validation. The pipeline achieved an overall F1 score of 0.94 on the internal set and 0.74 on external validation with the rule-based algorithm using expert input giving the best performance. Among the machine learning models, the Bi-LSTM_dropout model performed better than the ML model using XGBoost and the Bi-LSTM_simple model on internal set, whereas on external validation, the Bi-LSTM_simple model performed relatively better than other 2. This pipeline can be used for clinical concept-based classification of radiology reports related to lung carcinoma from a huge corpus and also for automated annotation of these reports