Coreference resolution in clinical discharge summaries, progress notes, surgical and pathology reports: a unified lexical approach

We developed a lexical rule-based system that uses a unified approach to resolving coreference across a wide variety of clinical records comprising discharge summaries, progress notes, pathology, radiology and surgical reports from two corpora (Ontology Development and Information Extraction (ODIE) and i2b2/VA) provided for the fifth i2b2/VA shared task. Taking the unweighted mean between 4 coreference metrics, validation of the system against the i2b2/VA corpus attained an overall F-score of 87.7% across all mention classes, with a maximum of 93.1% for coreference of persons, and a minimum of 77.2% for coreference of tests. For the ODIE corpus the overall F-score across all mention classes was 79.4%, with a maximum of 82.0% for coreference of persons and a minimum of 13.1% for coreference of diagnostic reagents. For the ODIE corpus our results are comparable to the mean reported inter-annotator agreement with the gold standard. We discuss the four categories of errors we identified, and how these might be addressed. The system uses a number of reusable modules and techniques that may be of benefit to the research community

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Lexical patterns, features and knowledge resources for coreference resolution in clinical notes

Generation of entity coreference chains provides a means to extract linked narrative events from clinical notes, but despite being a well-researched topic in natural language processing, general- purpose coreference tools perform poorly on clinical texts. This paper presents a knowledge-centric and pattern-based approach to resolving coreference across a wide variety of clinical records comprising discharge summaries, progress notes, pathology, radiology and surgical reports from two corpora (Ontology Development and Information Extraction (ODIE) and i2b2/VA). In addition, a method for generating coreference chains using progressively pruned linked lists is demonstrated that reduces the search space and facilitates evaluation by a number of metrics. Independent evaluation results show an F-measure for each corpus of 79.2% and 87.5%, respectively, which offers performance at least as good as human annotators, greatly increased performance over general- purpose tools, and improvement on previously reported clinical coreference systems. The system uses a number of open-source components that are available to download

The U-Net-based Active Learning Framework for Enhancing Cancer Immunotherapy

Breast cancer is the most common cancer in the world. According to the U.S. Breast Cancer Statistics, about 281,000 new cases of invasive breast cancer are expected to be diagnosed in 2021 (Smith et al., 2019). The death rate of breast cancer is higher than any other cancer type. Early detection and treatment of breast cancer have been challenging over the last few decades. Meanwhile, deep learning algorithms using Convolutional Neural Networks to segment images have achieved considerable success in recent years. These algorithms have continued to assist in exploring the quantitative measurement of cancer cells in the tumor microenvironment. However, detecting cancerous regions in whole-slide images has been challenging as it requires substantial annotation and training efforts from clinicians and biologists. In this thesis, a notable instructing process named U-Net-based Active Learning is proposed to improve the annotation and training procedure in a feedback learning process by utilizing a Deep Convolutional Neural Networks model. The proposed approach reduces the amount of time and effort required to analyze the whole slide images. During the Active Learning process, highly uncertain samples are iteratively selected to strategically supply characteristics of the whole slide images to the training process using a low-confidence sample selection algorithm. The performance results of the proposed approach indicated that the U-Net-based Active Learning framework has promising outcomes in the feedback learning process as it reaches 88.71% AUC-ROC when only using 64 image patches, while random lymphocyte prediction reaches 84.12% AUC-ROC at maximum

The use and function of gestures in word-finding difficulties in aphasia

Background: Gestures are spontaneous hand and arm movements that are part of everyday communication. The roles of gestures in communication are disputed. Most agree that they augment the information conveyed in speech. More contentiously, some argue that they facilitate speech, particularly when word-finding difficulties (WFD) occur. Exploring gestures in aphasia may further illuminate their role. Aims: This study explored the spontaneous use of gestures in the conversation of participants with aphasia (PWA) and neurologically healthy participants (NHP). It aimed to examine the facilitative role of gesture by determining whether gestures particularly accompanied WFD and whether those difficulties were resolved. Methods & Procedures: Spontaneous conversation data were collected from 20 PWA and 21 NHP. Video samples were analysed for gesture production, speech production, and WFD. Analysis 1 examined whether the production of semantically rich gestures in these conversations was affected by whether the person had aphasia, and/or whether there were difficulties in the accompanying speech. Analysis 2 identified all WFD in the data and examined whether these were more likely to be resolved if accompanied by a gesture, again for both groups of participants. Outcomes & Results: Semantically rich gestures were frequently employed by both groups of participants, but with no effect of group. There was an effect of the accompanying speech, with gestures occurring most commonly alongside resolved WFD. An interaction showed that this was particularly the case for PWA. NHP, on the other hand, employed semantically rich gestures most frequently alongside fluent speech. Analysis 2 showed that WFD were common in both groups of participants. Unsurprisingly, these were more likely to be resolved for NHP than PWA. For both groups, resolution was more likely if a WFD was accompanied by a gesture. Conclusions: These findings shed light on the different functions of gesture within conversation. They highlight the importance of gesture during WFD, both in aphasic and neurologically healthy language, and suggest that gesture may facilitate word retrieval

Context Aware Deep Learning for Brain Tumor Segmentation, Subtype Classification, and Survival Prediction Using Radiology Images

A brain tumor is an uncontrolled growth of cancerous cells in the brain. Accurate segmentation and classification of tumors are critical for subsequent prognosis and treatment planning. This work proposes context aware deep learning for brain tumor segmentation, subtype classification, and overall survival prediction using structural multimodal magnetic resonance images (mMRI). We first propose a 3D context aware deep learning, that considers uncertainty of tumor location in the radiology mMRI image sub-regions, to obtain tumor segmentation. We then apply a regular 3D convolutional neural network (CNN) on the tumor segments to achieve tumor subtype classification. Finally, we perform survival prediction using a hybrid method of deep learning and machine learning. To evaluate the performance, we apply the proposed methods to the Multimodal Brain Tumor Segmentation Challenge 2019 (BraTS 2019) dataset for tumor segmentation and overall survival prediction, and to the dataset of the Computational Precision Medicine Radiology-Pathology (CPM-RadPath) Challenge on Brain Tumor Classification 2019 for tumor classification. We also perform an extensive performance evaluation based on popular evaluation metrics, such as Dice score coefficient, Hausdorff distance at percentile 95 (HD95), classification accuracy, and mean square error. The results suggest that the proposed method offers robust tumor segmentation and survival prediction, respectively. Furthermore, the tumor classification results in this work is ranked at second place in the testing phase of the 2019 CPM-RadPath global challenge

Quality of Life and psychopathology in adults who underwent Hematopoietic Stem Cell Transplantation (HSCT) in childhood: a qualitative and quantitative analysis.

Background: Patients who undergo pediatric Hematopoietic Stem Cell Transplantation (HSCT) may experience long-term psychological sequelae and poor Quality of Life (QoL) in adulthood. This study aimed to investigate subjective illness experience, QoL, and psychopathology in young adults who have survived pediatric HSCT. Method: The study involved patients treated with HSCT in the Hematology-Oncology Department between 1984 and 2007. Psychopathology and QoL were investigated using the SCL-90-R and SF-36. Socio-demographic and medical information was also collected. Finally, participants were asked to write a brief composition about their experiences of illness and care. Qualitative analysis of the texts was performed using T-LAB, an instrument for text analysis that allows the user to highlight the occurrences and co-occurrences of lemma. Quantitative analyses were performed using non-parametric tests (Spearman correlations, Kruskal-Wallis and Mann-Whitney tests). Results: Twenty-one patients (9 males) participated in the study. No significant distress was found on the SCL-90 Global Severity Index, but it was found on specific scales. On the SF-36, lower scores were reported on scales referring to bodily pain, general health, and physical and social functioning. All the measures were significantly (p < 0.05) associated with specific socio-demographic and medical variables (gender, type of pathology, type of HSCT, time elapsed between communication of the need to transplant and effective transplantation, and days of hospitalization). With regard to the narrative analyses, males focused on expressions related to the body and medical therapies, while females focused on people they met during treatment, family members, and donors. Low general health and treatment with autologous HSCT were associated with memories about chemotherapy, radiotherapy, and the body parts involved, while high general health was associated with expressions focused on gratitude (V-Test \ub1 1.96). Conclusion: Pediatric HSCT survivors are more likely to experience psychological distress and low QoL in adulthood compared with the general population. These aspects, along with survivors' subjective illness experience, show differences according to specific medical and socio-demographic variables. Studies are needed in order to improve the care and long-term follow-up of these families

Differentiating Noninvasive Follicular Thyroid Neoplasm with Papillary-Like Nuclear Features from Classic Papillary Thyroid Carcinoma: Analysis of Cytomorphologic Descriptions Using a Novel Machine-Learning Approach.

Background:Recent studies show various cytomorphologic features that can assist in the differentiation of classic papillary thyroid carcinoma (cPTC) from noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP). Differentiating these two entities changes the clinical management significantly. We evaluated the performance of support vector machine (SVM), a machine learning algorithm, in differentiating cases of NIFTP and encapsulated follicular variant of papillary thyroid carcinoma with no capsular or lymphovascular invasion (EFVPTC) from cases of cPTC with the use of microscopic descriptions. SVM is a supervised learning algorithm used in classification problems. It assigns the input data to one of two categories by building a model based on a set of training examples (learning) and then using that learned model to classify new examples. Methods:Surgical pathology cases with the diagnosis of cPTC, NIFTP, and EFVPTC, were obtained from the laboratory information system. Only cases with existing fine-needle aspiration matching the tumor and available microscopic description were included. NIFTP cases with ipsilateral micro-PTC were excluded. The final cohort consisted of 59 cases (29 cPTCs and 30 NIFTP/EFVPTCs). Results:SVM successfully differentiated cPTC from NIFTP/EFVPTC 76.05 ± 0.96% of times (above chance, P \u3c 0.05) with the sensitivity of 72.6% and specificity of 81.6% in detecting cPTC. Conclusions:This machine learning algorithm was successful in distinguishing NIFTP/EFVPTC from cPTC. Our results are compatible with the prior studies, which show cytologic features are helpful in differentiating these two entities. Furthermore, this study shows the power and potential of this approach for clinical use and in developing data-driven scoring systems, which can guide cytopathology and surgical pathology diagnosis