Specializing for predicting obesity and its co-morbidities

AbstractWe present specializing, a method for combining classifiers for multi-class classification. Specializing trains one specialist classifier per class and utilizes each specialist to distinguish that class from all others in a one-versus-all manner. It then supplements the specialist classifiers with a catch-all classifier that performs multi-class classification across all classes. We refer to the resulting combined classifier as a specializing classifier.We develop specializing to classify 16 diseases based on discharge summaries. For each discharge summary, we aim to predict whether each disease is present, absent, or questionable in the patient, or unmentioned in the discharge summary. We treat the classification of each disease as an independent multi-class classification task. For each disease, we develop one specialist classifier for each of the present, absent, questionable, and unmentioned classes; we supplement these specialist classifiers with a catch-all classifier that encompasses all of the classes for that disease. We evaluate specializing on each of the 16 diseases and show that it improves significantly over voting and stacking when used for multi-class classification on our data

The 2022 n2c2/UW Shared Task on Extracting Social Determinants of Health

Objective: The n2c2/UW SDOH Challenge explores the extraction of social determinant of health (SDOH) information from clinical notes. The objectives include the advancement of natural language processing (NLP) information extraction techniques for SDOH and clinical information more broadly. This paper presents the shared task, data, participating teams, performance results, and considerations for future work. Materials and Methods: The task used the Social History Annotated Corpus (SHAC), which consists of clinical text with detailed event-based annotations for SDOH events such as alcohol, drug, tobacco, employment, and living situation. Each SDOH event is characterized through attributes related to status, extent, and temporality. The task includes three subtasks related to information extraction (Subtask A), generalizability (Subtask B), and learning transfer (Subtask C). In addressing this task, participants utilized a range of techniques, including rules, knowledge bases, n-grams, word embeddings, and pretrained language models (LM). Results: A total of 15 teams participated, and the top teams utilized pretrained deep learning LM. The top team across all subtasks used a sequence-to-sequence approach achieving 0.901 F1 for Subtask A, 0.774 F1 Subtask B, and 0.889 F1 for Subtask C. Conclusions: Similar to many NLP tasks and domains, pretrained LM yielded the best performance, including generalizability and learning transfer. An error analysis indicates extraction performance varies by SDOH, with lower performance achieved for conditions, like substance use and homelessness, that increase health risks (risk factors) and higher performance achieved for conditions, like substance abstinence and living with family, that reduce health risks (protective factors)

INFORMATIONAL SUPPORT OR EMOTIONAL SUPPORT: PRELIMINARY STUDY OF AN AUTOMATED APPROACH TO ANALYZE ONLINE SUPPORT COMMUNITY CONTENTS

Recognizing the need for analyzing large amounts of data in the study of online support communities, an automated content analysis method is introduced in this article. By adopting machine learning techniques and tools, this method requires minimal manual intervention while capable of analyzing large amounts of data automatically. Through this method, contents of messages from online support communities spanning over years are categorized as either informational support or emotional support. A case study on the analysis of online breast cancer and prostate cancer message boards is presented to demonstrate that the proposed method generates results comparable to results concluded from traditional manual qualitative content analysis methods

A cascade of classifiers for extracting medication information from discharge summaries

<p>Abstract</p> <p>Background</p> <p>Extracting medication information from clinical records has many potential applications, and recently published research, systems, and competitions reflect an interest therein. Much of the early extraction work involved rules and lexicons, but more recently machine learning has been applied to the task.</p> <p>Methods</p> <p>We present a hybrid system consisting of two parts. The first part, field detection, uses a cascade of statistical classifiers to identify medication-related named entities. The second part uses simple heuristics to link those entities into medication events.</p> <p>Results</p> <p>The system achieved performance that is comparable to other approaches to the same task. This performance is further improved by adding features that reference external medication name lists.</p> <p>Conclusions</p> <p>This study demonstrates that our hybrid approach outperforms purely statistical or rule-based systems. The study also shows that a cascade of classifiers works better than a single classifier in extracting medication information. The system is available as is upon request from the first author.</p

ASEMPTOMATİK BİR ÇOCUKTAKİ NADİR BİR İNTRATORASİK KİTLE NEDENİ

Pulmonary sequestration is an uncommon usually cystic mass of nonfunctioning primitive tissue that does not communicate with the tracheobronchial tree that receives it's blood supply from an anomalous systemic artery rather than the pulmonary arteries. Intralobar pulmonary sequestration is usually diagnosed later in childhood or adulthood when the patient presents with pulmonary infection. Many patients with extralobar pulmonary sequestration present during infancy with pulmonary respiratory distress and chronic cough. Here, we reported a case of extralobar pulmonary sequestration found coincidentally on chest radiograph in a nine year old girl during routine preoperative investigations for insertion of tympanostomy tube. Pulmoner sekestrasyon trakeobronşiyal ağaçla ilişkisi olmayan ve kan akımını pulmoner arterler yerine anormal bir sistemik arterden sağlayan, fonksiyon göstermeyen primitif dokudan oluşmuş, nadir görülen, genellikle kistik bir kitledir. İntralober pulmoner sekestrasyon genellikle geç çocukluk çağı veya erişkin çağda hasta enfeksiyon ile başvurduğunda tanınır. Ekstralober pulmoner sekestrasyonlu hastaların çoğu infant döneminde solunum sıkıntısı ve kronik öksürük ile başvurur. Burada, timpanostomi tüpü yerleştirilmek üzere rutin preoperatif tetkikler yapılırken akciğer filminde tesadüfen ekstralober pulmoner sekestrasyon saptanan dokuz yaşındaki bir kız olguyu sunduk

Challenges and opportunities for mining adverse drug reactions: perspectives from pharma, regulatory agencies, healthcare providers and consumers

Monitoring drug safety is a central concern throughout the drug life cycle. Information about toxicity and adverse events is generated at every stage of this life cycle, and stakeholders have a strong interest in applying text mining and artificial intelligence (AI) methods to manage the ever-increasing volume of this information. Recognizing the importance of these applications and the role of challenge evaluations to drive progress in text mining, the organizers of BioCreative VII (Critical Assessment of Information Extraction in Biology) convened a panel of experts to explore ‘Challenges in Mining Drug Adverse Reactions’. This article is an outgrowth of the panel; each panelist has highlighted specific text mining application(s), based on their research and their experiences in organizing text mining challenge evaluations. While these highlighted applications only sample the complexity of this problem space, they reveal both opportunities and challenges for text mining to aid in the complex process of drug discovery, testing, marketing and post-market surveillance. Stakeholders are eager to embrace natural language processing and AI tools to help in this process, provided that these tools can be demonstrated to add value to stakeholder workflows. This creates an opportunity for the BioCreative community to work in partnership with regulatory agencies, pharma and the text mining community to identify next steps for future challenge evaluations.M.K.: This work was supported in part through the collaboration between the Spanish Plan for the Advancement of Language Technology (Plan TL) and the Barcelona Supercomputing Center; we also acknowledge the 2020 Proyectos de I+D+i - RTI Tipo A (PID2020-119266RA-I00) for support. Ö.U.: This study was supported in part by the National Library of Medicine under Award Number R15LM013209 and R13LM013127.Peer ReviewedPostprint (published version