Population testing

To evaluate the feasibility, accuracy, and interoperability of a natural language processing (NLP) system that extracts diagnostic assertions of pneumonia in different clinical notes and institutions. A rule-based NLP system was designed to identify assertions of pneumonia in 3 types of clinical notes from electronic health records (EHRs): emergency department notes, radiology reports, and discharge summaries. The lexicon and classification logic were tailored for each note type. The system was first developed and evaluated using annotated notes from the Department of Veterans Affairs (VA). Interoperability was assessed using data from the University of Utah (UU). The NLP system was comprised of 782 rules and achieved moderate-to-high performance in all 3 note types in VA (precision/recall/f1: emergency = 88.1/86.0/87.1; radiology = 71.4/96.2/82.0; discharge = 88.3/93.0/90.1). When applied to UU data, performance was maintained in emergency and radiology but decreased in discharge summaries (emergency = 84.7/94.3/89.3; radiology = 79.7/100.0/87.9; discharge = 65.5/92.7/76.8). Customization with 34 additional rules increased performance for all note types (emergency = 89.3/94.3/91.7; radiology = 87.0/100.0/93.1; discharge = 75.0/95.1/83.4). NLP can be used to accurately identify the diagnosis of pneumonia across different clinical settings and institutions. A limited amount of customization to account for differences in lexicon, clinical definition of pneumonia, and EHR structure can achieve high accuracy without substantial modification.

BackgroundCognitive test results from electronic health records (EHRs) are key information for assessing the severity and progression of patients with mild cognitive impairment (MCI) and Alzheimer’s’ disease (AD). However, such information is often recorded in unstructured clinical notes rather than in an administrative database. We developed and validated a natural language processing (NLP) system to extract cognitive test results from clinical notes in the Veterans Affair (VA) Healthcare System.MethodAn NLP system was developed using regular expression‐based rules and Python to extract results for six tests that have been used most frequently in VA: Mini‐Mental State Exam (MMSE), Montreal Cognitive Assessment (MoCA), Saint Louis University Mental Status Examination (SLUMS), Mini‐cog, Boston Naming Test (BNT), and Benton Visual Retention Test (BVRT). The system extracted test results from each note in two steps: (1) searched a test name, or variations and abbreviation of the test name and, if successful, (2) searched the quantitative results (e.g., 12/30 for MMSE, 74th percentile for BNT) and/or descriptive results (e.g., “borderline impairment” for BVRT) within 5 words or one sentence before or after the test name. To balance the system performance and speed, we developed 3‐8 extraction rules per test based on a manual review of 30‐50 notes for each test. We further validated NLP performance on 6 held‐out datasets (50‐200 notes/test). We automatically sampled the development and held‐out notes by searching the test name and its variations/abbreviation to increase positive cases, i.e., notes that contained the above test results.ResultThe NLP system achieved 0.72‐0.92 predictive positive values (PPV), 0.96‐1.00 recall, and 0.83‐0.95 F1 scores on the validation test sets (Table 1). In addition, it demonstrated a scalable performance (processed 200,000 notes in 7 min), allowing an extraction of millions of notes from ∼1 million patients within hours.ConclusionRule‐based NLP can extract cognitive test results with adequate performance and scalable capability for clinical notes from patients with MCI or AD within the VA Healthcare System.

Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review

: The Neal-Montgomery NLP Evaluation Methodology was developed under the 'Benchmark Investigation/Identification' project as a means of determining the linguistic competence of Natural Language Processing (NLP) systems. Embodied in an evaluation tool based on a detailed classification of linguistic phenomena with, currently, over 350 test items, the methodology produces descriptive profiles of NLP system linguistic capabilities, and can be applied without regard to the current system application, domain, or specific language processing function. To date, it has been applied to three NL data base query systems and three text processing systems. Researchers are invited to apply the evaluation methodology (included within this report) to their systems and report their critique and recommendations towards the evolution of standard evaluation procedures for NLP systems.

Detecting adverse drug events (ADEs) and medications related information in clinical notes is important for both hospital medical care and medical research. We describe our clinical natural language processing (NLP) system to automatically extract medical concepts and relations related to ADEs and medications from clinical narratives. This work was part of the 2018 National NLP Clinical Challenges Shared Task and Workshop on Adverse Drug Events and Medication Extraction. The authors developed a hybrid clinical NLP system that employs a knowledge-based general clinical NLP system for medical concepts extraction, and a task-specific deep learning system for relations identification using attention-based bidirectional long short-term memory networks. The systems were evaluated as part of the 2018 National NLP Clinical Challenges challenge, and our attention-based bidirectional long short-term memory networks based system obtained an F-measure of 0.9442 for relations identification task, ranking fifth at the challenge, and had <2% difference from the best system. Error analysis was also conducted targeting at figuring out the root causes and possible approaches for improvement. We demonstrate the generic approaches and the practice of connecting general purposed clinical NLP system to task-specific requirements with deep learning methods. Our results indicate that a well-designed hybrid NLP system is capable of ADE and medication-related information extraction, which can be used in real-world applications to support ADE-related researches and medical decisions.

Welcome to the HICSS-33 minitrack on Electronic Commerce Systems Development -- Modeling Methods and Methodologies. This is the first time we organize this new minitrack on electronic commerce systems development. The flexibility, scalability, user-friendliness, privacy, security, intuitiveness, and other important characteristics of electronic commerce systems make the systems development process of such systems very multifaceted and complex. Today, most electronic commerce systems are built based on intuition and rule of thumbs.Many practitioners have the misconception that there is no need for a systematic modeling and design technique for these systems. The lack of appropriate methods and methodologies to support electronic commerce systems development is another stumbling block. Most of the widely used methods are weak in supporting the modeling of user interface, non-traditional data types (e.g. multimedia), security, and access control that are so important for electronic commerce systems. New modeling methods and methodologies need to be engineered to address this new breed of electronic commerce systems development projects.The field is still in its infancy. Innovative ideas and new systems development methods and methodologies are only starting to emerge. One thing, however, is sure. Electronic commerce systems development is a critical new area and will get more even more important as electronic commerce continues to grow and expand.We would like to take this opportunity to thank Eileen Dennis, track administrator, and Alan Dennis, track chair for Internet and the Digital Economy track, for their support. We also like to express our gratitude to the group of reviewers for this minitrack.If you have any comments or suggestions that will help us improve the minitrack, please do let us know. Have a great time in Hawaii and do keep in touch.

Lateralization of tonal and intonational pitch processing: An MEG study

ABSTRACTIn today's society, the demand for cybersecurity experts is increasing, as digital information systems are widely spread and targeted by malicious actors. This means that cybersecurity education should be effective in increasing students' knowledge, skills, self‐efficacy, and the ability to adapt and apply knowledge in rapidly evolving situations. Offering hands‐on training with real‐world‐like scenarios and exercises, for example, in the form of Capture the Flag (CTF) games, is one component in teaching students the needed skills. In this study, we measure and compare the effectiveness of cybersecurity teaching with the gamified CTF scenario in the Brno University of Technology Cyber Arena (BUTCA). We measure the effectiveness of the CTF scenario with pre‐ and post‐surveys among university students in Finland and Czechia, and examine effectiveness among different student groups. We also study student satisfaction and the perceived meaningfulness of the learning for different scenario elements, such as instructions, tasks, and gamification elements. The CTFs increased knowledge and skill variables, self‐efficacy variables, and interest variables. CTFs can have positive effects on learning‐related variables despite varying student's base skills or level of knowledge, but different types of students may benefit in different ways. Student satisfaction and perceived meaningfulness of learning with CTF were also high across different student groups.

Problem. This article addresses the issue of developing environmentally friendly vehicles and their electronic and electromechanical components. It provides an analytical review of advanced electromechanical technologies utilized in the creation of Tesla electric cars. Specifically, the study analyzes the electronic stability control system, active pneumatic suspension, and electric power steering. The perspective of electric cars lies in their ability to meet the economic requirements of modernity and align with the "Zero Emission" paradigm. The obtained results can serve as recommendations for the development of electronic and electromechanical systems and complexes of electric vehicles, benefiting scientific and technical professionals engaged in the creation of environmentally friendly vehicles. Goal. This study aims to analyze the advanced technologies used in the development of electronic and electromechanical systems of Tesla electric cars. Methodology. The research employs an analytical study of the characteristics of the electronic and electromechanical systems of Tesla electric cars to achieve the set goal. It includes a detailed analysis of the electronic stability control system, active pneumatic suspension, and electric power steering. Results. An analysis of scientific publications reveals that the problem of creating environmentally friendly and energy-efficient vehicles is currently being solved through the use of electric drives powered by traction batteries. Modern electric cars have overcome drawbacks such as low dynamics, limited range, and low maximum speed, surpassing similar internal combustion engine vehicles in these aspects. Tesla electric cars are currently the most technologically advanced and promising in this field. The analysis focuses on the technical solutions and advanced technologies employed in Tesla Model S electric cars, specifically the electronic stability control system, active pneumatic suspension, and electric power steering. The conducted research allows for an assessment of the current state of electronic and electromechanical systems in electric cars and provides insights into their future development. Originality. The results of this research provide a comprehensive understanding of the present state of electronic and electromechanical systems in Tesla electric cars. It enables a detailed analysis and exploration of the advanced technologies used in the development of Tesla electric cars and their distinctive features. Practical Value. The research findings contribute to identifying promising directions for the development of contemporary electronic and electromechanical systems in electric vehicles. They serve as a basis for creating recommendations concerning electronic stability control systems, active pneumatic suspension, and electric power steering, which can be valuable to scientific and technical professionals involved in the development of environmentally friendly vehicles.

This study identifies, analyses and describes the devices and functions of focus in information structuring in Hadiyya, a Highland East Cushitic language. Data, which were collected from native speakers, were dealt with based on the theory of Functional Grammar (Dik 1997). For focalization, the language uses morphological, lexical and syntactic means. The latter involves word-order change and cleft construction (including pseudo-cleft). Comparatively, the morphological mechanism is the most dynamic and frequent in use while the lexical means is limited both in the number of items employed and in frequency of occurrence.

This entry explores the integration of Artificial Intelligence (AI) tools into the teaching and research of Languages for Specific Purposes (LSP), examining its current applications, affordances, and challenges for educators and learners alike. It discusses the pedagogical particularities of LSP and connects them to the potentialities of AI‐powered tools. Drawing on recent developments in natural language processing, machine learning, and intelligent tutoring systems, and connecting them to the current research insights, the entry analyzes how AI‐enhanced tools—such as adaptive language platforms, automated writing evaluation systems, and conversation agents—are transforming LSP pedagogy. Particular attention is paid to the affordances of AI for tailoring instruction to specific professional linguistic needs, fostering learner autonomy, and boosting motivation and engagement. Through a critical review of research studies and a discussion of ethical and pedagogical considerations, the entry argues that while AI presents significant opportunities for innovation in LSP contexts, its adoption must be guided by domain‐specific expertise, transparency, and pedagogical soundness.

The research presented here focuses on implementation and efficiency issues associated with the use of word graphs for interfacing acoustic speech recognition systems with natural language processing systems. The effectiveness of various pruning methods for graph construction is examined, as well as techniques for word graph compression. In addition, the word graph representation is compared to another predominant interface method, the N-best sentence list.

BackgroundMany health care systems now allow patients to access their electronic health record (EHR) notes online through patient portals. Medical jargon in EHR notes can confuse patients, which may interfere with potential benefits of patient access to EHR notes.ObjectiveThe aim of this study was to develop and evaluate the usability and content quality of NoteAid, a Web-based natural language processing system that links medical terms in EHR notes to lay definitions, that is, definitions easily understood by lay people.MethodsNoteAid incorporates two core components: CoDeMed, a lexical resource of lay definitions for medical terms, and MedLink, a computational unit that links medical terms to lay definitions. We developed innovative computational methods, including an adapted distant supervision algorithm to prioritize medical terms important for EHR comprehension to facilitate the effort of building CoDeMed. Ten physician domain experts evaluated the user interface and content quality of NoteAid. The evaluation protocol included a cognitive walkthrough session and a postsession questionnaire. Physician feedback sessions were audio-recorded. We used standard content analysis methods to analyze qualitative data from these sessions.ResultsPhysician feedback was mixed. Positive feedback on NoteAid included (1) Easy to use, (2) Good visual display, (3) Satisfactory system speed, and (4) Adequate lay definitions. Opportunities for improvement arising from evaluation sessions and feedback included (1) improving the display of definitions for partially matched terms, (2) including more medical terms in CoDeMed, (3) improving the handling of terms whose definitions vary depending on different contexts, and (4) standardizing the scope of definitions for medicines. On the basis of these results, we have improved NoteAid’s user interface and a number of definitions, and added 4502 more definitions in CoDeMed.ConclusionsPhysician evaluation yielded useful feedback for content validation and refinement of this innovative tool that has the potential to improve patient EHR comprehension and experience using patient portals. Future ongoing work will develop algorithms to handle ambiguous medical terms and test and evaluate NoteAid with patients.

Evolution of a Mature Clinical Informationist Model

A group of computer scientists and linguists at international IBM locations around the world is developing an ‘intelligent’ natural language processing system which has multiple functions and which will serve different groups of users This paper will give a brief overview of the entire system and will focus on the grammar- and style-checking components, i. e. how errors will be detected, handled, and corrected, as well as the flexibility of the system which allows the user to choose among different levels of corrections as well as certain kinds of errors that will be detected and corrected