Interfacing acoustic models with natural language processing systems

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

Many ongoing clinical research projects, such as projects involving studies associated with cancer, involve manual capture of information in surgical pathology reports so that the information can be used to determine the eligibility of recruited patients for the study and to provide other information, such as cancer prognosis. Natural language processing (NLP) systems offer an alternative to automated coding, but pathology reports have certain features that are difficult for NLP systems. This paper describes how a preprocessor was integrated with an existing NLP system (MedLEE) in order to reduce modification to the NLP system and to improve performance. The work was done in conjunction with an ongoing clinical research project that assesses disparities and risks of developing breast cancer for minority women. An evaluation of the system was performed using manually coded data from the research project's database as a gold standard. The evaluation outcome showed that the extended NLP system had a sensitivity of 90.6% and a precision of 91.6%. Results indicated that this system performed satisfactorily for capturing information for the cancer research project.

Evaluation of Natural Language Processing (NLP) systems to annotate drug product labeling with MedDRA terminology

The purpose of this study is to evaluate the performance of a natural language processing (NLP) system in classifying a database of free-text knee MRI reports at two separate academic radiology practices. An NLP system that uses terms and patterns in manually classified narrative knee MRI reports was constructed. The NLP system was trained and tested on expert-classified knee MRI reports from two major health care organizations. Radiology reports were modeled in the training set as vectors, and a support vector machine framework was used to train the classifier. A separate test set from each organization was used to evaluate the performance of the system. We evaluated the performance of the system both within and across organizations. Standard evaluation metrics, such as accuracy, precision, recall, and F1 score (i.e., the weighted average of the precision and recall), and their respective 95% CIs were used to measure the efficacy of our classification system. The accuracy for radiology reports that belonged to the model's clinically significant concept classes after training data from the same institution was good, yielding an F1 score greater than 90% (95% CI, 84.6-97.3%). Performance of the classifier on cross-institutional application without institution-specific training data yielded F1 scores of 77.6% (95% CI, 69.5-85.7%) and 90.2% (95% CI, 84.5-95.9%) at the two organizations studied. The results show excellent accuracy by the NLP machine learning classifier in classifying free-text knee MRI reports, supporting the institution-independent reproducibility of knee MRI report classification. Furthermore, the machine learning classifier performed well on free-text knee MRI reports from another institution. These data support the feasibility of multiinstitutional classification of radiologic imaging text reports with a single machine learning classifier without requiring institution-specific training data.

: 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.

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.

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.

BackgroundThe vast amount of data published in the primary biomedical literature represents a challenge for the automated extraction and codification of individual data elements. Biological databases that rely solely on manual extraction by expert curators are unable to comprehensively annotate the information dispersed across the entire biomedical literature. The development of efficient tools based on natural language processing (NLP) systems is essential for the selection of relevant publications, identification of data attributes and partially automated annotation. One of the tasks of the Biocreative 2010 Challenge III was devoted to the evaluation of NLP systems developed to identify articles for curation and extraction of protein-protein interaction (PPI) data.ResultsThe Biocreative 2010 competition addressed three tasks: gene normalization, article classification and interaction method identification. The BioGRID and MINT protein interaction databases both participated in the generation of the test publication set for gene normalization, annotated the development and test sets for article classification, and curated the test set for interaction method classification. These test datasets served as a gold standard for the evaluation of data extraction algorithms.ConclusionThe development of efficient tools for extraction of PPI data is a necessary step to achieve full curation of the biomedical literature. NLP systems can in the first instance facilitate expert curation by refining the list of candidate publications that contain PPI data; more ambitiously, NLP approaches may be able to directly extract relevant information from full-text articles for rapid inspection by expert curators. Close collaboration between biological databases and NLP systems developers will continue to facilitate the long-term objectives of both disciplines.

This paper describes the integration of verbal idioms into an Natural Language Processing (NLP) system, adopting a construction approach, which is based on the prior parsing stage, so that these Multi-Word Expressions (MWE) can be taken into account in subsequent tasks, such as semantic role labeling or whole-part relation extraction. The paper focuses on body-part nouns, which are often part of many verbal idioms, and uses a manually annotated corpus to evaluate its parsing strategy. Results showed a precision of 0.92, 0.83 recall, 0.87 f-measure and an accuracy 0.99.

Natural language processing (NLP) systems often inadvertently encode and amplify social biases through entangled representations of demographic attributes and task-related attributes.To mitigate this, we propose a novel framework that combines causal analysis with practical intervention strategies.The method leverages attribute-specific prompting to isolate sensitive attributes while applying information-theoretic constraints to minimize spurious correlations.Experiments across six language models and two classification tasks demonstrate its effectiveness.We hope this work will provide the NLP community with a causal disentanglement perspective for achieving fairness in NLP systems.

Artificial Intelligence (AI) technologies are rapidly evolving and their integration into psychological practices has progressively expanded, offering new tools for diagnosis, treatment and therapeutic monitoring. This review examines the transformative role of AI, particularly Natural Language Processing (NLP) systems, in reshaping clinical psychology and digital mental health interventions (DMHIs). In particular, it explores how AI and NLP can facilitate human-machine interaction in therapy by analysing how language is used within clinical conversations and providing personalized, real-time interventions. Following PRISMA guidelines, a systematic review of literature from 2019 to 2025 identified 17 studies that met inclusion criteria, emphasizing AI's use in psychological assessment and intervention. The review focuses on two key aspects: the functions and applications of NLP-based systems in clinical practice and the advantages and benefits they offer for both psychologists and patients. Findings suggest that NLP-driven AI systems enhance both patient engagement and clinician efficiency, offering scalable, cost-effective solutions that improve access and personalization. However, challenges remain, including ethical concerns around data privacy, lack of standardization, limited generalizability across disorders and reduced human empathy. Moreover, current systems are primarily designed for well-defined conditions like anxiety and depression, with limited applicability to complex or comorbid psychological presentations. This review underscores the importance of supervised, ethically governed AI implementation. While AI holds substantial promise in augmenting clinical psychology, its success depends on maintaining human oversight, ensuring transparency and establishing shared scientific and ethical standards across the psychological community.

Social Risk Factors are Associated with Risk for Hospitalization in Home Health Care: A Natural Language Processing Study

This letter to the editor suggests adding a technical point to the new editorial policy expounded by Hosseini et al. on the mandatory disclosure of any use of natural language processing (NLP) systems, or generative AI, in writing scholarly publications. Such AI systems should naturally also be forbidden from being named as authors, because they would not have fulfilled prevailing authorship guidelines (such as the widely adopted ICMJE authorship criteria).

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