A Machine Learning Approach for the Automatic Classification of Schizophrenic Discourse

Decreased frontal cortical serotonin 2A receptors in schizophrenia

This study aimed to develop and apply natural language processing (NLP) algorithms to identify recurrent atrial fibrillation (AF) episodes following rhythm control therapy initiation using electronic health records (EHRs). We included adults with new-onset AF who initiated rhythm control therapies (ablation, cardioversion, or antiarrhythmic medication) within two US integrated healthcare delivery systems. A code-based algorithm identified potential AF recurrence using diagnosis and procedure codes. An automated NLP algorithm was developed and validated to capture AF recurrence from electrocardiograms, cardiac monitor reports, and clinical notes. Compared with the reference standard cases confirmed by physicians' adjudication, the F-scores, sensitivity, and specificity were all above 0.90 for the NLP algorithms at both sites. We applied the NLP and code-based algorithms to patients with incident AF (n=22 970) during the 12 months after initiating rhythm control therapy. Applying the NLP algorithms, the percentages of patients with AF recurrence for sites 1 and 2 were 60.7% and 69.9% (ablation), 64.5% and 73.7% (cardioversion), and 49.6% and 55.5% (antiarrhythmic medication), respectively. In comparison, the percentages of patients with code-identified AF recurrence for sites 1 and 2 were 20.2% and 23.7% for ablation, 25.6% and 28.4% for cardioversion, and 20.0% and 27.5% for antiarrhythmic medication, respectively. When compared with a code-based approach alone, this study's high-performing automated NLP method identified significantly more patients with recurrent AF. The NLP algorithms could enable efficient evaluation of treatment effectiveness of AF therapies in large populations and help develop tailored interventions.

Development and Validation of a Natural Language Processing Tool to Identify Injuries in Infants Associated With Abuse.

2043 Background: Electronic health records (EHR) are used for retrospective cancer outcomes analysis. Sites and timing of recurrence are not captured in structured EHR data. Novel computerized methods are necessary to use unstructured longitudinal EHR data for large scale studies. Methods: We previously developed a neural network-based NLP algorithm to identify no recurrence vs. metastatic recurrence cases by analyzing physician notes, pathology and radiology reports in Stanford’s breast cancer database, Oncoshare (Cohort A). To validate this algorithm for local vs. distant recurrence, we identified a distinct Oncoshare cohort (Cohort B). Cases were manually curated for longitudinal development of local or distant recurrence and metastatic sites. A two-sided t-test was used to compare mean probabilities between local and distant recurrence cases. Next, we combined cases in Cohorts A and B to train and validate a novel NLP classifier that identifies metastatic site. The combined cohort was randomly divided into training and validation sets. Sensitivity and specificity were calculated for the NLP algorithm’s ability to detect metastatic sites compared to manual curation. Results: In Cohort B: 350 metastatic cases were identified. Mean probability for local and distant recurrence was 0.43 and 0.79, respectively and differed significantly for patients with local vs. distant recurrence (p<0.01). In Cohorts A and B: 632 metastatic cases were used for determination of sites. Sensitivity and specificity were highest for detection of peritoneal metastasis followed by liver, lung, skin, bone and central nervous system (table). Conclusions: This NLP algorithm is a scalable tool that uses unstructured EHR data to capture breast cancer recurrence, distinguishing local from distant recurrence and identifying metastatic site. This method may facilitate analysis of large datasets and correlation of outcomes with metastatic site. [Table: see text]

The landscape of financial reporting is undergoing a profound transformation fueled by advancements in Artificial Intelligence (AI) technologies. This review explores the revolutionary impact of AI on financial reporting, with a specific focus on enhancing accuracy and timeliness. AI-driven technologies such as machine learning, natural language processing, and predictive analytics are reshaping traditional financial reporting processes. These technologies enable organizations to automate routine tasks, analyze vast volumes of financial data, and extract valuable insights with unprecedented speed and accuracy. By leveraging AI, organizations can streamline data collection, validation, and analysis, thereby reducing manual errors and improving the overall quality of financial reports. One of the key advantages of AI in financial reporting is its ability to identify patterns and anomalies in financial data that may go unnoticed by human analysts. Machine learning algorithms can detect irregularities in financial transactions, flag potential risks, and enhance fraud detection capabilities, thus bolstering the integrity and reliability of financial reports. Furthermore, AI-powered natural language processing (NLP) algorithms enable organizations to extract relevant information from unstructured data sources such as financial statements, regulatory filings, and news articles. By analyzing textual data, NLP algorithms can generate insights into market trends, competitive dynamics, and regulatory developments, providing decision-makers with valuable intelligence to inform financial reporting decisions. In addition to improving accuracy, AI plays a crucial role in enhancing the timeliness of financial reporting. By automating time-consuming tasks such as data entry, reconciliation, and financial statement preparation, AI enables organizations to expedite the reporting process and deliver financial information to stakeholders in a more timely manner. This not only meets regulatory deadlines but also enables stakeholders to make informed decisions based on up-to-date financial information. Moreover, AI facilitates real-time monitoring of financial performance metrics, enabling organizations to proactively identify emerging trends, risks, and opportunities. Predictive analytics algorithms can forecast future financial outcomes, enabling organizations to anticipate market changes and adjust their strategies accordingly, thereby enhancing agility and responsiveness in financial reporting. The integration of AI technologies is transforming financial reporting practices, enhancing both accuracy and timeliness. By automating routine tasks, analyzing vast datasets, and providing valuable insights, AI enables organizations to produce high-quality financial reports that meet the needs of stakeholders in a dynamic and rapidly evolving business environment. As AI continues to evolve, its role in financial reporting will only become more prominent, driving efficiency, transparency, and accountability across the financial reporting ecosystem. Keywords: Artificial Intelligence, Financial Reporting, Accuracy, Timeliness, Machine Learning.

In the era of digitalization, electronic gadgets such as Google Translate, Siri, and Alexa have at least one characteristic: They are all the products of natural language processing (NLP). “Natural Language” refers to a human language used for daily communication, such as English, Hindi, Bengali, etc. Natural languages, as opposed to artificial languages such as computer languages and mathematical nomenclature, have evolved as they have been transmitted from generation to generation and are challenging to explain with clear limits in the first instance. In natural language processing, artificial intelligence (Singh et al., 2021), linguistics, information processing, and cognitive science are all related fields (NLP). NLP aims to use intelligent computer techniques to process human language. However, NLP technologies such as voice recognition, language comprehension, and machine translation exist. With such limited obvious exclusions, machine learning algorithms in NLP sometimes lacked sufficient capacity to consume massive amounts of training data. In addition, the algorithms, techniques, and infrastructural facilities lack enough strength. Humans design features in traditional machine learning, and feature engineering is a limitation that requires significant human expertise. Simultaneously, the accompanying superficial algorithms lack depiction capability and, as a result, the ability to generate layers of duplicatable concepts that would naturally separate intricate aspects in forming visible linguistic data. Deep learning overcomes the challenges mentioned earlier by using deep, layered modelling architectures, often using neural networks and the corresponding full-stack learning methods. Deep learning has recently enhanced natural language processing by using artificial neural networks based on biological brain systems and Backpropagation. Deep learning approaches that use several processing layers to develop hierarchy data representations have produced cutting-edge results in various areas. This chapter introduces natural language processing (NLP) as an AI component. The history of NLP is next. Distributed language representations are the core of NLP's profound learning revolution. After the survey, the boundaries of deep learning for NLP are investigated. The paper proposes five NLP scientific fields.

ObjectivesUrinary tract infection (UTI) is common in home care but not easily captured with standard assessment. This study aimed to examine the value of nursing notes in detecting UTI signs and symptoms in home care. DesignThe study developed a natural language processing (NLP) algorithm to automatically identify UTI-related information in nursing notes. Setting and ParticipantsHome care visit notes (n = 1,149,586) and care coordination notes (n = 1,461,171) for 89,459 patients treated in the largest nonprofit home care agency in the United States during 2014. MeasuresWe generated 6 categories of UTI-related information from literature and used the Unified Medical Language System (UMLS) to identify a preliminary list of terms. The NLP algorithm was tested on a gold standard set of 300 clinical notes annotated by clinical experts. We used structured Outcome and Assessment Information Set data to extract the frequency of UTI-related emergency department (ED) visits or hospitalizations and explored time-patterns in documentation of UTI-related information. ResultsThe NLP system achieved very good overall performance (F measure = 0.9, 95% CI: 0.87-0.93) based on the test results obtained by using the notes for patients admitted to the ED or hospital due to UTI. UTI-related information was significantly more prevalent (P < .01 for all the tests) in home care episodes with UTI-related ED admission or hospitalization vs the general patient population; 81% of home care episodes with UTI-related hospitalization or ED admission had at least 1 category of UTI-related information vs 21.6% among episodes without UTI-related hospitalization or ED admission. Frequency of UTI-related information documentation increased in advance of UTI-related hospitalization or ED admission, peaking within a few days before the event. Conclusions and ImplicationsInformation in nursing notes is often overlooked by stakeholders and not integrated into predictive modeling for decision-making support, but our findings highlight their value in early risk identification and care guidance. Health care administrators should consider using NLP to extract clinical data from nursing notes to improve early detection and treatment, which may lead to quality improvement and cost reduction.

IntroductionDetailed, precise information on a pharmaceutical’s projected therapeutic use is required for horizon scanning. Inferring an estimated indication from trial protocols is a key skill of horizon scanners. The International Horizon Scanning Initiative (IHSI) database utilizes semi-automated data collection. This pilot aimed to verify that the extraction of relevant word sets to generate an estimated indication could be semi-automated.Methods Ten drugs approved in Europe in 2021 were selected as the pilot test set. The test set included drugs approved for the treatment of rare diseases (n=4), haemato-oncology (n=3), and non-oncology conditions (n=3). Eight of the drugs were approved based on phase III trials. The assessment comprised a review of the pivotal trial that supported product registration for these drugs. We undertook a comparison between a human curator and a natural language processing (NLP) algorithm in generating granular tags relating to key aspects of the drugs’ estimated indication (stage of disease, patient-specific subgroup, and place in treatment).ResultsIn 50 percent of cases, the NLP accurately tagged a word or word set related to stage of disease, patient-specific subgroup, or place in treatment, which was also tagged by human curators. In 50 percent of cases, the NLP did not identify words or word sets tagged by human curators. Where relevant, the NLP successfully tagged the same word sets relating to stage of disease for all drugs in the test set. The same word sets relating to patient-specific subgroup were successfully tagged for three drugs in the set. NLP successfully tagged word sets relating to place in treatment for two drugs.ConclusionsThe NLP algorithm is successful in extracting relevant word sets, which can be used to generate an estimated indication in an automated or semi-automated process. The pilot highlighted that further testing is required to advance the sensitivity of the algorithm. Further piloting exploring both unsupervised and supervised modeling approaches (named entity recognition and deep neural networks, respectively) is planned.

e19062 Background: The annual incidence of venous thromboembolism (VTE) is roughly 50-fold increased post allogeneic hematopoietic cell transplant (HSCT). Timely detection of VTE events has a significant impact on morbidity and mortality from these events. Manual chart review is commonly used for detection of VTE events; however, this is labor intensive and often not feasible. The International Classification of Diseases, 10th revision (ICD-10) has attempted to codify VTE more easily but has high false positive and false negative rates. Natural language processing (NLP) algorithms have been designed to improve detection of VTE events in electronic medical record systems over the past several years. This study aims to compare the accuracy of our institution’s NLP algorithm to the ICD-10 in detecting extremity VTE/pulmonary embolism (PE) events in hematopoietic stem cell transplant (HSCT) patients. Methods: This study was a retrospective analysis of adult patients who had allogeneic HSCT at MD Anderson between 2016-2020. Patient charts were assessed by three different methods for acute VTE events within 100 days of HSCT (index date). ICD-10 codes and our institution’s NLP algorithm were used separately to identify these VTE events. Afterwards, manual chart review was performed for confirmation. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for the ICD-10, NLP, and the NLP+ICD combination. Results: A total of 1,391 patient charts were analyzed in our study. There were 129 (9.3%) VTE events detected via manual chart review. Three methodologies were used for comparison: the NLP algorithm alone, the ICD code alone, and detection by NLP or ICD. The NLP algorithm alone had the best specificity (98.9%) and PPV (89.4%) of the three methods. The ICD-10 code had a statistically significant decrease in ability to detect acute VTE events compared to the NLP (p&lt;0.001) (Table). Of the 19 VTE events not identified by NLP, all were found in radiology or vascular laboratory reports. Conclusions: Our study shows the overall superior performance of the NLP algorithm, as it had higher sensitivity, specificity, PPV, and NPV compared to the ICD-10, as well as a better specificity and PPV compared to the combination of both methods. Future refinement of the NLP algorithm and combination with other detection methodologies will allow for more accurate detection of VTE in large cohorts. [Table: see text]

Idiopathic normal pressure hydrocephalus (iNPH) is an underdiagnosed, progressive, and disabling condition. Early treatment is associated with better outcomes and improved quality of life. In this paper, the authors aimed to identify features associated with patients with iNPH using natural language processing (NLP) to characterize this cohort, with the intention to later target the development of artificial intelligence-driven tools for early detection. The electronic health records of patients with shunt-responsive iNPH were retrospectively reviewed using an NLP algorithm. Participants were selected from a prospectively maintained single-center database of patients undergoing CSF diversion for probable iNPH (March 2008-July 2020). Analysis was conducted on preoperative health records including clinic letters, referrals, and radiology reports accessed through CogStack. Clinical features were extracted from these records as SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms) concepts using a named entity recognition machine learning model. In the first phase, a base model was generated using unsupervised training on 1 million electronic health records and supervised training with 500 double-annotated documents. The model was fine-tuned to improve accuracy using 300 records from patients with iNPH double annotated by two blinded assessors. Thematic analysis of the concepts identified by the machine learning algorithm was performed, and the frequency and timing of terms were analyzed to describe this patient group. In total, 293 eligible patients responsive to CSF diversion were identified. The median age at CSF diversion was 75 years, with a male predominance (69% male). The algorithm performed with a high degree of precision and recall (F1 score 0.92). Thematic analysis revealed the most frequently documented symptoms related to mobility, cognitive impairment, and falls or balance. The most frequent comorbidities were related to cardiovascular and hematological problems. This model demonstrates accurate, automated recognition of iNPH features from medical records. Opportunities for translation include detecting patients with undiagnosed iNPH from primary care records, with the aim to ultimately improve outcomes for these patients through artificial intelligence-driven early detection of iNPH and prompt treatment.

BackgroundAccurate identification of new diagnoses of human papillomavirus–associated cancers and precancers is an important step toward the development of strategies that optimize the use of human papillomavirus vaccines. The diagnosis of human papillomavirus cancers hinges on a histopathologic report, which is typically stored in electronic medical records as free-form, or unstructured, narrative text. Previous efforts to perform surveillance for human papillomavirus cancers have relied on the manual review of pathology reports to extract diagnostic information, a process that is both labor- and resource-intensive. Natural language processing can be used to automate the structuring and extraction of clinical data from unstructured narrative text in medical records and may provide a practical and effective method for identifying patients with vaccine-preventable human papillomavirus disease for surveillance and research.ObjectiveThis study's objective was to develop and assess the accuracy of a natural language processing algorithm for the identification of individuals with cancer or precancer of the cervix and anus.MethodsA pipeline-based natural language processing algorithm was developed, which incorporated machine learning and rule-based methods to extract diagnostic elements from the narrative pathology reports. To test the algorithm’s classification accuracy, we used a split-validation study design. Full-length cervical and anal pathology reports were randomly selected from 4 clinical pathology laboratories. Two study team members, blinded to the classifications produced by the natural language processing algorithm, manually and independently reviewed all reports and classified them at the document level according to 2 domains (diagnosis and human papillomavirus testing results). Using the manual review as the gold standard, the algorithm’s performance was evaluated using standard measurements of accuracy, recall, precision, and F-measure.ResultsThe natural language processing algorithm’s performance was validated on 949 pathology reports. The algorithm demonstrated accurate identification of abnormal cytology, histology, and positive human papillomavirus tests with accuracies greater than 0.91. Precision was lowest for anal histology reports (0.87, 95% CI 0.59-0.98) and highest for cervical cytology (0.98, 95% CI 0.95-0.99). The natural language processing algorithm missed 2 out of the 15 abnormal anal histology reports, which led to a relatively low recall (0.68, 95% CI 0.43-0.87).ConclusionsThis study outlines the development and validation of a freely available and easily implementable natural language processing algorithm that can automate the extraction and classification of clinical data from cervical and anal cytology and histology.

The prevalence of patients who are Incapacitated with No Evident Advance Directives or Surrogates (INEADS) remains unknown because such data are not routinely captured in structured electronic health records. This study sought to develop and validate a natural language processing (NLP) algorithm to identify information related to being INEADS from clinical notes. We used a publicly available dataset of critical care patients from 2001 through 2012 at a United States academic medical center, which contained 418,393 relevant clinical notes for 23,904 adult admissions. We developed 17 subcategories indicating reduced or elevated potential for being INEADS, and created a vocabulary of terms and expressions within each. We used an NLP application to create a language model and expand these vocabularies. The NLP algorithm was validated against gold standard manual review of 300 notes and showed good performance overall (F-score = 0.83). More than 80% of admissions had notes containing information in at least one subcategory. Thirty percent (n = 7,134) contained at least one of five social subcategories indicating elevated potential for being INEADS, and <1% (n = 81) contained at least four, which we classified as high likelihood of being INEADS. Among these, n = 8 admissions had no subcategory indicating reduced likelihood of being INEADS, and appeared to meet the definition of INEADS following manual review. Among the remaining n = 73 who had at least one subcategory indicating reduced likelihood of being INEADS, manual review of a 10% sample showed that most did not appear to be INEADS. Compared with the full cohort, the high likelihood group was significantly more likely to die during hospitalization and within four years, to have Medicaid, to have an emergency admission, and to be male. This investigation demonstrates potential for NLP to identify INEADS patients, and may inform interventions to enhance advance care planning for patients who lack social support.

Rare life-threatening conditions, such as multisystemic hereditary transthyretin amyloidosis (ATTRv) polyneuropathy, are often underdiagnosed or diagnosed late in the disease course, although early diagnosis is crucial for treatment success. Red flag symptoms have been identified, but manual screening of multidisciplinary medical records on this set of symptoms is time-consuming. This study aimed to validate a Natural Language Processing (NLP) algorithm to perform such a search in an automated manner, in order to improve early diagnosis and treatment. A novel state-of-the-art NLP procedure was applied to extract red flag symptoms from patients' electronic medical records and to select patients at risk for ATTRv polyneuropathy for further clinical review. Accuracy of the algorithm was assessed through comparison with a manual standard on a random sample of 300 patients. Out of a retrospective sample of 1015 patients, the NLP algorithm yielded 128 patients with three or more red flag symptoms of which 69 patients were considered eligible for genetic testing after clinical review. High accuracy was found in the detection of red flag symptoms, with F1 scores between 0.88 and 0.98. A relative increase of 48.6% in genetic testing, to identify patients with a rare disease earlier, was demonstrated. An NLP algorithm, after clinical validation, offers a valid and accurate tool to detect red flag symptoms in medical records across multiple disciplines, supporting better screening for patients with rare diseases. This opens the door to further NLP applications, facilitating rapid diagnosis and early treatment of rare diseases.

The aim of this review is to provide a comprehensive survey of statistical challenges in neuroimaging data analysis, from neuroimaging techniques to large-scale neuroimaging studies and statistical learning methods. We briefly review eight popular neuroimaging techniques and their potential applications in neuroscience research and clinical translation. We delineate four themes of neuroimaging data and review major image processing analysis methods for processing neuroimaging data at the individual level. We briefly review four large-scale neuroimaging-related studies and a consortium on imaging genomics and discuss four themes of neuroimaging data analysis at the population level. We review nine major population-based statistical analysis methods and their associated statistical challenges and present recent progress in statistical methodology to address these challenges.

The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium is a collaborative network of researchers working together on a range of large-scale studies that integrate data from 70 institutions worldwide. Organized into Working Groups that tackle questions in neuroscience, genetics, and medicine, ENIGMA studies have analyzed neuroimaging data from over 12,826 subjects. In addition, data from 12,171 individuals were provided by the CHARGE consortium for replication of findings, in a total of 24,997 subjects. By meta-analyzing results from many sites, ENIGMA has detected factors that affect the brain that no individual site could detect on its own, and that require larger numbers of subjects than any individual neuroimaging study has currently collected. ENIGMA’s first project was a genome-wide association study identifying common variants in the genome associated with hippocampal volume or intracranial volume. Continuing work is exploring genetic associations with subcortical volumes (ENIGMA2) and white matter microstructure (ENIGMA-DTI). Working groups also focus on understanding how schizophrenia, bipolar illness, major depression and attention deficit/hyperactivity disorder (ADHD) affect the brain. We review the current progress of the ENIGMA Consortium, along with challenges and unexpected discoveries made on the way.