Integrating predictive coding and a user-centric interface for enhanced auditing and quality in cancer registry data.

Data curation for a hospital-based cancer registry heavily relies on the labor-intensive manual abstraction process by cancer registrars to identify cancer-related information from free-text electronic health records. To streamline this process, a natural language processing system incorporating a hybrid of deep learning-based and rule-based approaches for identifying lung cancer registry-related concepts, along with a symbolic expert system that generates registry coding based on weighted rules, was developed. The system is integrated with the hospital information system at a medical center to provide cancer registrars with a patient journey visualization platform. The embedded system offers a comprehensive view of patient reports annotated with significant registry concepts to facilitate the manual coding process and elevate overall quality. Extensive evaluations, including comparisons with state-of-the-art methods, were conducted using a lung cancer dataset comprising 1428 patients from the medical center. The experimental results illustrate the effectiveness of the developed system, consistently achieving F1-scores of 0.85 and 1.00 across 30 coding items. Registrar feedback highlights the system's reliability as a tool for assisting and auditing the abstraction. By presenting key registry items along the timeline of a patient's reports with accurate code predictions, the system improves the quality of registrar outcomes and reduces the labor resources and time required for data abstraction. Our study highlights advancements in cancer registry coding practices, demonstrating that the proposed hybrid weighted neural-symbolic cancer registry system is reliable and efficient for assisting cancer registrars in the coding workflow and contributing to clinical outcomes.

Use of natural language processing to uncover racial bias in obstetrical documentation.

Natural Language Processing (NLP), a form of Artificial Intelligence, allows free-text based clinical documentation to be integrated in ways that facilitate data analysis, data interpretation and formation of individualized medical and obstetrical care. In this cross-sectional study, we identified all births during the study period carrying the radiology-confirmed diagnosis of fibroid uterus in pregnancy (defined as size of largest diameter of >5 cm) by using an NLP platform and compared it to non-NLP derived data using ICD10 codes of the same diagnosis. We then compared the two sets of data and stratified documentation gaps by race. Using fibroid uterus in pregnancy as a marker, we found that Black patients were more likely to have the diagnosis entered late into the patient's chart or had missing documentation of the diagnosis. With appropriate algorithm definitions, cross referencing and thorough validation steps, NLP can contribute to identifying areas of documentation gaps and improve quality of care.

Identification of pancreatic cancer risk factors from clinical notes using natural language processing.

OBJECTIVES: Screening for pancreatic ductal adenocarcinoma (PDAC) is considered in high-risk individuals (HRIs) with established PDAC risk factors, such as family history and germline mutations in PDAC susceptibility genes. Accurate assessment of risk factor status is provider knowledge-dependent and requires extensive manual chart review by experts. Natural Language Processing (NLP) has shown promise in automated data extraction from the electronic health record (EHR). We aimed to use NLP for automated extraction of PDAC risk factors from unstructured clinical notes in the EHR. METHODS: We first developed rule-based NLP algorithms to extract PDAC risk factors at the document-level, using an annotated corpus of 2091 clinical notes. Next, we further improved the NLP algorithms using a cohort of 1138 patients through patient-level training, validation, and testing, with comparison against a pre-specified reference standard. To minimize false-negative results we prioritized algorithm recall. RESULTS: In the test set (n = 807), the NLP algorithms achieved a recall of 0.933, precision of 0.790, and F1-score of 0.856 for family history of PDAC. For germline genetic mutations, the algorithm had a high recall of 0.851, while precision and F1-score were lower at 0.350 and 0.496 respectively. Most false positives for germline mutations resulted from erroneous recognition of tissue mutations. CONCLUSIONS: Rule-based NLP algorithms applied to unstructured clinical notes are highly sensitive for automated identification of PDAC risk factors. Further validation in a large primary-care patient population is warranted to assess real-world utility in identifying HRIs for pancreatic cancer screening.

Automating literature screening and curation with applications to computational neuroscience.

OBJECTIVE: ModelDB (https://modeldb.science) is a discovery platform for computational neuroscience, containing over 1850 published model codes with standardized metadata. These codes were mainly supplied from unsolicited model author submissions, but this approach is inherently limited. For example, we estimate we have captured only around one-third of NEURON models, the most common type of models in ModelDB. To more completely characterize the state of computational neuroscience modeling work, we aim to identify works containing results derived from computational neuroscience approaches and their standardized associated metadata (eg, cell types, research topics). MATERIALS AND METHODS: Known computational neuroscience work from ModelDB and identified neuroscience work queried from PubMed were included in our study. After pre-screening with SPECTER2 (a free document embedding method), GPT-3.5, and GPT-4 were used to identify likely computational neuroscience work and relevant metadata. RESULTS: SPECTER2, GPT-4, and GPT-3.5 demonstrated varied but high abilities in identification of computational neuroscience work. GPT-4 achieved 96.9% accuracy and GPT-3.5 improved from 54.2% to 85.5% through instruction-tuning and Chain of Thought. GPT-4 also showed high potential in identifying relevant metadata annotations. DISCUSSION: Accuracy in identification and extraction might further be improved by dealing with ambiguity of what are computational elements, including more information from papers (eg, Methods section), improving prompts, etc. CONCLUSION: Natural language processing and large language model techniques can be added to ModelDB to facilitate further model discovery, and will contribute to a more standardized and comprehensive framework for establishing domain-specific resources.

Getting to Know Named Entity Recognition: Better Information Retrieval.

Named entity recognition (NER) is a powerful computer system that utilizes various computing strategies to extract information from raw text input, since the early 1990s. With rapid advancement in AI and computing, NER models have gained significant attention and been serving as foundational tools across numerus professional domains to organize unstructured data for research and practical applications. This is particularly evident in the medical and healthcare fields, where NER models are essential in efficiently extract critical information from complex documents that are challenging for manual review. Despite its successes, NER present limitations in fully comprehending natural language nuances. However, the development of more advanced and user-friendly models promises to improve work experiences of professional users significantly.

Evaluation of co-speech gestures grounded in word-distributed representation.

The condition for artificial agents to possess perceivable intentions can be considered that they have resolved a form of the symbol grounding problem. Here, the symbol grounding is considered an achievement of the state where the language used by the agent is endowed with some quantitative meaning extracted from the physical world. To achieve this type of symbol grounding, we adopt a method for characterizing robot gestures with quantitative meaning calculated from word-distributed representations constructed from a large corpus of text. In this method, a "size image" of a word is generated by defining an axis (index) that discriminates the "size" of the word in the word-distributed vector space. The generated size images are converted into gestures generated by a physical artificial agent (robot). The robot's gesture can be set to reflect either the size of the word in terms of the amount of movement or in terms of its posture. To examine the perception of communicative intention in the robot that performs the gestures generated as described above, the authors examine human ratings on "the naturalness" obtained through an online survey, yielding results that partially validate our proposed method. Based on the results, the authors argue for the possibility of developing advanced artifacts that achieve human-like symbolic grounding.

The Role of Large Language Models in Transforming Emergency Medicine: Scoping Review.

BACKGROUND: Artificial intelligence (AI), more specifically large language models (LLMs), holds significant potential in revolutionizing emergency care delivery by optimizing clinical workflows and enhancing the quality of decision-making. Although enthusiasm for integrating LLMs into emergency medicine (EM) is growing, the existing literature is characterized by a disparate collection of individual studies, conceptual analyses, and preliminary implementations. Given these complexities and gaps in understanding, a cohesive framework is needed to comprehend the existing body of knowledge on the application of LLMs in EM. OBJECTIVE: Given the absence of a comprehensive framework for exploring the roles of LLMs in EM, this scoping review aims to systematically map the existing literature on LLMs' potential applications within EM and identify directions for future research. Addressing this gap will allow for informed advancements in the field. METHODS: Using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) criteria, we searched Ovid MEDLINE, Embase, Web of Science, and Google Scholar for papers published between January 2018 and August 2023 that discussed LLMs' use in EM. We excluded other forms of AI. A total of 1994 unique titles and abstracts were screened, and each full-text paper was independently reviewed by 2 authors. Data were abstracted independently, and 5 authors performed a collaborative quantitative and qualitative synthesis of the data. RESULTS: A total of 43 papers were included. Studies were predominantly from 2022 to 2023 and conducted in the United States and China. We uncovered four major themes: (1) clinical decision-making and support was highlighted as a pivotal area, with LLMs playing a substantial role in enhancing patient care, notably through their application in real-time triage, allowing early recognition of patient urgency; (2) efficiency, workflow, and information management demonstrated the capacity of LLMs to significantly boost operational efficiency, particularly through the automation of patient record synthesis, which could reduce administrative burden and enhance patient-centric care; (3) risks, ethics, and transparency were identified as areas of concern, especially regarding the reliability of LLMs' outputs, and specific studies highlighted the challenges of ensuring unbiased decision-making amidst potentially flawed training data sets, stressing the importance of thorough validation and ethical oversight; and (4) education and communication possibilities included LLMs' capacity to enrich medical training, such as through using simulated patient interactions that enhance communication skills. CONCLUSIONS: LLMs have the potential to fundamentally transform EM, enhancing clinical decision-making, optimizing workflows, and improving patient outcomes. This review sets the stage for future advancements by identifying key research areas: prospective validation of LLM applications, establishing standards for responsible use, understanding provider and patient perceptions, and improving physicians' AI literacy. Effective integration of LLMs into EM will require collaborative efforts and thorough evaluation to ensure these technologies can be safely and effectively applied.

Oversampling effect in pretraining for bidirectional encoder representations from transformers (BERT) to localize medical BERT and enhance biomedical BERT.

BACKGROUND: Pretraining large-scale neural language models on raw texts has made a significant contribution to improving transfer learning in natural language processing. With the introduction of transformer-based language models, such as bidirectional encoder representations from transformers (BERT), the performance of information extraction from free text has improved significantly in both the general and medical domains. However, it is difficult to train specific BERT models to perform well in domains for which few databases of a high quality and large size are publicly available. OBJECTIVE: We hypothesized that this problem could be addressed by oversampling a domain-specific corpus and using it for pretraining with a larger corpus in a balanced manner. In the present study, we verified our hypothesis by developing pretraining models using our method and evaluating their performance. METHODS: Our proposed method was based on the simultaneous pretraining of models with knowledge from distinct domains after oversampling. We conducted three experiments in which we generated (1) English biomedical BERT from a small biomedical corpus, (2) Japanese medical BERT from a small medical corpus, and (3) enhanced biomedical BERT pretrained with complete PubMed abstracts in a balanced manner. We then compared their performance with those of conventional models. RESULTS: Our English BERT pretrained using both general and small medical domain corpora performed sufficiently well for practical use on the biomedical language understanding evaluation (BLUE) benchmark. Moreover, our proposed method was more effective than the conventional methods for each biomedical corpus of the same corpus size in the general domain. Our Japanese medical BERT outperformed the other BERT models built using a conventional method for almost all the medical tasks. The model demonstrated the same trend as that of the first experiment in English. Further, our enhanced biomedical BERT model, which was not pretrained on clinical notes, achieved superior clinical and biomedical scores on the BLUE benchmark with an increase of 0.3 points in the clinical score and 0.5 points in the biomedical score. These scores were above those of the models trained without our proposed method. CONCLUSIONS: Well-balanced pretraining using oversampling instances derived from a corpus appropriate for the target task allowed us to construct a high-performance BERT model.

Machine learning to predict notes for chart review in the oncology setting: a proof of concept strategy for improving clinician note-writing.

OBJECTIVE: Leverage electronic health record (EHR) audit logs to develop a machine learning (ML) model that predicts which notes a clinician wants to review when seeing oncology patients. MATERIALS AND METHODS: We trained logistic regression models using note metadata and a Term Frequency Inverse Document Frequency (TF-IDF) text representation. We evaluated performance with precision, recall, F1, AUC, and a clinical qualitative assessment. RESULTS: The metadata only model achieved an AUC 0.930 and the metadata and TF-IDF model an AUC 0.937. Qualitative assessment revealed a need for better text representation and to further customize predictions for the user. DISCUSSION: Our model effectively surfaces the top 10 notes a clinician wants to review when seeing an oncology patient. Further studies can characterize different types of clinician users and better tailor the task for different care settings. CONCLUSION: EHR audit logs can provide important relevance data for training ML models that assist with note-writing in the oncology setting.

Vocabulary Matters: An Annotation Pipeline and Four Deep Learning Algorithms for Enzyme Named Entity Recognition.

Enzymes are indispensable in many biological processes, and with biomedical literature growing exponentially, effective literature review becomes increasingly challenging. Natural language processing methods offer solutions to streamline this process. This study aims to develop an annotated enzyme corpus for training and evaluating enzyme named entity recognition (NER) models. A novel pipeline, combining dictionary matching and rule-based keyword searching, automatically annotated enzyme entities in >4800 full-text publications. Four deep learning NER models were created with different vocabularies (BioBERT/SciBERT) and architectures (BiLSTM/transformer) and evaluated on 526 manually annotated full-text publications. The annotation pipeline achieved an F1-score of 0.86 (precision = 1.00, recall = 0.76), surpassed by fine-tuned transformers for F1-score (BioBERT: 0.89, SciBERT: 0.88) and recall (0.86) with BiLSTM models having higher precision (0.94) than transformers (0.92). The annotation pipeline runs in seconds on standard laptops with almost perfect precision, but was outperformed by fine-tuned transformers in terms of F1-score and recall, demonstrating generalizability beyond the training data. In comparison, SciBERT-based models exhibited higher precision, and BioBERT-based models exhibited higher recall, highlighting the importance of vocabulary and architecture. These models, representing the first enzyme NER algorithms, enable more effective enzyme text mining and information extraction. Codes for automated annotation and model generation are available from https://github.com/omicsNLP/enzymeNER and https://zenodo.org/doi/10.5281/zenodo.10581586.

Dataset for multimodal fake news detection and verification tasks.

The proliferation of online disinformation and fake news, particularly in the context of breaking news events, demands the development of effective detection mechanisms. While textual content remains the predominant medium for disseminating misleading information, the contribution of other modalities is increasingly emerging within online outlets and social media platforms. However, multimodal datasets, which incorporate diverse modalities such as texts and images, are not very common yet, especially in low-resource languages. This study addresses this gap by releasing a dataset tailored for multimodal fake news detection in the Italian language. This dataset was originally employed in a shared task on the Italian language. The dataset is divided into two data subsets, each corresponding to a distinct sub-task. In sub-task 1, the goal is to assess the effectiveness of multimodal fake news detection systems. Sub-task 2 aims to delve into the interplay between text and images, specifically analyzing how these modalities mutually influence the interpretation of content when distinguishing between fake and real news. Both sub-tasks were managed as classification problems. The dataset consists of social media posts and news articles. After collecting it, it was labeled via crowdsourcing. Annotators were provided with external knowledge about the topic of the news to be labeled, enhancing their ability to discriminate between fake and real news. The data subsets for sub-task 1 and sub-task 2 consist of 913 and 1350 items, respectively, encompassing newspaper articles and tweets.

Text summarization with ChatGPT for drug labeling documents.

Text summarization is crucial in scientific research, drug discovery and development, regulatory review, and more. This task demands domain expertise, language proficiency, semantic prowess, and conceptual skill. The recent advent of large language models (LLMs), such as ChatGPT, offers unprecedented opportunities to automate this process. We compared ChatGPT-generated summaries with those produced by human experts using FDA drug labeling documents. The labeling contains summaries of key labeling sections, making them an ideal human benchmark to evaluate ChatGPT's summarization capabilities. Analyzing >14000 summaries, we observed that ChatGPT-generated summaries closely resembled those generated by human experts. Importantly, ChatGPT exhibited even greater similarity when summarizing drug safety information. These findings highlight ChatGPT's potential to accelerate work in critical areas, including drug safety.

Preoperative prediction model for risk of readmission after total joint replacement surgery: a random forest approach leveraging NLP and unfairness mitigation for improved patient care and cost-effectiveness.

BACKGROUND: The Center for Medicare and Medicaid Services (CMS) imposes payment penalties for readmissions following total joint replacement surgeries. This study focuses on total hip, knee, and shoulder arthroplasty procedures as they account for most joint replacement surgeries. Apart from being a burden to healthcare systems, readmissions are also troublesome for patients. There are several studies which only utilized structured data from Electronic Health Records (EHR) without considering any gender and payor bias adjustments. METHODS: For this study, dataset of 38,581 total knee, hip, and shoulder replacement surgeries performed from 2015 to 2021 at Novant Health was gathered. This data was used to train a random forest machine learning model to predict the combined endpoint of emergency department (ED) visit or unplanned readmissions within 30 days of discharge or discharge to Skilled Nursing Facility (SNF) following the surgery. 98 features of laboratory results, diagnoses, vitals, medications, and utilization history were extracted. A natural language processing (NLP) model finetuned from Clinical BERT was used to generate an NLP risk score feature for each patient based on their clinical notes. To address societal biases, a feature bias analysis was performed in conjunction with propensity score matching. A threshold optimization algorithm from the Fairlearn toolkit was used to mitigate gender and payor biases to promote fairness in predictions. RESULTS: The model achieved an Area Under the Receiver Operating characteristic Curve (AUROC) of 0.738 (95% confidence interval, 0.724 to 0.754) and an Area Under the Precision-Recall Curve (AUPRC) of 0.406 (95% confidence interval, 0.384 to 0.433). Considering an outcome prevalence of 16%, these metrics indicate the model's ability to accurately discriminate between readmission and non-readmission cases within the context of total arthroplasty surgeries while adjusting patient scores in the model to mitigate bias based on patient gender and payor. CONCLUSION: This work culminated in a model that identifies the most predictive and protective features associated with the combined endpoint. This model serves as a tool to empower healthcare providers to proactively intervene based on these influential factors without introducing bias towards protected patient classes, effectively mitigating the risk of negative outcomes and ultimately improving quality of care regardless of socioeconomic factors.

Emerging Trends of Self-Harm Using Sodium Nitrite in an Online Suicide Community: Observational Study Using Natural Language Processing Analysis.

Background: There is growing concern around the use of sodium nitrite (SN) as an emerging means of suicide, particularly among younger people. Given the limited information on the topic from traditional public health surveillance sources, we studied posts made to an online suicide discussion forum, "Sanctioned Suicide," which is a primary source of information on the use and procurement of SN. Objective: This study aims to determine the trends in SN purchase and use, as obtained via data mining from subscriber posts on the forum. We also aim to determine the substances and topics commonly co-occurring with SN, as well as the geographical distribution of users and sources of SN. Methods: We collected all publicly available from the site's inception in March 2018 to October 2022. Using data-driven methods, including natural language processing and machine learning, we analyzed the trends in SN mentions over time, including the locations of SN consumers and the sources from which SN is procured. We developed a transformer-based source and location classifier to determine the geographical distribution of the sources of SN. Results: Posts pertaining to SN show a rise in popularity, and there were statistically significant correlations between real-life use of SN and suicidal intent when compared to data from the Centers for Disease Control and Prevention (CDC) Wide-Ranging Online Data for Epidemiologic Research (⍴=0.727; P<.001) and the National Poison Data System (⍴=0.866; P=.001). We observed frequent co-mentions of antiemetics, benzodiazepines, and acid regulators with SN. Our proposed machine learning-based source and location classifier can detect potential sources of SN with an accuracy of 72.92% and showed consumption in the United States and elsewhere. Conclusions: Vital information about SN and other emerging mechanisms of suicide can be obtained from online forums.

Characterising global antimicrobial resistance research explains why One Health solutions are slow in development: An application of AI-based gap analysis.

The global health crisis posed by increasing antimicrobial resistance (AMR) implicitly requires solutions based a One Health approach, yet multisectoral, multidisciplinary research on AMR is rare and huge knowledge gaps exist to guide integrated action. This is partly because a comprehensive survey of past research activity has never performed due to the massive scale and diversity of published information. Here we compiled 254,738 articles on AMR using Artificial Intelligence (AI; i.e., Natural Language Processing, NLP) methods to create a database and information retrieval system for knowledge extraction on research perfomed over the last 20 years. Global maps were created that describe regional, methodological, and sectoral AMR research activities that confirm limited intersectoral research has been performed, which is key to guiding science-informed policy solutions to AMR, especially in low-income countries (LICs). Further, we show greater harmonisation in research methods across sectors and regions is urgently needed. For example, differences in analytical methods used among sectors in AMR research, such as employing culture-based versus genomic methods, results in poor communication between sectors and partially explains why One Health-based solutions are not ensuing. Therefore, our analysis suggest that performing culture-based and genomic AMR analysis in tandem in all sectors is crucial for data integration and holistic One Health solutions. Finally, increased investment in capacity development in LICs should be prioritised as they are places where the AMR burden is often greatest. Our open-access database and AI methodology can be used to further develop, disseminate, and create new tools and practices for AMR knowledge and information sharing.

Fine-grained food image classification and recipe extraction using a customized deep neural network and NLP.

Global eating habits cause health issues leading people to mindful eating. This has directed attention to applying deep learning to food-related data. The proposed work develops a new framework integrating neural network and natural language processing for classification of food images and automated recipe extraction. It address the challenges of intra-class variability and inter-class similarity in food images that have received shallow attention in the literature. Firstly, a customized lightweight deep convolution neural network model, MResNet-50 for classifying food images is proposed. Secondly, automated ingredient processing and recipe extraction is done using natural language processing algorithms: Word2Vec and Transformers in conjunction. Thirdly, a representational semi-structured domain ontology is built to store the relationship between cuisine, food item, and ingredients. The accuracy of the proposed framework on the Food-101 and UECFOOD256 datasets is increased by 2.4% and 7.5%, respectively, outperforming existing models in literature such as DeepFood, CNN-Food, Wiser, and other pre-trained neural networks.

Deep continual learning for medical call incidents text classification under the presence of dataset shifts.

The aim of this work is to develop and evaluate a deep classifier that can effectively prioritize Emergency Medical Call Incidents (EMCI) according to their life-threatening level under the presence of dataset shifts. We utilized a dataset consisting of 1982746 independent EMCI instances obtained from the Health Services Department of the Region of Valencia (Spain), with a time span from 2009 to 2019 (excluding 2013). The dataset includes free text dispatcher observations recorded during the call, as well as a binary variable indicating whether the event was life-threatening. To evaluate the presence of dataset shifts, we examined prior probability shifts, covariate shifts, and concept shifts. Subsequently, we designed and implemented four deep Continual Learning (CL) strategies-cumulative learning, continual fine-tuning, experience replay, and synaptic intelligence-alongside three deep CL baselines-joint training, static approach, and single fine-tuning-based on DistilBERT models. Our results demonstrated evidence of prior probability shifts, covariate shifts, and concept shifts in the data. Applying CL techniques had a statistically significant (α=0.05) positive impact on both backward and forward knowledge transfer, as measured by the F1-score, compared to non-continual approaches. We can argue that the utilization of CL techniques in the context of EMCI is effective in adapting deep learning classifiers to changes in data distributions, thereby maintaining the stability of model performance over time. To our knowledge, this study represents the first exploration of a CL approach using real EMCI data.

Validation of a Natural Language Processing Algorithm using National Reporting Data to Improve Identification of Anesthesia-related ADVerse evENTs: The "ADVENTURE" Study.

BACKGROUND: Reporting and analysis of adverse events (AE) is associated with improved healthcare learning, quality outcomes, and patient safety. Manual text analysis is time-consuming, costly, and prone to human errors. We aimed to demonstrate the feasibility of machine learning and natural language processing (NLP) approaches for early predictions of adverse events and provide input to direct quality improvement and patient safety initiatives. METHODS: We used machine learning to analyze 9,559 continuously reported AE by clinicians and healthcare systems to the French National Health accreditor (HAS) between January 1, 2009, and December 31, 2020, for a total of 135,000 unique de-identified AE reports. We validated the labeling and determined the associations between different root causes and patient consequences. The model was validated by independent expert anesthesiologists. RESULTS: The machine learning and Artificial Intelligence (AI) model trained on 9,559 AE datasets accurately categorized 8800 (88%) of reported AE. The three most frequent AE types were "difficult orotracheal intubation" (16.9% of AE reports), "medication error" (10.5%), and "post-induction hypotension" (6.9%). The accuracy of the AI model reached 70.9% sensitivity, 96.6% specificity for "difficult intubation", 43.2% sensitivity, and 98.9% specificity for "medication error." CONCLUSIONS: This unsupervised method provides an accurate, automated, AI-supported search algorithm that ranks and helps to understand complex patterns of risky patient situations and has greater speed, precision, and clarity when compared to manual human data extraction. Machine learning (ML) and natural language processing models can effectively be used to process natural language AE reports and augment expert clinician input. This model can support clinical applications and methodological standards of implementations and be used to better inform and enhance decision-making for improved risk management and patient safety. TRIAL REGISTRATION: The study was approved by the ethics committee of the French Society of Anesthesiology (IRB 00010254-2020-20) and the CNIL (CNIL: 118 58 95) and the study was registered with ClinicalTrials.gov (NCT: NCT05185479).

Comparing natural language processing representations of coded disease sequences for prediction in electronic health records.

OBJECTIVE: Natural language processing (NLP) algorithms are increasingly being applied to obtain unsupervised representations of electronic health record (EHR) data, but their comparative performance at predicting clinical endpoints remains unclear. Our objective was to compare the performance of unsupervised representations of sequences of disease codes generated by bag-of-words versus sequence-based NLP algorithms at predicting clinically relevant outcomes. MATERIALS AND METHODS: This cohort study used primary care EHRs from 6 286 233 people with Multiple Long-Term Conditions in England. For each patient, an unsupervised vector representation of their time-ordered sequences of diseases was generated using 2 input strategies (212 disease categories versus 9462 diagnostic codes) and different NLP algorithms (Latent Dirichlet Allocation, doc2vec, and 2 transformer models designed for EHRs). We also developed a transformer architecture, named EHR-BERT, incorporating sociodemographic information. We compared the performance of each of these representations (without fine-tuning) as inputs into a logistic classifier to predict 1-year mortality, healthcare use, and new disease diagnosis. RESULTS: Patient representations generated by sequence-based algorithms performed consistently better than bag-of-words methods in predicting clinical endpoints, with the highest performance for EHR-BERT across all tasks, although the absolute improvement was small. Representations generated using disease categories perform similarly to those using diagnostic codes as inputs, suggesting models can equally manage smaller or larger vocabularies for prediction of these outcomes. DISCUSSION AND CONCLUSION: Patient representations produced by sequence-based NLP algorithms from sequences of disease codes demonstrate improved predictive content for patient outcomes compared with representations generated by co-occurrence-based algorithms. This suggests transformer models may be useful for generating multi-purpose representations, even without fine-tuning.

Redefining Healthcare With Artificial Intelligence (AI): The Contributions of ChatGPT, Gemini, and Co-pilot.

Artificial Intelligence (AI) in healthcare marks a new era of innovation and efficiency, characterized by the emergence of sophisticated language models such as ChatGPT (OpenAI, San Francisco, CA, USA), Gemini Advanced (Google LLC, Mountain View, CA, USA), and Co-pilot (Microsoft Corp, Redmond, WA, USA). This review explores the transformative impact of these AI technologies on various facets of healthcare, from enhancing patient care and treatment protocols to revolutionizing medical research and tackling intricate health science challenges. ChatGPT, with its advanced natural language processing capabilities, leads the way in providing personalized mental health support and improving chronic condition management. Gemini Advanced extends the boundary of AI in healthcare through data analytics, facilitating early disease detection and supporting medical decision-making. Co-pilot, by integrating seamlessly with healthcare systems, optimizes clinical workflows and encourages a culture of innovation among healthcare professionals. Additionally, the review highlights the significant contributions of AI in accelerating medical research, particularly in genomics and drug discovery, thus paving the path for personalized medicine and more effective treatments. The pivotal role of AI in epidemiology, especially in managing infectious diseases such as COVID-19, is also emphasized, demonstrating its value in enhancing public health strategies. However, the integration of AI technologies in healthcare comes with challenges. Concerns about data privacy, security, and the need for comprehensive cybersecurity measures are discussed, along with the importance of regulatory compliance and transparent consent management to uphold ethical standards and patient autonomy. The review points out the necessity for seamless integration, interoperability, and the maintenance of AI systems' reliability and accuracy to fully leverage AI's potential in advancing healthcare.