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Preparing clinical research data for artificial intelligence readiness: insights from the National Institute of Diabetes and Digestive and Kidney Diseases data centric challenge.

OBJECTIVES: The success of artificial intelligence (AI) and machine learning (ML) approaches in biomedical research depends on the quality of the underlying data. The National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) Data Centric Challenge was designed to address the challenge of making raw clinical research data AI ready, with a focus on type 1 diabetes studies available in the NIDDK Central Repository (NIDDK-CR). This paper aims to present a structured methodology for enhancing the AI readiness of clinical datasets. MATERIALS AND METHODS: We detail a systematic approa

A comparative analysis of privacy-preserving large language models for automated echocardiography report analysis.

BACKGROUND: Automated data extraction from echocardiography reports could facilitate large-scale registry creation and clinical surveillance of valvular heart diseases (VHD). We evaluated the performance of open-source large language models (LLMs) guided by prompt instructions and chain of thought (CoT) for this task. METHODS: From consecutive transthoracic echocardiographies performed in our center, we utilized 200 random reports from 2019 for prompt optimization and 1000 from 2023 for evaluation. Five instruction-tuned LLMs (Qwen2.0-72B, Llama3.0-70B, Mixtral8-46.7B, Llama3.0-8B, and Phi3.0

A self-report measure of digital skills needed to use digital health tools among older adults-the Skills Measurement and Readiness Training for Digital Health (SMART Digital Health) Scale.

OBJECTIVE: To identify a brief scale to accurately assess digital skills among older adults for use in identifying need for support to use digital health tools. MATERIALS AND METHODS: Patients age ≥50 speaking English, Spanish, or Cantonese completed surveys (n = 186) assessing digital health access, use, and skills. A subsample (n = 101) completed observational task assessments gauging competency on 4 tasks essential to digital health skills: (1) launch a video visit from an email/text message hyperlink, (2) visit a specific health website, (3) sign up for a patient portal, and (4) log in to

Zero-shot medical event prediction using a generative pretrained transformer on electronic health records.

ObjectivesLongitudinal data in electronic health records (EHRs) represent an individual's clinical history through a sequence of codified concepts, including diagnoses, procedures, medications, and laboratory tests. Generative pretrained transformers (GPT) can leverage this data to predict future events. While fine-tuning of these models can enhance task-specific performance, it becomes costly when applied to many clinical prediction tasks. In contrast, a pretrained foundation model can be used in zero-shot forecasting setting, offering a scalable alternative to fine-tuning separate models for

Predicting postoperative chronic opioid use with fair machine learning models integrating multi-modal data sources: a demonstration of ethical machine learning in healthcare.

OBJECTIVE: Building upon our previous work on predicting chronic opioid use using electronic health records (EHR) and wearable data, this study leveraged the Health Equity Across the AI Lifecycle (HEAAL) framework to (a) fine tune the previously built model with genomic data and evaluate model performance in predicting chronic opioid use and (b) apply IBM's AIF360 pre-processing toolkit to mitigate bias related to gender and race and evaluate the model performance using various fairness metrics. MATERIALS AND METHODS: Participants included approximately 271 All of Us Research Program subjects

Large language models accurately identify immunosuppression in intensive care unit patients.

OBJECTIVE: Rule-based structured data algorithms and natural language processing (NLP) approaches applied to unstructured clinical notes have limited accuracy and poor generalizability for identifying immunosuppression. Large language models (LLMs) may effectively identify patients with heterogenous types of immunosuppression from unstructured clinical notes. We compared the performance of LLMs applied to unstructured notes for identifying patients with immunosuppressive conditions or immunosuppressive medication use against 2 baselines: (1) structured data algorithms using diagnosis codes and

Assessment of health conditions from patient electronic health record portals vs self-reported questionnaires: an analysis of the INSPIRE study.

OBJECTIVES: Direct electronic access to multiple electronic health record (EHR) systems through patient portals offers a novel avenue for decentralized research. Given the critical value of patient characterization, we sought to compare computable evaluation of health conditions from patient-portal EHR against the traditional self-report. MATERIALS AND METHODS: In the nationwide Innovative Support for Patients with SARS-CoV-2 Infections Registry (INSPIRE) study, which linked self-reported questionnaires with multiplatform patient-portal EHR data, we compared self-reported health conditions ac

Communicating cancer treatment with pictogram-based timeline visualizations.

ObjectiveThis study evaluated the legibility, comprehension, and clinical usability of visual timelines for communicating cancer treatment paths. We examined how these visual aids enhance participants' and patients' understanding of their treatment plans.Materials and methodsThe study included 2 online surveys and 1 in-person survey with hematology cancer patients. The online surveys involved 306 and 160 participants, respectively, while the clinical evaluation included 30 patients (11 re-surveyed) and 24 medical doctors. Participants were assessed on their ability to understand treatment path

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… Supplementary material Supplementary material is available at Journal of the American Medical Informatics Association online. …

Utilizing large language models for detecting hospital-acquired conditions: an empirical study on pulmonary embolism.

OBJECTIVES: Adverse event detection from Electronic Medical Records (EMRs) is challenging due to the low incidence of the event, variability in clinical documentation, and the complexity of data formats. Pulmonary embolism as an adverse event (PEAE) is particularly difficult to identify using existing approaches. This study aims to develop and evaluate a Large Language Model (LLM)-based framework for detecting PEAE from unstructured narrative data in EMRs. MATERIALS AND METHODS: We conducted a chart review of adult patients (aged 18-100) admitted to tertiary-care hospitals in Calgary, Alberta

A machine learning framework to adjust for learning effects in medical device safety evaluation.

OBJECTIVES: Traditional methods for medical device post-market surveillance often fail to accurately account for operator learning effects, leading to biased assessments of device safety. These methods struggle with non-linearity, complex learning curves, and time-varying covariates, such as physician experience. To address these limitations, we sought to develop a machine learning (ML) framework to detect and adjust for operator learning effects. MATERIALS AND METHODS: A gradient-boosted decision tree ML method was used to analyze synthetic datasets that replicate the complexity of clinical

Human performance evaluation of a pediatric artificial intelligence sepsis model.

OBJECTIVE: To assess the influence of an implemented artificial intelligence model predicting pediatric sepsis (defined by IPSO-Improving Pediatric Sepsis Outcomes collaborative) in the emergency department (ED) on human performance measures. MATERIALS AND METHODS: Two ED sites within a large pediatric health system in the Southeastern United States between January 1, 2021 and April 1, 2024. We interviewed ED providers and nurses within 72 hours of caring for a patient identified as potentially having sepsis by the predictive model. Thematic analysis of qualitative data was combined with elec

SBDH-Reader: a large language model-powered method for extracting social and behavioral determinants of health from clinical notes.

OBJECTIVE: Social and behavioral determinants of health (SBDH) are increasingly recognized as essential for prognostication and informing targeted interventions. Clinical notes often contain details about SBDH in unstructured format. Conventional extraction methods for these data tend to be labor intensive, inaccurate, and/or unscalable. In this study, we aim to develop and validate a large language model (LLM)-powered method to extract structured SBDH data from clinical notes through prompt engineering. MATERIALS AND METHODS: We developed SBDH-Reader to extract 6 categories of granular SBDH

Evaluating robustly standardized explainable anomaly detection of implausible variables in cancer data.

OBJECTIVES: Explanations help to understand why anomaly detection algorithms identify data as anomalous. This study evaluates whether robustly standardized explanation scores correctly identify the implausible variables that make cancer data anomalous. MATERIALS AND METHODS: The dataset analyzed consists of 18 587 truncated real-world cancer registry records containing 8 categorical variables describing patients diagnosed with bladder and lung tumors. We identified 800 anomalous records using an autoencoder's per-record reconstruction error, which is a common neural network-based anomaly dete

Utility of word embeddings from large language models in medical diagnosis.

OBJECTIVE: This study evaluates the utility of word embeddings, generated by large language models (LLMs), for medical diagnosis by comparing the semantic proximity of symptoms to their eponymic disease embedding ("eponymic condition") and the mean of all symptom embeddings associated with a disease ("ensemble mean"). MATERIALS AND METHODS: Symptom data for 5 diagnostically challenging pediatric diseases-CHARGE syndrome, Cowden disease, POEMS syndrome, Rheumatic fever, and Tuberous sclerosis-were collected from PubMed. Using the Ada-002 embedding model, disease names and symptoms were transla

Comparison of rule- and large language model-based phenotype extraction from clinical notes for neurofibromatosis type 1.

INTRODUCTION: Neurofibromatosis type 1 (NF1) is a rare genetic disorder affecting multiple organ systems with significant clinical heterogeneity. Managing individuals with NF1 is challenging due to variability in disease progression and outcomes and limited early risk assessment tools. OBJECTIVE: This study aims to develop an effective, generalizable, user-friendly clinical entity extraction pipeline for identifying NF1-related phenotypes from unstructured clinical notes to enhance research and risk-modeling efforts. We compare the benefits of rule-based natural language processing (NLP) vs l

Descriptive epidemiology demonstrating the All of Us database as a versatile resource for the rare and undiagnosed disease community.

OBJECTIVE: We aim to demonstrate the versatility of the All of Us database as an important source of rare and undiagnosed disease (RUD) data, because of its large size and range of data types. MATERIALS AND METHODS: We searched the public data browser, electronic health record (EHR), and several surveys to investigate the prevalence, mental health, healthcare access, and other data of select RUDs. RESULTS: Several RUDs have participants in All of Us [eg, 75 of 100 rare infectious diseases (RIDs)]. We generated health-related data for undiagnosed, sickle cell disease (SCD), cystic fibrosis (C

Applying contrastive pre-training for depression and anxiety risk prediction in type 2 diabetes patients based on heterogeneous electronic health records: a primary healthcare case study.

OBJECTIVE: Due to heterogeneity and limited medical data in primary healthcare services (PHS), assessing the psychological risk of type 2 diabetes mellitus (T2DM) patients in PHS is difficult. Using unsupervised contrastive pre-training, we proposed a deep learning framework named depression and anxiety prediction (DAP) to predict depression and anxiety in T2DM patients. MATERIALS AND METHODS: The DAP model consists of two sub-models. Firstly, the pre-trained model of DAP used unlabeled discharge records of 85 085 T2DM patients from the First Affiliated Hospital of Nanjing Medical University

On the utility of using the All of Us Research Program as a resource to study military service members and veterans.

OBJECTIVES: To illustrate the utility of the All of Us Research Program for studying military and veteran health. MATERIALS AND METHODS: Results were derived from the All of Us Researcher Workbench Controlled Tier v7. Specific variables examined were family history of post-traumatic stress disorder (PTSD), medical encounters, and body mass index/body size. RESULTS: There are 37 363 military and veteran participants enrolled in the All of Us Research Program. The population is older (M = 63.3 years), White (71.3%), and male (83.2%), consistent with military and veteran populations. Participan

Augmenting clinicians' analytical workflow through task-based integration of data visualizations and algorithmic insights: a user-centered design study.

OBJECTIVES: To understand healthcare providers' experiences of using GlucoGuide, a mockup tool that integrates visual data analysis with algorithmic insights to support clinicians' use of patientgenerated data from Type 1 diabetes devices. MATERIALS AND METHODS: This qualitative study was conducted in three phases. In Phase 1, 11 clinicians reviewed data using commercial diabetes platforms in a think-aloud data walkthrough activity followed by semistructured interviews. In Phase 2, GlucoGuide was developed. In Phase 3, the same clinicians reviewed data using GlucoGuide in a think-aloud activi

Development of electronic health record based algorithms to identify individuals with diabetic retinopathy.

OBJECTIVES: To develop, validate, and implement algorithms to identify diabetic retinopathy (DR) cases and controls from electronic health care records (EHRs). MATERIALS AND METHODS: We developed and validated electronic health record (EHR)-based algorithms to identify DR cases and individuals with type I or II diabetes without DR (controls) in 3 independent EHR systems: Vanderbilt University Medical Center Synthetic Derivative (VUMC), the VA Northeast Ohio Healthcare System (VANEOHS), and Massachusetts General Brigham (MGB). Cases were required to meet 1 of the following 3 criteria: (1) 2 or