Journal of the American Medical Informatics Association最新文献

Objective: Early detection of surgical complications allows for timely therapy and proactive risk mitigation. Machine learning (ML) can be leveraged to identify and predict patient risks for postoperative complications. We developed and validated the effectiveness of predicting postoperative complications using a novel surgical Variational Autoencoder (surgVAE) that uncovers intrinsic patterns via cross-task and cross-cohort presentation learning.

Materials and methods: This retrospective cohort study used data from the electronic health records of adult surgical patients over 4 years (2018-2021). Six key postoperative complications for cardiac surgery were assessed: acute kidney injury, atrial fibrillation, cardiac arrest, deep vein thrombosis or pulmonary embolism, blood transfusion, and other intraoperative cardiac events. We compared surgVAE's prediction performance against widely-used ML models and advanced representation learning and generative models under 5-fold cross-validation.

Results: 89 246 surgeries (49% male, median [IQR] age: 57 [45-69]) were included, with 6502 in the targeted cardiac surgery cohort (61% male, median [IQR] age: 60 [53-70]). surgVAE demonstrated generally superior performance over existing ML solutions across postoperative complications of cardiac surgery patients, achieving macro-averaged AUPRC of 0.409 and macro-averaged AUROC of 0.831, which were 3.4% and 3.7% higher, respectively, than the best alternative method (by AUPRC scores). Model interpretation using Integrated Gradients highlighted key risk factors based on preoperative variable importance.

Discussion and conclusion: Our advanced representation learning framework surgVAE showed excellent discriminatory performance for predicting postoperative complications and addressing the challenges of data complexity, small cohort sizes, and low-frequency positive events. surgVAE enables data-driven predictions of patient risks and prognosis while enhancing the interpretability of patient risk profiles.

Background: Machine learning and deep learning are powerful tools for analyzing electronic health records (EHRs) in healthcare research. Although family health history has been recognized as a major predictor for a wide spectrum of diseases, research has so far adopted a limited view of family relations, essentially treating patients as independent samples in the analysis.

Methods: To address this gap, we present ALIGATEHR, which models inferred family relations in a graph attention network augmented with an attention-based medical ontology representation, thus accounting for the complex influence of genetics, shared environmental exposures, and disease dependencies.

Results: Taking disease risk prediction as a use case, we demonstrate that explicitly modeling family relations significantly improves predictions across the disease spectrum. We then show how ALIGATEHR's attention mechanism, which links patients' disease risk to their relatives' clinical profiles, successfully captures genetic aspects of diseases using longitudinal EHR diagnosis data. Finally, we use ALIGATEHR to successfully distinguish the 2 main inflammatory bowel disease subtypes with highly shared risk factors and symptoms (Crohn's disease and ulcerative colitis).

Conclusion: Overall, our results highlight that family relations should not be overlooked in EHR research and illustrate ALIGATEHR's great potential for enhancing patient representation learning for predictive and interpretable modeling of EHRs.

Objectives: Clinical trials (CTs) are essential for improving patient care by evaluating new treatments' safety and efficacy. A key component in CT protocols is the study population defined by the eligibility criteria. This study aims to evaluate the effectiveness of large language models (LLMs) in encoding eligibility criterion information to support CT-protocol design.

Materials and methods: We extracted eligibility criterion sections, phases, conditions, and interventions from CT protocols available in the ClinicalTrials.gov registry. Eligibility sections were split into individual rules using a criterion tokenizer and embedded using LLMs. The obtained representations were clustered. The quality and relevance of the clusters for protocol design was evaluated through 3 experiments: intrinsic alignment with protocol information and human expert cluster coherence assessment, extrinsic evaluation through CT-level classification tasks, and eligibility section generation.

Results: Sentence embeddings fine-tuned using biomedical corpora produce clusters with the highest alignment to CT-level information. Human expert evaluation confirms that clusters are well structured and coherent. Despite the high information compression, clusters retain significant CT information, up to 97% of the classification performance obtained with raw embeddings. Finally, eligibility sections automatically generated using clusters achieve 95% of the ROUGE scores obtained with a generative LLM prompted with CT-protocol details, suggesting that clusters encapsulate information useful to CT-protocol design.

Discussion: Clusters derived from sentence-level LLM embeddings effectively summarize complex eligibility criterion data while retaining relevant CT-protocol details. Clustering-based approaches provide a scalable enhancement in CT design that balances information compression with accuracy.

Conclusions: Clustering eligibility criteria using LLM embeddings provides a practical and efficient method to summarize critical protocol information. We provide an interactive visualization of the pipeline here.

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 (CF), and infectious (2 diseases) and chronic (4 diseases) disease pools.

Conclusion: Our results highlight the potential value of All of Us with both data breadth and depth to help identify possible solutions for shared and disease-specific biomedical and other problems such as healthcare access, thus enhancing diagnosis, treatment, prevention, and support for the RUD community.

Objectives: Advances in health informatics rapidly expanded use of big-data analytics and electronic health records (EHR) by clinical researchers seeking to optimize interprofessional ICU team care. This study developed and validated a program for extracting interprofessional teams assigned to each patient each shift from EHR event logs.

Materials and methods: A retrospective analysis of EHR event logs for mechanically-ventilated patients 18 and older from 5 ICUs in an academic medical center during 1/1/2018-12/31/2019. We defined interprofessional teams as all medical providers (physicians, physician assistants, and nurse practitioners), registered nurses, and respiratory therapists assigned to each patient each shift. We created an EHR event logs-mining program that extracts clinicians who interact with each patient's medical record each shift. The algorithm was validated using the Message Understanding Conference (MUC-6) method against manual chart review of a random sample of 200 patient-shifts from each ICU by two independent reviewers.

Results: Our sample included 4559 ICU encounters and 72 846 patient-shifts. Our program extracted 3288 medical providers, 2702 registered nurses, and 219 respiratory therapists linked to these encounters. Eighty-three percent of patient-shift teams included medical providers, 99.3% included registered nurses, and 74.1% included respiratory therapists; 63.4% of shift-level teams included clinicians from all three professions. The program demonstrated 95.9% precision, 96.2% recall, and high face validity.

Discussion: Our EHR event logs-mining program has high precision, recall, and validity for identifying patient-levelshift interprofessional teams in ICUs.

Conclusions: Algorithmic and artificial intelligence approaches have a strong potential for informing research to optimize patient team assignments and improve ICU care and outcomes.

Objectives: We sought to assess whether race, ethnicity, and preferred language were associated with digital health literacy in pediatric caregivers.

Materials and methods: We used linear regression to measure associations between 3 eHealth Literacy Questionnaire (eHLQ) domains (score range: 1-4) and demographic characteristics.

Results: Non-Latinx White respondents (n = 230) had highest adjusted mean eHLQ scores: 3.44 (95% confidence interval: 3.36-3.52) in "Ability to engage," 3.39 (3.31 to 3.47) in "Feel safe and in control," and 3.34 (3.25 to 3.41) in "Motivated." By contrast, Spanish-preferring Latinx respondents (n = 246) had lower adjusted mean scores across all 3 eHLQ domains: 2.97 (P < .0001), 3.21 (P = .004), and 3.19 (P = .033), respectively.

Discussion: Our study contributes insights in variations across ethnoracial and language preference groups by different eHLQ domains, with implications for addressing digital health inequities.

Conclusion: Digital health literacy was lower in Spanish-preferring Latinx pediatric caregivers compared to non-Latinx White caregivers across 3 eHLQ domains. It was lower than English-preferring Latinx caregivers in "Ability."

Objective: This article describes the implementation of preemptive clinical pharmacogenomics (PGx) testing linked to an automated clinical decision support (CDS) system delivering actionable PGx information to clinicians at the point of care at UCSF Health, a large Academic Medical Center.

Methods: A multidisciplinary team developed the strategic vision for the PGx program. Drug-gene interactions of interest were compiled, and actionable alleles identified. A genotyping platform was selected and validated in-house. Following HIPAA protocols, genotype results were electronically transferred and stored in electronic health records (EHRs). CDS was developed and integrated with electronic prescribing.

Results: We developed a customized PGx program for 56 medications and 15 genes. Two hundred thirty-three pharmacogenomic prescribing alerts and 15 pharmacogenomic testing prompts, approved by clinicians, were built into EHR to deliver actionable clinical PGx information to clinicians.

Conclusions: Our multidisciplinary team successfully implemented preemptive PGx testing linked to point-of-care CDS to guide clinicians with precise medication decision-making.

Objective: We aim to use large language models (LLMs) to detect mentions of nuanced psychotherapeutic outcomes and impacts than previously considered in transcripts of interviews with adolescent depression. Our clinical authors previously created a novel coding framework containing fine-grained therapy outcomes beyond the binary classification (eg, depression vs control) based on qualitative analysis embedded within a clinical study of depression. Moreover, we seek to demonstrate that embeddings from LLMs are informative enough to accurately label these experiences.

Materials and methods: Data were drawn from interviews, where text segments were annotated with different outcome labels. Five different open-source LLMs were evaluated to classify outcomes from the coding framework. Classification experiments were carried out in the original interview transcripts. Furthermore, we repeated those experiments for versions of the data produced by breaking those segments into conversation turns, or keeping non-interviewer utterances (monologues).

Results: We used classification models to predict 31 outcomes and 8 derived labels, for 3 different text segmentations. Area under the ROC curve scores ranged between 0.6 and 0.9 for the original segmentation and 0.7 and 1.0 for the monologues and turns.

Discussion: LLM-based classification models could identify outcomes important to adolescents, such as friendships or academic and vocational functioning, in text transcripts of patient interviews. By using clinical data, we also aim to better generalize to clinical settings compared to studies based on public social media data.

Conclusion: Our results demonstrate that fine-grained therapy outcome coding in psychotherapeutic text is feasible, and can be used to support the quantification of important outcomes for downstream uses.

Objectives: The All of Us Research Program is a precision medicine initiative aimed at establishing a vast, diverse biomedical database accessible through a cloud-based data analysis platform, the Researcher Workbench (RW). Our goal was to empower the research community by co-designing the implementation of SAS in the RW alongside researchers to enable broader use of All of Us data.

Materials and methods: Researchers from various fields and with different SAS experience levels participated in co-designing the SAS implementation through user experience interviews.

Results: Feedback and lessons learned from user testing informed the final design of the SAS application.

Discussion: The co-design approach is critical for reducing technical barriers, broadening All of Us data use, and enhancing the user experience for data analysis on the RW.

Conclusion: Our co-design approach successfully tailored the implementation of the SAS application to researchers' needs. This approach may inform future software implementations on the RW.

Objective: This study leverages the rich diversity of the All of Us Research Program (All of Us)'s dataset to devise a predictive model for cardiovascular disease (CVD) in breast cancer (BC) survivors. Central to this endeavor is the creation of a robust data integration pipeline that synthesizes electronic health records (EHRs), patient surveys, and genomic data, while upholding fairness across demographic variables.

Materials and methods: We have developed a universal data wrangling pipeline to process and merge heterogeneous data sources of the All of Us dataset, address missingness and variance in data, and align disparate data modalities into a coherent framework for analysis. Utilizing a composite feature set including EHR, lifestyle, and social determinants of health (SDoH) data, we then employed Adaptive Lasso and Random Forest regression models to predict 6 CVD outcomes. The models were evaluated using the c-index and time-dependent Area Under the Receiver Operating Characteristic Curve over a 10-year period.

Results: The Adaptive Lasso model showed consistent performance across most CVD outcomes, while the Random Forest model excelled particularly in predicting outcomes like transient ischemic attack when incorporating the full multi-model feature set. Feature importance analysis revealed age and previous coronary events as dominant predictors across CVD outcomes, with SDoH clustering labels highlighting the nuanced impact of social factors.

Discussion: The development of both Cox-based predictive model and Random Forest Regression model represents the extensive application of the All of Us, in integrating EHR and patient surveys to enhance precision medicine. And the inclusion of SDoH clustering labels revealed the significant impact of sociobehavioral factors on patient outcomes, emphasizing the importance of comprehensive health determinants in predictive models. Despite these advancements, limitations include the exclusion of genetic data, broad categorization of CVD conditions, and the need for fairness analyses to ensure equitable model performance across diverse populations. Future work should refine clinical and social variable measurements, incorporate advanced imputation techniques, and explore additional predictive algorithms to enhance model precision and fairness.

Conclusion: This study demonstrates the liability of the All of Us's diverse dataset in developing a multi-modality predictive model for CVD in BC survivors risk stratification in oncological survivorship. The data integration pipeline and subsequent predictive models establish a methodological foundation for future research into personalized healthcare.