Electronic Health Record Information Research Articles

Prevalence and risk factors for long COVID among adults in Scotland using electronic health records: a national, retrospective, observational cohort study

Long COVID is a debilitating multisystem condition. The objective of this study was to estimate the prevalence of long COVID in the adult population of Scotland, and to identify risk factors associated with its development. In this national, retrospective, observational cohort study, we analysed electronic health records (EHRs) for all adults (≥18 years) registered with a general medical practice and resident in Scotland between March 1, 2020, and October 26, 2022 (98-99% of the population). We linked data from primary care, secondary care, laboratory testing and prescribing. Four outcome measures were used to identify long COVID: clinical codes, free text in primary care records, free text on sick notes, and a novel operational definition. The operational definition was developed using Poisson regression to identify clinical encounters indicative of long COVID from a sample of negative and positive COVID-19 cases matched on time-varying propensity to test positive for SARS-CoV-2. Possible risk factors for long COVID were identified by stratifying descriptive statistics by long COVID status. Of 4,676,390 participants, 81,219 (1.7%) were identified as having long COVID. Clinical codes identified the fewest cases (n=1,092, 0.02%), followed by free text (n=8,368, 0.2%), sick notes (n=14,469, 0.3%), and the operational definition (n=64,193, 1.4%). There was limited overlap in cases identified by the measures; however, temporal trends and patient characteristics were consistent across measures. Compared with the general population, a higher proportion of people with long COVID were female (65.1% versus 50.4%), aged 38-67 (63.7% versus 48.9%), overweight or obese (45.7% versus 29.4%), had one or more comorbidities (52.7% versus 36.0%), were immunosuppressed (6.9% versus 3.2%), shielding (7.9% versus 3.4%), or hospitalised within 28 days of testing positive (8.8% versus 3.3%%), and had tested positive before Omicron became the dominant variant (44.9% versus 35.9%). The operational definition identified long COVID cases with combinations of clinical encounters (from four symptoms, six investigation types, and seven management strategies) recorded in EHRs within 4-26 weeks of a positive SARS-CoV-2 test. These combinations were significantly (p<0.0001) more prevalent in positive COVID-19 patients than in matched negative controls. In a case-crossover analysis, 16.4% of those identified by the operational definition had similar healthcare patterns recorded before testing positive. The prevalence of long COVID presenting in general practice was estimated to be 0.02-1.7%, depending on the measure used. Due to challenges in diagnosing long COVID and inconsistent recording of information in EHRs, the true prevalence of long COVID is likely to be higher. The operational definition provided a novel approach but relied on a restricted set of symptoms and may misclassify individuals with pre-existing health conditions. Further research is needed to refine and validate this approach. Chief Scientist Office (Scotland), Medical Research Council, and BREATHE.

Mapping the Landscape of Electronic Health Records and Health Information Exchange Through Bibliometric Analysis and Visualization

Mapping the Landscape of Electronic Health Records and Health Information Exchange Through Bibliometric Analysis and Visualization

Enhancing healthcare decision support through explainable AI models for risk prediction

Electronic health records (EHRs) are a valuable source of information that can aid in understanding a patient’s health condition and making informed healthcare decisions. However, modelling longitudinal EHRs with heterogeneous information is a challenging task. Although recurrent neural networks (RNNs) are frequently utilized in artificial intelligence (AI) models for capturing longitudinal data, their explanatory capabilities are limited. Predictive clustering stands as the most recent advancement within this domain, offering interpretable indications at the cluster level for predicting disease risk. Nonetheless, the challenge of determining the optimal number of clusters has put a brake on the widespread application of predictive clustering for disease risk prediction. In this paper, we introduce a novel non-parametric predictive clustering-based risk prediction model that integrates the Dirichlet Process Mixture Model (DPMM) with predictive clustering via neural networks. To enhance the model’s interpretability, we integrate attention mechanisms that enable the capture of local-level evidence in addition to the cluster-level evidence provided by predictive clustering. The outcome of this research is the development of a multi-level explainable artificial intelligence (AI) model. We evaluated the proposed model on two real-world datasets and demonstrated its effectiveness in capturing longitudinal EHR information for disease risk prediction. Moreover, the model successfully produced interpretable evidence to bolster its predictions.

Securing the Patient’s Breast Cancer Data using Blockchain-based IBE with Deep Learning Model in IoT

Computational intelligence (CI) and artificial intelligence (AI) have a portion to offer in the way of making healthcare systems smart and long-lasting. These technological advances have the potential to lessen our environmental impact while simultaneously increasing expectations for performance. The use of electronic health monitoring systems in healthcare management has been crucial. E-health has the potential to offer patients useful and efficient tracking tools. However, the existing E-Health system has protection disputes. However, there are safety concerns with the present online healthcare system. Doctors with bad intentions might collude with CSPs to compromise their patients' electronic health records (EHRs) or quickly leak EHR information to other opponents for financial gain. Patients might be manipulated by clinicians who work in collusion with a Patient Healthcare Monitoring Service Provider (PHMSP). EHRs for financial gain or disclose the HER content of EHRs directly to other adversaries. Recently, block-chain has emerged as one of the most potent strategies for maintaining privacy and security. The current security issues in e-health monitoring systems are expected to be replaced by this promising security method. To prevent unauthorised parties from accessing encrypted data, blockchain uses encryption. Using a computational intelligence approach, this study presented a blockchain-based encryption framework based on identity-based encryption (IBE) to deliver secure solutions. Furthermore, the study effort utilises optimised deep learning (ODL) to forecast the patient's condition from breast cancer input data. The suggested method improves performance, with an accuracy of 0.93 during training and 0.91 during validation. 

420 Computable Phenotyping with “Big Data” as a Foundation for Artificial Intelligence Algorithm Construction: Puberty as a Transdisciplinary Case Example

OBJECTIVES/GOALS: Artificial intelligence (AI) depends on quality machine learning (ML) algorithms constructed with high-quality training data. This TL1 trainee project develops a disease-agnostic computable phenotype framework for ML algorithm construction, modeling male puberty as a case example. METHODS/STUDY POPULATION: A computable phenotype of male puberty was constructed to answer the question: “Does early pubertal timing increase the risk of developing type II diabetes (T2D) in males?” A computable phenotype of males &lt; 18 years old was created in the TriNetX© Diamond Network utilizing Boolean operator data queries. TriNetX© contains patient electronic health record information (ICD-10 diagnoses, anthropometric measures). An exploratory analysis of patient counts reflecting various computable phenotypes allowed for outcome (T2D) comparison of males diagnosed with precocious puberty (E30.1, ICD code for early pubertal timing) to those without, controlling for body mass index (BMI). RESULTS/ANTICIPATED RESULTS: Subjects (n=12,996,132) displayed the following computable phenotype: Male, &lt; 18 years old, without ever having a BMI documented &gt;85th percentile. Males diagnosed with precocious puberty (E30.1) were 6.89 times more likely to develop T2D when aged 14-18 years old than those without (OR 6.89, 95% CI: 5.17-9.19, p&lt;0.0001). Next steps involve training a ML model on each computable phenotype groupings’ health data, with anticipated results identifying underlying salient pathophysiologic variables. A generalized computable phenotype approach is further developed to: 1) explore clinical questions in large databases like TriNetX©, and 2) model disease development with AI/ML algorithm construction. DISCUSSION/SIGNIFICANCE: Computed phenotypes reveal males with precocious puberty may have increased T2D risk. Next steps utilize subject data to train an AI/ML algorithm, model development to identify salient pathophysiologic variables, and synthesize a generalized AI/ML developmental research framework for dissemination.

Abstract 3569: Using AI to automatically process data from unstructured health records of patients with lung cancer

Abstract Introduction: Structured databases created from electronic health records (EHR) are crucial for cancer research. Manual data entry into databases is both labor-intensive and error-prone. We aimed to develop an artificial intelligence (AI)-driven approach for automatically inputting patient information from EHRs. Methods: A REDCap database for 53 patients with advanced lung cancer treated with first line immuno+/-chemotherapy at Gustave Roussy between 02/2021-06/2023 was manually populated by physicians with demographics, risk factors, cancer history, and treatment data, with 137 variables/patient. Given unstructured medical letters and a schematic description of each variable, generative AI was used to find, quote and process variables into a structured form. We directed large language model actions with prompt engineering and tailored few-shot examples. Mortality data were auto-extracted from the French public registry, INSEE. We assessed consistency between manual (MDE) and automated data entry (ADE), with a secondary manual review for mismatches performed by a senior physician. Results: In total, 7,261 data points were assessed. ADE averaged 10 minutes per patient inclusive of quality checks vs 1 h for MDA. The concordance rate between ADE and MDE was 90.2% (6,550/7,261) with a discordance of 6.8% (496/7,261). Data was missing in 0.8% (59/7,261) of cases for both methods, 1% (73/7,261) for ADE and 1,1% (83/7,261) for MDA. After checking discordances, ADE correctness was 95.3% (6,922/7,261) (Table), MDE was 93.8% (6,809/7,261). Errors in ADE (207/7,261) were due to algorithm refinement needs (70%) and missing EHR information (30%). Conclusion: Generative AI has a strong potential for identifying and structuring data from EHRs, yielding consistency superior to manual entry by physicians and 85% reduced amount of time. This may enhance the efficiency, accuracy, and scalability of EHR-to-database conversions. ADE for &amp;gt;1000 patients will be presented at the meeting. ADE correctness 100% 95-99% 90-94% 80-89% Sex; Age; Life status; Last follow-up; Metastatic from diagnosis Molecular alterations; Metastatic sites; Progression sites; Treatment type Tobacco &amp; cannabis consumption; Histology; PDL1 score; Progression event Pack years; Date of diagnosis &amp; first metastasis; Stage; Date of start treatment Citation Format: Mihaela Aldea, Pierre Rolland, Solenne Simon, Aliette Poplu, Muriel Wartelle, Benjamin Vignal, Jean-Charles Louis, Francois Lion, Arnaud Borie, David Planchard, Caroline Robert, Stefan Michiels, Fabrice Andre, Fabrice Barlesi, Franck Le Ouay, Benjamin Besse. Using AI to automatically process data from unstructured health records of patients with lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3569.

Characterisation and validation of lactation information from structured electronic health records for use in pharmacoepidemiological studies.

Breastfeeding information stored within electronic health records (EHR) has recently been used for pharmacoepidemiological research, however the data are primarily collected for clinical care. To characterise breastfeeding information recorded in structured fields in EHR during infant and postpartum health care visits, and to assess the validity of lactation status based on EHR data versus maternal report at research study visits. We assessed breastfeeding information recorded in structured fields in EHR from one health system for a subset of 211 patients who were also enrolled in a study on breast milk composition between 2014 and 2017 that required participants to exclusively breastfeed their infants until at least 1 month of age. We assessed the frequency of breastfeeding information in EHR during the first 12 months of age and compared lactation status based on EHR with maternal report at 1 and 6-month study visits (reference standard). The median number of breastfeeding records in the EHR per infant was six (interquartile range 3) with most observations clustering in the first few weeks of life and around well-infant visits. At the 6-month study visit, 93.8% of participants were breastfeeding and 80.1% were exclusively breastfeeding according to maternal report. Sensitivity of EHR data for identifying ever breastfeeding was at or near 100%, and sensitivity for identifying ever exclusive breastfeeding was 98.0% (95% CI: 95.0%, 99.2%). Sensitivities were 97.3% (95% CI: 93.9%, 98.9%) for identifying any breastfeeding and 94.4% (95% CI: 89.7%, 97.0%) for exclusive breastfeeding, and positive predictive values were 99.5% (95% CI: 97.0%, 99.9%) for any breastfeeding and 95.0% (95% CI: 90.4%, 97.4%) for exclusive breastfeeding. Breastfeeding information in structured EHR fields have the potential to accurately classify lactation status. The validity of these data should be assessed in populations with a lower breastfeeding prevalence.

Factors Associated with Online Patient Portal Utilization Experience in an Arkansas Phone Survey.

Introduction: Accessing electronic health record information through a patient portal is associated with numerous benefits to both health care providers and patients. However, patient portal utilization remains low. Little is known about the factors associated with patient portal utilization following the onset of the COVID-19 pandemic. Methods: In March 2022, we conducted a random digit dial phone survey of both cell phones and landlines of adults living in Arkansas that asked numerous demographic and health-related measures, including patient portal utilization in the past 12 months. A total of 2,201 adult Arkansans completed the survey between March 1 and March 28, 2022. Weighted estimates were generated using rank ratio estimation to approximate the 2019 American Community Survey 1-year Arkansas estimates for race/ethnicity (72% White, 15% Black/African American, 7.8% Hispanic, 4.9% other race/ethnicity), age (73% 18-39, 32% 40-59, and 31% 60+), and gender (49% male, 51% female). We fit the data to a logistic regression model. Results: We found that education, employment, prior telehealth experience, having a check-up in the past 2 years, and having a primary care provider were all positively associated with patient portal utilization. We also found that non-Hispanic Black/African-American respondents were less likely to access a patient portal relative to non-Hispanic White respondents. Discussion: Patient portal utilization is related to several demographic and health-related factors among an adult population in Arkansas. Given that the documented benefits of patient portal utilization are broad, under-utilization by groups that already experience relatively worse health outcomes could reproduce or even exacerbate existing health disparities. Additional research is needed to further investigate what barriers to patient portal utilization remain for these populations.

Trustworthy Data and AI Environments for Clinical Prediction: Application to Crisis-Risk in People with Depression.

Depression is a common mental health condition that often occurs in association with other chronic illnesses, and varies considerably in severity. Electronic Health Records (EHRs) contain rich information about a patient's medical history and can be used to train, test and maintain predictive models to support and improve patient care. This work evaluated the feasibility of implementing an environment for predicting mental health crisis among people living with depression based on both structured and unstructured EHRs. A large EHR from a mental health provider, Mersey Care, was pseudonymised and ingested into the Natural Language Processing (NLP) platform CogStack, allowing text content in binary clinical notes to be extracted. All unstructured clinical notes and summaries were semantically annotated by MedCAT and BioYODIE NLP services. Cases of crisis in patients with depression were then identified. Random forest models, gradient boosting trees, and Long Short-Term Memory (LSTM) networks, with varying feature arrangement, were trained to predict the occurrence of crisis. The results showed that all the prediction models can use a combination of structured and unstructured EHR information to predict crisis in patients with depression with good and useful accuracy. The LSTM network that was trained on a modified dataset with only 1000 most-important features from the random forest model with temporality showed the best performance with a mean AUC of 0.901 and a standard deviation of 0.006 using a training dataset and a mean AUC of 0.810 and 0.01 using a hold-out test dataset. Comparing the results from the technical evaluation with the views of psychiatrists shows that there are now opportunities to refine and integrate such prediction models into pragmatic point-of-care clinical decision support tools for supporting mental healthcare delivery.

Pay-for-performance for appropriate prescribing using routine healthcare data from general practices

Abstract Background A national pay-for-performance (P4P) scheme was introduced in the Netherlands to incentivize appropriate prescribing among general practitioners (GPs) using routine care data of electronic health records (EHRs). The goal of this program was to encourage GPs to choose the most clinically appropriate and cost-effective medication for their patients to promote an affordable and sustainable health system. Nivel facilitated and evaluated this program. Facilitators and barriers of this P4P program were assessed as part of this endeavour. Methods Semi-structured interviews were conducted with all stakeholder representatives (n = 15) to evaluate their experiences with the program. The interviews were analysed thematically. Results Our research was structured around the three ‘flows’ necessary for an effective learning health system. The ‘data to knowledge’ flow was impacted by: 1) effect of different EHR information systems on data quality, and 2) advantages of using EHR data for measuring quality of care, minimising GPs’ administrative burden. The ‘knowledge to practice’ flow was impacted by: 1) meaningfulness and robustness of indicators, and 2) measure for appropriate prescribing. The ‘practice to data’ flow was impacted by: 1) feedback to GPs, with most stakeholders emphasizing the need for GPs to reflect on their results with other GPs to stimulate learning, 2) impact of the P4P model, such as improvement in quality of GP registration in EHRs and better stakeholder collaboration, and 3) methods, communication and GP perspectives on the performance payment. Conclusions The study showed the factors that can support or hinder P4P programs in promoting appropriate care with EHR data for a sustainable and affordable health system. Enhancing uniformity of EHR information systems is crucial for more consistent quality of care assessment. To encourage GPs to deliver better care, it is recommended to complement monetary incentives with other forms of incentives. Key messages • This study showed that routine healthcare data are feasible for the use in P4P schemes promoting a sustainable and affordable health system. • P4P as a standalone approach may not be optimal for promoting a sustainable and affordable health system.

Vulnerable Patients with Inflammatory Bowel Disease

Crohn’s Disease and ulcerative colitis, collectively known as Inflammatory Bowel Disease (IBD), are conditions characterized by inflammation and ulceration of the intestines. This work aims to highlight disparities among vulnerable IBD populations using the OneFlorida Clinical Research Consortium, a data network housing claims and electronic health record information for more than 15 million patients in Florida. A retrospective analysis using data collected between 01/01/2012 and 06/30/2020 was performed using OneFlorida, and patients were identified using diagnosis and pharmacy codes for IBD. 10,578 patients met the inclusion criteria. Healthcare utilization (emergency department visits, inpatient stay, medications, procedures), disease severity (c-reactive protein, white blood cell count, hemoglobin, albumin), and outcomes (i.e., abdominal surgery) were assessed for patients of different ages, gender, ethnicities, races, and locations. Notably, adolescent and young adult (AYA) patients (ages 17-25) use significantly more corticosteroids than both the pediatric and older adult groups (p-values 0.005 and 0.0007). Contrary to prior literature, Hispanic IBD patients used more biologic medications compared to non-Hispanic White patients (p&lt;0.0001), whereas non-Hispanic Black patients used more steroids (p=0.0004). These findings illustrate a need for targeted educational interventions to support these vulnerable populations.

EHR-KnowGen: Knowledge-enhanced multimodal learning for disease diagnosis generation

Electronic health records (EHRs) contain diverse patient information, including medical notes, clinical events, and laboratory test results. Integrating this multimodal data can improve disease diagnoses using deep learning models. However, effectively combining different modalities for diagnosis remains challenging. Previous approaches, such as attention mechanisms and contrastive learning, have attempted to address this but do not fully integrate the modalities into a unified feature space. This paper presents EHR-KnowGen, a multimodal learning model enhanced with external domain knowledge, for improved disease diagnosis generation from diverse patient information in EHRs. Unlike previous approaches, our model integrates different modalities into a unified feature space with soft prompts learning and leverages large language models (LLMs) to generate disease diagnoses. By incorporating external domain knowledge from different levels of granularity, we enhance the extraction and fusion of multimodal information, resulting in more accurate diagnosis generation. Experimental results on real-world EHR datasets demonstrate the superiority of our generative model over comparative methods, providing explainable evidence to enhance the understanding of diagnosis results.

Predictive models of long COVID

The cause and symptoms of long COVID are poorly understood. It is challenging to predict whether a given COVID-19 patient will develop long COVID in the future. We used electronic health record (EHR) data from the National COVID Cohort Collaborative to predict the incidence of long COVID. We trained two machine learning (ML) models - logistic regression (LR) and random forest (RF). Features used to train predictors included symptoms and drugs ordered during acute infection, measures of COVID-19 treatment, pre-COVID comorbidities, and demographic information. We assigned the 'long COVID' label to patients diagnosed with the U09.9 ICD10-CM code. The cohorts included patients with (a) EHRs reported from data partners using U09.9 ICD10-CM code and (b) at least one EHR in each feature category. We analysed three cohorts: all patients (n=2,190,579; diagnosed with long COVID=17,036), inpatients (149,319; 3,295), and outpatients (2,041,260; 13,741). LR and RF models yielded median AUROC of 0.76 and 0.75, respectively. Ablation study revealed that drugs had the highest influence on the prediction task. The SHAP method identified age, gender, cough, fatigue, albuterol, obesity, diabetes, and chronic lung disease as explanatory features. Models trained on data from one N3C partner and tested on data from the other partners had average AUROC of 0.75. ML-based classification using EHR information from the acute infection period is effective in predicting long COVID. SHAP methods identified important features for prediction. Cross-site analysis demonstrated the generalizability of the proposed methodology. NCATS U24 TR002306, NCATS UL1 TR003015, Axle Informatics Subcontract: NCATS-P00438-B, NIH/NIDDK/OD, PSR2015-1720GVALE_01, G43C22001320007, and Director, Office of Science, Office of Basic Energy Sciences of the U.S. Department of Energy Contract No. DE-AC02-05CH11231.

Lab: an R package for generating analysis-ready data from laboratory records.

Electronic health records (EHRs) play a crucial role in healthcare decision-making by giving physicians insights into disease progression and suitable treatment options. Within EHRs, laboratory test results are frequently utilized for predicting disease progression. However, processing laboratory test results often poses challenges due to variations in units and formats. In addition, leveraging the temporal information in EHRs can improve outcomes, prognoses, and diagnosis predication. Nevertheless, the irregular frequency of the data in these records necessitates data preprocessing, which can add complexity to time-series analyses. To address these challenges, we developed an open-source R package that facilitates the extraction of temporal information from laboratory records. The proposed lab package generates analysis-ready time series data by segmenting the data into time-series windows and imputing missing values. Moreover, users can map local laboratory codes to the Logical Observation Identifier Names and Codes (LOINC), an international standard. This mapping allows users to incorporate additional information, such as reference ranges and related diseases. Moreover, the reference ranges provided by LOINC enable us to categorize results into normal or abnormal. Finally, the analysis-ready time series data can be further summarized using descriptive statistics and utilized to develop models using machine learning technologies. Using the lab package, we analyzed data from MIMIC-III, focusing on newborns with patent ductus arteriosus (PDA). We extracted time-series laboratory records and compared the differences in test results between patients with and without 30-day in-hospital mortality. We then identified significant variations in several laboratory test results 7 days after PDA diagnosis. Leveraging the time series-analysis-ready data, we trained a prediction model with the long short-term memory algorithm, achieving an area under the receiver operating characteristic curve of 0.83 for predicting 30-day in-hospital mortality in model training. These findings demonstrate the lab package's effectiveness in analyzing disease progression. The proposed lab package simplifies and expedites the workflow involved in laboratory records extraction. This tool is particularly valuable in assisting clinical data analysts in overcoming the obstacles associated with heterogeneous and sparse laboratory records.

Epidemiologic Questionnaire (EPI-Q) - a scalable, app-based health survey linked to electronic health record and genotype data.

The Epidemiologic Questionnaire (EPI-Q) was established to collect broad, uniform, self-reported health data to supplement electronic health record (EHR) and genotype information from participants in the University of Michigan (UM) Precision Health cohorts. Recruitment of EPI-Q participants, who were already enrolled in 1 of 3 ongoing UM Precision Health cohorts-the Michigan Genomics Initiative, Mental Health Biobank, and Metabolism, Endocrinology, and Diabetes cohorts-began in March 2020. Of 54,043 retrospective invitations, 5,577 individuals enrolled, representing a 10.3% response rate. Of these, 3,502 (63.7%) were female, and the average age was 56.1 years (standard deviation, 15.4). The baseline survey comprises 11 modules on topics including personal and family health history, lifestyle, and cancer screening and history. Additionally, 11 optional modules cover topics including financial toxicity, occupational exposure, and life meaning. The questions are based on standardized and validated instruments used in other cohorts, and we share resources to expedite development of similar surveys. Data are collected via the MyDataHelps platform, which enables current and future participants to share non-Michigan Medicine EHR data. Recruitment is ongoing. Cohort data are available to those with institutional review board approval; for details, contact the Data Office for Clinical and Translational Research (DataOffice@umich.edu).

A Privacy-Preserving Framework Using Hyperledger Fabric for EHR Sharing Applications

Electronic Health Records, or EHRs, include private and sensitive information of a patient. The privacy of personal healthcare data can be protected through Hyperledger Fabric, a permissioned blockchain framework. A few Hyperledger Fabric- integrated EHR solutions have emerged in recent years. However, none of them implements the privacy-preserving techniques of Hyperledger Fabric to make transactions anonymous or preserve the transaction data privacy during the consensus. Our proposed architecture is built on Hyperledger Fabric and its privacy-preserving mechanisms, such as Identity Mixer, Private Data Collections, Channels and Transient Fields to securely store and transfer patient-sensitive data while providing anonymity and unlinkability of transactions.

Electronic Health Records and Occupational Data: A Call for Promoting Interoperability.

Kowalski-Mcgraw, Michele MD, MPH; McLellan, Robert K. MD, MPH; Berenji, Manijeh MD, MPH; Saito, Kenji MD, JD; McKenzie, Judith MD, MPH; Thompson, Hannah MD; Hudson, Thomas Warner III MD; ACOEM Work Group on Electronic Health Records and Occupational Data Author Information

EHR privacy preservation using federated learning with DQRE-Scnet for healthcare application domains

A distributed learning technique named Federated Learning (FL) is utilized by mobile devices, clinical research labs, and hospitals for secure healthcare data sharing. FL has been utilized by several researchers for preserving the privacy of Electronic Health Record (EHR) information while updating the global server. Still, the privacy and higher number of communication rounds limit the performance of the model. Hence, this research introduces the privacy-preserving EHR information sharing technique, FL, based on deep Q reinforcement learning with spectral clustering (FL+DQRE-SCnet). In this, the local healthcare information is trained using the DCNN in the respective hospitals based on FL criteria. After learning the local model, the information is shared with the global model for updating the EHR to enhance the accuracy of disease diagnosis. The global model aggregates the information from the local models over several communication rounds by randomly selecting the local models, increasing the need for more communication rounds. Therefore, DQRE-SCnet is introduced before data aggregation to minimize the communication rounds. In addition, including a homomorphic encryption technique ensures privacy protection for FL data sharing. The analysis of FL+DQRE-SCnet based on the assessment measures of accuracy, precision, recall, and F-measure acquired maximal values of 95%, 94.9%, 94.94%, and 93.94%, respectively. Also, the error rate of 0.46 and the execution time of 1400 sec are accomplished by the FL+DQRE-SCnet method. Here, for comparing the performance of the model, the baseline methods like FedAvg, LoAdaBoost, BOFRF, PP-FedAvg and AdaFed are considered, wherein the proposed method acquired superior performance.

Text-mined Data Improves Machine Learning Predictions for Detecting Inborn Errors of Immunity

Patients with Inborn errors of immunity (IEI) often suffer protracted diagnostic odysseys; however, machine learning (ML) can facilitate patient detection via analysis of electronic health record (EHR) data. Despite availability of structured EHR data for analytic approaches, clinical-note text represents an untapped opportunity for expanding the computable phenotype of IEI to facilitate patient-finding. This study aims to quantify text-mined feature utility for improving ML-guided IEI prediction. We mined relevant clinical concepts using EHR text from over 6,000 verified IEI patients and over 28,000 controls. Enriched IEI concepts were used to augment a Bayesian Network (probabilistic ML-model). The ML-model was then tested by analyzing EHR clinical note text from 10 verified IEI patients. From this IEI test cohort, we extracted clinical note terms in two ways - manually and via an automated Human Phenotype Ontology (HPO) extraction tool. Terms were then mapped to IEI-relevant concepts for each subject and fed into the ML-model. Prediction improvement was assessed by comparing algorithmic sensitivity for both concept extraction approaches and for the degree to which text-derived concepts enhanced predictions over structured data alone.Text-mining identified 11 additional IEI-relevant concepts. Addition of these concepts improved algorithmic sensitivity from 0.8 to 1 and enabled detection of 2 patients not previously identified. Mean cohort risk score difference (baseline vs text-enhanced ML models) was significant for both manual and automatic concept extraction experiments (0.46 ± 0.40 vs. 0.94 ± 0.10 p = 0.004 and 0.51 ± 0.44 vs. 0.85 ± 0.28 p = 0.05; threshold = 0.06). The concordance rate between expert-determined text concepts and automatically extracted HPO terms was 21% (11/47 concepts) and concept frequency distribution differences between 'expert-derived’ and 'automated-HPO’ was significant for 'Exam Findings '(p = 0.021) and 'Failure to Thrive '(p = 0.011). [Display omitted] Top concepts extracted from text include exam findings, fatigue, failure to thrive, laboratory results, medications and infections which improved ML-model diagnostic sensitivity by 20%. Here, we show that structured and unstructured EHR information allowed for the prediction of IEI in all test subjects. In addition, automated extraction of HPO terms synergistically enhances manually extracted IEI features. In summary, text-mining facilitates discovery of IEI relevant concepts and ML-driven risk prediction.

57 Utility of Digital Phenotyping in Big Data to Answer Clinical Questions: Puberty as a Transdisciplinary Science Case Example

OBJECTIVES/GOALS: A disease-agnostic translational science framework for data mining is proposed for use across disciplines to: Answer clinical questions, justify future clinical research recruitment, and explore under-represented populations. As a case example, male puberty demonstrates utility of the framework. METHODS/STUDY POPULATION: As a case example using the generalizable framework, the following interdisciplinary question was asked: Does early pubertal timing increase the risk of developing type II diabetes (T2d) in boys? A digital phenotype of males &lt; 18 years old was created in the TriNetX Diamond Network utilizing Boolean operator data queries. TriNetX contains patient electronic health record information (ICD-10 diagnoses, anthropometric measures). A case control analysis leveraging patient counts from various digital phenotypes allowed for outcome (T2d) comparison of boys diagnosed with precocious puberty (E30.1, ICD code for early pubertal timing) to those without, while controlling for body mass index (BMI). RESULTS/ANTICIPATED RESULTS: Subjects (N=12,996,132) displayed the following digital phenotype: Male, &lt; 18 years old, without ever having a BMI documented &gt;85th percentile. Boys diagnosed with precocious puberty (E30.1) were 6.89 times more likely to develop T2d when aged 14-18 years old than those without (OR 6.89, 95%CI: 5.17-9.19, p. DISCUSSION/SIGNIFICANCE: Boys are under-represented in the early pubertal timing literature, justifying future human subjects research on male puberty. This case example demonstrates a broader disease-agnostic framework which can be adapted across disciplines. Opportunities may include public health digital phenotyping.