Phenotyping Algorithms Research Articles

Variation in Phenotype Definitions in Observational Clinical Research: A Review of Three Conditions.

Our study examined the heterogeneity of phenotype algorithms (PA) in literature on Alzheimer's disease (AD), major depressive disorder (MDD), and pulmonary arterial hypertension (PAI), focusing on the impact of PA differences on patient overlap and incidence rate variability across conditions in six observational databases. We reviewed 49 replicated PAs (13 for AD, 23 for MDD, and 13 for PAI) and found significant heterogeneity. These varied PAs identified distinct patient cohorts, resulting in significant incidence rate heterogeneity. Despite some papers reporting primary condition codes and inclusion. comprehensive documentation ensuring reproducibility was often lacking, underscoring the need for more transparent and robust research practices.

A Neuro Phenotypic Evolution Algorithm for Recognizing Human Motion Type

Living in the modern world requires having a precise, intelligent system that may be suggested to do various activities. Due to the scalability of AI algorithms, we have proposed a phenotypic Evolutionary Algorithm (EA)-based system to assist the Artificial Neural Network algorithm (ANN) in the learning process. Combining the two strategies can result in a smart neuro-evolutionary model that is effective in accomplishing significant duties in various domains. The suggested multi-layer neural algorithm's design creates the conditions for learning via the EA’s processes of crossover, mutation, and selection. To aid in the selection and crossover processes, the learning process phase breaks up the original ANN into multiple ANNs according to the number of hidden layers. ANNs are ranked from worst to best in the selection phase based on the soring function that is applied to the fitness list. The fitness list retains each ANN’s accuracy even after breaking apart the original ANN. The crossover procedure is then applied between the two worst and best ANNs. Mutation provides a means of improvement for the less effective ANNs. Following completion of these processes, the ANN algorithms are combined to create a single ANN algorithm. The Vicon mobile robot (SCITOS G5) system’s multi-dimensional data, which extracted both aggressive and typical human movement, as well as Human Activity Recognition (HAR) datasets extracted by smartphones, both have been applied using the suggested method. The system achieved a high performance and efficiency rate on the intended recognition problem

A Pain Desensitization Algorithm for Phenotyping and Treating Chronic Pelvic Pain.

Chronic pelvic pain remains challenging for physicians to manage due to central and peripheral sensitization and multiple pain generators including the bladder, pelvic floor, and pudendal nerve. Pain management providers have used nerve blocks for years for diagnosis and treatment. We developed a desensitization algorithm that provides a stepwise approach to improve patients pain scores. This is a prospective observational cohort study of 182 women aged 15-90 years old with chronic pelvic pain using an algorithm from 2016 to 2018. Treatment started with an Anesthetic Challenge Test of the bladder to guide us through a protocol of intravesical therapy and/or pudendal nerve blocks as a second step. ACT POSITIVE patients, who received intravesical therapy: 84% had a Visual Analog Score pain improvement of at least 50%, 64% improved at least 80% (41% pain-free). Those desiring additional relief that received further Pudendal Blocks: 83% had final improvement of at least 50% (67% pain-free). ACT NEGATIVE patients received Pudendal Blocks with 80% of subjects achieving at least 50% relief, 65% improved at least 80% (35% pain-free). All final groups showed a statistically significance of P < .05% when compared to their initial pain scores. Management of women with chronic pelvic pain would ideally start with treating a specific diagnosis which, in most cases, is difficult to establish since the majority have more than one pain generator. Our algorithm simplified the approach and reduced the severity of pain scores prior to any further necessary surgical interventions.

Using electronic health records to enhance surveillance of diabetes in children, adolescents and young adults: a study protocol for the DiCAYA Network

IntroductionTraditional survey-based surveillance is costly, limited in its ability to distinguish diabetes types and time-consuming, resulting in reporting delays. The Diabetes in Children, Adolescents and Young Adults (DiCAYA) Network seeks...

Demographics, Trends, and Clinical Characteristics of HIV Pre-Exposure Prophylaxis Recipients and People Newly Diagnosed with HIV from Large Electronic Health Records in Florida.

Florida is one of the HIV epicenters with high incidence and marked sociodemographic disparities. We analyzed a decade of statewide electronic health record/claims data-OneFlorida+-to identify and characterize pre-exposure prophylaxis (PrEP) recipients and newly diagnosed HIV cases in Florida. Refined computable phenotype algorithms were applied and a total of 2186 PrEP recipients and 7305 new HIV diagnoses were identified between January 2013 and April 2021. We examined patients' sociodemographic characteristics, stratified by self-reported sex, along with both frequency-driven and expert-selected descriptions of clinical conditions documented within 12 months before the first PrEP use or HIV diagnosis. PrEP utilization rate increased in both sexes; higher rates were observed among males with sex differences widening in recent years. HIV incidence peaked in 2016 and then decreased with minimal sex differences observed. Clinical characteristics were similar between the PrEP and new HIV diagnosis cohorts, characterized by a low prevalence of sexually transmitted infections (STIs) and a high prevalence of mental health and substance use conditions. Study limitations include the overrepresentation of Medicaid recipients, with over 96% of female PrEP users on Medicaid, and the inclusion of those engaged in regular health care. Although PrEP uptake increased in Florida, and HIV incidence decreased, sex disparity among PrEP recipients remained. Screening efforts beyond individuals with documented prior STI and high-risk behavior, especially for females, including integration of mental health care with HIV counseling and testing, are crucial to further equalize PrEP access and improve HIV prevention programs.

Red Meat Intake and the Risk of Cardiovascular Diseases: A Prospective Cohort Study in the Million Veteran Program

Red Meat Intake and the Risk of Cardiovascular Diseases: A Prospective Cohort Study in the Million Veteran Program

Semi-supervised ROC analysis for reliable and streamlined evaluation of phenotyping algorithms.

Semi-supervised ROC analysis for reliable and streamlined evaluation of phenotyping algorithms.

Data-driven automated classification algorithms for acute health conditions: applying PheNorm to COVID-19 disease.

Data-driven automated classification algorithms for acute health conditions: applying PheNorm to COVID-19 disease.

Automated microgreen phenotyping for yield estimation using a consumer-grade depth camera

Automated microgreen phenotyping for yield estimation using a consumer-grade depth camera

The Problem of Pain in Rheumatology: Variations in Case Definitions Derived From Chronic Pain Phenotyping Algorithms Using Electronic Health Records.

The aim of this study was to investigate and compare different case definitions for chronic pain to provide estimates of possible misclassification when researchers are limited by available electronic health record and administrative claims data, allowing for greater precision in case definitions. We compared the prevalence of different case definitions for chronic pain (N = 3042) in patients with autoimmune rheumatic diseases. We estimated the prevalence of chronic pain based on 15 unique combinations of pain scores, diagnostic codes, analgesic medications, and pain interventions. Chronic pain prevalence was lowest in unimodal pain phenotyping algorithms: 15% using analgesic medications, 18% using pain scores, 21% using pain diagnostic codes, and 22% using pain interventions. In comparison, the prevalence using a well-validated phenotyping algorithm was 37%. The prevalence of chronic pain also increased with the increasing number (bimodal to quadrimodal) of phenotyping algorithms that comprised the multimodal phenotyping algorithms. The highest estimated chronic pain prevalence (47%) was the multimodal phenotyping algorithm that combined pain scores, diagnostic codes, analgesic medications, and pain interventions. However, this quadrimodal phenotyping algorithm yielded a 10% overestimation of chronic pain compared to the well-validated algorithm. This is the first empirical study to our knowledge that shows that established common modes of phenotyping chronic pain can lead to substantially varying estimates of the number of patients with chronic pain. These findings can be a reference for biases in case definitions for chronic pain and could be used to estimate the extent of possible misclassifications or corrections in using datasets that cannot include specific data elements.

A machine learning approach toward automating spatial identification of LAG3+/CD3+ cells in ulcerative colitis

Over the past decade, automation of digital image analysis has become commonplace in both research and clinical settings. Spurred by recent advances in artificial intelligence and machine learning (AI/ML), tissue sub-compartments and cellular phenotypes within those compartments can be identified with higher throughput and accuracy than ever before. Recently, immune checkpoints have emerged as potential targets for auto-immune diseases. As such, spatial identification of these proteins along with immune cell markers (e.g., CD3+/LAG3+ T-cells) is a crucial step in understanding the potential and/or efficacy of such treatments. Here, we describe a semi-automated imaging and analysis pipeline that identifies CD3+/LAG3+ cells in colorectal tissue sub-compartments. While chromogenic staining has been a clinical mainstay and the resulting brightfield images have been utilized in AI/ML approaches in the past, there are associated drawbacks in phenotyping algorithms that can be overcome by fluorescence imaging. To address these tradeoffs, we developed an analysis pipeline combining the strengths of brightfield and fluorescence images. In this assay, immunofluorescence imaging was conducted to identify phenotypes followed by coverslip removal and hematoxylin and eosin staining of the same section to inform an AI/ML tissue segmentation algorithm. This assay proved to be robust in both tissue segmentation and phenotyping, was compatible with automated workflows, and revealed presence of LAG3+ T-cells in ulcerative colitis biopsies with spatial context preserved.

Validation of a Computable Phenotype for Successful Cognitive Aging

Abstract BackgroundStudying successful cognitive aging presents an opportunity to identify factors that may mitigate risk for Alzheimer’s disease and related disorders to inform public health initiatives, community interventions, and policy. Yet, identifying these cohorts is challenging, especially at advanced ages. Commutable phenotype algorithms using electronic health records may serve as an aid to identify cohorts of advanced age adults with intact cognitive function. The purpose of this study was to 1) establish, 2) apply, and 3) validate a machine learning‐based computing algorithm for recruitment of successful aging individuals for clinical cohort study.MethodWe identified EHR‐available variables representing successful aging among individuals 85 years and older (85+) by interviewing a panel comprised of 10 aging experts. Based on the identified variables, we developed a rule‐based computable phenotype algorithm composed of 17 inclusion or exclusion criteria. We applied the computable phenotype algorithm to screen all living persons age 85+ at UF Health and identified those meeting successful aging criteria and endeavored to directly evaluate them.There were 52,841 living 85+ individuals identified at UF Health. Of these 24,024 were identified as successful aged by the computable phenotype algorithm. This sample frame was 58% female, 59% White, and 69% non‐Hispanic. A priori permission to be contacted for research had been obtained on 11,898 of these persons, of whom 470 responded to study announcements and 333 consented to evaluation.The main outcomes measures were individuals identified by the computational algorithm subsequently being validated by virtue of having a modified Telephone Interview for Cognitive Status score &gt;27 and Geriatric Depression Scale &lt;6.ResultsForty‐five percent of all living persons 85+ in the UF Health EHR database were identified by the computable phenotype as successfully aged. Approximately 4% of these persons responded to study announcements and 333 were consented. Of these 333, 218 (65%) were ‘validated’ as meeting successful cognitive aging criteria via direct evaluation (See Figure).ConclusionsThe study validated a computable phenotype algorithm for recruitment of individuals for a successful aging study using large‐scale EHRs. Our study provides proof‐of‐concept of using big data and informatics as aids for recruitment of individuals for prospective cohort studies.

Plant-based diets and the incidence of cardiovascular disease: the Million Veteran Program

BackgroundA healthful plant-based diet was associated with lower risks of coronary heart disease and type 2 diabetes, and a favourable profile of adiposity-associated biomarkers, while an unhealthful plant-based diet was...

Unraveling the Role and Spatial Architecture of Cancer-Associated Fibroblasts in Classical Hodgkin Lymphoma

Unraveling the Role and Spatial Architecture of Cancer-Associated Fibroblasts in Classical Hodgkin Lymphoma

Development and External Validation of a Lymphoma-Specific Venous Thromboembolism Risk Assessment Model

Development and External Validation of a Lymphoma-Specific Venous Thromboembolism Risk Assessment Model

Iron Overload and Treatment Outcome in an Adult Sickle Cell Disease Cohort

Iron Overload and Treatment Outcome in an Adult Sickle Cell Disease Cohort

Connections between cross-tissue and intra-tissue biomarkers of aging biology in older adults

BackgroundSaliva measures are generally more accessible than blood, especially in vulnerable populations. However, connections between aging biology biomarkers in different body tissues remain unknown.MethodsThe present study included individuals (N = 2406) who consented for saliva and blood draw in the Health and Retirement Telomere length study in 2008 and the Venous blood study in 2016 who had complete data for both tissues. We assessed biological aging based on telomere length in saliva and DNA methylation and physiology measures in blood. DNA methylation clocks combine information from CpGs to produce the aging measures representative of epigenetic aging in humans. We analyzed DNA methylation clocks proposed by Horvath (353 CpG sites), Hannum (71 CpG sites), Levine or PhenoAge, (513 CpG sites), GrimAge, (epigenetic surrogate markers for select plasma proteins), Horvath skin and blood (391 CpG sites), Lin (99 CpG sites), Weidner (3 CpG sites), and VidalBralo (8 CpG sites). Physiology measures (referred to as phenotypic age) included albumin, creatinine, glucose, [log] C-reactive protein, lymphocyte percent, mean cell volume, red blood cell distribution width, alkaline phosphatase, and white blood cell count. The phenotypic age algorithm is based on parametrization of Gompertz proportional hazard models. Average telomere length was assayed using quantitative PCR (qPCR) by comparing the telomere sequence copy number in each patient’s sample (T) to a single-copy gene copy number (S). The resulting T/S ratio was proportional to telomere length, mean. Within individual, relationships between aging biology measures in blood and saliva and variations according to sex were assessed.ResultsSaliva-based telomere length showed inverse associations with both physiology-based and DNA methylation-based aging biology biomarkers in blood. Longer saliva-based telomere length was associated with 1 to 4 years slower biological aging based on blood-based biomarkers with the highest magnitude being Weidner (β = − 3.97, P = 0.005), GrimAge (β = − 3.33, P < 0.001), and Lin (β = − 3.45, P = 0.008) biomarkers of DNA methylation.ConclusionsThere are strong connections between aging biology biomarkers in saliva and blood in older adults. Changes in telomere length vary with changes in DNA methylation and physiology biomarkers of aging biology. We observed variations in the relationship between each body system represented by physiology biomarkers and biological aging, particularly at the DNA methylation level. These observations provide novel opportunities for integration of both blood-based and saliva-based biomarkers in clinical care of vulnerable and clinically difficult to reach populations where either or both tissues would be accessible for clinical monitoring purposes.

The role of health system penetration rate in estimating the prevalence of type 1 diabetes in children and adolescents using electronic health records.

The role of health system penetration rate in estimating the prevalence of type 1 diabetes in children and adolescents using electronic health records.

Development and application of pharmacological statin-associated muscle symptoms phenotyping algorithms using structured and unstructured electronic health records data.

Statins are widely prescribed cholesterol-lowering medications in the United States, but their clinical benefits can be diminished by statin-associated muscle symptoms (SAMS), leading to discontinuation. In this study, we aimed to develop and validate a pharmacological SAMS clinical phenotyping algorithm using electronic health records (EHRs) data from Minnesota Fairview. We retrieved structured and unstructured EHR data of statin users and manually ascertained a gold standard set of SAMS cases and controls using the published SAMS-Clinical Index tool from clinical notes in 200 patients. We developed machine learning algorithms and rule-based algorithms that incorporated various criteria, including ICD codes, statin allergy, creatine kinase elevation, and keyword mentions in clinical notes. We applied the best-performing algorithm to the statin cohort to identify SAMS. We identified 16 889 patients who started statins in the Fairview EHR system from 2010 to 2020. The combined rule-based (CRB) algorithm, which utilized both clinical notes and structured data criteria, achieved similar performance compared to machine learning algorithms with a precision of 0.85, recall of 0.71, and F1 score of 0.77 against the gold standard set. Applying the CRB algorithm to the statin cohort, we identified the pharmacological SAMS prevalence to be 1.9% and selective risk factors which included female gender, coronary artery disease, hypothyroidism, and use of immunosuppressants or fibrates. Our study developed and validated a simple pharmacological SAMS phenotyping algorithm that can be used to create SAMS case/control cohort to enable further analysis which can lead to the development of a SAMS risk prediction model.

An integrated pipeline for prediction of Clostridioides difficile infection

With the expansion of electronic health records(EHR)-linked genomic data comes the development of machine learning-enable models. There is a pressing need to develop robust pipelines to evaluate the performance of integrated models and minimize systemic bias. We developed a prediction model of symptomatic Clostridioides difficile infection(CDI) by integrating common EHR-based and genetic risk factors(rs2227306/IL8). Our pipeline includes (1) leveraging phenotyping algorithm to minimize temporal bias, (2) performing simulation studies to determine the predictive power in samples without genetic information, (3) propensity score matching to control for the confoundings, (4) selecting machine learning algorithms to capture complex feature interactions, (5) performing oversampling to address data imbalance, and (6) optimizing models and ensuring proper bias-variance trade-off. We evaluate the performance of prediction models of CDI when including common clinical risk factors and the benefit of incorporating genetic feature(s) into the models. We emphasize the importance of building a robust integrated pipeline to avoid systemic bias and thoroughly evaluating genetic features when integrated into the prediction models in the general population and subgroups.