Phenotype driven molecular genetic test recommendation for diagnosing pediatric rare disorders.

Patients with rare diseases often experience prolonged diagnostic delays. Ordering appropriate genetic tests is crucial yet challenging, especially for general pediatricians without genetic expertise. Recent American College of Medical Genetics (ACMG) guidelines embrace early use of exome sequencing (ES) or genome sequencing (GS) for conditions like congenital anomalies or developmental delays while still recommend gene panels for patients exhibiting strong manifestations of a specific disease.

Mini-mental status examination phenotyping for Alzheimer's disease patients using both structured and narrative electronic health record features.

Objective: This study aims to automate the prediction of Mini-Mental State Examination (MMSE) scores, a widely adopted standard for cognitive assessment in patients with Alzheimer's disease, using natural language processing (NLP) and machine learning (ML) on structured and unstructured EHR data.

Materials And Methods: We extracted demographic data, diagnoses, medications, and unstructured clinical visit notes from the EHRs. We used Latent Dirichlet Allocation (LDA) for topic modeling and Term-Frequency Inverse Document Frequency (TF-IDF) for n-grams.

Applying unsupervised machine learning approaches to characterize autologous breast reconstruction patient subgroups: an NSQIP analysis of 14,274 patients.

Background: Autologous breast reconstruction is composed of diverse techniques and results in a variety of outcome trajectories. We propose employing an unsupervised machine learning method to characterize such heterogeneous patterns in large-scale datasets.

Methods: A retrospective cohort study of autologous breast reconstruction patients was conducted through the National Surgical Quality Improvement Program database.

Phenotype-Driven Molecular Genetic Test Recommendation for Diagnosing Pediatric Rare Disorders.

Rare disease patients often endure prolonged diagnostic odysseys and may still remain undiagnosed for years. Selecting the appropriate genetic tests is crucial to lead to timely diagnosis. Phenotypic features offer great potential for aiding genomic diagnosis in rare disease cases.

Can Race-sensitive Biomedical Embeddings Improve Healthcare Predictive Models?

This reproducibility study presents an algorithm to weigh in race distribution data of clinical research study samples when training biomedical embeddings. We extracted 12,864 PubMed abstracts published between January 1, 2000 and January 1, 2022 and weighed them based on the race distribution data extracted from their corresponding clinical trials registered on ClinicalTrials.gov.

EvidenceMap: a three-level knowledge representation for medical evidence computation and comprehension.

Objective: To develop a computable representation for medical evidence and to contribute a gold standard dataset of annotated randomized controlled trial (RCT) abstracts, along with a natural language processing (NLP) pipeline for transforming free-text RCT evidence in PubMed into the structured representation.

Materials And Methods: Our representation, EvidenceMap, consists of 3 levels of abstraction: Medical Evidence Entity, Proposition and Map, to represent the hierarchical structure of medical evidence composition. Randomly selected RCT abstracts were annotated following EvidenceMap based on the consensus of 2 independent annotators to train an NLP pipeline.

Clinical and temporal characterization of COVID-19 subgroups using patient vector embeddings of electronic health records.

Objective: To identify and characterize clinical subgroups of hospitalized Coronavirus Disease 2019 (COVID-19) patients.

Materials And Methods: Electronic health records of hospitalized COVID-19 patients at NewYork-Presbyterian/Columbia University Irving Medical Center were temporally sequenced and transformed into patient vector representations using Paragraph Vector models. K-means clustering was performed to identify subgroups.

OARD: Open annotations for rare diseases and their phenotypes based on real-world data.

Diagnosis for rare genetic diseases often relies on phenotype-driven methods, which hinge on the accuracy and completeness of the rare disease phenotypes in the underlying annotation knowledgebase. Existing knowledgebases are often manually curated with additional annotations found in published case reports. Despite their potential, real-world data such as electronic health records (EHRs) have not been fully exploited to derive rare disease annotations.

Towards Better Diagnosis Prediction Using Bidirectional Recurrent Neural Networks.

Bidirectional recurrent neural networks (RNN) improved performance of various natural language processing tasks and recently have been used for diagnosis prediction. Advantages of general bidirectional RNN, however, are not readily applied to diagnosis prediction task. In this study, we present a simple way to efficiently apply bidirectional RNN for diagnosis prediction without using any additional networks or parameters.

Evaluation of Criteria2Query: Towards Augmented Intelligence for Cohort Identification.

Electronic healthcare records data promises to improve the efficiency of patient eligibility screening, which is an important factor in the success of clinical trials and observational studies. To bridge the sociotechnical gap in cohort identification by end-users, who are clinicians or researchers unfamiliar with underlying EHR databases, we previously developed a natural language query interface named Criteria2Query (C2Q) that automatically transforms free-text eligibility criteria to executable database queries. In this study, we present a comprehensive evaluation of C2Q to generate more actionable insights to inform the design and evaluation of future natural language user interfaces for clinical databases, towards the realization of Augmented Intelligence (AI) for clinical cohort definition via e-screening.

Progress toward a universal biomedical data translator.

Clinical, biomedical, and translational science has reached an inflection point in the breadth and diversity of available data and the potential impact of such data to improve human health and well-being. However, the data are often siloed, disorganized, and not broadly accessible due to discipline-specific differences in terminology and representation. To address these challenges, the Biomedical Data Translator Consortium has developed and tested a pilot knowledge graph-based "Translator" system capable of integrating existing biomedical data sets and "translating" those data into insights intended to augment human reasoning and accelerate translational science.

The potential role of EHR data in optimizing eligibility criteria definition for cardiovascular outcome trials.

Background: Cardiovascular outcome trials (CVOTs) include patients with high risks for cardiovascular events based on specific inclusion criteria. Little is known about the impact of such inclusion criteria on patient accrual and the incidence rate of cardiovascular events.

Materials And Methods: We evaluated the impact of criteria on the accrual and the number of cardiovascular events in a cohort of 1544 diabetes patients identified from the clinical data warehouse of New York Presbyterian Hospital / Columbia University Irving Medical Center.

Columbia Open Health Data for COVID-19 Research: Database Analysis.

Background: COVID-19 has threatened the health of tens of millions of people all over the world. Massive research efforts have been made in response to the COVID-19 pandemic. Utilization of clinical data can accelerate these research efforts to combat the pandemic since important characteristics of the patients are often found by examining the clinical data.

A Framework for Systematic Assessment of Clinical Trial Population Representativeness Using Electronic Health Records Data.

Background: Clinical trials are the gold standard for generating robust medical evidence, but clinical trial results often raise generalizability concerns, which can be attributed to the lack of population representativeness. The electronic health records (EHRs) data are useful for estimating the population representativeness of clinical trial study population.

Objectives: This research aims to estimate the population representativeness of clinical trials systematically using EHR data during the early design stage.

Severity Prediction for COVID-19 Patients via Recurrent Neural Networks.

The novel coronavirus disease-2019 (COVID-19) pandemic has threatened the health of tens of millions of people worldwide and imposed heavy burden on global healthcare systems. In this paper, we propose a model to predict whether a patient infected with COVID-19 will develop severe outcomes based only on the patient's historical electronic health records (EHR) prior to hospital admission using recurrent neural networks. The model predicts risk score that represents the probability for a patient to progress into severe status (mechanical ventilation, tracheostomy, or death) after being infected with COVID-19.