Disease Heritability Inferred from Familial Relationships Reported in Medical Records.

Heritability is essential for understanding the biological causes of disease but requires laborious patient recruitment and phenotype ascertainment. Electronic health records (EHRs) passively capture a wide range of clinically relevant data and provide a resource for studying the heritability of traits that are not typically accessible. EHRs contain next-of-kin information collected via patient emergency contact forms, but until now, these data have gone unused in research. We mined emergency contact data at three academic medical centers and identified 7.4 million familial relationships while maintaining patient privacy. Identified relationships were consistent with genetically derived relatedness. We used EHR data to compute heritability estimates for 500 disease phenotypes. Overall, estimates were consistent with the literature and between sites. Inconsistencies were indicative of limitations and opportunities unique to EHR research. These analyses provide a validation of the use of EHRs for genetics and disease research.

Mural Wnt/ß-catenin signaling regulates Lama2 expression to promote neurovascular unit maturation.

Neurovascular unit and barrier maturation rely on vascular basement membrane (vBM) composition. Laminins, a major vBM component, are crucial for these processes, yet the signaling pathway(s) that regulate their expression remain unknown. Here, we show that mural cells have active Wnt/ß-catenin signaling during central nervous system development in mice. Bulk RNA sequencing and validation using postnatal day 10 and 14 wild-type versus adenomatosis polyposis coli downregulated 1 (Apcdd1-/-) mouse retinas revealed that Lama2 mRNA and protein levels are increased in mutant vasculature with higher Wnt/ß-catenin signaling. Mural cells are the main source of Lama2, and Wnt/ß-catenin activation induces Lama2 expression in mural cells in vitro. Markers of mature astrocytes, including aquaporin 4 (a water channel in astrocyte endfeet) and integrin-α6 (a laminin receptor), are upregulated in Apcdd1-/- retinas with higher Lama2 vBM deposition. Thus, the Wnt/ß-catenin pathway regulates Lama2 expression in mural cells to promote neurovascular unit and barrier maturation.

No population left behind: Improving paediatric drug safety using informatics and systems biology.

Adverse drugs effects (ADEs) in children are common and may result in disability and death. The current paediatric drug safety landscape, including clinical trials, is limited as it rarely includes children and relies on extrapolation from adults. Children are not small adults but go through an evolutionarily conserved and physiologically dynamic process of growth and maturation. Novel quantitative approaches, integrating observations from clinical trials and drug safety databases with dynamic mechanisms, can be used to systematically identify ADEs unique to childhood. In this perspective, we discuss three critical research directions using systems biology methodologies and novel informatics to improve paediatric drug safety, namely child versus adult drug safety profiles, age-dependent drug toxicities and genetic susceptibility of ADEs across childhood. We argue that a data-driven framework that leverages observational data, biomedical knowledge and systems biology modelling will reveal previously unknown mechanisms of pediatric adverse drug events and lead to improved paediatric drug safety.

Deep learning on time series laboratory test results from electronic health records for early detection of pancreatic cancer.

The multi-modal and unstructured nature of observational data in Electronic Health Records (EHR) is currently a significant obstacle for the application of machine learning towards risk stratification. In this study, we develop a deep learning framework for incorporating longitudinal clinical data from EHR to infer risk for pancreatic cancer (PC). This framework includes a novel training protocol, which enforces an emphasis on early detection by applying an independent Poisson-random mask on proximal-time measurements for each variable. Data fusion for irregular multivariate time-series features is enabled by a "grouped" neural network (GrpNN) architecture, which uses representation learning to generate a dimensionally reduced vector for each measurement set before making a final prediction. These models were evaluated using EHR data from Columbia University Irving Medical Center-New York Presbyterian Hospital. Our framework demonstrated better performance on early detection (AUROC 0.671, CI 95% 0.667 - 0.675, p < 0.001) at 12 months prior to diagnosis compared to a logistic regression, xgboost, and a feedforward neural network baseline. We demonstrate that our masking strategy results greater improvements at distal times prior to diagnosis, and that our GrpNN model improves generalizability by reducing overfitting relative to the feedforward baseline. The results were consistent across reported race. Our proposed algorithm is potentially generalizable to other diseases including but not limited to cancer where early detection can improve survival.

Translational medicine in the Age of Big Data.

The ability to collect, store and analyze massive amounts of molecular and clinical data is fundamentally transforming the scientific method and its application in translational medicine. Collecting observations has always been a prerequisite for discovery, and great leaps in scientific understanding are accompanied by an expansion of this ability. Particle physics, astronomy and climate science, for example, have all greatly benefited from the development of new technologies enabling the collection of larger and more diverse data. Unlike medicine, however, each of these fields also has a mature theoretical framework on which new data can be evaluated and incorporated-to say it another way, there are no 'first principals' from which a healthy human could be analytically derived. The worry, and it is a valid concern, is that, without a strong theoretical underpinning, the inundation of data will cause medical research to devolve into a haphazard enterprise without discipline or rigor. The Age of Big Data harbors tremendous opportunity for biomedical advances, but will also be treacherous and demanding on future scientists.

Where Have All the Emergencies Gone? The Impact of the COVID-19 Pandemic on Obstetric and Gynecologic Procedures and Consults at a New York City Hospital.

STUDY OBJECTIVE: The purpose of this study was to assess the impact of the coronavirus disease 2019 (COVID-19) pandemic on surgical volume and emergency department (ED) consults across obstetrics-gynecology (OB-GYN) services at a New York City hospital. DESIGN: Retrospective cohort study. SETTING: Tertiary care academic medical center in New York City. PATIENTS: Women undergoing OB-GYN ED consults or surgeries between February 1, 2020 and April 15, 2020. INTERVENTIONS: March 16 institutional moratorium on elective surgeries. MEASUREMENTS AND MAIN RESULTS: The volume and types of surgeries and ED consults were compared before and after the COVID-19 moratorium. During the pandemic, the average weekly volume of ED consults and gynecology (GYN) surgeries decreased, whereas obstetric (OB) surgeries remained stable. The proportions of OB-GYN ED consults, GYN surgeries, and OB surgeries relative to all ED consults, all surgeries, and all labor and delivery patients were 1.87%, 13.8%, 54.6% in the pre-COVID-19 time frame (February 1-March 15) vs 1.53%, 21.3%, 79.7% in the COVID-19 time frame (March 16-April 15), representing no significant difference in proportions of OB-GYN ED consults (p = .464) and GYN surgeries (p = .310) before and during COVID-19, with a proportionate increase in OB surgeries (p <.002). The distribution of GYN surgical case types changed significantly during the pandemic with higher proportions of emergent surgeries for ectopic pregnancies, miscarriages, and concern for cancer (p <.001). Alternatively, the OB surgery distribution of case types remained relatively constant. CONCLUSION: This study highlights how the pandemic has affected the ways that patients in OB-GYN access and receive care. Institutional policies suspending elective surgeries during the pandemic decreased GYN surgical volume and affected the types of cases performed. This decrease was not appreciated for OB surgical volume, reflecting the nonelective and time-sensitive nature of obstetric care. A decrease in ED consults was noted during the pandemic begging the question "Where have all the emergencies gone?" Although the moratorium on elective procedures was necessary, "elective" GYN surgeries remain medically indicated to address symptoms such as pain and bleeding and to prevent serious medical sequelae such as severe anemia requiring transfusion. As we continue to battle COVID-19, we must not lose sight of those patients whose care has been deferred.

Identifying Symptom Information in Clinical Notes Using Natural Language Processing.

BACKGROUND: Symptoms are a core concept of nursing interest. Large-scale secondary data reuse of notes in electronic health records (EHRs) has the potential to increase the quantity and quality of symptom research. However, the symptom language used in clinical notes is complex. A need exists for methods designed specifically to identify and study symptom information from EHR notes. OBJECTIVES: We aim to describe a method that combines standardized vocabularies, clinical expertise, and natural language processing to generate comprehensive symptom vocabularies and identify symptom information in EHR notes. We piloted this method with five diverse symptom concepts: constipation, depressed mood, disturbed sleep, fatigue, and palpitations. METHODS: First, we obtained synonym lists for each pilot symptom concept from the Unified Medical Language System. Then, we used two large bodies of text (clinical notes from Columbia University Irving Medical Center and PubMed abstracts containing Medical Subject Headings or key words related to the pilot symptoms) to further expand our initial vocabulary of synonyms for each pilot symptom concept. We used NimbleMiner, an open-source natural language processing tool, to accomplish these tasks and evaluated NimbleMiner symptom identification performance by comparison to a manually annotated set of nurse- and physician-authored common EHR note types. RESULTS: Compared to the baseline Unified Medical Language System synonym lists, we identified up to 11 times more additional synonym words or expressions, including abbreviations, misspellings, and unique multiword combinations, for each symptom concept. Natural language processing system symptom identification performance was excellent. DISCUSSION: Using our comprehensive symptom vocabularies and NimbleMiner to label symptoms in clinical notes produced excellent performance metrics. The ability to extract symptom information from EHR notes in an accurate and scalable manner has the potential to greatly facilitate symptom science research.

Characterizing shared and distinct symptom clusters in common chronic conditions through natural language processing of nursing notes.

Data-driven characterization of symptom clusters in chronic conditions is essential for shared cluster detection and physiological mechanism discovery. This study aims to computationally describe symptom documentation from electronic nursing notes and compare symptom clusters among patients diagnosed with four chronic conditions-chronic obstructive pulmonary disease (COPD), heart failure, type 2 diabetes mellitus, and cancer. Nursing notes (N = 504,395; 133,977 patients) were obtained for the 2016 calendar year from a single medical center. We used NimbleMiner, a natural language processing application, to identify the presence of 56 symptoms. We calculated symptom documentation prevalence by note and patient for the corpus. Then, we visually compared documentation for a subset of patients (N = 22,657) diagnosed with COPD (n = 3339), heart failure (n = 6587), diabetes (n = 12,139), and cancer (n = 7269) and conducted multiple correspondence analysis and hierarchical clustering to discover underlying groups of patients who have similar symptom profiles (i.e., symptom clusters) for each condition. As expected, pain was the most frequently documented symptom. All conditions had a group of patients characterized by no symptoms. Shared clusters included cardiovascular symptoms for heart failure and diabetes; pain and other symptoms for COPD, diabetes, and cancer; and a newly-identified cognitive and neurological symptom cluster for heart failure, diabetes, and cancer. Cancer (gastrointestinal symptoms and fatigue) and COPD (mental health symptoms) each contained a unique cluster. In summary, we report both shared and distinct, as well as established and novel, symptom clusters across chronic conditions. Findings support the use of electronic health record-derived notes and NLP methods to study symptoms and symptom clusters to advance symptom science.

No Increased Risk of Colorectal Adenomas in Spouses of Patients with Colorectal Neoplasia.

Although genetic factors such as family history have been associated with increased risk of developing colorectal cancer (CRC), multiple lifestyle and environmental risk factors for CRC have been identified, including smoking, diet, obesity, and physical activity.1,2 Although couples typically have different genetic backgrounds, spouses are likely to share lifestyle and environmental exposures over the course of years, including similar home environments, geographical locations of residence, dietary exposures, and smoking exposures.3 As such, one might expect that an increased CRC incidence would be seen among spouses of patients with CRC; however, studies on this topic have inconsistent results.3-6 By using a large cohort of spouses who have undergone colonoscopy, we aimed to determine whether the risk of colorectal adenomas is increased among spouses of those with colorectal neoplasia (CRN) on colonoscopy.

PatientExploreR: an extensible application for dynamic visualization of patient clinical history from electronic health records in the OMOP common data model.

MOTIVATION: Electronic health records (EHRs) are quickly becoming omnipresent in healthcare, but interoperability issues and technical demands limit their use for biomedical and clinical research. Interactive and flexible software that interfaces directly with EHR data structured around a common data model (CDM) could accelerate more EHR-based research by making the data more accessible to researchers who lack computational expertise and/or domain knowledge. RESULTS: We present PatientExploreR, an extensible application built on the R/Shiny framework that interfaces with a relational database of EHR data in the Observational Medical Outcomes Partnership CDM format. PatientExploreR produces patient-level interactive and dynamic reports and facilitates visualization of clinical data without any programming required. It allows researchers to easily construct and export patient cohorts from the EHR for analysis with other software. This application could enable easier exploration of patient-level data for physicians and researchers. PatientExploreR can incorporate EHR data from any institution that employs the CDM for users with approved access. The software code is free and open source under the MIT license, enabling institutions to install and users to expand and modify the application for their own purposes. AVAILABILITY AND IMPLEMENTATION: PatientExploreR can be freely obtained from GitHub: https://github.com/BenGlicksberg/PatientExploreR. We provide instructions for how researchers with approved access to their institutional EHR can use this package. We also release an open sandbox server of synthesized patient data for users without EHR access to explore: http://patientexplorer.ucsf.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Silencing c-Myc translation as a therapeutic strategy through targeting PI3Kδ and CK1ε in hematological malignancies.

Phosphoinositide 3-kinase (PI3K) and the proteasome pathway are both involved in activating the mechanistic target of rapamycin (mTOR). Because mTOR signaling is required for initiation of messenger RNA translation, we hypothesized that cotargeting the PI3K and proteasome pathways might synergistically inhibit translation of c-Myc. We found that a novel PI3K δ isoform inhibitor TGR-1202, but not the approved PI3Kδ inhibitor idelalisib, was highly synergistic with the proteasome inhibitor carfilzomib in lymphoma, leukemia, and myeloma cell lines and primary lymphoma and leukemia cells. TGR-1202 and carfilzomib (TC) synergistically inhibited phosphorylation of the eukaryotic translation initiation factor 4E (eIF4E)-binding protein 1 (4E-BP1), leading to suppression of c-Myc translation and silencing of c-Myc-dependent transcription. The synergistic cytotoxicity of TC was rescued by overexpression of eIF4E or c-Myc. TGR-1202, but not other PI3Kδ inhibitors, inhibited casein kinase-1 ε (CK1ε). Targeting CK1ε using a selective chemical inhibitor or short hairpin RNA complements the effects of idelalisib, as a single agent or in combination with carfilzomib, in repressing phosphorylation of 4E-BP1 and the protein level of c-Myc. These results suggest that TGR-1202 is a dual PI3Kδ/CK1ε inhibitor, which may in part explain the clinical activity of TGR-1202 in aggressive lymphoma not found with idelalisib. Targeting CK1ε should become an integral part of therapeutic strategies targeting translation of oncogenes such as c-Myc.

Pathway analysis of genomic pathology tests for prognostic cancer subtyping.

Genomic test results collected during the provision of medical care and stored in Electronic Health Record (EHR) systems represent an opportunity for clinical research into disease heterogeneity and clinical outcomes. In this paper, we evaluate the use of genomic test reports ordered for cancer patients in order to derive cancer subtypes and to identify biological pathways predictive of poor survival outcomes. A novel method is proposed to calculate patient similarity based on affected biological pathways rather than gene mutations. We demonstrate that this approach identifies subtypes of prognostic value and biological pathways linked to survival, with implications for precision treatment selection and a better understanding of the underlying disease. We also share lessons learned regarding the opportunities and challenges of secondary use of observational genomic data to conduct such research.

Structural and functional cardiac profile after prolonged duration of mechanical unloading: potential implications for myocardial recovery.

Clinical and experimental studies have suggested that the duration of left ventricular assist device (LVAD) support may affect remodeling of the failing heart. We aimed to 1) characterize the changes in Ca2+/calmodulin-dependent protein kinase type-IIδ (CaMKIIδ), growth signaling, structural proteins, fibrosis, apoptosis, and gene expression before and after LVAD support and 2) assess whether the duration of support correlated with improvement or worsening of reverse remodeling. Left ventricular apex tissue and serum pairs were collected in patients with dilated cardiomyopathy ( n = 25, 23 men and 2 women) at LVAD implantation and after LVAD support at cardiac transplantation/LVAD explantation. Normal cardiac tissue was obtained from healthy hearts ( n = 4) and normal serum from age-matched control hearts ( n = 4). The duration of LVAD support ranged from 48 to 1,170 days (median duration: 270 days). LVAD support was associated with CaMKIIδ activation, increased nuclear myocyte enhancer factor 2, sustained histone deacetylase-4 phosphorylation, increased circulating and cardiac myostatin (MSTN) and MSTN signaling mediated by SMAD2, ongoing structural protein dysregulation and sustained fibrosis and apoptosis (all P < 0.05). Increased CaMKIIδ phosphorylation, nuclear myocyte enhancer factor 2, and cardiac MSTN significantly correlated with the duration of support. Phosphorylation of SMAD2 and apoptosis decreased with a shorter duration of LVAD support but increased with a longer duration of LVAD support. Further study is needed to define the optimal duration of LVAD support in patients with dilated cardiomyopathy. NEW & NOTEWORTHY A long duration of left ventricular assist device support may be detrimental for myocardial recovery, based on myocardial tissue experiments in patients with prolonged support showing significantly worsened activation of Ca2+/calmodulin-dependent protein kinase-IIδ, increased nuclear myocyte enhancer factor 2, increased myostatin and its signaling by SMAD2, and apoptosis as well as sustained histone deacetylase-4 phosphorylation, structural protein dysregulation, and fibrosis.

Pharmacological risk factors associated with hospital readmission rates in a psychiatric cohort identified using prescriptome data mining.

BACKGROUND: Worldwide, over 14% of individuals hospitalized for psychiatric reasons have readmissions to hospitals within 30 days after discharge. Predicting patients at risk and leveraging accelerated interventions can reduce the rates of early readmission, a negative clinical outcome (i.e., a treatment failure) that affects the quality of life of patient. To implement individualized interventions, it is necessary to predict those individuals at highest risk for 30-day readmission. In this study, our aim was to conduct a data-driven investigation to find the pharmacological factors influencing 30-day all-cause, intra- and interdepartmental readmissions after an index psychiatric admission, using the compendium of prescription data (prescriptome) from electronic medical records (EMR). METHODS: The data scientists in the project received a deidentified database from the Mount Sinai Data Warehouse, which was used to perform all analyses. Data was stored in a secured MySQL database, normalized and indexed using a unique hexadecimal identifier associated with the data for psychiatric illness visits. We used Bayesian logistic regression models to evaluate the association of prescription data with 30-day readmission risk. We constructed individual models and compiled results after adjusting for covariates, including drug exposure, age, and gender. We also performed digital comorbidity survey using EMR data combined with the estimation of shared genetic architecture using genomic annotations to disease phenotypes. RESULTS: Using an automated, data-driven approach, we identified prescription medications, side effects (primary side effects), and drug-drug interaction-induced side effects (secondary side effects) associated with readmission risk in a cohort of 1275 patients using prescriptome analytics. In our study, we identified 28 drugs associated with risk for readmission among psychiatric patients. Based on prescription data, Pravastatin had the highest risk of readmission (OR = 13.10; 95% CI (2.82, 60.8)). We also identified enrichment of primary side effects (n = 4006) and secondary side effects (n = 36) induced by prescription drugs in the subset of readmitted patients (n = 89) compared to the non-readmitted subgroup (n = 1186). Digital comorbidity analyses and shared genetic analyses further reveals that cardiovascular disease and psychiatric conditions are comorbid and share functional gene modules (cardiomyopathy and anxiety disorder: shared genes (n = 37; P = 1.06815E-06)). CONCLUSIONS: Large scale prescriptome data is now available from EMRs and accessible for analytics that could improve healthcare outcomes. Such analyses could also drive hypothesis and data-driven research. In this study, we explored the utility of prescriptome data to identify factors driving readmission in a psychiatric cohort. Converging digital health data from EMRs and systems biology investigations reveal a subset of patient populations that have significant comorbidities with cardiovascular diseases are more likely to be readmitted. Further, the genetic architecture of psychiatric illness also suggests overlap with cardiovascular diseases. In summary, assessment of medications, side effects, and drug-drug interactions in a clinical setting as well as genomic information using a data mining approach could help to find factors that could help to lower readmission rates in patients with mental illness.

Machine learning: novel bioinformatics approaches for combating antimicrobial resistance.

PURPOSE OF REVIEW: Antimicrobial resistance (AMR) is a threat to global health and new approaches to combating AMR are needed. Use of machine learning in addressing AMR is in its infancy but has made promising steps. We reviewed the current literature on the use of machine learning for studying bacterial AMR. RECENT FINDINGS: The advent of large-scale data sets provided by next-generation sequencing and electronic health records make applying machine learning to the study and treatment of AMR possible. To date, it has been used for antimicrobial susceptibility genotype/phenotype prediction, development of AMR clinical decision rules, novel antimicrobial agent discovery and antimicrobial therapy optimization. SUMMARY: Application of machine learning to studying AMR is feasible but remains limited. Implementation of machine learning in clinical settings faces barriers to uptake with concerns regarding model interpretability and data quality.Future applications of machine learning to AMR are likely to be laboratory-based, such as antimicrobial susceptibility phenotype prediction.

Predicting G protein-coupled receptor downstream signaling by tissue expression.

MOTIVATION: G protein-coupled receptors (GPCRs) are central to how cells respond to their environment and a major class of pharmacological targets. However, comprehensive knowledge of which pathways are activated and deactivated by these essential sensors is largely unknown. To better understand the mechanism of GPCR signaling system, we integrated five independent genome-wide expression datasets, representing 275 human tissues and cell lines, with protein-protein interactions and functional pathway data. RESULTS: We found that tissue-specificity plays a crucial part in the function of GPCR signaling system. Only a few GPCRs are expressed in each tissue, which are coupled by different combinations of G-proteins or ß-arrestins to trigger specific downstream pathways. Based on this finding, we predicted the downstream pathways of GPCR in human tissues and validated our results with L1000 knockdown data. In total, we identified 154,988 connections between 294 GPCRs and 690 pathways in 240 tissues and cell types. AVAILABILITY AND IMPLEMENTATION: The source code and results supporting the conclusions of this article are available at http://tatonettilab.org/resources/GOTE/source_code/ CONTACT: nick.tatonetti@columbia.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Coherent functional modules improve transcription factor target identification, cooperativity prediction, and disease association.

Transcription factors (TFs) are fundamental controllers of cellular regulation that function in a complex and combinatorial manner. Accurate identification of a transcription factor's targets is essential to understanding the role that factors play in disease biology. However, due to a high false positive rate, identifying coherent functional target sets is difficult. We have created an improved mapping of targets by integrating ChIP-Seq data with 423 functional modules derived from 9,395 human expression experiments. We identified 5,002 TF-module relationships, significantly improved TF target prediction, and found 30 high-confidence TF-TF associations, of which 14 are known. Importantly, we also connected TFs to diseases through these functional modules and identified 3,859 significant TF-disease relationships. As an example, we found a link between MEF2A and Crohn's disease, which we validated in an independent expression dataset. These results show the power of combining expression data and ChIP-Seq data to remove noise and better extract the associations between TFs, functional modules, and disease.

Connectivity Homology Enables Inter-Species Network Models of Synthetic Lethality.

Synthetic lethality is a genetic interaction wherein two otherwise nonessential genes cause cellular inviability when knocked out simultaneously. Drugs can mimic genetic knock-out effects; therefore, our understanding of promiscuous drugs, polypharmacology-related adverse drug reactions, and multi-drug therapies, especially cancer combination therapy, may be informed by a deeper understanding of synthetic lethality. However, the colossal experimental burden in humans necessitates in silico methods to guide the identification of synthetic lethal pairs. Here, we present SINaTRA (Species-INdependent TRAnslation), a network-based methodology that discovers genome-wide synthetic lethality in translation between species. SINaTRA uses connectivity homology, defined as biological connectivity patterns that persist across species, to identify synthetic lethal pairs. Importantly, our approach does not rely on genetic homology or structural and functional similarity, and it significantly outperforms models utilizing these data. We validate SINaTRA by predicting synthetic lethality in S. pombe using S. cerevisiae data, then identify over one million putative human synthetic lethal pairs to guide experimental approaches. We highlight the translational applications of our algorithm for drug discovery by identifying clusters of genes significantly enriched for single- and multi-drug cancer therapies.

Improving condition severity classification with an efficient active learning based framework.

Classification of condition severity can be useful for discriminating among sets of conditions or phenotypes, for example when prioritizing patient care or for other healthcare purposes. Electronic Health Records (EHRs) represent a rich source of labeled information that can be harnessed for severity classification. The labeling of EHRs is expensive and in many cases requires employing professionals with high level of expertise. In this study, we demonstrate the use of Active Learning (AL) techniques to decrease expert labeling efforts. We employ three AL methods and demonstrate their ability to reduce labeling efforts while effectively discriminating condition severity. We incorporate three AL methods into a new framework based on the original CAESAR (Classification Approach for Extracting Severity Automatically from Electronic Health Records) framework to create the Active Learning Enhancement framework (CAESAR-ALE). We applied CAESAR-ALE to a dataset containing 516 conditions of varying severity levels that were manually labeled by seven experts. Our dataset, called the "CAESAR dataset," was created from the medical records of 1.9 million patients treated at Columbia University Medical Center (CUMC). All three AL methods decreased labelers' efforts compared to the learning methods applied by the original CAESER framework in which the classifier was trained on the entire set of conditions; depending on the AL strategy used in the current study, the reduction ranged from 48% to 64% that can result in significant savings, both in time and money. As for the PPV (precision) measure, CAESAR-ALE achieved more than 13% absolute improvement in the predictive capabilities of the framework when classifying conditions as severe. These results demonstrate the potential of AL methods to decrease the labeling efforts of medical experts, while increasing accuracy given the same (or even a smaller) number of acquired conditions. We also demonstrated that the methods included in the CAESAR-ALE framework (Exploitation and Combination_XA) are more robust to the use of human labelers with different levels of professional expertise.