Corpus ID: 236428795

Clinical Utility of the Automatic Phenotype Annotation in Unstructured Clinical Notes: ICU Use Cases

  title={Clinical Utility of the Automatic Phenotype Annotation in Unstructured Clinical Notes: ICU Use Cases},
  author={Jingqing Zhang and Luis Bola{\~n}os and Ashwani Tanwar and Albert Sokol and Julia Ive and Vibhor Gupta and Yike Guo},
Clinical notes contain information not present elsewhere, including drug response and symptoms, all of which are highly important when predicting key outcomes in acute care patients. We propose the automatic annotation of phenotypes from clinical notes as a method to capture essential information to predict outcomes in the Intensive Care Unit (ICU). This information is complementary to typically used vital signs and laboratory test results. We demonstrate and validate our approach conducting… Expand


Validation of Prediction Models for Critical Care Outcomes Using Natural Language Processing of Electronic Health Record Data
Applying methods from machine learning and natural language processing to information already routinely collected in electronic health records, including laboratory test results, vital signs, and clinical free-text notes, significantly improves a prediction model for mortality in the intensive care unit compared with approaches that use only the most abnormal vital sign and laboratory values. Expand
ClinPhen extracts and prioritizes patient phenotypes directly from medical records to expedite genetic disease diagnosis
ClinPhen is a fast, high-accuracy tool that automatically converts clinical notes into a prioritized list of patient phenotypes using Human Phenotype Ontology (HPO) terms and makes a substantial contribution to improvements in efficiency critically needed to meet the surging demand for clinical diagnostic sequencing. Expand
Physician documentation matters. Using natural language processing to predict mortality in sepsis
Physician clinical judgement extracted from notes using NLP has greater performance in predicting mortality and survival in sepsis compared to structured data used in SOFA and qSOFA. Expand
A Supervised Learning Approach for ICU Mortality Prediction Based on Unstructured Electrocardiogram Text Reports
A neural network based method for predicting mortality risk of ICU patients using unstructured Electrocardiogram (ECG) text reports and an unsupervised data cleansing technique for identification and removal of anomalous data/special cases was designed for optimizing the patient data representation. Expand
Machine Learning and Decision Support in Critical Care
A range of applications addressing the collection and preprocessing of critical care data are covered, including the modernization of static acuity scoring; online patient tracking; personalized prediction and risk assessment; artifact detection; state estimation; and incorporation of multimodal data sources such as genomic and free text data. Expand
Monitoring ICU Mortality Risk with A Long Short-Term Memory Recurrent Neural Network
A new framework for dynamic monitoring of patients’ mortality risk uses the bag-of-words representation for all relevant medical events based on most recent history as inputs and shows that the deep learning based framework performs better than the existing severity scoring system, SAPS-II. Expand
Predicting mortality in critically ill patients with diabetes using machine learning and clinical notes
This study aimed to use Unified Medical Language System (UMLS) resources, involving machine learning and natural language processing (NLP) approaches to predict the risk of mortality in diabetic patients in the critical care setting. Expand
Identifying sub-phenotypes of acute kidney injury using structured and unstructured electronic health record data with memory networks
This study used a memory network-based deep learning approach to discover AKI sub-phenotypes using structured and unstructured electronic health record (EHR) data of patients before AKI diagnosis using a real world critical care EHR corpus including 37,486 ICU stays. Expand
Survival prediction in intensive-care units based on aggregation of long-term disease history and acute physiology: a retrospective study of the Danish National Patient Registry and electronic patient records.
Disease history can be used to differentiate mortality risk between patients with similar vital signs with more precision than SAPS II and APACHE II scores, and machine learning models can be deconvoluted to generate novel understandings of how ICU patient features from long-term and short-term events interact with each other. Expand
Deep phenotyping for precision medicine
This special issue of Human Mutation offers a number of articles describing computational solutions for current challenges in deep phenotyping, including semantic and technical standards for phenotype and disease data, digital imaging for facial phenotype analysis, model organism phenotypes, and databases for correlating phenotypes with genomic variation. Expand