ArticleBMC medical informatics and decision making2005
Automation of a problem list using natural language processing.
Article in BMC medical informatics and decision making, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
56 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Current approaches to identify sections within clinical narratives from electronic health records: a systematic review.BMC medical research methodology · 2019Pooled it
- Electronic problem lists: a thematic analysis of a systematic literature review to identify aspects critical to success.Journal of the American Medical Informatics Association : JAMIA · 2018Pooled it
- Improving the sensitivity of the problem list in an intensive care unit by using natural language processing.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2006Trial
- Symptom-based diagnostic models for common respiratory viral infections: a machine learning and natural language processing study.Infectious Disease Modelling · 2026Article
- Prediction of hospitalization and waiting time within 24 hours of emergency department patients with unstructured text data.Health care management science · 2024Article
- Building a Shared, Scalable, and Sustainable Source for the Problem-Oriented Medical Record: Developmental Study.JMIR medical informatics · 2021Article
- A fast, resource efficient, and reliable rule-based system for COVID-19 symptom identification.JAMIA open · 2021Article
- Data gaps in electronic health record (EHR) systems: An audit of problem list completeness during the COVID-19 pandemic.International journal of medical informatics · 2021Article
- Contextual property detection in Dutch diagnosis descriptions for uncertainty, laterality and temporality.BMC medical informatics and decision making · 2021Article
- Reducing Physicians' Cognitive Load During Chart Review: A Problem-Oriented Summary of the Patient Electronic Record.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2021Article
- Evaluation of a Concept Mapping Task Using Named Entity Recognition and Normalization in Unstructured Clinical Text.Journal of healthcare informatics research · 2020Article
- A Preliminary Characterization of Canonicalized and Non-Canonicalized Section Headers Across Variable Clinical Note Types.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
- Improving the Utility of Tobacco-Related Problem List Entries Using Natural Language Processing.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
- Recording problems and diagnoses in clinical care: developing guidance for healthcare professionals and system designers.BMJ health & care informatics · 2019Article
- Development of a Natural Language Processing Tool to Extract Radiation Treatment Sites.Cureus · 2019Article
- Discovering associations between problem list and practice setting.BMC medical informatics and decision making · 2019Article
- Issues in Building a Nursing Home Syndromic Surveillance System with Textmining: Longitudinal Observational Study.JMIR public health and surveillance · 2018Article
- A bibliometric analysis of natural language processing in medical research.BMC medical informatics and decision making · 2018Article
- Generalized Extraction and Classification of Span-Level Clinical Phrases.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- Comparison of Approaches for Heart Failure Case Identification From Electronic Health Record Data.JAMA cardiology · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe medical problem list is an important part of the electronic medical record in development in our institution. To serve the functions it is designed for, the problem list has to be as accurate and timely as possible. However, the current problem list is usually incomplete and inaccurate, and is often totally unused. To alleviate this issue, we are building an environment where the problem list can be easily and effectively maintained.
methodsFor this project, 80 medical problems were selected for their frequency of use in our future clinical field of evaluation (cardiovascular). We have developed an Automated Problem List system composed of two main components: a background and a foreground application. The background application uses Natural Language Processing (NLP) to harvest potential problem list entries from the list of 80 targeted problems detected in the multiple free-text electronic documents available in our electronic medical record. These proposed medical problems drive the foreground application designed for management of the problem list. Within this application, the extracted problems are proposed to the physicians for addition to the official problem list.
resultsThe set of 80 targeted medical problems selected for this project covered about 5% of all possible diagnoses coded in ICD-9-CM in our study population (cardiovascular adult inpatients), but about 64% of all instances of these coded diagnoses. The system contains algorithms to detect first document sections, then sentences within these sections, and finally potential problems within the sentences. The initial evaluation of the section and sentence detection algorithms demonstrated a sensitivity and positive predictive value of 100% when detecting sections, and a sensitivity of 89% and a positive predictive value of 94% when detecting sentences.
conclusionThe global aim of our project is to automate the process of creating and maintaining a problem list for hospitalized patients and thereby help to guarantee the timeliness, accuracy and completeness of this information.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.