ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2013
Developing predictive models using electronic medical records: challenges and pitfalls.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05311046 (Biomarker-enhanced Artificial Intelligence Based Pediatric Sepsis Screening Tool Towards Early Recognition and Personalized Therapeutics), which is not on this map. Cited by 33 papers, 1 of them a synthesis that pooled it.
What it found
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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.
Biomarker-enhanced Artificial Intelligence Based Pediatric Sepsis Screening Tool Towards Early Recognition and Personalized Therapeutics
Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning prediction models in orthopedic surgery: A systematic review in transparent reporting.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2022Pooled it
- Rapid identification of slow healing wounds.Wound repair and regeneration : official publication of the Wound Healing Society [and] the European Tissue Repair SocietyTrial
- Machine Learning and Deep Learning Models for Early Sepsis Prediction: A Scoping Review.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2025Review
- Predictive Factors of Apparent Treatment Resistant Hypertension Among Patients With Hypertension Identified Using Electronic Health Records.Journal of general internal medicine · 2025Article
- Trustworthiness of a machine learning early warning model in medical and surgical inpatients.JAMIA open · 2025Article
- The Role of Artificial Intelligence and Machine Learning Models in Antimicrobial Stewardship in Public Health: A Narrative Review.Antibiotics (Basel, Switzerland) · 2025Review
- Early prediction of mortality upon intensive care unit admission.BMC medical informatics and decision making · 2024Article
- Transportability of bacterial infection prediction models for critically ill patients.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Identification and Prediction of Clinical Phenotypes in Hospitalized Patients With COVID-19: Machine Learning From Medical Records.JMIR formative research · 2023Article
- The Impact of Longitudinal Data-Completeness of Electronic Health Record Data on the Prediction Performance of Clinical Risk Scores.Clinical pharmacology and therapeutics · 2023Article
- Performance drift in a mortality prediction algorithm among patients with cancer during the SARS-CoV-2 pandemic.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Expectations for Artificial Intelligence (AI) in Psychiatry.Current psychiatry reports · 2022Review
- Predicting Healthcare-associated Infections: are Point of Prevalence Surveys data useful?Journal of preventive medicine and hygiene · 2022Article
- Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare.NPJ digital medicine · 2022Review
- DIAGNOSING PHYSICIAN ERROR: A MACHINE LEARNING APPROACH TO LOW-VALUE HEALTH CARE.The quarterly journal of economics · 2022Article
- Performance Drift in a Mortality Prediction Algorithm during the SARS-CoV-2 Pandemic.medRxiv : the preprint server for health sciences · 2022Article
- Applications of Machine Learning to the Problem of Antimicrobial Resistance: an Emerging Model for Translational Research.Journal of clinical microbiology · 2021Review
- Early Detection of Sepsis With Machine Learning Techniques: A Brief Clinical Perspective.Frontiers in medicine · 2021Article
- Machine Learning for Early Warning of Septic Shock in Children With Hematological Malignancies Accompanied by Fever or Neutropenia: A Single Center Retrospective Study.Frontiers in oncology · 2021Article
- As if sand were stone. New concepts and metrics to probe the ground on which to build trustable AI.BMC medical informatics and decision making · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While Electronic Medical Records (EMR) contain detailed records of the patient-clinician encounter - vital signs, laboratory tests, symptoms, caregivers' notes, interventions prescribed and outcomes - developing predictive models from this data is not straightforward. These data contain systematic biases that violate assumptions made by off-the-shelf machine learning algorithms, commonly used in the literature to train predictive models. In this paper, we discuss key issues and subtle pitfalls specific to building predictive models from EMR. We highlight the importance of carefully considering both the special characteristics of EMR as well as the intended clinical use of the predictive model and show that failure to do so could lead to developing models that are less useful in practice. Finally, we describe approaches for training and evaluating models on EMR using early prediction of septic shock as our example application.
Indexed as
Identifiers
24551396PMC3900132What 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.