A unified framework for collecting, annotating, modeling and prediction from phenotypes in multi-institutional pediatric data resources
Notice bibliographique
Résumé
Predictive models on sequential observational clinical data have helped make headway utilizing large sets of this data collected from health systems from electronic medical record (EMR) and billing systems to predict outcomes, help physicians make decisions through clinical decision support (CDS) and possibly change the course of treatment of patients suffering from some of the most common diseases and illnesses. For diseases that affect a large portion of the population, standardized clinical data observations can be used to create new models that predict precise treatment options for individuals. This is referred to as precision medicine, and these treatments are derived from techniques in machine learning, artificial intelligence and other areas breaking to the surface by data scientists in healthcare. The question remains, is this data useful for those affected by rare diseases? Unlike diseases that common in the population like asthma or diabetes, rare diseases do not have characteristics well described or derived from the clinical records. Pediatric cancer, specifically brain tumors in children are severely lacking the sample size, consistent data capture and simple infrastructure to collect data. This hinders the speed at which scientists can discover breakthroughs in precision medicine as is seen in more common diseases. With the attention and buzz around the discipline under the umbrella of "artificial intelligence" in precision medicine especially in cancer, collaborations have been created to pool resources, data and biomaterials. In parallel, the government in the United States over the years has dedicated money and resources to large scale health data programs called Clinical Data Resource Networks (CDRNs) where standard observational clinical data is integrated across institutions to made large relevant sample sizes for observational research in one common data model. In this research, we investigate the intersection of both a specific rare disease repository, The Children's Brain Tumor Tissue Consortium (CBTTC) and a CDRN, PEDSnet, with the goal of leveraging the CDRN to help ease the burden of human data entry in the CBTTC and make data available faster to more researchers over multiple disciplines. We use PEDSnet data from children diagnosed with malignant brain tumors to try to predict a highly malignant brain tumor, Primitive neuroectodermal tumors (PNETs), and reproduce a similar data set used in a physical laboratory project developed by researchers at the Children's Hospital of Seattle (CHS), Children's Hospital of Philadelphia (CHOP) and Toronto Hospital for Sick Children. We use a threefold pipeline of exploratory data analysis, multi-modal data transformation and predictive analytics to derive a data driven phenotype from a sample of over 1900 brain tumor cases from PEDSnet originating at CHOP and SCH. The hypothesis herein is that the CDRN could contain a set of data that can be derived through traditional machine learning techniques and used to supplement the current hand data entry and abstraction process to bring automation to data harmonization across institutions where longitudinal data is required for biomedical research. This research is supported by the iFellowship Program administered by the University of Pittsburgh as part of the iSchool Consortium and funded by the Andrew W. Mellon Foundation. This research reflects the goals of the Committee on Coherence at Scale for Higher Education in that this research aims to take information that is usually 'siloed' in a lab and make it available to more minds from different domains by using a national organization with large sets of patient data to drive an analogue, human data entry intensive project to scale.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,034 | 0,042 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,003 |
| Méta-épidémiologie (sens large) | 0,003 | 0,006 |
| Bibliométrie | 0,008 | 0,010 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,012 | 0,011 |
| Science ouverte | 0,008 | 0,013 |
| Intégrité de la recherche | 0,003 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».