PHDMF: A Flexible and Scalable Personal Health Data Management Framework Based on Blockchain Technology
Notice bibliographique
Résumé
Currently, most of the personal health data (PHD) are managed and stored separately by individual medical institutions. When these data need to be shared, they must be transferred to a trusted management center and approved by data owners through the third-party endorsement technology. Therefore, it is difficult for personal health data to be shared and circulated over multiple medical institutions. On the other hand, the use of directly exchanging and sharing the original data has become inconsistent with the data rapid growth of medical institutions because of the need of massive data transferring across agencies. In order to secure sharing and managing the mass personal health data generated by various medical institutions, a federal personal health data management framework (PHDMF, https://hvic.biosino.org/PHDMF) has been developed, which had the following advantages: 1) the blockchain technology was used to establish a data consortium over multiple medical institutions, which could provide a flexible and scalable technical solution for member extension and solve the problem of third-party endorsement during data sharing; 2) using data distributed storage technology, personal health data could be majorly stored in their original medical institutions, and the massive data transferring process was of no further use, which could match up with the data rapid growth of these institutions; 3) the distributed ledger technology was utilized to record the hash value of data, given the anti-tampering feature of the technology, malicious modification of data could be identified by comparing the hash value; 4) the smart contract technology was introduced to manage users' access and operation of data, which made the data transaction process traceable and solved the problem of data provenance; and 5) a trusted computing environment was provided for meta-analysis with statistic information instead of original data, the trusted computing environment could be further applied to more health data, such as genome sequencing data, protein expression data, and metabolic profile data through combining the federated learning and blockchain technology. In summary, the framework provides a convenient, secure, and trusted environment for health data supervision and circulation, which facilitate the consortium establish over medical institutions and help achieve the value of data sharing and mining.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».