CN-122024985-A - Hemodialysis data management method and system based on whole course multidimensional data integration

CN122024985ACN 122024985 ACN122024985 ACN 122024985ACN-122024985-A

Abstract

The invention relates to a hemodialysis data management method and system based on full-course multidimensional data integration. The method comprises the steps of obtaining multi-source hemodialysis data of a patient from first treatment and processing the multi-source hemodialysis data into patient dialysis data under a unified timestamp, distributing unique dialysis session identifiers for each basic course unit by taking a single dialysis session as a basic course unit, constructing a whole course dialysis data chain taking the unique patient identifier as a root node and the dialysis session identifier as a core anchor point, hanging multi-mode data of a sample dimension and a histology dimension to the whole course dialysis data chain based on the association relation of the sample and the histology, and therefore achieving data tracing based on the whole course dialysis data chain and performing longitudinal modeling analysis based on data of a plurality of basic course units obtained by tracing. The invention effectively realizes the traceability and interpretable synergy of clinical, equipment, samples and histology parameters on the same whole course time axis under the hemodialysis scene.

Inventors

CHEN JING
WANG MENGJING
QIAN JING
CHENG PING
Lu Chuhan
ZHANG FENGMING

Assignees

复旦大学附属华山医院

Dates

Publication Date: 20260512
Application Date: 20260402

Claims (10)

1. A hemodialysis data management method based on whole course multidimensional data integration, comprising the steps of: Acquiring multi-source hemodialysis data of a patient from a first visit, the multi-source hemodialysis data including multi-modal data of a clinical dimension, a sample dimension, a device dimension, and a histology dimension; taking the unique patient identifier as a main index, performing data deduplication and association on the multi-source hemodialysis data, and then converting the multi-source hemodialysis data into a uniform timestamp to form patient hemodialysis data; Assigning a unique dialysis session identifier to each basic course unit by taking a single dialysis session as a basic course unit, thereby constructing a first whole course dialysis data chain taking the unique patient identifier as a root node and the dialysis session identifier as a core anchor point; Respectively generating a sample circulation chain of each biological sample based on multi-mode data of sample dimensions, creating a digital recording node for each circulation node in the sample circulation chain, and hanging the digital recording node to the first full-course dialysis data chain to obtain a second full-course dialysis data chain; mapping multi-mode data of a histology dimension to the second full-course dialysis data chain according to the corresponding relation between the multi-mode data and the biological sample to obtain a third full-course dialysis data chain; and tracing to obtain data of a plurality of basic course units by utilizing the third whole course dialysis data chain, and performing longitudinal modeling analysis based on the traced data.
2. The hemodialysis data management method of claim 1, wherein the constructing a first full course dialysis data chain with the patient unique identification as a root node and the dialysis session identifier as a core anchor, comprises: for any basic course unit, after the dialysis data of the patient before, during and after dialysis are unfolded in a time domain, forming dialysis data sub-chains respectively and binding with the corresponding dialysis session identifiers; and serially connecting the dialysis data subchains to the corresponding unique patient identifiers according to the time sequence to form the first full-course dialysis data chain.
3. The hemodialysis data management method of claim 2, wherein the pre-dialysis multimodal data includes laboratory indicators, vital signs, and medication information.
4. The method according to claim 3, wherein when the digital recording node is connected to the first full-course dialysis data chain, the digital recording node is connected to the corresponding dialysis data sub-chain according to the laboratory index generated by the digital recording node corresponding to the biological sample detection.
5. The hemodialysis data management method of claim 2, wherein the multi-modal data in dialysis includes dialysis equipment numbers and corresponding real-time operating parameters including blood flow, dialysate flow, transmembrane pressure, ultrafiltration volume, and alarm events.
6. The hemodialysis data management method of claim 2, wherein the post-dialysis multimodal data includes complication records, weight changes, blood pressure recovery, and order adjustment information.
7. The hemodialysis data management method of claim 1, further comprising: Acquiring all non-dialysis events of a patient from the first visit and converting the non-dialysis events to a uniform time stamp; Respectively constructing corresponding type identifiers and total event data aiming at any non-dialysis event; And inserting the type identifier and the full event data into a gap between two core anchor points adjacent to the type identifier and the full event data in time sequence.
8. The hemodialysis data management method of claim 1, wherein the flow nodes include collection, storage, processing, detection, and destruction, and the sample flow chain is constructed based on blockchain.
9. A hemodialysis data management system based on whole course multidimensional data integration, comprising: The processing module is used for acquiring multi-source hemodialysis data of a patient after the first treatment, carrying out data deduplication and association on the multi-source hemodialysis data by taking a patient unique identifier as a main index, and then converting the multi-source hemodialysis data into a uniform timestamp to form patient hemodialysis data, wherein the multi-source hemodialysis data comprises multi-mode data of clinical dimension, sample dimension, equipment dimension and histology dimension; The construction module is used for taking a single dialysis session as a basic course unit, and distributing a unique dialysis session identifier to each basic course unit so as to construct a first whole course dialysis data chain taking the unique patient identifier as a root node and the dialysis session identifier as a core anchor point; The correlation module is used for respectively generating a sample circulation chain of each biological sample based on the multi-modal data of the sample dimension, creating a digital recording node for each circulation node in the sample circulation chain, and hooking the digital recording node to the first full-course dialysis data chain to obtain a second full-course dialysis data chain; and the analysis module is used for tracing and obtaining multi-dimensional full-course hemodialysis data around a single dialysis session by utilizing the third full-course dialysis data chain, and performing longitudinal modeling analysis based on the traced data.
10. The hemodialysis data management system of claim 9, further comprising a privacy protection and hierarchy authorization module for field level desensitization, differential privacy noise addition, and role-based access control of the patient dialysis data.

Description

Hemodialysis data management method and system based on whole course multidimensional data integration Technical Field The invention relates to the technical field of medical big data, in particular to a hemodialysis data management method and system based on full course multidimensional data integration. Background The diagnosis and treatment effect evaluation of hemodialysis, the complication early warning and the personalized scheme adjustment are highly dependent on long-term data comparison of the same patient, and relevant data of the patient in the current industry are stored in heterogeneous systems such as HIS (hospital information system), LIS (inspection information system), dialysis equipment, biological sample library, histology detection platform and the like in a scattered manner, so that the following defects exist: The existing system does not realize uniform time stamp alignment of cross-system data, clinical, equipment and test data of different sources cannot be accurately matched to corresponding single dialysis session, and the insufficient relevance of the underlying data leads to insufficient accuracy of data sources of longitudinal disease course analysis; The existing biological samples only mark basic sampling time in a circulation device and are respectively and independently managed with clinical course data, and the histology detection result cannot exclude mixed factor interference such as sampling time, sample freeze thawing times, detection batches and the like due to the lack of a sample circulation chain, so that the repeatability of accurate medical research is difficult to support; The existing dialysis management system only performs statistical display on equipment parameters and single clinical indexes of single dialysis, does not establish mapping relation between the histology data and corresponding dialysis session and biological samples, cannot realize joint longitudinal analysis of clinical phenotypes, equipment parameters and histology characteristics, cannot find underlying molecular mechanisms of dialysis related complications and curative effect differences, and cannot support training of a personalized early warning model. Disclosure of Invention The invention aims to solve the technical problem of providing a hemodialysis data management method and system based on full-course multidimensional data integration, which can effectively realize the traceability and interpretable coordination of clinical, equipment, samples and histology parameters on the same full-course time axis in a hemodialysis scene. The invention provides a hemodialysis data management method based on full course multidimensional data integration, which comprises the following steps: Acquiring multi-source hemodialysis data of a patient from a first visit, the multi-source hemodialysis data including multi-modal data of a clinical dimension, a sample dimension, a device dimension, and a histology dimension; taking the unique patient identifier as a main index, performing data deduplication and association on the multi-source hemodialysis data, and then converting the multi-source hemodialysis data into a uniform timestamp to form patient hemodialysis data; Assigning a unique dialysis session identifier to each basic course unit by taking a single dialysis session as a basic course unit, thereby constructing a first whole course dialysis data chain taking the unique patient identifier as a root node and the dialysis session identifier as a core anchor point; Respectively generating a sample circulation chain of each biological sample based on multi-mode data of sample dimensions, creating a digital recording node for each circulation node in the sample circulation chain, and hanging the digital recording node to the first full-course dialysis data chain to obtain a second full-course dialysis data chain; mapping multi-mode data of a histology dimension to the second full-course dialysis data chain according to the corresponding relation between the multi-mode data and the biological sample to obtain a third full-course dialysis data chain; and tracing to obtain data of a plurality of basic course units by utilizing the third whole course dialysis data chain, and performing longitudinal modeling analysis based on the traced data. Further, the constructing a first full course dialysis data chain with the patient unique identifier as a root node and the dialysis session identifier as a core anchor point, includes: for any basic course unit, after the dialysis data of the patient before, during and after dialysis are unfolded in a time domain, forming dialysis data sub-chains respectively and binding with the corresponding dialysis session identifiers; and serially connecting the dialysis data subchains to the corresponding unique patient identifiers according to the time sequence to form the first full-course dialysis data chain. Further, the pre-dialysis multimodal data includes laboratory in