Application of the Modified Real-Time Medical Information Standard for U-Healthcare Systems by Using HL7 and Modified MFER(TS-MFER)
Bibliographic record
Abstract
U-healthcare 환경에서는 시간, 장소에 무관하게 사용자의 건강을 관리해준다. 이를 위해 이기종 의료 장비간 정보공유와 호환성 보장을 위한 의료 정보 표준화는 필수다. 적합한 표준이란 다양한 타입의 정보와 장비의 특성을 포괄하며 적용이 쉬운 표준이다. HL7은 대표적 예이지만 비텍스트 기반 신호, 특히 파형 정보를 다루는 데부족한 점이 있다. 이 점을 보완하기 위해 JAHIS에서 의료 파형에 적합한 표준(MFER)을 제시하였다. MFER은 파형정보 측면에서 HL7이 가지지 못한 장점을 가지고 있으나 실시간 적용에는 적합하지 않다. U-healthcare system의 본래 목적상 실시간 응용에 대한 요구는 크다. 따라서 앞의 표준들의 장점은 유지하고 단점은 보완할 수 있는 표준이 필요하다. 본 논문에서는 첫째, U-healthcare system을 위한 의료 정보 표준중 대표적인 HL7, MFER에 대한 리뷰와 두 표준관련 연구 동향을 소개한다. 둘째, 앞의 표준을 수정하여 실시간 응용에도 접목할 수 있는 scheme(TS-MFER with HL7)을 제안하고 실제 적용 결과를 제시한다. U-healthcare is maintaining of users' health without limitations from where and when they are. As it is important to guarantee compatibility between heterogeneous systems in U-healthcare, a medical information standard is compulsory. An adequate standard means that it is easy to understand and it can cover wide range of information types and various medical devices. Among them, HL7(Helath Level 7) has those traits, but HL7 is not adequate for non-text message, especially for medical waveform. JAHIS suggested an appropriate standard, that is MFER. MFER has many advantages for representation of medical waveform, but it is still not good for real-time applications. In U-healthcare, there are lots of needs for real-time application, so we need a standard that can have useful properties of MFER and HL7, and support real-time. In this article, there are two main topics. The first one is introducing MFER and HL7. Second, the new scheme(TS-MFER with HL7) is developed by modifying MFER and HL7 for real-time applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".