Evidence map›Paper›PMID 40417511›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2024

Early Disease Prediction Using a Text-Numerical Hybrid Model Using Large-Scale Clinical Real-World Data.

Ayaka Oka, Tatsuya Yamaguchi, Masaki Ishihara, Takayuki Baba, Tatsuya Sato, Kazuki Iwamoto, Ryo Iwamura, Shigetaka Toma, Kaho Ogura, Masahiro Kimura and 2 more

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Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Ayaka OkaFujitsu Limited, Kanagawa, Japan.
Tatsuya YamaguchiFujitsu Limited, Kanagawa, Japan.
Masaki IshiharaFujitsu Limited, Kanagawa, Japan.
Takayuki BabaFujitsu Limited, Kanagawa, Japan.
Tatsuya SatoFujitsu Limited, Kanagawa, Japan.
Kazuki IwamotoFujitsu Limited, Kanagawa, Japan.
Ryo IwamuraFujitsu Limited, Kanagawa, Japan.
Shigetaka TomaFujitsu Limited, Kanagawa, Japan.
Kaho OguraFujitsu Limited, Kanagawa, Japan.
Masahiro KimuraFujitsu Limited, Kanagawa, Japan.
Hokuto MorohoshiDepartment of Hygiene, Public Health and Preventive Medicine, Showa University School of Medicine, Tokyo, Japan.
Akio NakamuraOffice of Medical Information Technology, Showa University, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To assist physicians in predicting diseases, most natural language processing (NLP) models have focused on progress notes in electronic medical records with full descriptions from the initial stage of patient diagnosis to the final stage of discharge. However, accurately predicting diseases in the early stage using initial notes is challenging due to limited information. To address this, a text-numerical hybrid method is developed to improve disease prediction accuracy. The method identifies "Reliably predicted diseases (RPD)" that can be robustly predicted in the NLP and Random Forest models even if there are missing values in the numerical data or the amount of text data is small. Results show that, among the predicted disease groups of the two models, diseases matching the RPD are preferentially adopted and integrated. Precision@10 reveals that our developed method has a relatively higher accuracy of 67.0% than the traditional NLP model.

Indexed as

Electronic Health RecordsNatural Language ProcessingHumans

Identifiers

PMID40417511
PMCPMC12099446

What OpenQuestion holds

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Registered trials

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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.