IDMBD: Intelligent Diagnostic Modelling of Bipolar Disorder at its early onset.

K A Yashaswini, S Kokila, Arunkumar Balakrishnan, K Madhura, S Anbukkarasi, Raghavendra M Devadas

Abstract read

In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

6 authors.

K A YashaswiniManipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India.

S KokilaManipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India. kokila.s@manipal.edu.

Arunkumar BalakrishnanManipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India.

K MadhuraManipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India.

S AnbukkarasiManipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India.

Raghavendra M DevadasManipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The proposed study presents an Intelligent Diagnostic Modelling of Bipolar Disorder (IDMBD), which represents an underlying framework operating on a Mobile Computing Device (MCD). The approach is meant to acquire multi-modal heterogeneous features in order to profile the participants more effectively with the highest granularity. IDMBD adopts a standard scale for forecasting the criticality state of Bipolar Disorder (BD). The feature-based profiling is carried out based on five discrete attributes collected by MCD, which, after undergoing a series of analytical processing, is subjected to a deep neural network-based learning model. An extensive evaluation is being carried out in multiple combinations of both machine learning and deep learning models to find that IDMBD exhibits optimal performance when integrated with Random Forest and Decision Tree with respect to multiple performance metrics.

Indexed as

Bipolar disorderDeep neural networkFeatureForecastingMachine learningMobile computing device

Identifiers

PMID42288667

DOI10.1038/s41598-026-57662-4

PMCPMC13448807

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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.