mindLAMPVis as a Co-Designed Clinician-Facing Data Visualization Portal to Integrate Clinical Observations From Digital Phenotyping in Schizophrenia: User-Centered Design Process and Pilot Implementation
Notice bibliographique
Résumé
Background: The potential of digital mental health to transform care delivery in low- and middle-income countries is well established. However, there remains the need to clinically and organically adapt current tools to local needs. This paper explores the process of creating a novel data visualization system for a digital mental health app and outlines the necessary steps in the process. This work demonstrates co-design involving collaboration between teams across geographies and disciplines based on clinicians' requirements. Objective: This study aims to co-design a visualization dashboard app for clinicians through a design study with a multidisciplinary team consisting of clinicians in Boston and Bangalore, mindLAMP software developers in Boston, and computer scientists with visualization expertise in Bangalore. The app is designed to visualize derivatives of both active and passive data of patients with schizophrenia to support the research contexts of digital psychiatry clinics in India. Methods: The mindLAMP app, already used in many countries today, is adapted to offer a new clinician-facing data visualization portal, mindLAMPVis. The novel web-based portal is designed to improve clinical integration for use in India. After building the new portal, the insights from this new portal are corroborated with known clinical observations of relapse using comparative visualization. The data were taken from the mindLAMP app and processed using multivariate analysis and dimensionality reduction to make it easy and manageable for clinicians to analyze. These techniques are integrated in mindLAMPVis, thus making it a locally co-designed, developed, and deployed tool. A feasibility study of the pilot implementation of the app was completed through a domain expert study with clinician-driven case studies. Results: To assess the system, we preloaded data from 24 patients with schizophrenia, including those with relapses. Through case examples focusing on relapse risk prediction in schizophrenia, mindLAMPVis is used to identify different visualization methods to compare different analytical results for each patient. In partnership with clinicians for co-designing the app, we explored the feasibility of a comparative visualization tool for discovering patterns across different time stamps for a single patient or any patterns across patients related to the relapse episode. As an example of reverse translation, mindLAMPVis offers new features that complement the original features of mindLAMP, highlighting the mutual benefit of software adaptation and collaborative design. Conclusions: mindLAMPVis is a tailored tool designed for use in India, but it can aid in identifying and comparing behavioral patterns that may indicate clinical risk for patients in any country. mindLAMPVis offers an example of how, through technical design, feedback, and real-world clinical testing, it is feasible to adapt current software tools to meet local needs and even exceed the use cases of the original technology. mindLAMPVis also successfully incorporates both active and passive digital phenotyping data.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,018 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».