Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Making sense of personal health information: challenges for information visualization.

Health informatics journal - 1 Sept 2013

Faisal Sarah, Blandford Ann, Potts Henry W W

Abstract excerpt

This article presents a systematic review of the literature on information visualization for making sense of personal health information. Based on this review, five application themes were identified: treatment planning, examination of patients' medical records, representation of pedigrees and family history, communication and shared decision making, and life management and health monitoring. While there are...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Communication
Share to Decision Making
Share to Genetic Predisposition to Disease

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Health Behavior

Share to Life Style

Share to Medical Records

Share to Patient Participation

Source

Identifiers

PMID: 23981395
DOI: 10.1177/1460458212465213

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Making sense of personal health information: challenges for information visualization.PMID 23981395Read ↗

VisAGE: Integrating external knowledge into electronic medical record visualization.Semantic proximity Read ↗

V-Model: a new perspective for EHR-based phenotyping.Semantic proximity Read ↗

Using set visualization techniques to investigate and explain patterns of missing values in electronic health recordsSemantic proximity Read ↗

Lineage: Visualizing Multivariate Clinical Data in Genealogy GraphsSemantic proximity Read ↗

A systematic method for surveying data visualizations and a resulting genomic epidemiology visualization typology: GEViTSemantic proximity Read ↗

Perceptions and willingness of patients and caregivers on the utilization of patient-generated health data: a cross-sectional surveySemantic proximity Read ↗

MediSyn: uncertainty-aware visualization of multiple biomedical datasets to support drug treatment selectionSemantic proximity Read ↗

Visual-Conversational Interface for Evidence-Based Explanation of Diabetes Risk PredictionSemantic proximity Read ↗

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.Semantic proximity Read ↗

Data integration between clinical research and patient care: a framework for context-depending data sharing and in silico predictionsSemantic proximity Read ↗

The role of visual analytics in asthma phenotyping and biomarker discovery.Semantic proximity Read ↗

XAI-based Data Visualization in Multimodal Medical DataSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.