ArticlePloS one2019
Using path signatures to predict a diagnosis of Alzheimer's disease.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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.
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.
Who cites it
10 citing papers in PubMed.
- Evoked emotions in anorexia nervosa: neural and behavioural correlates of social-emotional processing.Translational psychiatry · 2026Article
- Predicting high dengue incidence in municipalities of Brazil using path signatures.Scientific reports · 2025Article
- Deep multimodal representations and classification of first-episode psychosis via live face processing.Frontiers in psychiatry · 2025Article
- Signature methods for brain-computer interfaces.Scientific reports · 2023Article
- Harnessing the potential of machine learning and artificial intelligence for dementia research.Brain informatics · 2023Review
- Neural-signature methods for structured EHR prediction.BMC medical informatics and decision making · 2022Article
- Identifying psychiatric diagnosis from missing mood data through the use of log-signature features.PloS one · 2022Article
- Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders.Sensors (Basel, Switzerland) · 2020Article
- Influence of medical domain knowledge on deep learning for Alzheimer's disease prediction.Computer methods and programs in biomedicine · 2020Article
- Multi-time-point data preparation robustly reveals MCI and dementia risk factors.Alzheimer's & dementia (Amsterdam, Netherlands) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The path signature is a means of feature generation that can encode nonlinear interactions in data in addition to the usual linear terms. It provides interpretable features and its output is a fixed length vector irrespective of the number of input points or their sample times. In this paper we use the path signature to provide features for identifying people whose diagnosis subsequently converts to Alzheimer's disease. In two separate classification tasks we distinguish converters from 1) healthy individuals, and 2) individuals with mild cognitive impairment. The data used are time-ordered measurements of the whole brain, ventricles and hippocampus from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We find two nonlinear interactions which are predictive in both cases. The first interaction is change of hippocampal volume with time, and the second is a change of hippocampal volume relative to the volume of the whole brain. While hippocampal and brain volume changes are well known in Alzheimer's disease, we demonstrate the power of the path signature in their identification and analysis without manual feature selection. Sequential data is becoming increasingly available as monitoring technology is applied, and the path signature method is shown to be a useful tool in the processing of this data.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.