Evidence map›Paper›PMID 26406881›Full record

ArticleComputers in biology and medicine2015

Machine learning classification of medication adherence in patients with movement disorders using non-wearable sensors.

Conrad S Tucker, Ishan Behoora, Harriet Black Nembhard, Mechelle Lewis, Nicholas W Sterling, Xuemei Huang

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computers in biology and medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 1 pooled it
3.3field-weighted citation impact, top 8% of its field
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

27 citing papers in PubMed, 1 synthesis or guideline pooled it, 86 citations in OpenAlex.

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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 at 2 institutions in 1 country.

Conrad S TuckerIndustrial and Manufacturing Engineering, Engineering Design, The Pennsylvania State University, University Park, PA 16802, USA; Computer Science and Engineering, The Pennsylvania State University, University Park, PA 16802, USA. Electronic address: ctucker4@psu.edu.
Ishan BehooraComputer Science and Engineering, The Pennsylvania State University, University Park, PA 16802, USA.
Harriet Black NembhardIndustrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA 16802, USA.
Mechelle LewisDepartment of Neurology, Penn State, Milton S. Hershey Medical Center, Hershey, PA 17033, USA.
Nicholas W SterlingDepartment of Neurology, Penn State, Milton S. Hershey Medical Center, Hershey, PA 17033, USA.
Xuemei HuangDepartment of Neurology, Penn State, Milton S. Hershey Medical Center, Hershey, PA 17033, USA.
Penn State Milton S. Hershey Medical Center · USPennsylvania State University · US

Funding

Multimodal MRI Markers of Nigrostriatal Pathology in Parkinson's DiseaseU01NS082151 · NINDS · PENNSYLVANIA STATE UNIV HERSHEY MED CTR · PI HUANG, XUEMEI · 2012 to 2016
$4.2M
Structural MRI marker(s) of Parkinson's Disease's ProgressionR01NS060722 · NINDS · PENNSYLVANIA STATE UNIV HERSHEY MED CTR · PI HUANG, XUEMEI · 2009 to 2013
$2.9M
NINDS NIH HHS R01 NS060722NINDS NIH HHS U01 NS082151
6 · The paper itself

Abstract

Medication non-adherence is a major concern in the healthcare industry and has led to increases in health risks and medical costs. For many neurological diseases, adherence to medication regimens can be assessed by observing movement patterns. However, physician observations are typically assessed based on visual inspection of movement and are limited to clinical testing procedures. Consequently, medication adherence is difficult to measure when patients are away from the clinical setting. The authors propose a data mining driven methodology that uses low cost, non-wearable multimodal sensors to model and predict patients' adherence to medication protocols, based on variations in their gait. The authors conduct a study involving Parkinson's disease patients that are "on" and "off" their medication in order to determine the statistical validity of the methodology. The data acquired can then be used to quantify patients' adherence while away from the clinic. Accordingly, this data-driven system may allow for early warnings regarding patient safety. Using whole-body movement data readings from the patients, the authors were able to discriminate between PD patients on and off medication, with accuracies greater than 97% for some patients using an individually customized model and accuracies of 78% for a generalized model containing multiple patient gait data. The proposed methodology and study demonstrate the potential and effectiveness of using low cost, non-wearable hardware and data mining models to monitor medication adherence outside of the traditional healthcare facility. These innovations may allow for cost effective, remote monitoring of treatment of neurological diseases.

Indexed as

Machine LearningMedication AdherenceAlgorithmsData CollectionData MiningGaitHumansMedical InformaticsModels, StatisticalMovement DisordersParkinson DiseasePatient SafetyPhysician-Patient RelationsSoftwareWalkingDecision support systemHealthcare dataHealthcare deliveryMachine learningMedication adherenceMultimodal sensorParkinson's disease (PD) diagnosisSmart healthcare system

Identifiers

PMID26406881
PMCPMC5729888
OpenAlexW1425868093

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.