title: Machine learning classification of medication adherence in patients with movement disorders using non-wearable sensors.
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.
authors.affiliation: Industrial 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.
authors.forename: Conrad S
authors.initials: CS
authors.lastname: Tucker
authors.affiliation: Computer Science and Engineering, The Pennsylvania State University, University Park, PA 16802, USA.
authors.forename: Ishan
authors.initials: I
authors.lastname: Behoora
authors.affiliation: Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA 16802, USA.
authors.forename: Harriet Black
authors.initials: HB
authors.lastname: Nembhard
authors.affiliation: Department of Neurology, Penn State, Milton S. Hershey Medical Center, Hershey, PA 17033, USA.
authors.forename: Mechelle
authors.initials: M
authors.lastname: Lewis
authors.affiliation: Department of Neurology, Penn State, Milton S. Hershey Medical Center, Hershey, PA 17033, USA.
authors.forename: Nicholas W
authors.initials: NW
authors.lastname: Sterling
authors.affiliation: Department of Neurology, Penn State, Milton S. Hershey Medical Center, Hershey, PA 17033, USA.
authors.forename: Xuemei
authors.initials: X
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D003625:Data Collection; D057225:Data Mining; D005684:Gait / Q000187:drug effects; D006801:Humans; D000069550:Machine Learning; D008490:Medical Informatics; D055118:Medication Adherence; D015233:Models, Statistical; D009069:Movement Disorders / Q000628:therapy*; D010300:Parkinson Disease / Q000188:drug therapy*; D061214:Patient Safety; D010817:Physician-Patient Relations; D012984:Software; D016138:Walking
keywords: Decision support system; Healthcare data; Healthcare delivery; Machine learning; Medication adherence; Multimodal sensor; Parkinson's disease (PD) diagnosis; Smart healthcare system