A reinforcement learning and deep learning based intelligent system for the support of impaired patients in home treatment

Abstract A clinical treatment process typically carries out in two stages; i.e., hospital stay and treatment at home after hospitalization. The correct completion of the treatment process is essential, but it becomes challenging for elders and patients with any physical or cognitive disability since they need assistance in the execution of the treatment itself. This work presents an intelligent system able to provide automatic assistance to those patients that have to follow a planned treatment at home. The system can support the patient with both customized reminders whenever it is the time to take medication and alerts to avoid possible medication errors when the patient is going to assume an incorrect drug by mistake. The core of the proposed solution consists of a multi-agent system that relies on algorithms of both Reinforcement Learning and Deep Learning. Experimental results show that the system improves the quality of home assistance services reducing medication errors.

[1]  Kathleen Dracup,et al.  A text messaging intervention to promote medication adherence for patients with coronary heart disease: a randomized controlled trial. , 2014, Patient education and counseling.

[2]  Antonio Piccinno,et al.  EUDroid: a formal language specifying the behaviour of IoT devices , 2018, IET Softw..

[3]  S. De Geest,et al.  Adherence to Long-Term Therapies: Evidence for Action , 2003, European journal of cardiovascular nursing : journal of the Working Group on Cardiovascular Nursing of the European Society of Cardiology.

[4]  M. Perri,et al.  Adverse Outcomes Associated with Inappropriate Drug Use in Nursing Homes , 2005, The Annals of pharmacotherapy.

[5]  António J. S. Teixeira,et al.  Design and development of Medication Assistant: older adults centred design to go beyond simple medication reminders , 2017, Universal Access in the Information Society.

[6]  Jayesh Patil The Autonomous Pill Dispenser with Alarm and Mobile Notifications , 2019 .

[7]  S. Hanauer,et al.  Medication nonadherence and the outcomes of patients with quiescent ulcerative colitis. , 2003, The American journal of medicine.

[8]  K. Stockl,et al.  Impact of a text messaging pilot program on patient medication adherence. , 2012, Clinical therapeutics.

[9]  Alfredo Cuzzocrea,et al.  An Innovative Risk Assessment Methodology for Medical Information Systems , 2022, IEEE Transactions on Knowledge and Data Engineering.

[10]  Qiong Wu,et al.  AI empowered context-aware smart system for medication adherence , 2017 .

[11]  Milan Ramljak Smart home medication reminder system , 2017, 2017 25th International Conference on Software, Telecommunications and Computer Networks (SoftCOM).

[12]  David Andre,et al.  Model based Bayesian Exploration , 1999, UAI.

[13]  Benjamin Van Roy,et al.  A Tutorial on Thompson Sampling , 2017, Found. Trends Mach. Learn..

[14]  Antonio Coronato,et al.  A Reinforcement Learning Based Intelligent System for the Healthcare Treatment Assistance of Patients with Disabilities , 2019, I-SPAN.

[15]  Rebecca Schnall,et al.  A Multi-step Usability Evaluation of a Self-Management App to Support Medication Adherence in Persons Living with HIV , 2019, Int. J. Medical Informatics.

[16]  B. Tai,et al.  Improving medication adherence with adjuvant aromatase inhibitor in women with breast cancer: study protocol of a randomised controlled trial to evaluate the effect of short message service (SMS) reminder , 2018, BMC Cancer.

[17]  Michael Unser,et al.  Deep Convolutional Neural Network for Inverse Problems in Imaging , 2016, IEEE Transactions on Image Processing.

[18]  Antonio Coronato,et al.  Gait Anomaly Detection of Subjects With Parkinson’s Disease Using a Deep Time Series-Based Approach , 2018, IEEE Access.

[19]  Pill Care-The Smart Pill Box with Remind, Authenticate and Confirmation Function , 2018, 2018 International Conference on Emerging Trends and Innovations In Engineering And Technological Research (ICETIETR).

[20]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[21]  Giovanni Paragliola,et al.  Risk management for nuclear medical department using reinforcement learning algorithms , 2019, Journal of Reliable Intelligent Environments.

[22]  Andrew W. Moore,et al.  Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..

[23]  Giuseppe De Pietro,et al.  Reinforcement learning for intelligent healthcare applications: A survey , 2020, Artif. Intell. Medicine.

[24]  Mario Ciampi,et al.  A clustering based methodology to support the translation of medical specifications to software models , 2018, Appl. Soft Comput..

[25]  R. Reves,et al.  Noncompliance with directly observed therapy for tuberculosis. Epidemiology and effect on the outcome of treatment. , 1997, Chest.

[26]  James R. Carpenter,et al.  Care homes’ use of medicines study: prevalence, causes and potential harm of medication errors in care homes for older people , 2009, Quality and Safety in Health Care.

[27]  Antonio Coronato,et al.  Adaptive Treatment Assisting System for Patients Using Machine Learning , 2019, 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS).

[28]  Huai-Kuei Wu,et al.  A smart pill box with remind and consumption confirmation functions , 2015, 2015 IEEE 4th Global Conference on Consumer Electronics (GCCE).

[29]  Jen-Tzung Chien,et al.  Deep reinforcement learning for automated radiation adaptation in lung cancer , 2017, Medical physics.

[30]  Mohamed Abdelfattah,et al.  Smart drugs:Improving healthcare using Smart Pill Box for Medicine Reminder and Monitoring System , 2018, Future Computing and Informatics Journal.

[31]  Giuseppe De Pietro,et al.  A situation-aware system for the detection of motion disorders of patients with Autism Spectrum Disorders , 2014, Expert Syst. Appl..

[32]  Giuseppe De Pietro,et al.  Deep neural network for hierarchical extreme multi-label text classification , 2019, Appl. Soft Comput..

[33]  Harriet Black Nembhard,et al.  Machine learning classification of medication adherence in patients with movement disorders using non-wearable sensors , 2015, Comput. Biol. Medicine.

[34]  Hesham A. Hefny,et al.  An enhanced deep learning approach for brain cancer MRI images classification using residual networks , 2020, Artif. Intell. Medicine.

[35]  Peter Szolovits,et al.  Deep Reinforcement Learning for Sepsis Treatment , 2017, ArXiv.

[36]  Michael R. Cohen Medication Errors. , 2019, Nursing.