MamaBot: a System based on ML and NLP for supporting Women and Families during Pregnancy

Artificial intelligence is transforming healthcare with a profound paradigm shift impacting diagnostic techniques, drug discovery, health analytics, interventions and much more. In this paper we focus on exploiting AI-based chatbot systems, mainly based on machine learning algorithms and Natural Language Processing, to understand and respond to needs of patients and their families. In particular, we describe an application scenario for an AI-chatbot delivering support to pregnant women, mothers, and families with young children, by giving them help and instructions in relevant situations.

[1]  Illhoi Yoo,et al.  A Systematic Review of Healthcare Applications for Smartphones , 2012, BMC Medical Informatics and Decision Making.

[2]  John Woods,et al.  Survey on Chatbot Design Techniques in Speech Conversation Systems , 2015 .

[3]  Eric J. Topol,et al.  Stop the privatization of health data , 2016, Nature.

[4]  Amit Patil,et al.  Comparative study of cloud platforms to develop a Chatbot , 2017 .

[5]  Silvia Gabrielli,et al.  Addressing challenges in promoting healthy lifestyles: the al-chatbot approach , 2017, PervasiveHealth.

[6]  Jonathan White,et al.  ‘It's on my iPhone’: attitudes to the use of mobile computing devices in medical education, a mixed-methods study , 2012, BMJ Open.

[7]  B. Comendador,et al.  Pharmabot: A Pediatric Generic Medicine Consultant Chatbot , 2014 .

[8]  D. Lupton,et al.  Playing pregnancy: the ludification and gamification of expectant motherhood in Smartphone apps , 2015 .

[9]  Ralph Nanan,et al.  An emerging model of maternity care: smartphone, midwife, doctor? , 2014, Women and birth : journal of the Australian College of Midwives.

[10]  Lindsey E. Dayer,et al.  Smartphone medication adherence apps: potential benefits to patients and providers. , 2013, Journal of the American Pharmacists Association : JAPhA.

[11]  Joseph Weizenbaum,et al.  ELIZA—a computer program for the study of natural language communication between man and machine , 1966, CACM.

[12]  Stefano Marrone,et al.  Chatbots Meet eHealth: Automatizing Healthcare , 2017, WAIAH@AI*IA.

[13]  Anton Leuski,et al.  SHIHbot: A Facebook chatbot for Sexual Health Information on HIV/AIDS , 2017, SIGDIAL Conference.

[14]  Ronald Tamler,et al.  An evaluation of diabetes self-management applications for Android smartphones , 2012, Journal of telemedicine and telecare.

[15]  Joseph Flaherty,et al.  Evolution of Data Management Tools for Managing Self-Monitoring of Blood Glucose Results: A Survey of iPhone Applications , 2010, Journal of diabetes science and technology.

[16]  V.Manoj Kumar,et al.  Sanative Chatbot For Health Seekers , 2016 .

[17]  M. Sigman,et al.  Automated analysis of free speech predicts psychosis onset in high-risk youths , 2015, npj Schizophrenia.

[18]  Livia Bellina,et al.  Mobile cell-phones (M-phones) in telemicroscopy: increasing connectivity of isolated laboratories , 2009, Diagnostic pathology.

[19]  Robert Dale,et al.  The return of the chatbots , 2016, Natural Language Engineering.

[20]  Emily C. Pike,et al.  Mobile Phone Applications for the Care and Prevention of HIV and Other Sexually Transmitted Diseases: A Review , 2013, Journal of medical Internet research.