Overview: In this assignment, you will write a (3 pages) addressing artificial intelligence according to the prompt below. 


  1. Read and review the material attached
  2. Discern the similarities and differences between artificial intelligence, machine learning, and predictive analytics. 
  3. Explain the relationship between artificial intelligence, machine learning, and predictive analytics.  

Transactions of the SDPS:
Journal of Integrated Design and Process Science
DOI 10.3233/JID200002

1092-0617/$27.50© 2020 – Society for Design and Process Science. All rights reserved. Published by IOS Press

Convergence of Artificial Intelligence Research in
Healthcare: Trends and Approaches

Thomas T.H. Wan *

Professor of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Taiwan
and Professor Emeritus of the Department of Health Management and Informatics, University of Center
Florida, Orlando, USA

Abstract A value-based strategy relies on the implementation of a patient-centered care system that will directly
benefit patient care outcomes and reduce costs of care. This paper identifies the trends and approaches to artificial
intelligence (AI) research in healthcare. The convergence of multiple disciplines in the conduct of healthcare research
requires partnerships to be established among academic scholars, healthcare practitioners, and industrial experts in
software design and data science. This collaborative work will greatly enhance the formulation of theoretically
relevant frameworks to guide empirical research and application, particularly relevant in the search for causal
mechanisms to reduce costly and avoidable hospital readmissions for chronic conditions. An example of implementing
patient-centered care at the community level is presented and entails the influence of the context, design, process,
performance and outcomes on personal and population health, employing AI research and informational technology.

Keywords: AI research, context-design-performance-outcomes framework, predictive analytics, shared decision
support, patient-centered care

1. Introduction

The Institute of Medicine (IOM) of the National Academies of Science has estimated that 44,000 to
98,000 Americans die annually due to preventable mistakes in healthcare each year (Kohn, Corrigan, &
Donaldson, 2000). The IOM has doggedly hounded the nation’s health care delivery system because it
“…has fallen far short in its ability to translate knowledge into practice and to apply new technology safely
and appropriately (Institute of Medicine, 2001)”. The IOM (2003) has made continuity of care a primary
goal of its comprehensive call for transforming the quality of care in the United States. In 2006, the
American College of Physicians (ACP) established continuity of care as a central theme for restructuring
or reengineering healthcare. Recent research of life-limited patients receiving patient-centered care
management showed a notable 38% reduction of hospital utilizations and a 26% reduction of overall costs
with high patient satisfaction (Sweeney, Waranoff, & Halpert, 2007). Thus, it is imperative to establish
scientific evidence in support of t


www.thelancet.com Vol 395 May 16, 2020 1579

Artificial intelligence and the future of global health
Nina Schwalbe*, Brian Wahl*

Concurrent advances in information technology infrastructure and mobile computing power in many low and
middle-income countries (LMICs) have raised hopes that artificial intelligence (AI) might help to address challenges
unique to the field of global health and accelerate achievement of the health-related sustainable development goals. A
series of fundamental questions have been raised about AI-driven health interventions, and whether the tools,
methods, and protections traditionally used to make ethical and evidence-based decisions about new technologies can
be applied to AI. Deployment of AI has already begun for a broad range of health issues common to LMICs, with
interventions focused primarily on communicable diseases, including tuberculosis and malaria. Types of AI vary, but
most use some form of machine learning or signal processing. Several types of machine learning methods are
frequently used together, as is machine learning with other approaches, most often signal processing. AI-driven
health interventions fit into four categories relevant to global health researchers: (1) diagnosis, (2) patient morbidity
or mortality risk assessment, (3) disease outbreak prediction and surveillance, and (4) health policy and planning.
However, much of the AI-driven intervention research in global health does not describe ethical, regulatory, or
practical considerations required for widespread use or deployment at scale. Despite the field remaining nascent,
AI-driven health interventions could lead to improved health outcomes in LMICs. Although some challenges of
developing and deploying these interventions might not be unique to these settings, the global health community will
need to work quickly to establish guidelines for development, testing, and use, and develop a user-driven research
agenda to facilitate equitable and ethical use.

AI is changing how health services are delivered in many
high-income settings, particularly in specialty care
(eg, radiology and pathology).1–3 This development has
been facilitated by the growing availability of large
datasets and novel analytical methods that rely on such
datasets. Concurrent advances in information technology
(IT) infrastructure and mobile computing power have
raised hopes that AI might also provide opportunities to
address health challenges in LMICs.4 These challenges,
including acute health workforce shortages and weak
public health surveillance systems, undermine global
progress towards achieving the health-related sustainable
development goals (SDGs).5,6 Although not unique to
such countries, these challenges are particularly relevant
given their contribution to morbidity and mortality.7,8

AI-driven health technologies could be used to

38 www.e-enm.org

Endocrinol Metab 2016;31:38-44
pISSN 2093-596X · eISSN 2093-5978


How to Establish Clinical Prediction Models
Yong-ho Lee1, Heejung Bang2, Dae Jung Kim3

1Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea; 2Division of Biostatistics, Department
of Public Health Sciences, University of California Davis School of Medicine, Davis, CA, USA; 3Department of Endocrinology
and Metabolism, Ajou University School of Medicine, Suwon, Korea

A clinical prediction model can be applied to several challenging clinical scenarios: screening high-risk individuals for asymp-
tomatic disease, predicting future events such as disease or death, and assisting medical decision-making and health education.
Despite the impact of clinical prediction models on practice, prediction modeling is a complex process requiring careful statisti-
cal analyses and sound clinical judgement. Although there is no definite consensus on the best methodology for model develop-
ment and validation, a few recommendations and checklists have been proposed. In this review, we summarize five steps for de-
veloping and validating a clinical prediction model: preparation for establishing clinical prediction models; dataset selection;
handling variables; model generation; and model evaluation and validation. We also review several studies that detail methods
for developing clinical prediction models with comparable examples from real practice. After model development and vigorous
validation in relevant settings, possibly with evaluation of utility/usability and fine-tuning, good models can be ready for the use
in practice. We anticipate that this framework will revitalize the use of predictive or prognostic research in endocrinology, leading
to active applications in real clinical practice.

Keywords: Clinical prediction model; Development; Validation; Clinical usefulness


Hippocrates emphasized prognosis as a principal component of
medicine [1]. Nevertheless, current medical investigation
mostly focuses on etiological and therapeutic research, rather
than prognostic methods such as the development of clinical
prediction models. Numerous studies have investigated wheth-
er a single variable (e.g., biomarkers or novel clinicobiochemi-
cal parameters) can predict or is associated with certain out-

comes, whereas establishing clinical prediction models by in-
corporating multiple variables is rather complicated, as it re-
quires a multi-step and multivariable/multifactorial approach to
design and analysis [1].
Clinical prediction models can inform patients and their
physicians or other healthcare providers of the patient’s proba-
bility of having or developing a certain disease and help them
with associated decision-making (e.g., facilitating patient-do


Analysing the power of deep learning techniques over the
traditional methods using medicare utilisation and provider data
Varadraj P. Gurupura, Shrirang A. Kulkarnib, Xinliang Liua, Usha Desai c and Ayan Nasird

aDepartment of Health Management and Informatics, University of Central Florida, Orlando, FL, USA; bSchool of
Computer Science and Engineering, Vellore Institute of Technology, Vellore, India; cDepartment of Electronics and
Communication Engineering, Nitte Mahalinga Adyanthaya Memorial Institute of Technology, Nitte, Udupi, India;
dUCF School of Medicine, University of Central Florida, Orlando, FL, USA

Deep Learning Technique (DLT) is the sub-branch of Machine
Learning (ML) which assists to learn the data in multiple levels of
representation and abstraction and shows impressive performance
on many Artificial Intelligence (AI) tasks. This paper presents a new
method to analyse the healthcare data using DLT algorithms and
associated mathematical formulations. In this study, we have first
developed a DLT to programme two types of deep learning neural
networks, namely: (a) a two-hidden layer network, and (b) a three-
hidden layer network. The data was analysed for predictability in
both of these networks. Additionally, a comparison was also made
with simple and multiple Linear Regression (LR). The demonstration
of successful application of this method is carried out using the
dataset that was constructed based on 2014 Medicare Provider
Utilization and Payment Data. The results indicate a stronger case
to use DLTs compared to traditional techniques like LR. Furthermore,
it was identified that adding more hidden layers to neural network
constructed for performing deep learning analysis did not have
much impact on predictability for the dataset considered in this
study. Therefore, the experimentation described in this article sets
up a case for using DLTs over the traditional predictive analytics. The
investigators assume that the algorithms described for deep learning
is repeatable and can be applied for other types of predictive ana-
lysis on healthcare data. The observed results indicate, the accuracy
obtained by DLT was 40% more accurate than the traditional multi-
variate LR analysis.

Received 16 April 2018
Accepted 30 August 2018

Deep Learning Technique
(DLT); medicare data;
Machine Learning (ML);
Linear Regression (LR);
Confusion Matrix (CM)


Methods involving Artificial Intelligence (AI) associated with Deep Learning Technique (DLT)
and Machine Learning (ML) are slowly but surely being used in medical and health infor-
matics. Traditionally, techniques such as Linear Regression (LR) (Nimon & Oeswald, 2013),
Analysis of Variance (ANOVA) (Kim, 2014), and Multivariate Analysis of Variance (MANOVA)
(Xu, 2014) (Malehi et al., 2015) have been used for predicting outcomes in healthcare.