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FACULTY OF ENGINEERING AND INFORMATION
TECHNOLOGY
Health Recommender System for
Personalized Clinical Prescription
Qian Zhang
A thesis submitted for the Degree of
Doctor of Philosophy
University of Technology, Sydney
July, 2018
i
ABSTRACT
General practitioners are faced with a great challenge of clinical prescription owing
to the increase of new drugs and their complex functions to different diseases. A
personalized recommender system can help practitioners discover mass of medical
knowledge hidden in history medical records to deal with information overload
problem in prescription. To support practitioner’s decision making in prescription, a
literature review is conducted to find the research gaps and propose research
questions in implementing health recommender system for prescription. Three
research gaps are considered in this system framework: (1) to define a patient’s need
by giving his/her symptom, (2) to mine features from free text in medical records and
(3) to analyze temporal efficiency of drugs. The recommender system is expected to
help general practitioners to improve their efficiency and reduce risks of making
errors in daily clinical consultation with patients.
Keywords:
recommender
systems,
personalization, text mining
ii
case-based
reasoning,
healthcare
ACKNOWLEDGEMENTS
Insert acknowledgements here
iii
TABLE OF CONTENTS
ABSTRACT ................................................................................................................. ii
ACKNOWLEDGEMENTS ........................................................................................ iii
TABLE OF CONTENTS ............................................................................................ iv
LIST OF FIGURES ..................................................................................................... v
LIST OF TABLES ...................................................................................................... vi
Chapter 1 Introduction ................................................................................................. 1
1.1 Background ........................................................................................................ 1
Chapter 2 Literature Review ........................................................................................ 3
2.1 Recommender System........................................................................................ 3
2.2 Health Recommender System ............................................................................ 4
2.3 Application of RS techniques to HRS ................................................................ 5
2.4 Research Gap in Literature................................................................................. 7
2.4.1 The relationship between patient and drug ................................................. 7
2.4.2 Unstructured data in medical records .......................................................... 7
2.4.3 Temporal effectiveness of drug................................................................... 8
Chapter 3 A hybrid recommendation approach using feature mined from free text ... 9
Chapter 4 Temporal recommendation on drugs via long-and short-term efficiency . 10
Chapter 5 Multi-label classification based drug recommender system ..................... 11
Chapter 6 A hybrid recommender system for personalized clinical prescription ...... 12
Chapter 7 Conclusion and future study ...................................................................... 13
REFERENCES........................................................................................................... 14
APPENDIX A TITLE ................................................................................................ 20
APPENDIX B TITLE ................................................................................................ 21
iv
LIST OF FIGURES
v
LIST OF TABLES
vi
CHAPTER 1 INTRODUCTION
1.1 Background
Due to the large number of patients general practitioners (GPs) are meeting every
day, efficiency and accuracy of decision making are quite crucial in clinical
medicine. Prescription is an important element in every consultation with patient.
Because of the fast variation of diseases, numerous new drugs are developed to deal
with different situations occurred to patients. It is sometimes difficult to keep up to
date with plenty and complex drug information even for an experienced GP, let alone
the ones in internship. Moreover, contraindications of many drugs may bring chronic
harm to unsuitable patients or what’s worse may cause lethal effect. Therefore, GPs
are facing a big challenge in the information explosion era with many new drugs
flooded into market every year. To reduce mistakes and cut off the time consumed in
encounter with patients, there is a requirement of medical E-services to help GPs
search and select drugs when they are making decisions on prescription.
Some surveys show that GPs in clinics use an Electronic Medical Record
System (EMRS) mainly for prescribing together with ordering laboratory tests and
recording patient’s progress (McInnes, Saltman & Kidd 2006). Wildly used EMRS
lead to fast accumulation of medical data where potential medical knowledge is to be
extracted to guide GPs in prescription (Jensen, Jensen & Brunak 2012). Prescription
decision support to GPs is to supply them with personalized drug list based on
patients’ medical symptoms, test results and other characteristics collected by
EMRS. Similarly, to find appropriate item for specific customers in E-commerce is a
problem solved by personalized recommendation (Lu 2004).
Recommender systems (RS) aim to predict users’ potential interests on items
they haven’t bought according to users’ preferences (explicitly as ratings on
historical item and implicitly as social relations) thus making a personalized product
list for customers to choose (Lu et al. 2015). RS is broadly adopted to different areas
both in academia and in industry in the past two decades (Huang et al. 2012). Some
RSs have been developed to support medical prescription (Wiesner & Pfeifer 2010).
Collaborative filtering (CF) is used together with a trust-based relationship among
clinicians using drug frequency in prescribing to represent its rating of a clinician
(Begum et al. 2011). Moreover, since clinicians often solve a new problem based on
previous medical experience (solved case) with some adjustments implemented
(Ting et al. 2010), application of knowledge based technique in RS or specifically,
case-based reasoning (CBR) method is applied in this problem. CBR is refined by
association rule mining and Bayesian reasoning to integrate general knowledge from
relationship between clinical manifestation and prescribed medicine into cases
(Sesen et al. 2014; Ting et al. 2011). However, due to the patient privacy in
medicine, implementation of recommender system techniques in drug
recommendation has gained less attention (Ricci, Rokach & Shapira 2011).
Accordingly, a health recommender system (HRS) is ‘a specialization of a RS’
(Wiesner & Pfeifer 2014, p. 2582). Similarly, HRSs make predictions based on
patients’ medical records the same as RSs do based on user’s profile. But in the
context of medicine, HRS assists clinicians in decision making according to the
practical condition of a patient rather than user’s interest. The decision support for
clinicians can be presented in three ways. Chiefly, it helps clinicians to save time
1
when making diagnosis and prescribing medicines if the system can give proper
recommendation based on the similar history records in the database. Also, it reduces
the probability of false diagnosis if the system can acquire knowledge related to the
clinical case based on other clinician’s experience. Last but not least, this system
may help discover latent patterns that may be significant in medicine after analysis of
different patients’ cases. Consequently, exploiting a health recommender system is a
good way to help clinicians reduce irrelevant information.
Although these systems mentioned above filtered some unrelated drugs and
achieved good performance on test, some questions are remained to be solved. First,
unlike the items recommendation which depend on user’s preference in E-commerce,
drugs prescribed by GP are corresponding to patient’s symptoms. How to define a
patient’s need given a patient’s symptom is a primary problem in prescription
recommendation. Existing method choose CBR to evade this issue or simplify it to a
single one-to-one symptom-drug relationship. However, the relationship between
symptom and drug is complex to deal with but seriously influence the final
recommendation accuracy. Second, free text message in EMRS is not taken into
consideration. Free text written by GP is the most flexible part containing latent
characteristics of different patients, where distinguishable feature extracted are
valuable for personalization. Third, since numerous new drugs are developed each
year to improve therapy performance, analysing temporal efficiency of drugs will
help GP grasp the trend in the leading edge. The three aspects mentioned above will
affect the accuracy and practical utility of the prescription recommender system,
which are not well developed in previous research.
To solve the identified issues, a new hybrid recommender system which uses
artificial neural network and case-based reasoning to personalize prescriptions in a
comprehensive way, should be exploited. This system relates patient’s need to
different drug clusters includes meticulous patient features in free text and mines upto-date drug trends to support GP in drug decision making. And my research will
focus on developing methods based on RS techniques for clinical environment,
especially in personalized prescription.
2
CHAPTER 2 LITERATURE REVIEW
In this part, it begins with the detailed description of formulated recommendation and
categorization of different methods used in RSs. Then the differences between RS
and HRS will be provided based on the content of RS. At last, recent applications of
RS techniques used in HRS will be reviewed to give a light on how to exploit a
hybrid recommender system for in health service and why it is significant.
2.1 Recommender System
Recommender system (RS) is first applied in E-commerce to solve information
overload problem brought by Web 2.0 and quickly expanded to personalization of egovernment, e-learning, e-tourism etc. (Lu et al. 2015). They gave an overall
summarization on the development of RSs in the last two decades. Basically,
recommendation is to be formulated as follows (Adomavicius & Tuzhilin 2005):
𝑢: 𝐶 × 𝑆 → 𝑅
This is a utility function about how to define the preference of a user c ∈ C on a
specific item s ∈ S. Usually, the preference of a user is presented as ratings on items.
RS is to find the item for the user to maximize the utility function, formulated as
follows (Adomavicius & Tuzhilin 2005):
∀𝑐 ∈ 𝐶, 𝑎𝑟𝑔𝑚𝑎𝑥 𝑢(𝑐, 𝑠)
𝑠∈𝑆
Classification of RSs are different in papers, but here it is categorised in 4 types
according to methods used in the system: content-based, collaborative filtering (CF)based, knowledge-based and hybrid approaches (Adomavicius & Tuzhilin 2005;
Burke 2002; Ekstrand, Riedl & Konstan 2011; Trewin 2000). Different methods have
its own advantages and drawbacks, detailed information were summarised in table 1.
Hybrid method combines two or more techniques to overcome the limitation of any
individual one. There are seven different types of hybrids: ‘weighted, switching,
mixed, feature combination, feature augmentation, cascade and meta-level’ (Burke
2002, p. 340).
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Table 1 Comparison of different methods used in RSs
Method
Basic Description
Content
based
Recommendations
were based on the
content of the item
user preferred before.
CF based
Recommendations
were based on other
users who have similar
tastes.
Knowledge
based
Recommendations
were based on user's
needs and item's
function.
Hybrid
Method
Combination of the
above methods.
Representative
Techniques
Heuristic method:
Keyword-based
Vector Space
Model,
TF-IDF
Statistical model
and machine
learning method
Advantages
Drawbacks
1. New item can
be recommended
2. User
independent
3. Transparency
1. New user problem
2. Overspecialization
(lack of serendipity)
Memory-based:
Neighbour-based
CF
Top-N CF
Model-based:
Dimension
reduction
Latent semantic
Hybrid CF methods
Case-based
reasoning
Ontology-based
semantic web
1. No need to
consider the
content
2. Easy
implementation
1. Cold-start problem
2. Scalability
3. Sparsity
1. New item and
user can be
recommended
2. Reasonable
recommendation
and specific
needs can be
meet
Overcome the
limitations of
each single
method
1. Complex
knowledge database
management
2. monotonous
recommendation
Knowledge-based
CF
Content-based CF
Implementation
expense is relatively
high
2.2 Health Recommender System
Accordingly, a health recommender system (HRS) is ‘a specialization of a RS’
(Wiesner & Pfeifer 2014, p. 2582). Similarly, HRSs make predictions based on
patients’ medical records the same as RSs do based on user’s profile. But in the
context of medicine, HRS assists clinicians in decision making according to the
practical condition of a patient rather than user’s interest. The decision support for
clinicians can be presented in three ways. Chiefly, it helps clinicians to save time
when making diagnosis and prescribing medicines if the system can give proper
recommendation based on the similar history records in the database (Lu, Huang &
Duan 2012). Also, it reduces the probability of false diagnosis if the system can
acquire knowledge related to the clinical case based on other clinician’s experience
(Huang et al. 2012). Last but not least, this system may help discover latent patterns
that may be significant in medicine after analysis of different patients’ cases (Huang,
Lu & Duan 2012).
4
Unlike the user-item matrix in RS, medical record is the primary information in
health recommender system. Recommendations are based on user’s medical history.
Before applying the techniques used in RS to the medical context, it is necessary to
conduct analogy between RS and HRS. A classical e-commerce RS in most of
research papers is to recommend a movie to a customer based on the customer’s
rating to movies he has already seen. Besides, other information beyond the useritem rating matrix like user social network/demographic feature, movie
genre/tag/taxonomy can be considered as user’s profile and item’s profile. All the
factors mentioned in the scenario above can be matched in HRS. In HRS (Komkhao,
Lu & Zhang 2012),
1. Users are corresponding to patients;
2. Items are corresponding to medicines;
3. User’s profile is corresponding to patient’s disease history, age, weight and
other attribute in PHRS;
4. Item’s profile is corresponding to medicine’s attribute such as ADR(adverse
drug reaction) and dose;
5. Rating is corresponding to the frequency of a specific medicine found in the
database containing all the prescriptions.
Although the analogy of RS and HRS indicate the two systems to be quite
similar, there are still some different aspects between the two systems. For instance,
in the context of medicine, features extracted from patient’s medical history or from
medicine attribute have its own special meaning in clinic. Whether these features will
lead to final decision made by clinicians or they were chosen in coincidence should
be estimated by clinicians. The whole process should be under surveillance of a
professional expert like a general practitioner so that the risk of misjudgement by the
system can be lowest. Another difference is that patient’s privacy must be considered
in HRS. Despite privacy problem also exists in classical RS, the security level of
HRS is much higher since the data is related to patient’s medical history. Data from
hospital must be de-identified before they come to research or practical application.
This, from an aspect, explains the reason why the development of HRS is not fast. It
is because the research is restrained by the availability of data from clinical area
(Ricci, Rokach & Shapira 2011).
Relevant information or items can be recommended to users scale from
clinicians who are domain experts and need the system to keep them up-to-date to
patients who are laymen but care about their own health and want a better
explanation to their current health status (Kashfi 2011). Here, we focus on the HRS
for clinicians considering that decision making in medicine is crucial to human life
so that trust a machine may not be reasonable. Therefore, HRS is a tool to support
clinicians to improve their efficiency in diagnosis and prescription rather than to
replace them.
2.3 Application of RS techniques to HRS
In recent years, different data mining techniques of recommender system are applied
in medical data to acquire medical knowledge to support diagnosis, treatment or
prognosis (Hassan & Syed 2010). Wiesner & Pfeifer (2010) proposed a framework
of how HRS can be implemented in electronic medical system. This is a first
approach showing how the system can be put into practical use in medicine. And it is
about how to use free-texted encyclopedia and Wikipedia to manage medical
5
information and transform it to features that can be used in recommender system
techniques. Rodríguez et al. (2009) applied semantic web and ontology in HRS and a
hierarchy of ontology is given. Although these papers are among the first
applications of RS in the health area, their development is insufficient and no
experiment is implemented, thus making the conclusions to be doubted.
CF is the classical method used in RS and its performance is quite well during
the test from both academia and industry (Hassan & Syed 2010). According to the
analogies in Section 3.2, user-item matrix can be identified in HRS so that a CFmethod can be applied. Wiesner & Pfeifer (2014) argued CF is ‘an inappropriate
approach’ because the confidential health record should not be used in the system to
protect patients’ privacy. However, problem caused by privacy can be solved by deidentify the medical records or by other privacy friendly frameworks which are
proved effective in (Hoens et al. 2013).
Besides, some research about CF techniques used in HRS proved that CF is a
suitable method in the context of medicine. Hassan & Syed (2010) predicted the risks
in heart attacks using CF and compared this method with traditional classification
methods like SVM or LR. Huang et al. (2012) implemented CF algorithm together
with a trust-based relationship among clinicians into the medical knowledge
recommender system. Concretely, one of the case studies uses drug frequency in
prescribing to represent its rating of a clinician. The performance of the proposed
method is good, suggesting that the assumption above is valid and CF algorithm
performs well in practical use. Komkhao, Lu & Zhang (2012) adopted an
incremental CF algorithm, W-InCF, with Mahalanobis distance and fuzzy
membership and compares it with classical K-NN and K-means algorithm. Davis et
al. (2010) proposed a system named iCARE based on CF and ensemble learning to
make predictions of patient’s disease risks according to their disease history. All the
evidence above have proved the feasibility of CF application in HRS.
Except CF techniques, other techniques are also considered by researchers.
Since clinicians will solve a new problem based on the previous medical
experience(solved case) with some adjustments implemented (Begum et al. 2011).
And a medical case not only contains the medicine ‘ratings’ but also other kinds of
knowledge such as severity of a symptom or special health condition of a patient
related to the diagnosis and medicine prescription. The application of knowledge
based technique in RS or specifically, a case-based reasoning method in HRS seems
reasonable.
Case based reasoning has been used in clinical problems in (Kolodner &
Kolodner 1987). That is the first time to propose a framework based on CBS and test
this method by medical data. Many systems are developed since then based on CBS
and they are reviewed by(Bichindaritz & Marling 2006). Particularly, some recent
research also proved this method is applicable to decision making in clinical area. A
recommendation system to predict treatment to different diabetes cases according to
different features selected using rough set feature reduction (Lu, Huang & Duan
2012). A CBR method is also applied to determine radial dose in cancer treatment
and weights of features are optimized by Bee Colony Optimization (Teodorović,
Šelmić & Mijatović-Teodorović 2013).
6
2.4 Research Gap in Literature
Evidence shows that CF-based and knowledge-based techniques are feasible in
HRS. In addition, because medical cases and treatment plans in clinical context can
be quite complex and no simple and direct rules can be traced, a hybrid method could
be an advisable choice since this combination performs well in RS for restaurants
(Burke 2002). Therefore, a hybrid method of knowledge-based CF algorithm specific
to medical environment is to be exploited. And this is the whole view of what I will
do in my research.
2.4.1 The relationship between patient and drug
Literatures reviewed previously take lab test results in numerical form as patient
feature to retrieve most similar case in predicting the unknown case. The prediction
made in these literatures are quite simple either a binary yes-or-no decision or a
choice from less than 10 candidates. Usually, the suggested decisions are made from
treatments in broad categories like tablet/injection/surgery (Lu, Huang & Duan 2012;
Sesen et al. 2014). However, medical cases and treatment plans in clinical context
can be quite complex and no simple and direct rules can be traced. Decision making
in prescription is to be made among hundreds or thousands of drugs. Moreover,
combinations and relationships between different drugs will increase the difficulty of
the choice. Till now, no adequate system to support decision making of GP’s
prescription has been provided. Therefore, how to deal with the relationship between
patient feature space and drug he/she needs is the question to be answered.
This issue is worth researching because the accuracy of recommender system
for personalized prescription depends on how to mine this relationship. To get a
more reasonable and accurate recommendation list to guide GP, the relationship is
the most crucial and dispensable question to be solved.
2.4.2 Unstructured data in medical records
Unstructured data are totally un-predefined. Unlike structured data which have
unified standards and are easy to select and compare, unstructured data are usually
not easy to deal with. In medical records, lab test and patient’s demographic features
are structured data that have a range of values like the blood glucose concentration or
a binary option like gender. These features can be directly used as patient features
and used in data mining and machine learning methods. At the same time, clinicians
like to write free text to describe patient’s symptom or other issues to keep patients’
diversiform characters in record. However, unstructured data is not well explored and
most literatures focus on structured data and classical classification methods
(Esfandiari et al. 2014). As a consequence, how to mine useful features from free text
written by GP is another research question in this research area.
Some literatures study free text in medical records, but they either focused on
text classification or named entity recognition (e.g. finding keywords to classify the
records or finding the terms of disease or syndrome). Free text information is seldom
used in personalized prescription. Since clinicians are keen to use free text and much
dynamic information about patients are to be mined, it is important to dig up free text
to provide GPs with dynamic and personalized recommendation service.
7
2.4.3 Temporal effectiveness of drug
The amount of information is increasing nowadays, so user’s interests are
changing rapidly. The challenge of recommender systems in e-commerce is to keep
up with the changing of user’s preferences. This leads to a lot of research in
recommender system in temporal dynamics(Ding & Li 2005; Koren 2010).
Same situation occurs to health recommender system. The effectiveness of a
drug is changing with the fast development of medical research. For instance, a drug
can be eliminated once patients develop resistance to a certain drug; a drug can be
substituted by new drugs which have better therapy performance or less side effects.
With the changing of a curative effect of a drug, the frequency of this drug
prescribed by GP will be reduced once its clinical effect is not as good as new drugs.
This is time drifting data widely known as concept drift.
Referred to literature review, personalized recommender system for prescription
is not well developed, let alone to take temporal effectiveness of drugs into
consideration. Still, this problem is practical in medial prescription, especially for
patients with severe diseases such as cancer or AIDS.
8
CHAPTER
3
A HYBRID RECOMMENDATION
APPROACH USING FEATURE MINED FROM FREE
TEXT
9
CHAPTER 4 TEMPORAL RECOMMENDATION ON
DRUGS VIA LONG-AND SHORT-TERM EFFICIENCY
10
CHAPTER
5
MULTI-LABEL CLASSIFICATION
BASED DRUG RECOMMENDER SYSTEM
11
CHAPTER 6 A HYBRID RECOMMENDER SYSTEM
FOR PERSONALIZED CLINICAL PRESCRIPTION
12
CHAPTER 7 CONCLUSION AND FUTURE STUDY
13
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15
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APPENDIX A TITLE
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APPENDIX B TITLE
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