Sequential drug decision problems in long-term medical conditions: a case Study of Primary Hypertension Eunju Kim ba, ma, msc


Appendix 2. The target papers for the systematic review on approximate optimisation methods



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Appendix 2. The target papers for the systematic review on approximate optimisation methods





 

Author

Year

Optimisation problem

Optimisation

Method

Disease

CEA model

1

Rauner [192]

2010

To identify mitigation strategies that minimize the total costs.

o

The Pareto ant colony optimisation

Breast cancer

o

2

Zhao [190]

2011

To discover optimal individualized treatment regimens in clinical trial that maximize the overall survival time

o

Adaptive reinforcement learning approach (Q-learning)

Lung Cancer

x

3

van Gerven [60]

2007

To approximate the optimal treatment strategy, which maximizes the global utility defined as a discounted additive combination of the quality of life and the cost.

o

Single policy updating, single rule updating and simulated annealing algorithms

Oncology

o

4

Vahedi [187]

2009

To find a control strategy that minimizes the expected total discounted cost in the long run.

o

Probabilistic Boolean networks (PBNs)

Cancer

x

5

Tse [188]

2007

To optimise the multidrug cancer chemotherapy schedule, which minimizes the tumour size under a set of constraints.

o

Memetic algorithm

Cancer

x

6

Chi [429]

2008

To describe a data mining model for constructing an optimal diagnostic sequence that assists cost-effective sequential decisions.

o

Hill climbing and genetic algorithms

?

 

7

Martín-Guerrero [430]

2009

To individualize Erythropoietin dosages to optimise Hb levels in the long-term.

o

Reinforcement learning

Hemodialysis

x

8

Lee [129]

2008

To derive approximately optimal strategies (dialysis dose per week and hours) that maximize patient welfare.

o

Reinforcement learning

Dialysis (chronic kidney failure)

o

9

He [132]

2010

To identify the optimal dosage policy, which minimizes the total costs.

o

Dynamic programming

Controlled ovarian hyperstimulation

o


Appendix 3. Full search strategies of the systematic review on approximate optimisation methods

A3.1 Web of Science



Set

Search history

Results

A

((TI=("NP hard” or “nondeterministic polynomial-time hard” or sequen* or dynamic or "time-dependent" or Marov* or multistage* or multi-stage*) OR TS=("NP hard” or “nondeterministic polynomial-time hard” or sequen* or dynamic or "time-dependent" or Marov* or multistage* or multi-stage*)))

2,040,041

A'

(TI=(("NP hard” or “nondeterministic polynomial-time hard” or sequen* or dynamic or "time-dependent" or Marov* or multistage* or multi-stage*) near/5 (decision* or problem* or pathway* or polic* or strateg*)) OR TS=(("NP hard” or “nondeterministic polynomial-time hard” or sequen* or dynamic or "time-dependent" or Marov* or multistage* or multi-stage*) near/5 (decision* or problem* or pathway* or polic* or strateg*)))

53,400

A''

(TI=(("NP hard” or “nondeterministic polynomial-time hard” or sequen* or dynamic or "time-dependent" or Marov* or multistage* or multi-stage* or multidrug* or multi-drug* or individualiz*) near/5 (decision* or problem* or pathway* or polic* or strateg* or treatment* or regimen* or therap*)) OR TS=(("NP hard” or “nondeterministic polynomial-time hard” or sequen* or dynamic or "time-dependent" or Marov* or multistage* or multi-stage* or multidrug* or multi-drug* or individualiz*) near/5 (decision* or problem* or pathway* or polic* or strateg* or treatment* or regimen* or therap*)))

77,965

B

(TI=(heuristic* or metaheuristic* or algorithm* or (approximat* near/5 (approach* or method* or function* or search*))) OR TS=(heuristic* or metaheuristic* or algorithm* or (approximat* near/5 (approach* or method* or function* or search*))))

585,439

B'

(TI=((heuristic* or metaheuristic* or approximat* or iterative or cyclic or adaptive or backward) near/5 (algorithm* or approach* or method* or function* or search*)) OR TS=((heuristic* or metaheuristic* or approximat* or iterative or cyclic or adaptive or backward) near/5 (algorithm* or approach* or method* or function* or search*)))

172,268

C

(TI=(optim* or minim* or maxim*) OR TS=(optim* or minim* or maxim*))

2,064,178

A∩B

#1 AND #4

125,449

A'∩B'

#2 AND #5

6,011

A''∩B'

#3 AND #5

6,170

A∩B∩C

#1 AND #4 AND #6

51,068

A'∩B'∩C

#2 AND #5 AND #6

4,339

A''∩B'∩C

#3 AND #5 AND #6

4,342

A∩(B∪C)

#1 AND (#4 OR #6)

378,184

A'∩(B'∪C)

#2 AND (#5 OR #6)

21,771

A''∩(B'∪C)

#3 AND (#5 OR #6)

26,339

1) The row in grey is the final search result used for the systematic review.

A3.2. Scopus



Set

Search history

Results

A

TITLE-ABS-KEY("NP hard" OR "nondeterministic polynomial-time hard" OR sequen* OR dynamic OR "time-dependent" OR marov* OR multistage* OR multi-stage*)

1,948,450

A'

TITLE-ABS-KEY(("NP hard" OR "nondeterministic polynomial-time hard" OR sequen* OR dynamic OR "time-dependent" OR marov* OR multistage* OR multi-stage*) W/5 (decision* OR problem* OR pathway* OR polic* OR strateg*))

50,124

A''

TITLE-ABS-KEY(("NP hard" OR "nondeterministic polynomial-time hard" OR sequen* OR dynamic OR "time-dependent" OR marov* OR multistage* OR multi-stage* OR multidrug* OR multi-drug* OR individualiz*) W/5 (decision* OR problem* OR pathway* OR polic* OR strateg* OR treatment* OR regimen* OR therap*))

80,102

B

TITLE-ABS-KEY(heuristic* OR metaheuristic* OR algorithm* OR (approximat* W/5 (approach* OR method* OR function* OR search*)))

684,764

B'

TITLE-ABS-KEY((heuristic* OR metaheuristic* OR approximat* OR iterative OR cyclic OR adaptive OR backward) W/5 (algorithm* OR approach* OR method* OR function* OR search*))

233,930

C

TITLE-ABS-KEY(optim* OR minim* OR maxim*)

2,222,123

A∩B

#1 AND #4

119,961

A'∩B'

#2 AND #5

7,653

A''∩B'

#3 AND #5

7,817

A∩B∩C

#1 AND #4 AND #6

47,774

A'∩B'∩C

#2 AND #5 AND #6

5,394

A''∩B'∩C

#3 AND #5 AND #6

5,834

A∩(B∪C)

#1 AND (#4 OR #6)

338,674

A'∩(B'∪C)

#2 AND (#5 OR #6)

23,102

A''∩(B'∪C)

#3 AND (#5 OR #6)

28,578

1) The row in grey is the final search result used for the systematic review.


9.1


A3.3. Ovid

Set

Search history

Results

A

("NP hard" or "nondeterministic polynomial-time hard" or sequen$ or dynamic or "time-dependent" or Markov$ or multistage$ or multi-stage$).ab,kw,ti.

858,663

A'

(("NP hard" or "nondeterministic polynomial-time hard" or sequen$ or dynamic or "time-dependent" or Markov$ or multistage$ or multi-stage$) adj5 (decision$ or problem$ or pathway$ or polic$ or strateg$)).ab,kw,ti.

9,409

A''

(("NP hard" or "nondeterministic polynomial-time hard" or sequen$ or dynamic or "time-dependent" or Markov$ or multistage$ or multi-stage$ or multidrug$ or multi-drug$ or individualiz$) adj5 (decision$ or problem$ or pathway$ or polic$ or strateg$ or treatment$ or regimen$ or therap$)).ab,kw,ti.

30,134

B

(heuristic$ or metaheuristic$ or algorithm$ or (approximat$ adj5 (approach$ or method$ or function$ or search$))).ab,kw,ti.

109,279

B'

((heuristic$ or metaheuristic$ or approximat$ or iterative or cyclic or adaptive or backward) adj5 (algorithm$ or approach$ or method$ or function$ or search$)).ab,kw,ti.

25,113

C

(optim$ or minim$ or maxim$).ab,kw,ti.

961,438

A∩B

#1 AND #4

20,703

A'∩B'

#2 AND #5

248

A''∩B'

#3 AND #5

307

A∩B∩C

#1 AND #4 AND #6

6,414

A'∩B'∩C

#2 AND #5 AND #6

158

A''∩B'∩C

#3 AND #5 AND #6

182

A∩(B∪C)

#1 AND (#4 OR #6)

111,976

A'∩(B'∪C)

#2 AND (#5 OR #6)

1,990

A''∩(B'∪C)

#3 AND (#5 OR #6)

6,141

1) The row in grey is the final search result used for the systematic review.



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