Treatment Planning in Smart Medical: A Sustainable Strategy

Article information

Abstract

1. Introduction

In today’s society, healthcare has been upgraded to an important social issue related to people’s work and study. This past decade has witnessed the dramatic development of modern medical technologies by virtue of the wireless internet, the Internet of Things, and other ubiquitous technologies. Importantly, smart medical, which is one of the results of this big data era, emphasizes effective treatment planning and processing for various diseases that occur in patients. Generally speaking, the process of treatment planning begins after a doctor has initially diagnosed a disease. The doctor must determine if the patient’s health needs are best met by providing potential treatments and maintaining their healthcare. This question is becoming more complex than ever before because of the wide array of treatment modalities available. Particularly, smart medical aims to facilitate economic development, social progress, and environmental protection [1,2]. This is because medical organizations must take the responsibility to value natural resources by complying with regulations, while they also pursue their own maximum profit and work to reduce treatment complexities in this context.

As a result, designing an eco-efficient strategy for treatment planning is becoming a sustainable development issue. Typically, because sustainable treatment planning has multiple and mutually conflicting objectives, it is necessary and important to evaluate all available alternatives and to determine preferable strategies that conform to sustainable development.

1.1 Motivating Example

Fig. 1 shows a motivating example on the selection of treatment plans for a computer autism patient. In this example, suppose patient Joe suffers from ‘computer autism’ disease [3], which leads to obstacles in regards to communications and creates language barriers between people. First of all, Joe requests for medical treatment from the medical doctor, then, the medical doctor might carefully consider his situation and make selections from historical medical treatment plans. This means that many existing treatment plans might be selected for him—e.g., training intervention (applied behaviors analysis, TEACCH autism program, and interpersonal training methods) and medicine. However, some of these treatment plans cannot satisfy social and economical sustainability (i.e., higher medicine costs, expensive treatments, more pollution). Therefore, how to efficiently select the best treatment plan is a quite complicated problem. After a sustainable treatment plan is selected, it will be given to Joe.

Fig. 1

A motivating example on treatment planning for a computer autism patient.

2. Formalism on Treatment Planning and Solution Framework

The treatment plan is the road map that a patient will follow on his/her journey through treatment. The treatment planning system begins as soon as the initial assessments are completed. In fact, the potential treatment plans can vary for the same disease. Hence, it is necessary to have a formal and accurate treatment plan for each particular patient. A sustainable strategy for treatment planning can result in the benefits (http://www.accuray.com/solutions/treatment-planning) listed below.

We understand that a sustainable treatment plan must be selected quickly and confidently. By taking into account, a formalism of the addressed problem is described as:

where, f(*) is a comprehensive multi-objectives evaluation function, it might be a nonlinear function. Therefore, the problem of sustainable strategy for treatment planning is transformed into an optimization process upon this multi-objectives evaluation function that is associated with treatment cost, complexity, side effects, pollution, and so forth.

Table 1 lists the important variables and corresponding descriptions used throughout this paper.

Table 1

Important variables used in the paper

In smart medical systems or sustainable development medical industries, treatment planning is regarded as a fuzzy multiple criteria decision-making problem in which the fuzziness and uncertainty of subjective perception should be considered [1]. As for the evaluation of treatment plans, users usually employ natural language to express their thinking and subjective preferences. With natural language, the meaning of a word might be well defined, but when using the word as a label for a set, the boundaries within which objects do or do not belong to the set become fuzzy or vague.

Fig. 2 shows a generic solution framework for the problem addressed in this paper. Obviously, this framework contains two modules: Module 1, data preprocessing and representation and Module 2, data optimization and selection. In Module 1, the row medical data (numerical evaluation data) is preprocessed and tensorized as a 3-order tensor where the objective, treatment phase, and treatment plans are regarded as three dimensions. After the tensoriztion of medical data, we obtained the linguistic-represented tensor by establishing the membership functions for the linguistic terms and aggregating the calculated degree of membership on various terms (as shown in Module 2). We are going to elaborate the above-mentioned two modules in Sections 3 and 4, respectively.

Fig. 2

Solution framework for the problem addressed in this paper.

3. Tensor-Based Representation of the Evaluation of Medical Treatment Plans

This section is devoted to presenting the conceptual structure of the three-dimensional evaluation model. As described in the introduction section, the objectives of the sustainable medical treatment plan are to provide an economical and social sustainable treatment scheme. An efficient treatment plan should save the design cost, reduce medical pollution, and lower the side-effects for patients during the different life cycle of the selected treatment plan, such as medicine selection, diagnosis, and recovery processing. Based on these motivations, a three-dimensional evaluation model for a treatment plan was constructed with a 3-order tensor [9] and is depicted as shown below.

Fig. 3 shows a 3-order tensor equipped with the 3 dimensions of: objectives, treatment lifecycle (treatment phase), and treatment plans.

Fig. 3

Representation of evaluation on medical treatment plan with three-dimensional evaluation model.

4. The Proposed Strategy

Smart medical industries emphasize users’ intelligence and experiences, an efficient treatment plan evaluation is facilitating these industries sharply [10,11]. During the evaluation of a treatment plan, users usually describe and evaluate them in natural language [8], such as

Based on the above observations, the question of, “Which treatment plan is the sustainable one and how should they be evaluated?” naturally arises. To answer this question, this section presents a sustainable strategy for treatment planning based on a three-dimension fuzzy evaluation model. Our proposed strategy is composed of the steps listed below.

4.1 Establishing the Membership Functions

Fully integrating with users’ objective opinions, we investigated the frequently used fuzzy linguistic terms “worst,” “worse,” “average,” “better,” and “best,” which are normally used to evaluate the treatment plans in terms of different objectives during different phases of it.

Let L = {l₁,l₂,...l₅} be the set of fuzzy linguistic terms, P = {p₁,p₂,p₃} be the different phases of the treatment plan, and O = {o₁,o₂,o₃} be the evaluation objectives. The corresponding numerical evaluation matrix for a given treatment plan T_k is then expressed as Eijk={eijk}. Consequently, the membership functions for all possible treatment plans are characterized with triangle membership functions [12–15], as shown in Fig. 4.

Fig. 4

The membership functions for evaluation of treatment plan.

1. For the linguistic term “worst,” the membership function is as follows:

   (2) μijl1(eij)={1eij≤r1(r2-eij)/(r2-r1)r1<eij≤r20eij≥r2

2. For the linguistic terms “worse,” “average,” and “better,” the membership functions are unified as follows:

   (3) μijl2∣3∣4(eij)={(eij-rl-1)/(rl-rl-1)rl-1≤eij≤rl(rl+1-eij)/(rl+1-rl)rl<eij≤rl+10eij∉(rl-1,rl+1)

3. For the linguistic term “best,” the membership function is defined as:

   (4) μijl5(eij)={1eij≥r5(eij-r4)/(r5-r4)r4<eij≤r50eij≤r4

4.4 Algorithm

Based on the above-proposed strategy, the corresponding algorithm is devised, as shown in Algorithm 1.

Algorithm 1

A Sustainable Algorithm for Treatment Planning in Smart Medical.

In this algorithm, the inputs are a set of treatment plans T, phases of treatment plan P, evaluations for a given treatment plan T_k, termed eijk, system parameters r₁,r₂,r₃,r₄,r₅ and weights w₁,w₂,w₃. The output is the most sustainable medical treatment plan T_Sustainable. Line 1 initializes two variables pro and sum that are used for storing the results of Ai{V} and Ai{H}. Lines 2–6 are for obtaining the vertical aggregation Ai{V}. Lines 7–11 are for calculating the horizontal aggregation Ai{V}. Finally, Lines 12–14 are in charge of re-ranking treatment plans according to the Ai{H}, that is to say, it will return the maximum Ai{H}.

5. Case Study

This section presents a case study on a sustainable medical treatment planning service for computer autism in smart medical, which includes the three dimensions of: a treatment plan, treatment phase, and objectives. Similar to Fig. 3, we collected the existing five mainstream treatment plans T₁,T₂,...T₅ and the evaluations for each treatment plan T_i. These numerical evaluation values can be stored with a 3-order tensor, as shown in Fig. 5.

Fig. 5

An illustration for selecting the sustainable treatment plan for patients with computer autism.

For the sake of better illustration, the parameters r₁,r₂,r₃,r₄,r₅ were set as 0.2, 0.4, 0.6, 0.8, 1 in this case study. After fuzzification, we were able to easily obtain a reasonable linguistic-expressed evaluation, as shown in Fig. 5. As a matter of fact, no matter what we set the weights for the treatment phases, the treatment plan T₂ was selected as the most sustainable one for older acute myocardial infarction patients by using the vertical and horizontal aggregations. This coincides with the user’s intuition since the treatment plan T₂ exhibits the best benefits on the aspects of the environment, the economy, and patients’ health.

This case study is composed of three main objectives: 1) to represent the evaluation data with a 3-order tensor; 2) to establish the membership functions for various linguistic terms that are used for expressing the evaluations on the treatment plans; and 3) to obtain a sustainable one via aggregations. Overall, it made possible to model the complex medical evaluation data with tensor and to convert it into a linguistic-expressed tensor.

6. Conclusions

Aiming to evaluate and select the most sustainable option from all possible treatment plans for a certain disease, this paper studies a practical issue on treatment planning in smart medical. First, this paper presented a three-dimensional evaluation model and then proposed a sustainable strategy for medical treatment planning based on a three-dimensional fuzzy evaluation model. Furthermore, a concrete case study on the disease ‘computer autism’ was provided for demonstrating the feasibility of the proposed approach. The results analysis of the case study embodied good consistency with users’ intuitions. It is believed that this research can provide good insights for the development of smart medical in the future.

Article information Continued