یک روش داده کاوی برای شناسایی دامنه توانبخشی اعصاب شناختی در بیماران پس از ضربه مغزی

Cognitive rehabilitation (CR) treatment consists of hierarchically organized tasks that require repetitive use of impaired cognitive functions in a progressively more demanding sequence. Active monitoring of the progress of the subjects is therefore required, and the difficulty of the tasks must be progressively increased, always pushing the subjects to reach a goal just beyond what they can attain. There is an important lack of well-established criteria by which to identify the right tasks to propose to the patient. In this paper, the NeuroRehabilitation Range (NRR) is introduced as a means of identifying formal operational models. These are to provide the therapist with dynamic decision support information for assigning the most appropriate CR plan to each patient. Data mining techniques are used to build data-driven models for NRR. The Sectorized and Annotated Plane (SAP) is proposed as a visual tool by which to identify NRR, and two data-driven methods to build the SAP are introduced and compared. Application to a specific representative cognitive task is presented. The results obtained suggest that the current clinical hypothesis about NRR might be reconsidered. Prior knowledge in the area is taken into account to introduce the number of task executions and task performance into NRR models and a new model is proposed which outperforms the current clinical hypothesis. The NRR is introduced as a key concept to provide an operational model identifying when a patient is experiencing activities in his or her Zone of Proximal Development and, consequently, experiencing maximum improvement. For the first time, data collected through a CR platform has been used to find a model for the NRR.

Acquired Brain Injury (ABI) of either vascular or traumatic nature is one of the most important causes of neurological disabilities. According to the World Health Organization, Traumatic Brain Injury (TBI) is the leading cause of death and disability in children and young adults around the world and is a factor in nearly half of all trauma deaths (Walsh, Donal, Stephen, & Muldoon, 2012). In Europe, brain injuries from trauma are responsible for more years of disability than any other cause (Maas, Stocchetti, & Bullock, 2008). Despite new techniques for early intervention and intensive ABI, both of which increase the survival rate, there is still no surgical or pharmacological treatment for the re-establishment of lost functions following brain injury. Cognitive rehabilitation (CR) is currently considered the therapeutic process for re-establishing functioning in everyday life (Pascual-Leone & et al., 2005). A typical CR program mainly provides exercises which require repetitive use of the impaired cognitive system in a progressively more demanding (Sohlberg, 2001) sequence of tasks. The rehabilitating impact of a task or exercise depends on the ratio between the skills of the treated patient and the challenges involved in the execution of the task itself. Thus, determining the correct training schedule requires a quite precise trade-off between sufficient stimulation and sufficiently achievable tasks, which is far from intuition, and is still an open issue, both empirically and theoretically ( Green & Bavelier, 2005). It is difficult to identify this maximum effective level of stimulation and therapists use their expertise in daily practice, without precise guidelines on these issues. In this work, the NeuroRehabilitation Range (NRR) is introduced as the conceptual framework to describe the degree of performance of a CR task that produces maximum rehabilitation effects. A data mining approach is used to induce an operational model for the NRR of CR tasks. The aim is to help create useful guidelines for CR therapists that can help them select the most appropriate tasks for each single patient at a given moment in their rehabilitation plan, as well as correctly to determine the most appropriate level of difficulty for the proposed task. The Sectorized and Annotated Plane (SAP) is proposed here as a visual tool to find both the NRR and operational definitions to be used in real clinical practice. Two data-driven methods to build the SAP are introduced and compared. One of them (DT-SAP) is based on a decision tree model, the other (Vis-SAP) on a visualization of available data that promotes model induction from a graphical representation. A quality criterion to assess NRR models is also introduced, based on the correct prediction ratio provided by the tool. The performance of NRR model obtained with both DT-SAP and Vis-SAP approaches is evaluated and the advantages and drawbacks are analyzed over a real application. Data comes from the PREVIRNEC© platform (Tormos, Garcia-Molina, Garcia Rudolph, & Roig, 2009) which contains rich data monitoring the CR process on real neurorehabilitation patients. The real performance of a representative cognitive task is analyzed under both approaches and discussed for a sample of patients following a CR treatment at Institut Guttmann (IG) hospital de Neurorehabilitació, Barcelona, Spain. The structure of the paper is: Section 2 briefly presents the state of the art. Section 3 presents the IG conceptual framework for the research of NRR. Section 4 introduces the analysis methodology and Section 5 its application to a typical cognitive rehabilitation task in the proposed framework. Section 6 presents a discussion of the results obtained and Section 7 the conclusions and future lines of research.

This work is a contribution towards the personalized, predictable, and data driven CR design from both a theoretical and practical point of view. From the theoretical point of view, the paper introduces a new concept, the NeuroRehabilitation Range (NRR) as the framework to describe the degree of performance of a CR task which produces maximum rehabilitation effects. The NRR contributes to provide an operational definition for the zone of maximum rehabilitation potential and represents an operationalization of the Zone of Proximal Development referred in Vygotsky (1934). Analytical and visual tools are also proposed in this paper, defined and validated, to find an operational definition of a NRR from a data driven approach. On the one hand, the SAP has been introduced as a general visualization tool to find areas with high probability of occurrence of a target event. A particular case of SAP for detecting cognitive improvement in relation with results and repetitions of a certain cognitive rehabilitation task is presented in the paper. For this particular application, the SAP identifies areas with high probability of cognitive improvement. Although SAP is not a complex concept, it has shown a great potential to find the NRR region of a cognitive rehabilitation task in a quick, simple and very intuitive way, which has shown to be highly useful at clinical practice level. Also, for the first time, the NRR is defined as a bivariate structure involving conditions in both results and repetitions of the tasks. Another contribution of the paper is to propose two different methodologies to build the SAP in a given real problem: Direct construction of SAP by visualization of raw data (Vis-SAP method); and DT-SAP, which is based on decision-tree induction and could be automated. Decision trees have been considered because their inherent structure is directly providing the NRR model, which is built as the OR of all branches bringing to a leaf labeled as improvement. Both methods effectively determine the areas where probability of improvement are higher; a statistical two-proportion test has been used to assess the goodness of the NRR models, by checking whereas the probability of improvement is significantly higher when tasks are performed according to NRR than out of it. Whereas DT-SAP is a deterministic method that can be automated, the Vis-SAP is a semi-deterministic method that requires visual inspection in its last step. However, it seems to produce better results in practical applications, as the incomplete sectorization of the plane in very homogeneous areas provided by Vis-SAP outperforms the results induced from a DT where the leaves are often contaminated, containing both improving and non-improving patients. The last theoretical contribution of this work is the definition of a quality criterion to assess NRR models, based on pooled confidence and pooled specificity. The defined criterion is based on the capacity of a NRR model to detect the patients improving with the execution of a task. This is somehow giving a global performance indicator, although ROC curves have also been used to test the quality of obtained models, and it confirms that both proposed methods outperform the univariate and static NRR [65,85] currently used by the experts, as well as that Vis-SAP performs slightly better than DT-SAP. Finally, all those elements have been applied to a real case study. The application shows how the proposed methodology could identify NRR for a given cognitive rehabilitation task, and how the NRR obtained provides clear guidelines to the therapists about the number of repetitions of the task to be proposed to the patient together with the acceptable range of performance desirable to maximize the effect of the rehabilitation. From the clinical point of view, main contributions of this paper are to show that repetition of tasks is really relevant for rehabilitation (as stated by Luria in 1978), to provide an intuitive tool that permits the therapists to obtain guidelines about how much repetitions of the task must be proposed to the patient, and to show that the desirable difficulty level of a task is specific for each task. This is an excellent complement to the previous state of the art in which some advances were done regarding how to manage the difficulty level of the tasks, but no works assessing repetitions were addressed. Clinicians established an initial hypothesis about the NRR, assuming it is fixed and task independent (NRR(T) = [65, 85]); these bounds have been defined according to CR therapists’ expertise. PREVIRNEC© allows a systematic pre and post evaluation of participants covering the major cognitive domains. This provides empirical data useful to validate or clarify clinical hypothesis. For the first time, data collected through PREVIRNEC© platform has been used to learn more about the NRR. Although the ratio of improvement of patients in that initial NRR was not low, this work provided evidence that a simple formulation for NRR regarding only the Results obtained is insufficient to identify the group of patients with better response to CR treatment. According to our results NRR cannot be defined by means of univariant analysis (considering only the Result of performing a task). A predictive model considering other implied covariables needs to be developed. The present analysis is a first attempt into that direction. It has been shown that the number of repetitions that a patient performs of a certain task is also relevant for the patient’s outcome, according to literature. Bidimensional NRR, depending not only on performances, but also on repetition, significantly improve the CR treatment design. On the other hand, the range of therapeutic performances might change from task to task. This work points to target a specific performance-range for each task, instead of the current [65, 85] range used for the whole set of cognitive tasks available. An old wondering of the Institute Guttmann was to better understand how NRR could be found, and since 2007, the institute has been leading research in this line. This work is providing objective criteria for NRR that can be integrated in daily clinical practice of the institution, as well as operationalized for PREVIRNEC© platform, that provides the support to verify clinical hypothesis. As discussed in previous section, the Vis-SAP provides a semi-deterministic criterion that outperforms DT-SAP, but the later is automatable. On the other hand, the proposed analysis considers each task individually Till now, CR plans are mainly built from scratch for every patient, on the basis of the expertise of therapist and the follow-up of the patient, as no standard guidelines are available in this domain yet. The findings from the present study led to new actionable knowledge in the field of rehabilitation practice, opening the door towards more precise, predictable and powerful CR treatments, customized for the individual patient. Some clinical hypothesis are being formulated by specialists on the basis of these results and currently under validation, as a previous step to the establishment of a methodology for personalized therapeutic interventions based on clinical evidence. As future research lines, the automatic construction of SAP still requires more work since decision trees imply, by construction, some intrinsic error taxes in every branch that will be always propagated to the NRR performance and automation from Vis-SAP has to be faced from scratch. This work is currently being enriched by analyzing how patients walk through the SAP areas (or sectors) during their rehabilitation process. This can be analyzed by connecting the points corresponding to a same patient in the SAP and finding prototypical patterns according to the form of the paths designed on the SAP. This dynamic analysis can be later generalized to find dynamic patterns on the global treatment of the patient involving the whole sequence of tasks performed during the treatment, and providing information about the possible positive interactions among tasks that empower the improvement capacity. Although the NRR models using number of executions and results seem to provide quite high sensitiveness and specificity, there are other factors supposed to be highly determinant of cognitive improvement, like task difficulty. Extension of the current proposals to include such other factors is currently being explored. Finally, obtained results are expected to be more interpretable by clinicians when other demographic and clinical variables are included in the model, e.g. participants’ educational level, age, time since injury, obtained results in pre-treatment evaluation.