We present a novel architecture for an AI system that allows a priori knowledge to combine with deep learning. In traditional neural networks, all available data is pooled at the input layer. Our alternative neural network is constructed so that partial representations (invariants) are learned in the intermediate layers, which can then be combined with a priori knowledge or with other predictive analyses of the same data. This leads to smaller training datasets due to more efficient learning. In addition, because this architecture allows inclusion of a priori knowledge and interpretable predictive models, the interpretability of the entire system increases while the data can still be used in a black box neural network. Our system makes use of networks of neurons rather than single neurons to enable the representation of approximations (invariants) of the output.
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Background: Previous studies found that 40-60% of the sarcoidosis patients suffer from small fiber neuropathy (SFN), substantially affecting quality of life. SFN is difficult to diagnose, as a gold standard is still lacking. The need for an easily administered screening instrument to identify sarcoidosis-associated SFN symptoms led to the development of the SFN Screening List (SFNSL). The usefulness of any questionnaire in clinical management and research trials depends on its interpretability. Obtaining a clinically relevant change score on a questionnaire requires that the smallest detectable change (SDC) and minimal important difference (MID) are known. Objectives: The aim of this study was to determine the SDC and MID for the SFNSL in patients with sarcoidosis. Methods: Patients with neurosarcoidosis and/or sarcoidosis-associated SFN symptoms (N=138) included in the online Dutch Neurosarcoidosis Registry participated in a prospective, longitudinal study. Anchor-based and distribution-based methods were used to estimate the MID and SDC, respectively. Results: The SFNSL was completed both at baseline and at 6-months’ follow-up by 89/138 patients. A marginal ROC curve (0.6) indicated cut-off values of 3.5 points, with 73% sensitivity and 49% specificity for change. The SDC was 11.8 points. Conclusions: The MID on the SFNSL is 3.5 points for a clinically relevant change over a 6-month period. The MID can be used in the follow-up and management of SFN-associated symptoms in patients with sarcoidosis, though with some caution as the SDC was found to be higher.
Background: Healthcare practitioner beliefs influence patients’ beliefs and health outcomes in musculoskeletal (MSK) pain. A validated questionnaire based on modern pain neuroscience assessing Knowledge and Attitudes ofPain (KNAP) was unavailable.Objectives: The aim of this study was to develop and test measurement properties of KNAP.Design: Phase 1; Development of KNAP reflecting modern pain neuroscience and expert opinion. Phase 2; a crosssectional and longitudinal study among Dutch physiotherapy students.Method: In the cross-sectional study (n = 424), internal consistency, structural validity, hypotheses testing, and Rasch analysis were examined. Longitudinal designs were applied to analyse test-retest reliability (n = 156), responsiveness, and interpretability (n = 76).Results: A 30-item KNAP was developed in 4 stages. Test-retest reliability: ICC (2,1) 0.80. Internal consistency: Cronbach’s α 0.80. Smallest Detectable Difference 90%: 4.99 (4.31; 5.75). Structural validity: exploratory factor analysis showed 2 factors. Hypotheses testing: associations with the Pain Attitudes and Beliefs Scale for Physiotherapists biopsychosocial subscale r = 0.60, with biomedical subscale r = 0.58, with the Neurophysiology of Pain Questionnaire r = 0.52. Responsiveness: 93% improved on KNAP after studying pain education. MinimalImportant Change: 4.84 (95%CI: 2.77; 6.91).Conclusions: The KNAP has adequate measurement properties. This new questionnaire could be useful to evaluate physiotherapy students’ knowledge and attitudes of modern pain neuroscience that could help to create awareness and evaluate physiotherapy education programs, and ultimately provide better pain management.
