This study addresses the characterization of normal gait and pathological deviations induced by neurological diseases, considering knee angular kinematics in the sagittal plane. We propose an unsupervised approach based on Dynamic Time Warping (DTW) to identify different normal gait profiles (NGPs) corresponding to real cycles representing the overall behavior of healthy subjects, instead of considering an average reference, as done in the literature. The obtained NGPs are then used to measure the deviations of pathological gait cycles from normal gait with DTW. Hierarchical Clustering is applied to stratify deviations into clusters. Results show that three NGPs are necessary to finely characterize the heterogeneity of normal gait and accurately quantify pathological deviations. In particular, we automatically identify which lower limb is affected for Hemiplegic patients and characterize the severity of motor impairment for Paraplegic patients. Concerning Tetraplegic patients, different profiles appear in terms of impairment severity. These promising results are obtained by considering the raw description of gait signals. Indeed, we have shown that normalizing signals removes the temporal properties of signals, inducing a loss of dynamic information that is crucial for accurately measuring pathological deviations. Our methodology could be exploited to quantify the impact of therapies on gait rehabilitation.

Background: Accurate mortality prediction for liver transplant candidates with hepatocellular carcinoma (HCC) remains a critical challenge. Traditional scoring systems, including Child–Pugh, Albumin–Bilirubin, Model for End-Stage Liver Disease (MELD), MELD-Na, MELD 3.0, and Alpha-fetoprotein scores, are widely used but often fail to provide precise risk assessments. This limitation arises from the dual burden of liver dysfunction and tumor progression, which complicates prognosis. Consequently, there is a need for a comprehensive approach addressing both considerations to better manage HCC patients. Methods: We propose an advanced machine learning-based scoring system exploiting Ensemble Learning and SHapley Additive exPlanations (SHAP) for a better understanding of key mortality risk factors. SHAP offers valuable insights into the decision-making process by providing both global and local explanations. By embedding SHAP values in the Uniform Manifold Approximation and Projection space, we perform supervised clustering to infer latent subgroups, providing a higher granularity on the contribution of key variables for mortality risk assessment. Results: Our system based on LightGBM outperforms conventional scores leveraging only 8 relevant variables selected by SHAP analysis. These variables respond to the challenging dual risk problem set in this work. With supervised clustering, we uncover 7 subgroups showing an increasing mortality risk level and a fine assessment of risk factors’ contribution. Conclusion: By contrast to existing studies, our approach offers an integrative data-driven framework for handling the dual risk challenge set by HCC patients with liver dysfunction. Also, it provides a valuable tool for a more precise risk evaluation that may guide treatment decisions and help monitoring patient progression.

This study addresses the characterization of normal gait and pathological deviations caused by neurological diseases. We focus on the angular knee kinematics in the sagittal plane and we propose to exploit Hidden Markov Models to build a statistical model of normal gait. Such model provides a log-likelihood score that quantifies gait quality. Hence allowing to assess deviations of pathological cycles from normal gait. Our approach allows a refined characterization of motor impairments of three different patients' groups. In particular, it detects the affected lower limb in Hemiparetic patients. Comparatively to the Gait Variable Score and a Dynamic Time Warping-based metric, our results show that our statistical method is more effective for finely quantifying pathological deviations. Finally, we show the potential use of our methodology to assess therapeutic impacts during gait rehabilitation, which represents a promising avenue for improving patient care.