Particle filter-based parameter estimation algorithm for prognostic risk assessment of progression in non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) is a malignant tumor that threatens human life and health. The development of a new NSCLC risk assessment model based on electronic medical records has great potential for reducing the risk of cancer recurrence. In this process, machine learning is a powerful method for automatically extracting risk factors and indicating impact weights for NSCLC deaths. However, when the number of samples reaches a certain value, it is difficult for machine learning to improve the prediction accuracy, and it is also challenging to use the characteristic data of subsequent patients effectively. Therefore, this study aimed to build a postoperative survival risk assessment model for patients with NSCLC that updates the model parameters and improves model accuracy based on new patient data. The model perspective was a combination of particle filtering and parameter estimation. To demonstrate the feasibility and further evaluate the performance of our approach, we performed an empirical analysis experiment. The study showed that our method achieved an overall accuracy of 92% and a recall of 71% for deceased patients. Compared with traditional machine learning models, the accuracy of the model estimated by particle filter parameters has been improved by 2%, and the recall rate for dead patients has been improved by 11%. Additionally, this study outcome shows that this method can better utilize subsequent patients' characteristic data, be more relevant to different patients, and help achieve precision medicine.

A new development in magnetic particle tracking technology and its application in a sheared dense granular flow.

This paper presents a new development in the magnetic particle tracking (MPT) technology that measures the translational and rotational motions of a small particle. A main advantage of MPT is that it is able to track objects in an opaque environment without using radioactive material or X-rays. In addition, it can provide information about the orientation and rotation of the object, which is difficult to obtain using other technologies. However, the reconstruction process of MPT using standard optimization approaches is very time consuming and, therefore, limits its applications. In this work, two new MPT reconstruction algorithms are examined and the results are compared with the optimization approach. The extended Kalman filter (EKF) algorithm has the same accuracy as the optimization method but is orders of magnitude faster. The speed of the sequential importance sampling approach is between those of the above two methods. The accuracy of position obtained using EKF is about 0.6%, and the uncertainty of orientation is less than 1.5°. The MPT is applied to measure a dense granular shear flow to investigate the spatial distribution of a tracer particle.

Implicit sampling for particle filters.

We present a particle-based nonlinear filtering scheme, related to recent work on chainless Monte Carlo, designed to focus particle paths sharply so that fewer particles are required. The main features of the scheme are a representation of each new probability density function by means of a set of functions of Gaussian variables (a distinct function for each particle and step) and a resampling based on normalization factors and Jacobians. The construction is demonstrated on a standard, ill-conditioned test problem.