Reinforcement learning strategies in cancer chemotherapy treatments: A review.

Background And Objective: Cancer is one of the major causes of death worldwide and chemotherapies are the most significant anti-cancer therapy, in spite of the emerging precision cancer medicines in the last 2 decades. The growing interest in developing the effective chemotherapy regimen with optimal drug dosing schedule to benefit the clinical cancer patients has spawned innovative solutions involving mathematical modeling since the chemotherapy regimens are administered cyclically until the futility or the occurrence of intolerable adverse events. Thus, in this present work, we reviewed the emerging trends involved in forming a computational solution from the aspect of reinforcement learning.

Supervised Optimal Chemotherapy Regimen Based on Offline Reinforcement Learning.

In recent years, reinforcement learning (RL) has achieved a remarkable achievement and it has attracted researchers' attention in modeling real-life scenarios by expanding its research beyond conventional complex games. Prediction of optimal treatment regimens from observational real clinical data is being popularized, and more advanced versions of RL algorithms are being implemented in the literature. However, RL-generated medications still need careful supervision of expertise parties or doctors in healthcare.

IMU Consensus Exception Detection with Dynamic Time Warping-A Comparative Approach.

A dynamic time warping (DTW) algorithm has been suggested for the purpose of devising a motion-sensitive microelectronic system for the realization of remote motion abnormality detection. In combination with an inertial measurement unit (IMU), the algorithm is potentially applicable for remotely monitoring patients who are at risk of certain exceptional motions. The fixed interval signal sampling mechanism has normally been adopted when devising motion detection systems; however, dynamically capturing the particular motion patterns from the IMU motion sensor can be difficult.