Resonant acoustic rheometry for assessing plasma coagulation in bleeding patients.

Disordered hemostasis associated with life-threatening hemorrhage commonly afflicts patients in the emergency department, critical care unit, and perioperative settings. Rapid and sensitive hemostasis phenotyping is needed to guide administration of blood components and hemostatic adjuncts to reverse aberrant hemostasis. Here, we report the use of resonant acoustic rheometry (RAR), a technique that quantifies the viscoelastic properties of soft biomaterials, for assessing plasma coagulation in a cohort of 38 bleeding patients admitted to the hospital.

Intricacies of human-AI interaction in dynamic decision-making for precision oncology.

AI decision support systems can assist clinicians in planning adaptive treatment strategies that can dynamically react to individuals' cancer progression for effective personalized care. However, AI's imperfections can lead to suboptimal therapeutics if clinicians over or under rely on AI. To investigate such collaborative decision-making process, we conducted a Human-AI interaction study on response-adaptive radiotherapy for non-small cell lung cancer and hepatocellular carcinoma.

Resonant Acoustic Rheometry for Real Time Assessment of Plasma Coagulation in Bleeding Patients.

Disordered hemostasis associated with life-threatening hemorrhage commonly afflicts patients in the emergency room, critical care unit, and perioperative settings. Rapid and sensitive hemostasis phenotyping is needed to guide administration of blood components and hemostatic adjuncts to reverse aberrant coagulofibrinolysis. Here, resonant acoustic rheometry (RAR), a technique that quantifies the viscoelastic properties of soft biomaterials, was applied to assess plasma coagulation in a cohort of bleeding patients with concomitant clinical coagulation assays and whole blood thromboelastography (TEG) as part of their routine care.

Intricacies of Human-AI Interaction in Dynamic Decision-Making for Precision Oncology: A Case Study in Response-Adaptive Radiotherapy.

Background: Adaptive treatment strategies that can dynamically react to individual cancer progression can provide effective personalized care. Longitudinal multi-omics information, paired with an artificially intelligent clinical decision support system (AI-CDSS) can assist clinicians in determining optimal therapeutic options and treatment adaptations. However, AI-CDSS is not perfectly accurate, as such, clinicians' over/under reliance on AI may lead to unintended consequences, ultimately failing to develop optimal strategies.

WOOD: Wasserstein-Based Out-of-Distribution Detection.

The training and testing data for deep-neural-network-based classifiers are usually assumed to be sampled from the same distribution. When part of the testing samples are drawn from a distribution that is sufficiently far away from that of the training samples (a.k.

A clinical decision support system for AI-assisted decision-making in response-adaptive radiotherapy (ARCliDS).

Involvement of many variables, uncertainty in treatment response, and inter-patient heterogeneity challenge objective decision-making in dynamic treatment regime (DTR) in oncology. Advanced machine learning analytics in conjunction with information-rich dense multi-omics data have the ability to overcome such challenges. We have developed a comprehensive artificial intelligence (AI)-based optimal decision-making framework for assisting oncologists in DTR.

Influence of morphological variation on brain impact responses among youth and young adults.

Tissue-level brain responses to sport-related head impacts may be stronger predictors of brain injury risk than head kinematics alone. Despite the importance of accurate impact response estimation, the influence of head morphological variations has not been properly considered due to the limited sizes and shapes of existing computational head models. In this study, we developed 101 subject-specific finite element (FE) head-brain models based on CT scans and a parametric modeling approach to estimate tissue-level brain impact responses (maximal principal strain, MPS) under three head impact conditions.

Analysis of human motion variation patterns using UMPCA.

The rapid development of motion capture technologies has greatly increased the use of human motion data in many applications. This has increased the demand to have an effective means to systematically analyze those massive data in order to understand human motion variation patterns. This paper studies one typical type of motion data, which are recorded as multi-stream trajectories of human joints.

Development of seating accommodation models for soldiers in vehicles.

Data from a previous study of soldier driving postures and seating positions were analysed to develop statistical models for defining accommodation of driver seating positions in military vehicles. Regression models were created for seating accommodation applicable to driver positions with a fixed heel point and a range of steering wheel locations in typical tactical vehicles. The models predict the driver-selected seat position as a function of population anthropometry and vehicle layout.

A survey on statistical methods for health care fraud detection.

Fraud and abuse have led to significant additional expense in the health care system of the United States. This paper aims to provide a comprehensive survey of the statistical methods applied to health care fraud detection, with focuses on classifying fraudulent behaviors, identifying the major sources and characteristics of the data based on which fraud detection has been conducted, discussing the key steps in data preprocessing, as well as summarizing, categorizing, and comparing statistical fraud detection methods. Based on this survey, some discussion is provided about what has been lacking or under-addressed in the existing research, with the purpose of pinpointing some future research directions.