Aiding Difficult and High-Stakes Medical Decision Making - Research on Parental Tracheostomy Decisions for Critically Ill Children

This dissertation illustrated the multiple approaches necessary to improving decision making in applied settings. It consists of three studies that aimed to understand high-stakes pediatric tracheostomy decisions and aid parents’ abilities to make these decisions. Chapter 1 involved an interview study of parents who had recently made a tracheostomy decision for their critically ill child. We found that parents were stressed and worried about future outcomes. They sought and desired information and emotional support for making this difficult decision. Despite these efforts, there seemed to exist opportunities to improve their understanding and forecasting of long-term challenges of a tracheostomy placement. Based on the literature of forecasting errors and narrative-form communication, Chapter 2 involved a survey experiment to test a possible intervention approach. It showed that narratives describing challenges that affect the child’s and/or the family’s quality of life from the point of view of parents who had already experienced them reduced parents’ tendency to choose tracheostomy. The effect was particularly strong when the narratives focused on challenges in the child’s quality of life. These narratives also led to less optimistic forecasting. Based on findings from Chapters 1 and 2, Chapter 3 presented a user-centered design process used to create education materials that were designed to help parents understand major challenges in life after a tracheostomy placement. This dissertation extends the literature on using narrative-form communication to help decision makers anticipate future experiences and reduce forecasting errors. It also demonstrates the multiple types of research needed to develop educational communication that is ready for implementation in clinical settings.PHDPsychologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162973/1/haoyangy_1.pd

Patterns of Emoji Use for Individual Twitter Users: An Exploratory Analysis

This paper provides a new perspective of looking at emoji: Users. On 100 Twitter users represented by emojis from 200 tweets, an exploratory analysis is conducted to find patterns of emoji use for individual users. We use k-means clustering, principal component analysis and hierarchical clustering on different distance measures, with special focus on outlying users with unique using patterns. Our findings could give insights of how the ways people use emoji converge and diverge, show hidden connections between emojis, and help people better understand this novel language in the digital era.Master of Science in Information Scienc

Development and validation of a web-based predictive model for preoperative diagnosis of localized colorectal cancer and colorectal adenoma

BackgroundLocalized colorectal cancer (LCC) has obscure clinical signs, which are difficult to distinguish from colorectal adenoma (CA). This study aimed to develop and validate a web-based predictive model for preoperative diagnosis of LCC and CA.MethodsWe conducted a retrospective study that included data from 500 patients with LCC and 980 patients with CA who were admitted to Dongyang People’s Hospital between November 2012 and June 2022. Patients were randomly divided into the training (n=1036) and validation (n=444) cohorts. Univariate logistic regression, least absolute shrinkage and selection operator regression, and multivariate logistic regression were used to select the variables for predictive models. The area under the curve (AUC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were used to evaluate the performance of the model.ResultsThe web-based predictive model was developed, including nine independent risk factors: age, sex, drinking history, white blood cell count, lymphocyte count, red blood cell distribution width, albumin, carcinoembryonic antigen, and fecal occult blood test. The AUC of the prediction model in the training and validation cohorts was 0.910 (0.892–0.929) and 0.894 (0.862–0.925), respectively. The calibration curve showed good consistency between the outcome predicted by the model and the actual diagnosis. DCA and CIC showed that the predictive model had a good clinical application value.ConclusionThis study first developed a web-based preoperative prediction model, which can discriminate LCC from CA and can be used to quantitatively assess the risks and benefits in clinical practice

Potato: A Data-Oriented Programming 3D Simulator for Large-Scale Heterogeneous Swarm Robotics

Large-scale simulation with realistic nonlinear dynamic models is crucial for algorithms development for swarm robotics. However, existing platforms are mainly developed based on Object-Oriented Programming (OOP) and either use simple kinematic models to pursue a large number of simulating nodes or implement realistic dynamic models with limited simulating nodes. In this paper, we develop a simulator based on Data-Oriented Programming (DOP) that utilizes GPU parallel computing to achieve large-scale swarm robotic simulations. Specifically, we use a multi-process approach to simulate heterogeneous agents and leverage PyTorch with GPU to simulate homogeneous agents with a large number. We test our approach using a nonlinear quadrotor model and demonstrate that this DOP approach can maintain almost the same computational speed when quadrotors are less than 5,000. We also provide two examples to present the functionality of the platform.Comment: 4 pages, 5 figures, accepted by ICRA 2023 Workshop on "The Role of Robotics Simulators for Unmanned Aerial Vehicles

StructChart: Perception, Structuring, Reasoning for Visual Chart Understanding

Charts are common in literature across different scientific fields, conveying rich information easily accessible to readers. Current chart-related tasks focus on either chart perception which refers to extracting information from the visual charts, or performing reasoning given the extracted data, e.g. in a tabular form. In this paper, we aim to establish a unified and label-efficient learning paradigm for joint perception and reasoning tasks, which can be generally applicable to different downstream tasks, beyond the question-answering task as specifically studied in peer works. Specifically, StructChart first reformulates the chart information from the popular tubular form (specifically linearized CSV) to the proposed Structured Triplet Representations (STR), which is more friendly for reducing the task gap between chart perception and reasoning due to the employed structured information extraction for charts. We then propose a Structuring Chart-oriented Representation Metric (SCRM) to quantitatively evaluate the performance for the chart perception task. To enrich the dataset for training, we further explore the possibility of leveraging the Large Language Model (LLM), enhancing the chart diversity in terms of both chart visual style and its statistical information. Extensive experiments are conducted on various chart-related tasks, demonstrating the effectiveness and promising potential for a unified chart perception-reasoning paradigm to push the frontier of chart understanding.Comment: SimChart9K is available for downloading at: https://github.com/UniModal4Reasoning/SimChart9K. 21 pages, 11 figure

Interplay of Structural Chirality, Electron Spin and Topological Orbital in Chiral Molecular Spin Valves

Chirality has been a property of central importance in chemistry and biology for more than a century, and is now taking on increasing relevance in condensed matter physics. Recently, electrons were found to become spin polarized after transmitting through chiral molecules, crystals, and their hybrids. This phenomenon, called chirality-induced spin selectivity (CISS), presents broad application potentials and far-reaching fundamental implications involving intricate interplays among structural chirality, topological states, and electronic spin and orbitals. However, the microscopic picture of how chiral geometry influences electronic spin remains elusive. In this work, via a direct comparison of magnetoconductance (MC) measurements on magnetic semiconductor-based chiral molecular spin valves with normal metal electrodes of contrasting strengths of spin-orbit coupling (SOC), we unambiguously identified the origin of the SOC, a necessity for the CISS effect, given the negligible SOC in organic molecules. The experiments revealed that a heavy-metal electrode provides SOC to convert the orbital polarization induced by the chiral molecular structure to spin polarization. Our results evidence the essential role of SOC in the metal electrode for engendering the CISS spin valve effect. A tunneling model with a magnetochiral modulation of the potential barrier is shown to quantitatively account for the unusual transport behavior. This work hence produces critical new insights on the microscopic mechanism of CISS, and more broadly, reveals a fundamental relation between structure chirality, electron spin, and orbital