Representation learning for uncertainty-aware clinical decision support

Over the last decade, there has been an increasing trend towards digitalization in healthcare, where a growing amount of patient data is collected and stored electronically. These recorded data are known as electronic health records. They are the basis for state-of-the-art research on clinical decision support so that better patient care can be delivered with the help of advanced analytical techniques like machine learning. Among various technical fields in machine learning, representation learning is about learning good representations from raw data to extract useful information for downstream prediction tasks. Deep learning, a crucial class of methods in representation learning, has achieved great success in many fields such as computer vision and natural language processing. These technical breakthroughs would presumably further advance the research and development of data analytics in healthcare. This thesis addresses clinically relevant research questions by developing algorithms based on state-of-the-art representation learning techniques. When a patient visits the hospital, a physician will suggest a treatment in a deterministic manner. Meanwhile, uncertainty comes into play when the past statistics of treatment decisions from various physicians are analyzed, as they would possibly suggest different treatments, depending on their training and experiences. The uncertainty in clinical decision-making processes is the focus of this thesis. The models developed for supporting these processes will therefore have a probabilistic nature. More specifically, the predictions are predictive distributions in regression tasks and probability distributions over, e.g., different treatment decisions, in classification tasks. The first part of the thesis is concerned with prescriptive analytics to provide treatment recommendations. Apart from patient information and treatment decisions, the outcome after the respective treatment is included in learning treatment suggestions. The problem setting is known as learning individualized treatment rules and is formulated as a contextual bandit problem. A general framework for learning individualized treatment rules using data from observational studies is presented based on state-of-the-art representation learning techniques. From various offline evaluation methods, it is shown that the treatment policy in our proposed framework can demonstrate better performance than both physicians and competitive baselines. Subsequently, the uncertainty-aware regression models in diagnostic and predictive analytics are studied. Uncertainty-aware deep kernel learning models are proposed, which allow the estimation of the predictive uncertainty by a pipeline of neural networks and a sparse Gaussian process. By considering the input data structure, respective models are developed for diagnostic medical image data and sequential electronic health records. Various pre-training methods from representation learning are adapted to investigate their impacts on the proposed models. Through extensive experiments, it is shown that the proposed models delivered better performance than common architectures in most cases. More importantly, uncertainty-awareness of the proposed models is illustrated by systematically expressing higher confidence in more accurate predictions and less confidence in less accurate ones. The last part of the thesis is about missing data imputation in descriptive analytics, which provides essential evidence for subsequent decision-making processes. Rather than traditional mean and median imputation, a more advanced solution based on generative adversarial networks is proposed. The presented method takes the categorical nature of patient features into consideration, which enables the stabilization of the adversarial training. It is shown that the proposed method can better improve the predictive accuracy compared to traditional imputation baselines

Trichinella spiralis: nurse cell formation with emphasis on analogy to muscle cell repair

Trichinella infection results in formation of a capsule in infected muscles. The capsule is a residence of the parasite which is composed of the nurse cell and fibrous wall. The process of nurse cell formation is complex and includes infected muscle cell response (de-differentiation, cell cycle re-entry and arrest) and satellite cell responses (activation, proliferation and differentiation). Some events that occur during the nurse cell formation are analogous to those occurring during muscle cell regeneration/repair. This article reviews capsule formation with emphasis on this analogy

Reliability Assessment of Nanoscale System on Chip Depending on Neturon Irradiation

The atmospheric neutron poses a serious hazard to nanoscale electronics reliability. Spallation neutron irradiations on a nanoscale system on chip (SoC) were conducted applying the China Spallation Neutron Source (CSNS), and the results were compared and analyzed using Monte Carlo simulation. The contribution from thermal neutron on the SoC single event effect (SEE) was analyzed. Analysis indicated the SoC atmospheric neutron SEE vulnerability can be reduced by 44.4% if the thermal neutron was absorbed. The influences of the B and Hf elements on the SEEs were evaluated, too. It can be concluded that 10 B interacting with thermal neutron is the reason for thermal neutron inducing SEE in the SoC. Although the Hf element has no contribution to the 28 nm SoC atmospheric neutron SEE cross section, it increases the total dose risk 5 times during atmospheric neutron irradiation

GROVE: A Retrieval-augmented Complex Story Generation Framework with A Forest of Evidence

Conditional story generation is significant in human-machine interaction, particularly in producing stories with complex plots. While Large language models (LLMs) perform well on multiple NLP tasks, including story generation, it is challenging to generate stories with both complex and creative plots. Existing methods often rely on detailed prompts to guide LLMs to meet target conditions, which inadvertently restrict the creative potential of the generated stories. We argue that leveraging information from exemplary human-written stories facilitates generating more diverse plotlines. Delving deeper into story details helps build complex and credible plots. In this paper, we propose a retrieval-au\textbf{G}mented sto\textbf{R}y generation framework with a f\textbf{O}rest of e\textbf{V}id\textbf{E}nce (GROVE) to enhance stories' complexity. We build a retrieval repository for target conditions to produce few-shot examples to prompt LLMs. Additionally, we design an ``asking-why'' prompting scheme that extracts a forest of evidence, providing compensation for the ambiguities that may occur in the generated story. This iterative process uncovers underlying story backgrounds. Finally, we select the most fitting chains of evidence from the evidence forest and integrate them into the generated story, thereby enhancing the narrative's complexity and credibility. Experimental results and numerous examples verify the effectiveness of our method.Comment: Findings of EMNLP 202

Toll-like receptor activation by helminths or helminth products to alleviate inflammatory bowel disease

Helminth infection may modulate the expression of Toll like receptors (TLR) in dendritic cells (DCs) and modify the responsiveness of DCs to TLR ligands. This may regulate aberrant intestinal inflammation in humans with helminthes and may thus help alleviate inflammation associated with human inflammatory bowel disease (IBD). Epidemiological and experimental data provide further evidence that reducing helminth infections increases the incidence rate of such autoimmune diseases. Fine control of inflammation in the TLR pathway is highly desirable for effective host defense. Thus, the use of antagonists of TLR-signaling and agonists of their negative regulators from helminths or helminth products should be considered for the treatment of IBD

Long-term effects of fire and harvest on carbon stocks of boreal forests in northeastern China

International audienceAbstractKey messageFire, harvest, and their spatial interactions are likely to affect boreal forest carbon stocks. Repeated disturbances associated with short fire return intervals and harvest rotations resulted in landscapes with a higher proportion of young stands that store less carbon than mature stands.ContextBoreal forests represent about one third of forest area and one third of forest carbon stocks on the Earth. Carbon stocks of boreal forests are sensitive to climate change, natural disturbances, and human activities.AimsThe objectives of this study were to evaluate the effects of fire, harvest, and their spatial interactions on boreal forest carbon stocks of northeastern China.MethodsWe used a coupled forest landscape model (LANDIS PRO) and a forest ecosystem model (LINKAGES) framework to simulate the landscape-level effects of fire, harvest, and their spatial interactions over 150 years.ResultsOur simulation suggested that aboveground carbon and soil organic carbon are significantly reduced by fire and harvest over the whole simulation period. The long-term effects of fire and harvest on carbon stocks were greater than the short-term effects. The combined effects of fire and harvest on carbon stocks are less than the sum of the separate effects of fire and harvest. The response of carbon stocks was impacted by the spatial variability of fire and harvest regimes.ConclusionThese results emphasize that the spatial interactions of fire and harvest play an important role in regulating boreal forest carbon stocks