Siamese Survival Analysis with Competing Risks

Survival analysis in the presence of multiple possible adverse events, i.e., competing risks, is a pervasive problem in many industries (healthcare, finance, etc.). Since only one event is typically observed, the incidence of an event of interest is often obscured by other related competing events. This nonidentifiability, or inability to estimate true cause-specific survival curves from empirical data, further complicates competing risk survival analysis. We introduce Siamese Survival Prognosis Network (SSPN), a novel deep learning architecture for estimating personalized risk scores in the presence of competing risks. SSPN circumvents the nonidentifiability problem by avoiding the estimation of cause-specific survival curves and instead determines pairwise concordant time-dependent risks, where longer event times are assigned lower risks. Furthermore, SSPN is able to directly optimize an approximation to the C-discrimination index, rather than relying on well-known metrics which are unable to capture the unique requirements of survival analysis with competing risks

Deep Learning for Survival Analysis: A Review

The influx of deep learning (DL) techniques into the field of survival analysis in recent years, coupled with the increasing availability of high-dimensional omics data and unstructured data like images or text, has led to substantial methodological progress; for instance, learning from such high-dimensional or unstructured data. Numerous modern DL-based survival methods have been developed since the mid-2010s; however, they often address only a small subset of scenarios in the time-to-event data setting - e.g., single-risk right-censored survival tasks - and neglect to incorporate more complex (and common) settings. Partially, this is due to a lack of exchange between experts in the respective fields. In this work, we provide a comprehensive systematic review of DL-based methods for time-to-event analysis, characterizing them according to both survival- and DL-related attributes. In doing so, we hope to provide a helpful overview to practitioners who are interested in DL techniques applicable to their specific use case as well as to enable researchers from both fields to identify directions for future investigation. We provide a detailed characterization of the methods included in this review as an open-source, interactive table: https://survival-org.github.io/DL4Survival. As this research area is advancing rapidly, we encourage the research community to contribute to keeping the information up to date.Comment: 24 pages, 6 figures, 2 tables, 1 interactive tabl

Potential welfare issues of the Siamese fighting fish (Betta splendens) at the retailer and in the hobbyist aquarium

Betta splendens is an extremely popular ornamental fish among hobby aquarists. It has an interesting behavioral repertoire, particularly where male aggression and territoriality are concerned. The lack of scientific studies investigating optimal housing conditions in combination with the wide variety of commercially available husbandry products, raises questions about the welfare status of these fish in captivity. In this article, an overview of the available literature on the biology of the betta and general considerations of ornamental fish keeping is given, and environment- and animal-related factors with potential impact on the welfare of Betta splendens are examined. Although more research using biological and physiological indicators is needed, the following factors constituting welfare problems have been identified: an aquarium of limited dimensions, prevalence of Mycobacterium spp. infection, aggression to and from conspecifics or other species in the same aquarium and the limited ability to escape, potential for stress due to prolonged visual contact between males in shops and during shows, and the lack of environmental enrichment in the form of sheltering vegetation

Potential welfare issues of the Siamese fighting fish (Betta splendens) at the retailer and in the hobbyist aquarium

Betta splendens is an extremely popular ornamental fish among hobby aquarists. It has an interesting behavioral repertoire, particularly where male aggression and territoriality are concerned. The lack of scientific studies investigating optimal housing conditions in combination with the wide variety of commercially available husbandry products, raises questions about the welfare status of these fish in captivity. In this article, an overview of the available literature on the biology of the betta and general considerations of ornamental fish keeping is given, and environment- and animal-related factors with potential impact on the welfare of Betta splendens are examined. Although more research using biological and physiological indicators is needed, the following factors constituting welfare problems have been identified: an aquarium of limited dimensions, prevalence of Mycobacterium spp. infection, aggression to and from conspecifics or other species in the same aquarium and the limited ability to escape, potential for stress due to prolonged visual contact between males in shops and during shows, and the lack of environmental enrichment in the form of sheltering vegetation

Resource value as a mediating factor of aggression during within-sex competition between female Siamese fighting fish (Betta splendens)

In order to acquire and defend resources, individuals make decisions about the benefits of acquiring a resource weighed against the potential cost of injury and lost time and energy. Individual investment into aggression is expected to be proportional to the value of the contested resource, and that investment in defense of different categories of resources (e.g. food, mates) will vary depending on the value of each category of resource. Female Siamese fighting fish (Betta splendens) competed in dyadic trials with other female bettas in two experiments: one for food of either high or low value (small or large amount of food), and second, for males of either high or low value (large or small bodied male). Aggression was quantified as the number of behavioral displays and physical attacks performed during each competitive interaction. Within a dyad, female bettas expressed more physical aggression when competing over a small amount of food compared to a large amount of food, yet had fewer displays when competing over a small amount of food. Shorter display time and more attacks are consistent with the hypothesis that females perceive a small amount of food as more valuable because the resource may be depleted more quickly. In male stimulus trials, females increased aggression when they were presented with a small-bodied male compared to a large-bodied male. This is consistent with the hypothesis that females exhibit a choice for smaller males, which may pose less threat to females, as they have reduced aggression during courtship compared to larger males. To examine the type of selective pressures driving female aggression, I analyzed the difference between female aggression over food or males. Females showed more aggression when competing over food compared to competing over males, indicating stronger social selection for female aggression for obtaining food, while sexual selection for acquisition to mates has favored lower levels of aggression in mate-based competition. These results indicate that females modulate aggression depending on resource value, irrespective of the category of contested resource, and that a limitation of food resources appears to be a strong driving factor in female aggression, while competition for access to mates, although present, is less robust

Strategic Hedging and Middle Power Foreign Policy: The Case of Thailand as Viewed Through Neoclassical Realism

The Sino-American competition poses a challenge for all states, including Thailand. As a Southeast Asian middle power, Thailand must balance long-term security and short-term economic interests. Strategic hedging, deeply rooted in Thai history, involves engaging with competing great powers. This thesis explores Bangkok's foreign policy by examining its past, using neoclassical realist theory to analyze policy development and external factors. By studying centuries of strategy, this thesis fills a gap in literature on hedging and offers insights into Thailand's approach to the superpower rivalry

A review of arthritis diagnosis techniques in artificial intelligence era: Current trends and research challenges

Deep learning, a branch of artificial intelligence, has achieved unprecedented performance in several domains including medicine to assist with efficient diagnosis of diseases, prediction of disease progression and pre-screening step for physicians. Due to its significant breakthroughs, deep learning is now being used for the diagnosis of arthritis, which is a chronic disease affecting young to aged population. This paper provides a survey of recent and the most representative deep learning techniques (published between 2018 to 2020) for the diagnosis of osteoarthritis and rheumatoid arthritis. The paper also reviews traditional machine learning methods (published 2015 onward) and their application for the diagnosis of these diseases. The paper identifies open problems and research gaps. We believe that deep learning can assist general practitioners and consultants to predict the course of the disease, make treatment propositions and appraise their potential benefits

A Multistate Survival Analysis for Sequential Follicular Lymphoma Treatment Leveraging National Lymphocare Study

Follicular lymphoma (FL), caused due to the abnormal growth of B-cells, is the most common form of indolent Non-Hodgkin\u27s Lymphoma (NHL) in the United States. Majority of the patients diagnosed with FL are subjected to a series of treatment during their clinical course, thereby providing researchers an opportunity to evaluate sequential effect of different treatment regimens and identify significant risk factors affecting the survival of patients. National Lymphocare Study (NLCS) is the largest (n = 2,740) prospective study conducted in the United States that collected information about different prognostic factors and outcomes required to conduct a comprehensive multi-state survival analysis. Using the data set, we analyzed the effect of 11 stationary and 5 dynamic variables on the death-specific transition and compared the results obtained from two multi-state models. We identified significant clinical factors impacting all-cause and FL-specific death using Cox proportional hazard model and predicted the course of disease using Aalen-Johansen estimator. The risk of all-cause death was 16.37\% following 5-years from diagnosis. At the same time point, patients initially kept under watchful waiting (WW) were at a lower risk of FL-specific death, 4.52\%, compared to death due to other-causes, 8.25\%. Similarly, patients receiving an induction treatment without WW had an reduced risk of FL-specific death compared to death due to other cause at the 5-years time point. We further identified that the risk of FL-specific death increase after first-, second-, and third-line treatment with an increase in age of diagnosed patients. Dynamic variables, such as low albumin and elevated lactate dehydrogenase, were associated with poor outcomes after first-, second-, and third-line treatment. The presence of B-symptoms at diagnosis was not associated with an increased risk of FL death. On the other hand, being female reduced the risk (hazard ratio: 0.46 [0.3 - 0.8]) of FL death following treatment 2. Using a multi-state survival analysis framework allowed to quantify the effect of prognostic factors on cause-specific death. We identified that the risk of death due to other-cause is higher compared to the risk of death due to FL

A review on competing risks methods for survival analysis

When modelling competing risks survival data, several techniques have been proposed in both the statistical and machine learning literature. State-of-the-art methods have extended classical approaches with more flexible assumptions that can improve predictive performance, allow high dimensional data and missing values, among others. Despite this, modern approaches have not been widely employed in applied settings. This article aims to aid the uptake of such methods by providing a condensed compendium of competing risks survival methods with a unified notation and interpretation across approaches. We highlight available software and, when possible, demonstrate their usage via reproducible R vignettes. Moreover, we discuss two major concerns that can affect benchmark studies in this context: the choice of performance metrics and reproducibility.Comment: 22 pages, 2 table