Novel Perspectives and Applications of Knowledge Graph Embeddings: From Link Prediction to Risk Assessment and Explainability

Knowledge graph representation is an important embedding technology that supports a variety of machine learning related applications. By learning the distributed representation of multi-relational data, knowledge embedding models are supposed to efficiently deal with the semantic relatedness of their constituents. However, failing in the fundamental task of creating an appropriate form to represent knowledge harms any attempt of designing subsequent machine learning tasks. Several knowledge embedding methods have been proposed in the last decade. Although there is a consensus on the idea that enhanced approaches are more efficient, more complex projections in the hyperspace that indeed favor link prediction (or knowledge graph completion) can result in a loss of semantic similarity. We propose a new evaluation task that aims at performing risk assessment on domain-specific categorized multi-relational datasets, designed as a classification problem based on the resulting embeddings. We assess the quality of embedding representations based on the synergy of the resulting clusters of target subjects. We show that more sophisticated embedding approaches do not necessarily favor embedding quality, and the traditional link prediction validation protocol is a weak metric to measure the quality of embedding representation. Finally, we present insights about using the synergy analysis to provide risk assessment explainability based on the probability distribution of feature-value pairs within embedded clusters

Improving Risk Assessment of Miscarriage During Pregnancy with Knowledge Graph Embeddings

Miscarriages are the most common type of pregnancy loss, mostly occurring in the first 12 weeks of pregnancy. Pregnancy risk assessment aims to quantify evidence to reduce such maternal morbidities, and personalized decision support systems are the cornerstone of high-quality, patient-centered care to improve diagnosis, treatment selection, and risk assessment. However, data sparsity and the increasing number of patient-level observations require more effective forms of representing clinical knowledge to encode known information that enables performing inference and reasoning. Whereas knowledge embedding representation has been widely explored in the open domain data, there are few efforts for its application in the clinical domain. In this study, we contrast differences among multiple embedding strategies, and we demonstrate how these methods can assist in performing risk assessment of miscarriage before and during pregnancy. Our experiments show that simple knowledge embedding approaches that utilize domain-specific metadata perform better than complex embedding strategies, although both can improve results comparatively to a population probabilistic baseline in both AUPRC, F1-score, and a proposed normalized version of these evaluation metrics that better reflects accuracy for unbalanced datasets. Finally, embedding approaches provide evidence about each individual, supporting explainability for its model predictions in such a way that humans understand

Use of backpropagation and differential evolution algorithms to training MLPs

Artificial Neural Networks (ANNs) are often used (trained) to find a general solution in problems where a pattern needs to be extracted, such as data classification. Feedforward (FFNN) is one of the ANN architectures and multilayer perceptron (MLP) is a type of FFNN. Based on gradient descent, backpropagation (BP) is one of the most used algorithms for MLP training. Evolutionary algorithms can be also used to train MLPs, including Differential Evolution (DE) algorithm. In this paper, BP and DE are used to train MLPs and they are both compared in four different approaches: (a) backpropagation, (b) DE with fixed parameter values, (c) DE with adaptive parameter values and (d) a hybrid alternative using both DE+BP algorithms. © 2013 IEEE

Natural language processing for mimicking clinical trial recruitment in critical care: a semi-automated simulation based on the LeoPARDS trial

Clinical trials often fail to recruit an adequate number of appropriate patients. Identifying eligible trial participants is resource-intensive when relying on manual review of clinical notes, particularly in critical care settings where the time window is short. Automated review of electronic health records (EHR) may help, but much of the information is in free text rather than a computable form. We applied natural language processing (NLP) to free text EHR data using the CogStack platform to simulate recruitment into the LeoPARDS study, a clinical trial aiming to reduce organ dysfunction in septic shock. We applied an algorithm to identify eligible patients using a moving 1-hour time window, and compared patients identified by our approach with those actually screened and recruited for the trial, for the time period that data were available. We manually reviewed records of a random sample of patients identified by the algorithm but not screened in the original trial. Our method identified 376 patients, including 34 patients with EHR data available who were actually recruited to LeoPARDS in our centre. The sensitivity of CogStack for identifying patients screened was 90% (95% CI 85%, 93%). Of the 203 patients identified by both manual screening and CogStack, the index date matched in 95 (47%) and CogStack was earlier in 94 (47%). In conclusion, analysis of EHR data using NLP could effectively replicate recruitment in a critical care trial, and identify some eligible patients at an earlier stage, potentially improving trial recruitment if implemented in real time

Acute hepatitis associated with Q fever in a man in Greece: a case report

Coxiella burnetii is the causative agent of Q fever. Q fever is a worldwide zoonosis that is responsible for various clinical manifestations. However, in Greece hepatitis due to Coxiella is rarely encountered. A case of Q fever associated with hepatitis is reported here. Diagnosis was made by specific serological investigation (enzyme-linked immunosorbent and indirect immunofluorescene assays) for Coxiella burnetii

Active surveillance of Q fever in human and animal population of Cyprus

BACKGROUND: A long-term active surveillance of Q fever was conducted in Cyprus organized in two phases. METHODS: Following serological tests and identification of seropositive humans and animals for C. burnetii in two villages (VIL1 and VIL2), all seronegative individuals were followed up for one year on a monthly basis by trained physicians to detect possible seroconversion for Q fever. In the second phase of the study, active surveillance for one year was conducted in the entire Cyprus. Physicians were following specific case definition criteria for Q fever. Standardized questionnaires, a geographical information system on a regional level, Immunofluorescence Assay (IFA) examinations and shell vial technique were used. RESULTS: Eighty-one seronegative humans and 239 seronegative animals from both villages participated in the first phase surveillance period of Q fever. Despite the small number of confirmed clinical cases (2 humans and 1 goat), a significant percentage of new seropositives for C. burnetii (44.4% of human participants and 13.8% of animals) was detected at the end of the year. During the second phase of surveillance, 82 humans, 100 goats, and 76 sheep were considered suspected cases of Q fever. However, only 9 human, 8 goat, and 4 sheep cases were serologically confirmed, while C. burnetii was isolated from three human and two animal samples. The human incidence rate was estimated at 1.2 per 100,000 population per year. CONCLUSION: A small number of confirmed clinical cases of Q fever were observed despite the high seroprevalence for C. burnetii in human and animal population of Cyprus. Most of the cases in the local population of Cyprus appear to be subclinical. Moreover further studies should investigate the role of ticks in the epidemiology of Q fever and their relation to human seropositivity

Equivalence of plasma p-tau217 with cerebrospinal fluid in the diagnosis of Alzheimer's disease

INTRODUCTION: Plasma biomarkers are promising tools for Alzheimer's disease (AD) diagnosis, but comparisons with more established biomarkers are needed. METHODS: We assessed the diagnostic performance of p-tau181, p-tau217, and p-tau231 in plasma and CSF in 174 individuals evaluated by dementia specialists and assessed with amyloid-PET and tau-PET. Receiver operating characteristic (ROC) analyses assessed the performance of plasma and CSF biomarkers to identify amyloid-PET and tau-PET positivity. RESULTS: Plasma p-tau biomarkers had lower dynamic ranges and effect sizes compared to CSF p-tau. Plasma p-tau181 (AUC = 76%) and p-tau231 (AUC = 82%) assessments performed inferior to CSF p-tau181 (AUC = 87%) and p-tau231 (AUC = 95%) for amyloid-PET positivity. However, plasma p-tau217 (AUC = 91%) had diagnostic performance indistinguishable from CSF (AUC = 94%) for amyloid-PET positivity. DISCUSSION: Plasma and CSF p-tau217 had equivalent diagnostic performance for biomarker-defined AD. Our results suggest that plasma p-tau217 may help reduce the need for invasive lumbar punctures without compromising accuracy in the identification of AD. Highlights: p-tau217 in plasma performed equivalent to p-tau217 in CSF for the diagnosis of AD, suggesting the increased accessibility of plasma p-tau217 is not offset by lower accuracy. p-tau biomarkers in plasma had lower mean fold-changes between amyloid-PET negative and positive groups than p-tau biomarkers in CSF. CSF p-tau biomarkers had greater effect sizes than plasma p-tau biomarkers when differentiating between amyloid-PET positive and negative groups. Plasma p-tau181 and plasma p-tau231 performed worse than p-tau181 and p-tau231 in CSF for AD diagnosis

Plasma pTau-217 and N-terminal tau (NTA) enhance sensitivity to identify tau PET positivity in amyloid-β positive individuals

INTRODUCTION: We set out to identify tau PET-positive (A+T+) individuals among amyloid-beta (Aβ) positive participants using plasma biomarkers. METHODS: In this cross-sectional study we assessed 234 participants across the AD continuum who were evaluated by amyloid PET with [18F]AZD4694 and tau-PET with [18F]MK6240 and measured plasma levels of total tau, pTau-181, pTau-217, pTau-231, and N-terminal tau (NTA-tau). We evaluated the performances of plasma biomarkers to predict tau positivity in Aβ+ individuals. RESULTS: Highest associations with tau positivity in Aβ+ individuals were found for plasma pTau-217 (AUC [CI95%] = 0.89 [0.82, 0.96]) and NTA-tau (AUC [CI95%] = 0.88 [0.91, 0.95]). Combining pTau-217 and NTA-tau resulted in the strongest agreement (Cohen's Kappa = 0.74, CI95% = 0.57/0.90, sensitivity = 92%, specificity = 81%) with PET for classifying tau positivity. DISCUSSION: The potential for identifying tau accumulation in later Braak stages will be useful for patient stratification and prognostication in treatment trials and in clinical practice. Highlights: We found that in a cohort without pre-selection pTau-181, pTau-217, and NTA-tau showed the highest association with tau PET positivity. We found that in Aβ+ individuals pTau-217 and NTA-tau showed the highest association with tau PET positivity. Combining pTau-217 and NTA-tau resulted in the strongest agreement with the tau PET-based classification

A super-spreading ewe infects hundreds with Q fever at a farmers' market in Germany

BACKGROUND: In May 2003 the Soest County Health Department was informed of an unusually large number of patients hospitalized with atypical pneumonia. METHODS: In exploratory interviews patients mentioned having visited a farmers' market where a sheep had lambed. Serologic testing confirmed the diagnosis of Q fever. We asked local health departments in Germany to identiy notified Q fever patients who had visited the farmers market. To investigate risk factors for infection we conducted a case control study (cases were Q fever patients, controls were randomly selected Soest citizens) and a cohort study among vendors at the market. The sheep exhibited at the market, the herd from which it originated as well as sheep from herds held in the vicinity of Soest were tested for Coxiella burnetii (C. burnetii). RESULTS: A total of 299 reported Q fever cases was linked to this outbreak. The mean incubation period was 21 days, with an interquartile range of 16–24 days. The case control study identified close proximity to and stopping for at least a few seconds at the sheep's pen as significant risk factors. Vendors within approximately 6 meters of the sheep's pen were at increased risk for disease compared to those located farther away. Wind played no significant role. The clinical attack rate of adults and children was estimated as 20% and 3%, respectively, 25% of cases were hospitalized. The ewe that had lambed as well as 25% of its herd tested positive for C. burnetii antibodies. CONCLUSION: Due to its size and point source nature this outbreak permitted assessment of fundamental, but seldom studied epidemiological parameters. As a consequence of this outbreak, it was recommended that pregnant sheep not be displayed in public during the 3(rd )trimester and to test animals in petting zoos regularly for C. burnetii

Inflammation-Associated Nitrotyrosination Affects TCR Recognition through Reduced Stability and Alteration of the Molecular Surface of the MHC Complex

Nitrotyrosination of proteins, a hallmark of inflammation, may result in the production of MHC-restricted neoantigens that can be recognized by T cells and bypass the constraints of immunological self-tolerance. Here we biochemically and structurally assessed how nitrotyrosination of the lymphocytic choriomeningitis virus (LCMV)-associated immunodominant MHC class I-restricted epitopes gp33 and gp34 alters T cell recognition in the context of both H-2Db and H-2Kb. Comparative analysis of the crystal structures of H-2Kb/gp34 and H-2Kb/NY-gp34 demonstrated that nitrotyrosination of p3Y in gp34 abrogates a hydrogen bond interaction formed with the H-2Kb residue E152. As a consequence the conformation of the TCR-interacting E152 was profoundly altered in H-2Kb/NY-gp34 when compared to H-2Kb/gp34, thereby modifying the surface of the nitrotyrosinated MHC complex. Furthermore, nitrotyrosination of gp34 resulted in structural over-packing, straining the overall conformation and considerably reducing the stability of the H-2Kb/NY-gp34 MHC complex when compared to H-2Kb/gp34. Our structural analysis also indicates that nitrotyrosination of the main TCR-interacting residue p4Y in gp33 abrogates recognition of H-2Db/gp33-NY complexes by H-2Db/gp33-specific T cells through sterical hindrance. In conclusion, this study provides the first structural and biochemical evidence for how MHC class I-restricted nitrotyrosinated neoantigens may enable viral escape and break immune tolerance