Objective: The 2022 n2c2 NLP Challenge posed identification of social determinants of health (SDOH) in clinical narratives. We present three systems that we developed for the Challenge and discuss the distinctive task formulation used in each of the three systems. Materials and Methods: The first system identifies target pieces of information independently using machine learning classifiers. The second system uses a large language model (LLM) to extract complete structured outputs per document. The third system extracts candidate phrases using machine learning and identifies target relations with hand-crafted rules. Results: The three systems achieved F1 scores of 0.884, 0.831, and 0.663 in the Subtask A of the Challenge, which are ranked third, seventh, and eighth among the 15 participating teams. The review of the extraction results from our systems reveals characteristics of each approach and those of the SODH extraction task. Discussion: Phrases and relations annotated in the task is unique and diverse, not conforming to the conventional event extraction task. These annotations are difficult to model with limited training data. The system that extracts information independently, ignoring the annotated relations, achieves the highest F1 score. Meanwhile, LLM with its versatile capability achieves the high F1 score, while respecting the annotated relations. The rule-based system tackling relation extraction obtains the low F1 score, while it is the most explainable approach. Conclusion: The F1 scores of the three systems vary in this challenge setting, but each approach has advantages and disadvantages in a practical application. The selection of the approach depends not only on the F1 score but also on the requirements in the application.

One of the most important aspects of data science is building trust. This is especially true when you're working with machine learning and AI technologies, which are new and unfamiliar to many people. When something goes wrong, what do you tell your customer? What do they think will happen next? With explainable AI, you can provide answers that prove your product's legitimacy.

Process analytic approaches play a critical role in supporting the practice of business process management and continuous process improvement by leveraging process-related data to identify performance bottlenecks, extracting insights about reducing costs and optimizing the utilization of available resources. Process analytic techniques often have to contend with real-world settings where available logs are noisy or incomplete. In this paper we present an approach that permits process analytics techniques to deliver value in the face of noisy/incomplete event logs. Our approach leverages knowledge graphs to mitigate the effects of noise in event logs while supporting process analysts in understanding variability associated with event logs. Our approach is verified and validated on a sepsis event-log taken from a standard repository.

In this article, we study the approximate solutions set $\Lambda_b$ of an inconsistent system of $\max-\min$ fuzzy relational equations $(S): A \Box_{\min}^{\max}x =b$. Using the $L_\infty$ norm, we compute by an explicit analytical formula the Chebyshev distance $\Delta~=~\inf_{c \in \mathcal{C}} \Vert b -c \Vert$, where $\mathcal{C}$ is the set of second members of the consistent systems defined with the same matrix $A$. We study the set $\mathcal{C}_b$ of Chebyshev approximations of the second member $b$ i.e., vectors $c \in \mathcal{C}$ such that $\Vert b -c \Vert = \Delta$, which is associated to the approximate solutions set $\Lambda_b$ in the following sense: an element of the set $\Lambda_b$ is a solution vector $x^\ast$ of a system $A \Box_{\min}^{\max}x =c$ where $c \in \mathcal{C}_b$. As main results, we describe both the structure of the set $\Lambda_b$ and that of the set $\mathcal{C}_b$. We then introduce a paradigm for $\max-\min$ learning weight matrices that relates input and output data from training data. The learning error is expressed in terms of the $L_\infty$ norm. We compute by an explicit formula the minimal value of the learning error according to the training data. We give a method to construct weight matrices whose learning error is minimal, that we call approximate weight matrices. Finally, as an application of our results, we show how to learn approximately the rule parameters of a possibilistic rule-based system according to multiple training data.

Natural Language Processing (NLP) has come a long way since its inception in the 1960s. In the early days, NLP focused primarily on syntactic and grammatical analysis of text. However, as technology has advanced, so too has the field of NLP. Today, NLP encompasses a wide range of techniques and applications, from sentiment analysis to machine translation to language generation. The NLP landscape of the 1960s was dominated by rule-based systems.

Rule-based explanations provide simple reasons explaining the behavior of machine learning classifiers at given points in the feature space. Several recent methods (Anchors, LORE, etc.) purport to generate rule-based explanations for arbitrary or black-box classifiers. But what makes these methods work in general? We introduce a topological framework for rule-based explanation methods and provide a characterization of explainability in terms of the definability of a classifier relative to an explanation scheme. We employ this framework to consider various explanation schemes and argue that the preferred scheme depends on how much the user knows about the domain and the probability measure over the feature space.

The situation in the eurozone is much more fragile than in the U.S. Gopinath: It's true, high-energy prices are a particular burden on countries like Germany, whose economy is very dependent on energy imports. At least the Federal Republic has done better than expected; we had expected GDP growth to have slowed to 1.5 percent in 2022. Measured against that, it has done better, up 1.9 percent. Now, it seems that overall inflation may have already peaked. Gopinath: But core inflation – i.e., price increases excluding energy and food prices – is stubbornly high and will probably only start to fall toward the end of the year.

Machine learning has the potential to revolutionize the field of cybersecurity, automating many tasks that were previously done by human penetration testers. This has led to speculation that machine learning will replace a significant portion of the cybersecurity workforce overnight, just as it has done with other industries such as content graphic design. One of the main advantages of machine learning in cybersecurity is its ability to detect and respond to cyber threats in real time. Machine learning algorithms can process large amounts of data and identify patterns that may indicate a cyber attack, allowing them to quickly and efficiently respond to threats. This is in contrast to traditional cybersecurity methods, which rely on manual analysis and rule-based systems that can be slow and error-prone.

The field of artificial intelligence (AI) has its roots in the 1950s, when a group of researchers at Dartmouth College in Hanover, New Hampshire, proposed a research program to investigate the feasibility of building "thinking machines." They held a conference in 1956, which is considered by many to be the birth of the field of AI. The conference brought together many of the leading researchers in the field, and it was at this conference that the term "artificial intelligence" was first coined. However, the concept of creating machines that can mimic or emulate human intelligence is an old one, with ideas and proposals going back centuries. Some of the earliest references to the concept of artificial intelligence can be found in the myths, stories, and writings of ancient civilizations. During the 20th century, the field of AI developed slowly.

Multimodal knowledge graph link prediction aims to improve the accuracy and efficiency of link prediction tasks for multimodal data. However, for complex multimodal information and sparse training data, it is usually difficult to achieve interpretability and high accuracy simultaneously for most methods. To address this difficulty, a new model is developed in this paper, namely Interpretable Multimodal Knowledge Graph Answer Prediction via Sequence Modeling (IMKGA-SM). First, a multi-modal fine-grained fusion method is proposed, and Vgg16 and Optical Character Recognition (OCR) techniques are adopted to effectively extract text information from images and images. Then, the knowledge graph link prediction task is modelled as an offline reinforcement learning Markov decision model, which is then abstracted into a unified sequence framework. An interactive perception-based reward expectation mechanism and a special causal masking mechanism are designed, which "converts" the query into an inference path. Then, an autoregressive dynamic gradient adjustment mechanism is proposed to alleviate the insufficient problem of multimodal optimization. Finally, two datasets are adopted for experiments, and the popular SOTA baselines are used for comparison. The results show that the developed IMKGA-SM achieves much better performance than SOTA baselines on multimodal link prediction datasets of different sizes.