scholarly journals Neural Temporal Point Processes: A Review

2021 ◽  
Author(s):  
Oleksandr Shchur ◽  
Ali Caner Türkmen ◽  
Tim Januschowski ◽  
Stephan Günnemann
Keyword(s):  
Continuous Time ◽  
Point Processes ◽  
Review Paper ◽  
Generative Models ◽  
Learning Approaches ◽  
Event Sequences ◽  
Body Of Knowledge ◽  
Future Work ◽  
Important Design ◽  
Significant Attention

Temporal point processes (TPP) are probabilistic generative models for continuous-time event sequences. Neural TPPs combine the fundamental ideas from point process literature with deep learning approaches, thus enabling construction of flexible and efficient models. The topic of neural TPPs has attracted significant attention in the recent years, leading to the development of numerous new architectures and applications for this class of models. In this review paper we aim to consolidate the existing body of knowledge on neural TPPs. Specifically, we focus on important design choices and general principles for defining neural TPP models. Next, we provide an overview of application areas commonly considered in the literature. We conclude this survey with the list of open challenges and important directions for future work in the field of neural TPPs.

scholarly journals A Neural Approach for Modeling Continuous Time Sequences with Intermittent Observations

2021 ◽  
Author(s):  
Vinayak Gupta ◽  
Srikanta Bedathur
Keyword(s):  
Real World ◽  
Continuous Time ◽  
Point Processes ◽  
Large Fraction ◽  
Modeling Framework ◽  
Event Sequences ◽  
Event Time ◽  
Sequence Modeling ◽  
Recent Success ◽  
Real World Datasets

A large fraction of data generated via human activities such as online purchases, health records, spatial mobility etc. can be represented as continuous-time event sequences (CTES) i.e. sequences of discrete events over a continuous time. Learning neural models over CTES is a non-trivial task as it involves modeling the ever-increasing event timestamps, inter-event time gaps, event types, and the influences between different events within and across different sequences. Moreover, existing sequence modeling techniques consider a complete observation scenario i.e. the event sequence being modeled is completely observed with no missing events – an ideal setting that is rarely applicable in real-world applications. In this paper, we highlight our approach[8] for modeling CTES with intermittent observations. Buoyed by the recent success of neural marked temporal point processes (MTPP) for modeling the generative distribution of CTES, we provide a novel unsupervised model and inference method for learning MTPP in presence of event sequences with missing events. Specifically, we first model the generative processes of observed events and missing events using two MTPP, where the missing events are represented as latent random variables. Then, we devise an unsupervised training method that jointly learns both the MTPP using variational inference. Experiments across real-world datasets show that our modeling framework outperforms state-of-the-art techniques for future event prediction and imputation. This work appeared in AISTATS 2021.

scholarly journals A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2514
Author(s):  
Tharindu Kaluarachchi ◽  
Andrew Reis ◽  
Suranga Nanayakkara
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Review Paper ◽  
Learning Systems ◽  
Learning Approaches ◽  
Application Development ◽  
Research Gaps ◽  
Domain Experts ◽  
Working Definition ◽  
Real World Application

After Deep Learning (DL) regained popularity recently, the Artificial Intelligence (AI) or Machine Learning (ML) field is undergoing rapid growth concerning research and real-world application development. Deep Learning has generated complexities in algorithms, and researchers and users have raised concerns regarding the usability and adoptability of Deep Learning systems. These concerns, coupled with the increasing human-AI interactions, have created the emerging field that is Human-Centered Machine Learning (HCML). We present this review paper as an overview and analysis of existing work in HCML related to DL. Firstly, we collaborated with field domain experts to develop a working definition for HCML. Secondly, through a systematic literature review, we analyze and classify 162 publications that fall within HCML. Our classification is based on aspects including contribution type, application area, and focused human categories. Finally, we analyze the topology of the HCML landscape by identifying research gaps, highlighting conflicting interpretations, addressing current challenges, and presenting future HCML research opportunities.

scholarly journals Training Public Health Professionals to Tackle Women, Children, and Families Health Issues: A “Skill-Based Qualitative Learning Approach”

2021 ◽  
Vol 9 ◽  
Author(s):  
Cecilia Obeng
Keyword(s):  
Public Health ◽  
Health Professionals ◽  
Teaching And Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Health Issues ◽  
Children And Families ◽  
Arduous Task ◽  
The One ◽  
Future Work

Purpose: There are several teaching and learning approaches but finding the one that is appropriate for a particular field or training program is an arduous task. The purpose of this paper is to introduce the “Skill Based Qualitative Learning Approach” (SBQLA) in training health professionals.Description: The SBQLA is a pedagogical approach via which learners are trained in developing qualitative questionnaires and interview skills to learn from experts in the Public Health (PH) field. This teaching approach arms students with interview skills that help them identify and address PH roadblocks and get them authentic information from experts. It also equips them with techniques on how to do formalized presentations and come up with projects and interventions that help mitigate and eliminate drivers of health problems among women, children and families.Assessment: Learners' field experiences are shared in a professional presentation style in a class to help trainees benefit from each other's information and to get formalized feedback on their presentation. Assessment in this learning approach is based on a synthesis and an analysis of data collected from professionals.Conclusion: Findings from this learning approach enables experts to shed light on true stories shared by real and authentic individuals whose faces can be associated with their shared experiences. This learning approach makes it possible for trainees to also initiate projects that help them tackle existing and emerging public health issues in their future work.

scholarly journals State Variable Effects in Graphical Event Models

2020 ◽  
Author(s):  
Debarun Bhattacharjya ◽  
Dharmashankar Subramanian ◽  
Tian Gao
Keyword(s):  
Real World ◽  
Continuous Time ◽  
Point Processes ◽  
Temporal Dynamics ◽  
Search Algorithm ◽  
State Variables ◽  
Discrete State ◽  
State Variable ◽  
Event Models ◽  
Real World Datasets

Many real-world domains involve co-evolving relationships between events, such as meals and exercise, and time-varying random variables, such as a patient's blood glucose levels. In this paper, we propose a general framework for modeling joint temporal dynamics involving continuous time transitions of discrete state variables and irregular arrivals of events over the timeline. We show how conditional Markov processes (as represented by continuous time Bayesian networks) and multivariate point processes (as represented by graphical event models) are among various processes that are covered by the framework. We introduce and compare two simple and interpretable yet practical joint models within the framework with relevant baselines on simulated and real-world datasets, using a graph search algorithm for learning. The experiments highlight the importance of jointly modeling event arrivals and state variable transitions to better fit joint temporal datasets, and the framework opens up possibilities for models involving even more complex dynamics whenever suitable.

Review of Business Intelligence and Intelligent Systems in Healthcare Domain

2018 ◽  
pp. 2073-2086
Author(s):  
Halil Ibrahim Cebeci ◽  
Abdulkadir Hiziroglu
Keyword(s):  
Business Intelligence ◽  
Research Agenda ◽  
Intelligent Systems ◽  
Health Management ◽  
Health Science ◽  
Disease Treatment ◽  
Health Community ◽  
Future Work ◽  
Significant Attention ◽  
Made In

Business intelligence and corresponding intelligent components and tools have been one of those instruments that receive significant attention from health community. In order to raise more awareness on the potentials of business intelligence and intelligent systems, this paper aims to provide an overview of business intelligence in healthcare context by specifically focusing on the applications of intelligent systems. This study reviewed the current applications into three main categories and presented some important findings of that research in a systematic manner. The literature is wide with respect to the applications of business intelligence covering the issues from health management and policy related topics to more operational and tactical ones such as disease treatment, diagnostics, and hospital management. The discussions made in this article can also facilitate the researchers in that area to generate a research agenda for future work in applied health science, particularly within the context of health management and policy and health analytics.

Review of Business Intelligence and Intelligent Systems in Healthcare Domain

2016 ◽  
pp. 192-206
Author(s):  
Halil Ibrahim Cebeci ◽  
Abdulkadir Hiziroglu
Keyword(s):  
Business Intelligence ◽  
Research Agenda ◽  
Intelligent Systems ◽  
Health Management ◽  
Health Science ◽  
Disease Treatment ◽  
Health Community ◽  
Future Work ◽  
Significant Attention ◽  
Made In

Business intelligence and corresponding intelligent components and tools have been one of those instruments that receive significant attention from health community. In order to raise more awareness on the potentials of business intelligence and intelligent systems, this paper aims to provide an overview of business intelligence in healthcare context by specifically focusing on the applications of intelligent systems. This study reviewed the current applications into three main categories and presented some important findings of that research in a systematic manner. The literature is wide with respect to the applications of business intelligence covering the issues from health management and policy related topics to more operational and tactical ones such as disease treatment, diagnostics, and hospital management. The discussions made in this article can also facilitate the researchers in that area to generate a research agenda for future work in applied health science, particularly within the context of health management and policy and health analytics.

scholarly journals Likelihood Methods for Point Processes with Refractoriness

2014 ◽  
Vol 26 (2) ◽  
pp. 237-263 ◽  
Author(s):  
Luca Citi ◽  
Demba Ba ◽  
Emery N. Brown ◽  
Riccardo Barbieri
Keyword(s):  
Point Process ◽  
Continuous Time ◽  
Process Model ◽  
Point Processes ◽  
Linear Models ◽  
Continuous Time Model ◽  
Time Model ◽  
Fitting Procedures ◽  
Continuous Time Process ◽  
Conditional Intensity Function

Likelihood-based encoding models founded on point processes have received significant attention in the literature because of their ability to reveal the information encoded by spiking neural populations. We propose an approximation to the likelihood of a point-process model of neurons that holds under assumptions about the continuous time process that are physiologically reasonable for neural spike trains: the presence of a refractory period, the predictability of the conditional intensity function, and its integrability. These are properties that apply to a large class of point processes arising in applications other than neuroscience. The proposed approach has several advantages over conventional ones. In particular, one can use standard fitting procedures for generalized linear models based on iteratively reweighted least squares while improving the accuracy of the approximation to the likelihood and reducing bias in the estimation of the parameters of the underlying continuous-time model. As a result, the proposed approach can use a larger bin size to achieve the same accuracy as conventional approaches would with a smaller bin size. This is particularly important when analyzing neural data with high mean and instantaneous firing rates. We demonstrate these claims on simulated and real neural spiking activity. By allowing a substantive increase in the required bin size, our algorithm has the potential to lower the barrier to the use of point-process methods in an increasing number of applications.

scholarly journals On the weak convergence of stochastic processes with embedded point processes

1988 ◽  
Vol 20 (2) ◽  
pp. 473-475 ◽  
Author(s):  
Panagiotis Konstantopoulos ◽  
Jean Walrand
Keyword(s):  
Stochastic Process ◽  
Weak Convergence ◽  
Stochastic Processes ◽  
Point Process ◽  
Real Line ◽  
Continuous Time ◽  
Point Processes ◽  
Mixing Condition ◽  
The Real ◽  
Palm Distribution

We consider a stochastic process in continuous time and two point processes on the real line, all jointly stationary. We show that under a certain mixing condition the values of the process at the points of the second point process converge weakly under the Palm distribution with respect to the first point process, and we identify the limit. This result is a supplement to two other known results which are mentioned below.

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