Forest-Based Point Process for Event Prediction from Electronic Health Records

Weiss, Jeremy C.; Page, David

doi:10.1007/978-3-642-40994-3_35

Cited by 15 publications

(9 citation statements)

References 10 publications

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“…The latter fact opens up the possibility to predict future diseases of patients based on their medical history. This has been accomplished with approaches that include collaborative filtering [21,22], frequent itemsets [23,24], learning of transition probabilities between states represented by binary vectors [25,26], deep learning [27,28] and point processes [29].…”

Section: Introductionmentioning

confidence: 99%

High-risk multimorbidity patterns on the road to cardiovascular mortality

Haug

Deischinger

Gyimesi

et al. 2020

BMC Med

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Background: Multimorbidity, the co-occurrence of two or more diseases in one patient, is a frequent phenomenon. Understanding how different diseases condition each other over the lifetime of a patient could significantly contribute to personalised prevention efforts. However, most of our current knowledge on the long-term development of the health of patients (their disease trajectories) is either confined to narrow time spans or specific (sets of) diseases. Here, we aim to identify decisive events that potentially determine the future disease progression of patients. Methods: Health states of patients are described by algorithmically identified multimorbidity patterns (groups of included or excluded diseases) in a population-wide analysis of 9,000,000 patient histories of hospital diagnoses observed over 17 years. Over time, patients might acquire new diagnoses that change their health state; they describe a disease trajectory. We measure the age-and sex-specific risks for patients that they will acquire certain sets of diseases in the future depending on their current health state. Results: In the present analysis, the population is described by a set of 132 different multimorbidity patterns. For elderly patients, we find 3 groups of multimorbidity patterns associated with low (yearly in-hospital mortality of 0.2-0.3%), medium (0.3-1%) and high in-hospital mortality (2-11%). We identify combinations of diseases that significantly increase the risk to reach the high-mortality health states in later life. For instance, in men (women) aged 50-59 diagnosed with diabetes and hypertension, the risk for moving into the high-mortality region within 1 year is increased by the factor of 1.96 ± 0.11 (2.60 ± 0.18) compared with all patients of the same age and sex, respectively, and by the factor of 2.09 ± 0.12 (3.04 ± 0.18) if additionally diagnosed with metabolic disorders. Conclusions: Our approach can be used both to forecast future disease burdens, as well as to identify the critical events in the careers of patients which strongly determine their disease progression, therefore constituting targets for efficient prevention measures. We show that the risk for cardiovascular diseases increases significantly more in females than in males when diagnosed with diabetes, hypertension and metabolic disorders.

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Section: Introductionmentioning

confidence: 99%

High-risk multimorbidity patterns on the road to cardiovascular mortality

Haug

Deischinger

Gyimesi

et al. 2020

BMC Med

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“…There has been a lot of work on various parametric models for learning conditional intensity functions for event streams. Notable amongst them are Poisson networks (Rajaram, Graepel, and Herbrich 2005), Poisson cascades (Simma and Jordan 2010), piecewise-constant conditional intensity models (Gunawardana, Meek, and Xu 2011), forest-based point processes (Weiss and Page 2013), etc. Recent work has also considered neural network architectures (Du et al 2016;Xiao et al 2017).…”

Section: Related Workmentioning

confidence: 99%

Event-Driven Continuous Time Bayesian Networks

Bhattacharjya¹,

Shanmugam²,

Gao³

et al. 2020

AAAI

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We introduce a novel event-driven continuous time Bayesian network (ECTBN) representation to model situations where a system's state variables could be influenced by occurrences of events of various types. In this way, the model parameters and graphical structure capture not only potential “causal” dynamics of system evolution but also the influence of event occurrences that may be interventions. We propose a greedy search procedure for structure learning based on the BIC score for a special class of ECTBNs, showing that it is asymptotically consistent and also effective for limited data. We demonstrate the power of the representation by applying it to model paths out of poverty for clients of CityLink Center, an integrated social service provider in Cincinnati, USA. Here the ECTBN formulation captures the effect of classes/counseling sessions on an individual's life outcome areas such as education, transportation, employment and financial education.

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“…Piecewise-constant intensity models (PCIMs) (Gunawardana, Meek, & Xu, 2012;Parikh, Gunamwardana, & Meek, 2012) define the intensity function as a decision tree, with internal nodes' tests drawn from a set of pre-specified binary tests of the history. Forest-based point processes (Weiss & Page, 2013) extend this by allowing the intensity function to be the product of a set of functions, each a PCIM-like tree. Didelez (2008) presents a generalization of the continuous-time Bayesian networks (see Section 5) to inhomogeneous point processes, but without specific parameterizations or algorithms.…”

Section: Related Modelsmentioning

confidence: 99%

Tutorial on Structured Continuous-Time Markov Processes

Shelton¹,

Ciardo²

2014

jair

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A continuous-time Markov process (CTMP) is a collection of variables indexed by a continuous quantity, time. It obeys the Markov property that the distribution over a future variable is independent of past variables given the state at the present time. We introduce continuous-time Markov process representations and algorithms for filtering, smoothing, expected sufficient statistics calculations, and model estimation, assuming no prior knowledge of continuous-time processes but some basic knowledge of probability and statistics. We begin by describing "flat" or unstructured Markov processes and then move to structured Markov processes (those arising from state spaces consisting of assignments to variables) including Kronecker, decision-diagram, and continuous-time Bayesian network representations. We provide the first connection between decision-diagrams and continuoustime Bayesian networks. Tutorial GoalsThis tutorial is intended for readers interested in learning about continuous-time Markov processes, and in particular compact or structured representations of them. It is assumed that the reader is familiar with general probability and statistics and has some knowledge of discrete-time Markov chains and perhaps hidden Markov model algorithms.While this tutorial deals only with Markovian systems, we do not require that all variables be observed. Thus, hidden variables can be used to model long-range interactions among observations. In these models, at any given instant the assignment to all state variables is sufficient to describe the future evolution of the system. The variables themselves real-valued (continuous) times. We consider evidence or observations that can be regularly spaced, irregularly spaced, or continuous over intervals. These evidence patterns can change by model variable and time.We deal exclusively with discrete-state continuous-time systems. Real-valued variables are important in many situations, but to keep the scope manageable, we will not treat them here. We refer to the work of Särkkä (2006) for a machine-learning-oriented treatment of filtering and smoothing in such models. The literature on parameter estimation is more scattered. We will further constrain our discussion to systems with finite states, although many of the concepts can be extended to countably infinite state systems.We will be concerned with two main problems: inference and learning (parameter estimation). These were chosen as those most familiar to and applicable for researchers in artificial intelligence. At points we will also discuss the computation of steady-state properties, especially for model for which most research concentrates on this computation.

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Forest-Based Point Process for Event Prediction from Electronic Health Records

Cited by 15 publications

References 10 publications

High-risk multimorbidity patterns on the road to cardiovascular mortality

High-risk multimorbidity patterns on the road to cardiovascular mortality

Event-Driven Continuous Time Bayesian Networks

Tutorial on Structured Continuous-Time Markov Processes

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