Clairvoyance: A Pipeline Toolkit for Medical Time Series

Time-series learning is the bread and butter of data-driven *clinical decision support*, and the recent explosion in ML research has demonstrated great potential in various healthcare settings. At the same time, medical time-series problems in the wild are challenging due to their highly *composite* nature: They entail design choices and interactions among components that preprocess data, impute missing values, select features, issue predictions, estimate uncertainty, and interpret models. Despite exponential growth in electronic patient data, there is a remarkable gap between the potential and realized utilization of ML for clinical research and decision support. In particular, orchestrating a real-world project lifecycle poses challenges in engineering (i.e. hard to build), evaluation (i.e. hard to assess), and efficiency (i.e. hard to optimize). Designed to address these issues simultaneously, Clairvoyance proposes a unified, end-to-end, autoML-friendly pipeline that serves as a (i) software toolkit, (ii) empirical standard, and (iii) interface for optimization. Our ultimate goal lies in facilitating transparent and reproducible experimentation with complex inference workflows, providing integrated pathways for (1) personalized prediction, (2) treatment-effect estimation, and (3) information acquisition. Through illustrative examples on real-world data in outpatient, general wards, and intensive-care settings, we illustrate the applicability of the pipeline paradigm on core tasks in the healthcare journey. To the best of our knowledge, Clairvoyance is the first to demonstrate viability of a comprehensive and automatable pipeline for clinical time-series ML

Sistema Inteligente de Manutenção Preditiva

Maintenance Tasks in a shopfloor are one of the most critical tasks regarding the direct effect on production costs and, consequently, profit. Up until now, maintenance tasks were based on both Run-To-Failure and Reactive paradigms, fixing a machine only when it breaks or at a regular time intervals, regardless of the assets needed the maintenance or not. However, with the Industry 4.0 Paradigm and the Smart Factories concept, machines are now equipped with sensors that monitor a large number of different and varied variables which are afterwards stored. This data can be used to predict machine failures, called Predictive Maintenance, with the aid of the manual registries of asset breakdowns. This project, carried out in the scope of the subject TMDEI of the Master in Informatics Engineering (MEI), aims to conceive and build a system capable of doing Predictive Maintenance, by combining sensors and manual inputted data on ERP systems. PrediMain employs different Machine Learning techniques, with a special emphasis on Ensemble Methods, making the generated machine learning models more robust and accurate, by not using a single algorithm for the predictions. For sensor predictions, before classifying them as failure or not, PrediMain uses the auto-ARIMA technique, being an autoparemetrized method generating more accurate predictions. In the end, the system correctly classifies a set of observations with an estimated 90% of accuracy. This system is also developed to be served as a Software-as-a-Service, allowing multiple Data Sources, and therefore shopfloors, to use the same software instance, consequently not compromising the performance of the existing systems.As tarefas de manutenção, num contexto de chão de fábrica, são uma das tarefas mais críticas relativamente ao efeito direto nos custos de produção e consequentes lucros. Tradicionalmente, estas tarefas eram baseadas em técnicas rudimentares, seja a manutenção quando a máquina tem uma avariar ou então manutenções regulares no tempo, independentemente de a máquina necessitar ou não. No entanto, com o paradigma da Indústria 4.0 e Smart Factories, as máquinas estão cada vez mais equipadas com sensores que monitorizam um grande conjunto de variáveis e estatísticas que posteriormente são guardadas. Estes dados, em conjunto com os dados introduzidos manualmente nos sistemas ERP e MIS dos chão-de-fábrica, podem ser utilizados para prever falhas, utilizando técnicas de Machine Learning. Este projecto, PrediMain, desenvolvido no âmbito da unidade curricular TMDEI, do Mestrado de Engenharia Informática (MEI), tem como objectivo conceber um sistema capaz de realizar Manutenção Preditiva, dando previsões ao departamento de manutenção de quando é que uma determinada máquina irá ter algum tipo de falha. O PrediMain, tem como suporte técnicas de machine learning, com especial ênfase em técnicas de Ensemble, misturando diferentes algoritmos e técnicas, obtendo assim uma previsão mais fiável e precisa, contrariamente a utilizando apenas um tipo de algoritmo. Para a previsão dos valores de sensores, ainda antes de classificar uma determinada observação como possível falha, é utilizado um método auto-parametrizável e auto-ajustável, autoARIMA, gerando previsões mais fiáveis. No final, o sistema é capaz de classificar um conjunto de observações com uma taxa de acerto rondando os 90%. Por fim, este sistema foi concebido para ser servido a partir da Cloud, com as fontes de dados configuráveis, dando assim uma maior flexibilidade aos potenciais utilizadores e prevenir falhas ou diminuições de performance nos sistemas existentes

Analysis of Daily Streamflow Complexity by Kolmogorov Measures and Lyapunov Exponent

Analysis of daily streamflow variability in space and time is important for water resources planning, development, and management. The natural variability of streamflow is being complicated by anthropogenic influences and climate change, which may introduce additional complexity into the phenomenological records. To address this question for daily discharge data recorded during the period 1989-2016 at twelve gauging stations on Brazos River in Texas (USA), we use a set of novel quantitative tools: Kolmogorov complexity (KC) with its derivative associated measures to assess complexity, and Lyapunov time (LT) to assess predictability. We find that all daily discharge series exhibit long memory with an increasing downflow tendency, while the randomness of the series at individual sites cannot be definitively concluded. All Kolmogorov complexity measures have relatively small values with the exception of the USGS (United States Geological Survey) 08088610 station at Graford, Texas, which exhibits the highest values of these complexity measures. This finding may be attributed to the elevated effect of human activities at Graford, and proportionally lesser effect at other stations. In addition, complexity tends to decrease downflow, meaning that larger catchments are generally less influenced by anthropogenic activity. The correction on randomness of Lyapunov time (quantifying predictability) is found to be inversely proportional to the Kolmogorov complexity, which strengthens our conclusion regarding the effect of anthropogenic activities, considering that KC and LT are distinct measures, based on rather different techniques

Multi-step Ahead Inflow Forecasting for a Norwegian Hydro-Power Use-Case, Based on Spatial-Temporal Attention Mechanism

Hydrological forecasting has been an ongoing area of research due to its importance to improve decision making on water resource management, flood management, and climate change mitigation. With the increasing availability of hydrological data, Machine Learning (ML) techniques have started to play an important role, enabling us to better understand and predict complex hydrological events. However, some challenges remain. Hydrological processes have spatial and temporal dependencies that are not always easy to capture with traditional ML models, and a thorough understanding of these dependencies is essential when developing accurate predictive models. This thesis explores the use of ML techniques in hydrological forecasting and consists of an introduction, two papers, and an application developed alongside the case study. The motivation for this research is to enhance our understanding of the spatial and temporal dependencies in hydrological processes and to explore how ML techniques, particularly those incorporating attention mechanisms, can aid in hydrological forecasting. The first paper is a chronological literature review that explores the development of data-driven forecasting in hydrology, and highlighting the potential application of attention mechanisms in hydrological forecasting. These attention mechanisms have proven to be successful in various domains, allowing models to focus on the most relevant parts of the input for making predictions, which is particularly useful when dealing with spatial and temporal data. The second paper is a case study of a specific ML model incorporating these attention mechanisms. The focus is to illustrate the influence of spatial and temporal dependencies in a real-world hydrological forecasting scenario, thereby showcasing the practical application of these techniques. In parallel with the case study, an application has been developed, employing the principles and techniques discovered throughout the course of this research. The application aims to provide a practical demonstration of the concepts explored in the thesis, contributing to the field of hydrological forecasting by introducing a tool for hydropower suppliers.Masteroppgave i Programvareutvikling samarbeid med HVLPROG399MAMN-PRO