| 1. |
- Ashfaq, Awais, 1990-, et al.
(författare)
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Readmission prediction using deep learning on electronic health records
- 2019
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Ingår i: Journal of Biomedical Informatics. - Maryland Heights, MO : Elsevier BV. - 1532-0464 .- 1532-0480. ; 97
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Tidskriftsartikel (refereegranskat)abstract
- Unscheduled 30-day readmissions are a hallmark of Congestive Heart Failure (CHF) patients that pose significant health risks and escalate care cost. In order to reduce readmissions and curb the cost of care, it is important to initiate targeted intervention programs for patients at risk of readmission. This requires identifying high-risk patients at the time of discharge from hospital. Here, using real data from over 7500 CHF patients hospitalized between 2012 and 2016 in Sweden, we built and tested a deep learning framework to predict 30-day unscheduled readmission. We present a cost-sensitive formulation of Long Short-Term Memory (LSTM) neural network using expert features and contextual embedding of clinical concepts. This study targets key elements of an Electronic Health Record (EHR) driven prediction model in a single framework: using both expert and machine derived features, incorporating sequential patterns and addressing the class imbalance problem. We evaluate the contribution of each element towards prediction performance (ROC-AUC, F1-measure) and cost-savings. We show that the model with all key elements achieves higher discrimination ability (AUC: 0.77; F1: 0.51; Cost: 22% of maximum possible savings) outperforming the reduced models in at least two evaluation metrics. Additionally, we present a simple financial analysis to estimate annual savings if targeted interventions are offered to high risk patients. © 2019 The Authors
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| 2. |
- Blom, Mathias Carl, et al.
(författare)
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Training machine learning models to predict 30-day mortality in patients discharged from the emergency department: A retrospective, population-based registry study
- 2019
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Ingår i: BMJ Open. - London : BMJ. - 2044-6055. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- Objectives The aim of this work was to train machine learning models to identify patients at end of life with clinically meaningful diagnostic accuracy, using 30-day mortality in patients discharged from the emergency department (ED) as a proxy. Design Retrospective, population-based registry study. Setting Swedish health services. Primary and secondary outcome measures All cause 30-day mortality. Methods Electronic health records (EHRs) and administrative data were used to train six supervised machine learning models to predict all-cause mortality within 30 days in patients discharged from EDs in southern Sweden, Europe. Participants The models were trained using 65 776 ED visits and validated on 55 164 visits from a separate ED to which the models were not exposed during training. Results The outcome occurred in 136 visits (0.21%) in the development set and in 83 visits (0.15%) in the validation set. The model with highest discrimination attained ROC-AUC 0.95 (95% CI 0.93 to 0.96), with sensitivity 0.87 (95% CI 0.80 to 0.93) and specificity 0.86 (0.86 to 0.86) on the validation set. Conclusions Multiple models displayed excellent discrimination on the validation set and outperformed available indexes for short-term mortality prediction interms of ROC-AUC (by indirect comparison). The practical utility of the models increases as the data they were trained on did not require costly de novo collection but were real-world data generated as a by-product of routine care delivery.
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| 3. |
- Calikus, Ece, 1990-, et al.
(författare)
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A data-driven approach for discovering heat load patterns in district heating
- 2019
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Ingår i: Applied Energy. - Oxford : Elsevier. - 0306-2619 .- 1872-9118. ; 252
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Tidskriftsartikel (refereegranskat)abstract
- Understanding the heat usage of customers is crucial for effective district heating operations and management. Unfortunately, existing knowledge about customers and their heat load behaviors is quite scarce. Most previous studies are limited to small-scale analyses that are not representative enough to understand the behavior of the overall network. In this work, we propose a data-driven approach that enables large-scale automatic analysis of heat load patterns in district heating networks without requiring prior knowledge. Our method clusters the customer profiles into different groups, extracts their representative patterns, and detects unusual customers whose profiles deviate significantly from the rest of their group. Using our approach, we present the first large-scale, comprehensive analysis of the heat load patterns by conducting a case study on many buildings in six different customer categories connected to two district heating networks in the south of Sweden. The 1222 buildings had a total floor space of 3.4 million square meters and used 1540 TJ heat during 2016. The results show that the proposed method has a high potential to be deployed and used in practice to analyze and understand customers’ heat-use habits. © 2019 Calikus et al. Published by Elsevier Ltd.
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| 4. |
- Calikus, Ece, 1990-, et al.
(författare)
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Interactive-cosmo : Consensus self-organized models for fault detection with expert feedback
- 2019
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Ingår i: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019. - New York : Association for Computing Machinery (ACM). - 9781450362962 ; , s. 1-9
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Konferensbidrag (refereegranskat)abstract
- Diagnosing deviations and predicting faults is an important task, especially given recent advances related to Internet of Things. However, the majority of the efforts for diagnostics are still carried out by human experts in a time-consuming and expensive manner. One promising approach towards self-monitoring systems is based on the "wisdom of the crowd" idea, where malfunctioning equipments are detected by understanding the similarities and differences in the operation of several alike systems.A fully autonomous fault detection, however, is not possible, since not all deviations or anomalies correspond to faulty behaviors; many can be explained by atypical usage or varying external conditions. In this work, we propose a method which gradually incorporates expert-provided feedback for more accurate self-monitoring. Our idea is to support model adaptation while allowing human feedback to persist over changes in data distribution, such as concept drift. © 2019 Association for Computing Machinery.
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| 5. |
- Calikus, Ece, 1990-, et al.
(författare)
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No free lunch but a cheaper supper : A general framework for streaming anomaly detection
- 2020
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Ingår i: Expert systems with applications. - Oxford : Elsevier. - 0957-4174 .- 1873-6793. ; 155
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Tidskriftsartikel (refereegranskat)abstract
- In recent years, there has been increased research interest in detecting anomalies in temporal streaming data. A variety of algorithms have been developed in the data mining community, which can be divided into two categories (i.e., general and ad hoc). In most cases, general approaches assume the one-size-fits-all solution model where a single anomaly detector can detect all anomalies in any domain. To date, there exists no single general method that has been shown to outperform the others across different anomaly types, use cases and datasets. On the other hand, ad hoc approaches that are designed for a specific application lack flexibility. Adapting an existing algorithm is not straightforward if the specific constraints or requirements for the existing task change. In this paper, we propose SAFARI, a general framework formulated by abstracting and unifying the fundamental tasks in streaming anomaly detection, which provides a flexible and extensible anomaly detection procedure. SAFARI helps to facilitate more elaborate algorithm comparisons by allowing us to isolate the effects of shared and unique characteristics of different algorithms on detection performance. Using SAFARI, we have implemented various anomaly detectors and identified a research gap that motivates us to propose a novel learning strategy in this work. We conducted an extensive evaluation study of 20 detectors that are composed using SAFARI and compared their performances using real-world benchmark datasets with different properties. The results indicate that there is no single superior detector that works well for every case, proving our hypothesis that "there is no free lunch" in the streaming anomaly detection world. Finally, we discuss the benefits and drawbacks of each method in-depth and draw a set of conclusions to guide future users of SAFARI.
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| 6. |
- Calikus, Ece, 1990-, et al.
(författare)
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Ranking Abnormal Substations by Power Signature Dispersion
- 2018
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Ingår i: Energy Procedia. - Amsterdam : Elsevier. - 1876-6102. ; 149, s. 345-353
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Tidskriftsartikel (refereegranskat)abstract
- The relation between heat demand and outdoor temperature (heat power signature) is a typical feature used to diagnose abnormal heat demand. Prior work is mainly based on setting thresholds, either statistically or manually, in order to identify outliers in the power signature. However, setting the correct threshold is a difficult task since heat demand is unique for each building. Too loose thresholds may allow outliers to go unspotted, while too tight thresholds can cause too many false alarms.Moreover, just the number of outliers does not reflect the dispersion level in the power signature. However, high dispersion is often caused by fault or configuration problems and should be considered while modeling abnormal heat demand.In this work, we present a novel method for ranking substations by measuring both dispersion and outliers in the power signature. We use robust regression to estimate a linear regression model. Observations that fall outside of the threshold in this model are considered outliers. Dispersion is measured using coefficient of determination R2 which is a statistical measure of how close the data are to the fitted regression line.Our method first produces two different lists by ranking substations using number of outliers and dispersion separately. Then, we merge the two lists into one using the Borda Count method. Substations appearing on the top of the list should indicate higher abnormality in heat demand compared to the ones on the bottom. We have applied our model on data from substations connected to two district heating networks in the south of Sweden. Three different approaches i.e. outlier-based, dispersion-based and aggregated methods are compared against the rankings based on return temperatures. The results show that our method significantly outperforms the state-of-the-art outlier-based method. © 2018 The Authors. Published by Elsevier Ltd.
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| 7. |
- Camelo, Guilherme Antonio, et al.
(författare)
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Control of Smart Environments Using Brain Computer Interface Based on Genetic Algorithm
- 2016
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Ingår i: Lecture Notes in Computer Science. - Berlin/Heidelberg : Springer Berlin/Heidelberg. - 0302-9743 .- 1611-3349. ; 9622, s. 773-781
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Tidskriftsartikel (refereegranskat)abstract
- This work deals with the development of an interface to control a smart conference room using passive BCI (Brain Computer Interface). It compares a genetic algorithm developed in a previous project to control the smart conference room with a random control algorithm. The system controls features of the conference room such as air conditioner, lightning systems, electric shutters, entertainment devices, etc. The parameters of the algorithm are extracted from users biosignal using Emotiv Epoc Headset while the user performs an attention test. The tests indicate that the decisions made by the genetic algorithm lead to better results, but in a single execution cannot be considered an effective optimization algorithm. © Springer-Verlag Berlin Heidelberg 2016.
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| 8. |
- Cooney, Martin, 1980-, et al.
(författare)
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Avoiding Playfulness Gone Wrong: Exploring Multi-objective Reaching Motion Generation in a Social Robot
- 2017
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Ingår i: International Journal of Social Robotics. - Dordrecht : Springer Netherlands. - 1875-4791 .- 1875-4805. ; 9:4, s. 545-562
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Tidskriftsartikel (refereegranskat)abstract
- Companion robots will be able to perform useful tasks in homes and public places, while also providing entertainment through playful interactions. “Playful” here means fun, happy, and humorous. A challenge is that generating playful motions requires a non-trivial understanding of how people attribute meaning and intentions. The literature suggests that playfulness can lead to some undesired impressions such as that a robot is obnoxious, untrustworthy, unsafe, moving in a meaningless fashion, or boring. To generate playfulness while avoiding such typical failures, we proposed a model for the scenario of a robot arm reaching for an object: some simplified movement patterns such as sinusoids are structured toward appearing helpful, clear about goals, safe, and combining a degree of structure and anomaly. We integrated our model into a mathematical framework (CHOMP) and built a new robot, Kakapo, to perform dynamically generated motions. The results of an exploratory user experiment were positive, suggesting that: Our proposed system was perceived as playful over the course of several minutes. Also a better impression resulted compared with an alternative playful system which did not use our proposed heuristics; furthermore a negative effect was observed for several minutes after showing the alternative motions, suggesting that failures are important to avoid. And, an inverted u-shaped correlation was observed between motion length and degree of perceived playfulness, suggesting that motions should neither be too short or too long and that length is also a factor which can be considered when generating playful motions. A short follow-up study provided some additional support for the idea that playful motions which seek to avoid failures can be perceived positively. Our intent is that these exploratory results will provide some insight for designing various playful robot motions, toward achieving some good interactions. © 2017, The Author(s).
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| 9. |
- Cooney, Martin, 1980-, et al.
(författare)
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Pitfalls of Affective Computing : How can the automatic visual communication of emotions lead to harm, and what can be done to mitigate such risks?
- 2018
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Ingår i: WWW '18 Companion Proceedings of the The Web Conference 2018. - New York, NY : ACM Publications. ; , s. 1563-1566
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Konferensbidrag (refereegranskat)abstract
- What would happen in a world where people could "see'' others' hidden emotions directly through some visualizing technology Would lies become uncommon and would we understand each other better Or to the contrary, would such forced honesty make it impossible for a society to exist The science fiction television show Black Mirror has exposed a number of darker scenarios in which such futuristic technologies, by blurring the lines of what is private and what is not, could also catalyze suffering. Thus, the current paper first turns an eye towards identifying some potential pitfalls in emotion visualization which could lead to psychological or physical harm, miscommunication, and disempowerment. Then, some countermeasures are proposed and discussed--including some level of control over what is visualized and provision of suitably rich emotional information comprising intentions--toward facilitating a future in which emotion visualization could contribute toward people's well-being. The scenarios presented here are not limited to web technologies, since one typically thinks about emotion recognition primarily in the context of direct contact. However, as interfaces develop beyond today's keyboard and monitor, more information becomes available also at a distance--for example, speech-to-text software could evolve to annotate any dictated text with a speaker's emotional state.
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| 10. |
- de Morais, Wagner Ourique, et al.
(författare)
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A Wearable Accelerometer Based Platform to Encourage Physical Activity for the Elderly
- 2008
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Ingår i: Gerontechnology : international journal on the fundamental aspects of technology to serve the ageing society. - Eindhoven : International Society for Gerontechnology. - 1569-1101. ; 07:02, s. 129-181
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- The growth in the elderly population will pose great pressure on the healthcare system to treat common geriatric problem. Preventive approaches like encouraging elderly people to perform physical exercises can decrease the risk of developing chronic diseases. In cases when diseases already have developed, further developments could possibly be retarded. In this work a wearable platform to recognize user’ s movements presented. The platform provides interactions with simple computer games designed to promote physical activity.
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