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Pinheiro Sant'Anna, AnitaORCID iD iconorcid.org/0000-0002-3495-2961
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Publications (10 of 34) Show all publications
Ashfaq, A., Pinheiro Sant'Anna, A., Lingman, M. & Nowaczyk, S. (2019). Readmission prediction using deep learning on electronic health records. Journal of Biomedical Informatics, 97, Article ID 103256.
Open this publication in new window or tab >>Readmission prediction using deep learning on electronic health records
2019 (English)In: Journal of Biomedical Informatics, ISSN 1532-0464, E-ISSN 1532-0480, Vol. 97, article id 103256Article in journal (Refereed) Published
Abstract [en]

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 7,500 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 show that the model with all key elements achieves a higher discrimination ability (AUC 0.77) compared to the rest. Additionally, we present a simple financial analysis to estimate annual savings if targeted interventions are offered to high risk patients. © 2019 The Authors

Place, publisher, year, edition, pages
Maryland Heights, MO: Academic Press, 2019
Keywords
Electronic health records, Readmission prediction, Long short-term memory networks, Contextual embeddings
National Category
Health Care Service and Management, Health Policy and Services and Health Economy
Identifiers
urn:nbn:se:hh:diva-39297 (URN)10.1016/j.jbi.2019.103256 (DOI)31351136 (PubMedID)2-s2.0-85069858722 (Scopus ID)
Projects
HiCube - behovsmotiverad hälsoinnovation
Funder
European Regional Development Fund (ERDF)
Note

Funding: The authors thank the European Regional Development Fund (ERDF), Health Technology Center and CAISR at Halmstad University and Hallands Hospital for financing the research work under the project HiCube - behovsmotiverad hälsoinnovation.

Available from: 2019-04-30 Created: 2019-04-30 Last updated: 2019-09-10Bibliographically approved
Mashad Nemati, H., Pinheiro Sant'Anna, A., Nowaczyk, S., Jürgensen, J. H. & Hilber, P. (2019). Reliability Evaluation of Power Cables Considering the Restoration Characteristic. International Journal of Electrical Power & Energy Systems, 105, 622-631
Open this publication in new window or tab >>Reliability Evaluation of Power Cables Considering the Restoration Characteristic
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2019 (English)In: International Journal of Electrical Power & Energy Systems, ISSN 0142-0615, E-ISSN 1879-3517, Vol. 105, p. 622-631Article in journal (Refereed) Published
Abstract [en]

In this paper Weibull parametric proportional hazard model (PHM) is used to estimate the failure rate of every individual cable based on its age and a set of explanatory factors. The required information for the proposed method is obtained by exploiting available historical cable inventory and failure data. This data-driven method does not require any additional measurements on the cables, and allows the cables to be ranked for maintenance prioritization and repair actions.

Furthermore, the results of reliability analysis of power cables are compared when the cables are considered as repairable or non-repairable components. The paper demonstrates that the methods which estimate the time-to-the-first failure (for non-repairable components) lead to incorrect conclusions about reliability of repairable power cables.

The proposed method is used to evaluate the failure rate of each individual Paper Insulated Lead Cover (PILC) underground cables in a distribution grid in the south of Sweden. © 2018 Elsevier Ltd

Place, publisher, year, edition, pages
London: Elsevier, 2019
Keywords
Power cable, historical data, reliability, proportional hazard model, preventive maintenance.
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-35470 (URN)10.1016/j.ijepes.2018.08.047 (DOI)000449447200055 ()2-s2.0-85053080255 (Scopus ID)
Available from: 2017-11-24 Created: 2017-11-24 Last updated: 2019-03-19Bibliographically approved
Blom, M. C., Ashfaq, A., Pinheiro Sant'Anna, A., Anderson, P. D. & Lingman, M. (2019). Training machine learning models to predict 30-day mortality in patients discharged from the emergency department: a retrospective, population based registry study. BMJ Open, 9(8), Article ID e028015.
Open this publication in new window or tab >>Training machine learning models to predict 30-day mortality in patients discharged from the emergency department: a retrospective, population based registry study
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2019 (English)In: BMJ Open, ISSN 2044-6055, E-ISSN 2044-6055, Vol. 9, no 8, article id e028015Article in journal (Refereed) Published
Abstract [en]

Background: Aggressive treatment at end-of-life (EOL) can be traumatic to patients and may not add clinical benefit. Absent an accurate prognosis of death, individual level biases may prevent timely discussions about the scope of EOL care and patients are at risk of being subject to care against their desire. The aim of this work is to develop predictive algorithms for identifying patients at EOL, with clinically meaningful discriminatory power.

Methods: Retrospective, population-based study of patients utilizing emergency departments (EDs) in Sweden, Europe. Electronic health records (EHRs) were used to train supervised learning algorithms to predict all-cause mortality within 30 days following ED discharge. Algorithm performance was validated out of sample on EHRs from a separate hospital, to which the algorithms were previously unexposed.

Results: Of 65,776 visits in the development set, 136 (0.21%) experienced the outcome. The algorithm with highest discrimination attained ROC-AUC 0.945 (95% CI 0.933 - 0.956), with sensitivity 0.869 (95% CI 0.802, 0.931) and specificity 0.858 (0.855, 0.860) on the validation set.

Conclusions: Multiple algorithms displayed excellent discrimination and outperformed available indexes for short-term mortality prediction. The practical utility of the algorithms increases as the required data were captured electronically and did not require de novo data collection.

Trial registration number: Not applicable.

Place, publisher, year, edition, pages
London: BMJ Publishing Group Ltd, 2019
National Category
Social and Clinical Pharmacy
Identifiers
urn:nbn:se:hh:diva-39307 (URN)10.1136/bmjopen-2018-028015 (DOI)31401594 (PubMedID)
Note

Funding: This work was partly funded by Region Halland, Sweden.The initial stage of MCBs involvement in the work was funded by a grant for post-doctoral research from the Tegger Foundation.

Available from: 2019-05-02 Created: 2019-05-02 Last updated: 2019-08-15Bibliographically approved
Menezes, M. L., Pinheiro Sant'Anna, A., Pavel, M., Jimison, H. & Alonso-Fernandez, F. (2018). Affective Ambient Intelligence: from Domotics to Ambient Intelligence. In: A2IC 2018: Artificial Intelligence International Conference: Book of Abstract. Paper presented at Artificial Intelligence International Conference, A2IC 2018, November 21-23, 2018, Barcelona, Spain (pp. 25-25).
Open this publication in new window or tab >>Affective Ambient Intelligence: from Domotics to Ambient Intelligence
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2018 (English)In: A2IC 2018: Artificial Intelligence International Conference: Book of Abstract, 2018, p. 25-25Conference paper, Oral presentation with published abstract (Refereed)
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-38503 (URN)
Conference
Artificial Intelligence International Conference, A2IC 2018, November 21-23, 2018, Barcelona, Spain
Available from: 2018-12-06 Created: 2018-12-06 Last updated: 2018-12-06Bibliographically approved
Menezes, M. L., Pinheiro Sant'Anna, A. & Alonso-Fernandez, F. (2018). Methodology for Subject Authentification and Identification through EEG signal: equipment's and positioning artifacts. In: A2IC 2018: Artificial Intelligence International Conference: Book of Abstract. Paper presented at Artificial Intelligence International Conference, A2IC 2018, November 21-23, 2018, Barcelona, Spain (pp. 37-37).
Open this publication in new window or tab >>Methodology for Subject Authentification and Identification through EEG signal: equipment's and positioning artifacts
2018 (English)In: A2IC 2018: Artificial Intelligence International Conference: Book of Abstract, 2018, p. 37-37Conference paper, Oral presentation with published abstract (Refereed)
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-38502 (URN)
Conference
Artificial Intelligence International Conference, A2IC 2018, November 21-23, 2018, Barcelona, Spain
Available from: 2018-12-06 Created: 2018-12-06 Last updated: 2018-12-06Bibliographically approved
Nowaczyk, S., Pinheiro Sant'Anna, A., Calikus, E. & Fan, Y. (2018). Monitoring equipment operation through model and event discovery. In: Hujun Yin, David Camacho Paulo Novais & Antonio J. Tallón-Ballesteros (Ed.), Intelligent Data Engineering and Automated Learning – IDEAL 2018: 19th International Conference, Madrid, Spain, November 21–23, 2018, Proceedings, Part II. Paper presented at Intelligent Data Engineering and Automated Learning – IDEAL 2018, 19th International Conference, Madrid, Spain, November 21–23, 2018 (pp. 41-53). Cham: Springer, 11315
Open this publication in new window or tab >>Monitoring equipment operation through model and event discovery
2018 (English)In: Intelligent Data Engineering and Automated Learning – IDEAL 2018: 19th International Conference, Madrid, Spain, November 21–23, 2018, Proceedings, Part II / [ed] Hujun Yin, David Camacho Paulo Novais & Antonio J. Tallón-Ballesteros, Cham: Springer, 2018, Vol. 11315, p. 41-53Conference paper, Published paper (Refereed)
Abstract [en]

Monitoring the operation of complex systems in real-time is becoming both required and enabled by current IoT solutions. Predicting faults and optimising productivity requires autonomous methods that work without extensive human supervision. One way to automatically detect deviating operation is to identify groups of peers, or similar systems, and evaluate how well each individual conforms with the group. We propose a monitoring approach that can construct knowledge more autonomously and relies on human experts to a lesser degree: without requiring the designer to think of all possible faults beforehand; able to do the best possible with signals that are already available, without the need for dedicated new sensors; scaling up to “one more system and component” and multiple variants; and finally, one that will adapt to changes over time and remain relevant throughout the lifetime of the system. © Springer Nature Switzerland AG 2018.

Place, publisher, year, edition, pages
Cham: Springer, 2018
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11315
Keywords
Artificial intelligence, Computer science, Computers, Event discoveries, Human expert, Human supervision, Monitoring approach, Monitoring equipment, Multiple variants, Real time, Scaling-up, Real time systems
National Category
Embedded Systems
Identifiers
urn:nbn:se:hh:diva-38732 (URN)10.1007/978-3-030-03496-2_6 (DOI)2-s2.0-85057087564 (Scopus ID)9783030034955 (ISBN)978-3-030-03496-2 (ISBN)
Conference
Intelligent Data Engineering and Automated Learning – IDEAL 2018, 19th International Conference, Madrid, Spain, November 21–23, 2018
Available from: 2019-01-08 Created: 2019-01-08 Last updated: 2019-01-08Bibliographically approved
Cooney, M., Pashami, S., Pinheiro Sant'Anna, A., Fan, Y. & Nowaczyk, S. (2018). Pitfalls of Affective Computing: How can the automatic visual communication of emotions lead to harm, and what can be done to mitigate such risks?. In: WWW '18 Companion Proceedings of the The Web Conference 2018: . Paper presented at The Web Conference 2018 (WWW '18), Lyon, France, April 23-27, 2018 (pp. 1563-1566). New York, NY: ACM Publications
Open this publication in new window or tab >>Pitfalls of Affective Computing: How can the automatic visual communication of emotions lead to harm, and what can be done to mitigate such risks?
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2018 (English)In: WWW '18 Companion Proceedings of the The Web Conference 2018, New York, NY: ACM Publications, 2018, p. 1563-1566Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
New York, NY: ACM Publications, 2018
Keywords
Affective computing, emotion visualization, Black Mirror, privacy, ethics, intention recognition
National Category
Robotics
Identifiers
urn:nbn:se:hh:diva-37664 (URN)10.1145/3184558.3191611 (DOI)
Conference
The Web Conference 2018 (WWW '18), Lyon, France, April 23-27, 2018
Projects
CAISR 2010/0271
Funder
Knowledge Foundation, CAISR 2010/0271
Note

Funding: Swedish Knowledge Foundation (CAISR 2010/0271 and Sidus AIR no. 20140220)

Available from: 2018-07-25 Created: 2018-07-25 Last updated: 2019-04-12Bibliographically approved
Calikus, E., Nowaczyk, S., Pinheiro Sant'Anna, A. & Byttner, S. (2018). Ranking Abnormal Substations by Power Signature Dispersion. Paper presented at 16th International Symposium on District Heating and Cooling, DHC2018, Hamburg, Germany, 9-12 September, 2018. Energy Procedia, 149, 345-353
Open this publication in new window or tab >>Ranking Abnormal Substations by Power Signature Dispersion
2018 (English)In: Energy Procedia, ISSN 1876-6102, E-ISSN 1876-6102, Vol. 149, p. 345-353Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2018
Keywords
abnormal heat demand, district heating, anomaly detection, fault detection, power signature
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-38253 (URN)10.1016/j.egypro.2018.08.198 (DOI)2-s2.0-85054100441 (Scopus ID)
Conference
16th International Symposium on District Heating and Cooling, DHC2018, Hamburg, Germany, 9-12 September, 2018
Funder
Knowledge Foundation, 20160103
Available from: 2018-11-04 Created: 2018-11-04 Last updated: 2018-11-12Bibliographically approved
Mashad Nemati, H., Laso, A., Manana, M., Pinheiro Sant'Anna, A. & Nowaczyk, S. (2018). Stream Data Cleaning for Dynamic Line Rating Application. Energies, 11(8), Article ID 2007.
Open this publication in new window or tab >>Stream Data Cleaning for Dynamic Line Rating Application
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2018 (English)In: Energies, ISSN 1996-1073, E-ISSN 1996-1073, Vol. 11, no 8, article id 2007Article in journal (Refereed) Published
Abstract [en]

The maximum current that an overhead transmission line can continuously carry depends on external weather conditions, most commonly obtained from real-time streaming weather sensors. The accuracy of the sensor data is very important in order to avoid problems such as overheating. Furthermore, faulty sensor readings may cause operators to limit or even stop the energy production from renewable sources in radial networks. This paper presents a method for detecting and replacing sequences of consecutive faulty data originating from streaming weather sensors. The method is based on a combination of (a) a set of constraints obtained from derivatives in consecutive data, and (b) association rules that are automatically generated from historical data. In smart grids, a large amount of historical data from different weather stations are available but rarely used. In this work, we show that mining and analyzing this historical data provides valuable information that can be used for detecting and replacing faulty sensor readings. We compare the result of the proposed method against the exponentially weighted moving average and vector autoregression models. Experiments on data sets with real and synthetic errors demonstrate the good performance of the proposed method for monitoring weather sensors.

Place, publisher, year, edition, pages
Basel: MDPI, 2018
Keywords
smart grids, dynamic line rating, stream data cleaning, data mining
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-37676 (URN)000446604100086 ()2-s2.0-85052824538 (Scopus ID)
Note

Funding: This research was partially funded by Spanish Government under Spanish R+D initiative with reference ENE2013-42720-R and RETOS RTC-2015-3795-3.

Available from: 2018-08-02 Created: 2018-08-02 Last updated: 2019-01-08Bibliographically approved
Cooney, M. & Sant'Anna, A. (2017). Avoiding Playfulness Gone Wrong: Exploring Multi-objective Reaching Motion Generation in a Social Robot. International Journal of Social Robotics, 9(4), 545-562
Open this publication in new window or tab >>Avoiding Playfulness Gone Wrong: Exploring Multi-objective Reaching Motion Generation in a Social Robot
2017 (English)In: International Journal of Social Robotics, ISSN 1875-4791, E-ISSN 1875-4805, Vol. 9, no 4, p. 545-562Article in journal (Refereed) Published
Abstract [en]

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).

Place, publisher, year, edition, pages
Dordrecht: Springer Netherlands, 2017
Keywords
entertainment robotics, motion generation, social robotics, playfulness, reaching
National Category
Environmental Sciences
Identifiers
urn:nbn:se:hh:diva-35044 (URN)10.1007/s12369-017-0411-1 (DOI)000408405800008 ()2-s2.0-85028359414 (Scopus ID)
Available from: 2017-09-20 Created: 2017-09-20 Last updated: 2017-09-21Bibliographically approved
Projects
Multi-modal emotion recognition from bio-signals [2015-04074_Vinnova]; Halmstad University
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-3495-2961

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