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Therapy Decision Support Based on Recommender System Methods

Felix Gräßer, Stefanie Beckert, Denise Küster, Jochen Schmitt, Susanne Abraham, Hagen Malberg, Sebastian Zaunseder
2017 Journal of Healthcare Engineering  
We present a system for data-driven therapy decision support based on techniques from the field of recommender systems.  ...  Two methods for therapy recommendation, namely,Collaborative RecommenderandDemographic-based Recommender, are proposed.  ...  The authors further acknowledge the support by the German Research Foundation and the Open Access Publication Funds of the TU Dresden.  ... 
doi:10.1155/2017/8659460 pmid:29065657 pmcid:PMC5387813 fatcat:zeggpebtonewref522azcdis7i

Defining architectures for recommended systems for medical treatment. A Systematic Literature Review

Cristina Jimenez, Ivan Carrera
2018 Zenodo  
research is encouraged in order to build an intelligentrecommender system based on the features analyzed in this work.  ...  This paper presents a Systematic Literature Review(SLR) related to recommender system for medical treatment, aswell as analyze main elements that may provide flexible, accurate,and comprehensive recommendations  ...  , clustering SR7 [17] Similarity matrices, collaborative filtering SR8 [12] Decision making in medical domain.  ... 
doi:10.5281/zenodo.5708488 fatcat:h42va3ywszbblijxa2efudfv5m

Disease Risk Prediction using SVM based on Geographical Location

Abarna A R, A Umamakeswari
2018 International Journal of Engineering & Technology  
Plenty of health-care related information is available in social media where people spend more time in it.  ...  The experimental output shows that the proposed method is more effective when compared with Collaborative Filtering based Disease Risk Assessment.  ...  It is widely used in decision support for medically related searches. An analysis of disease based on the heart can be supported by developing a decision support method based on MLP.  ... 
doi:10.14419/ijet.v7i2.24.11989 fatcat:j7w4gfe4qffvtkdy77atemt6ni

Hybrid Recommender System for Therapy Recommendation

V Vishwajith, S Kaviraj, R Vasanth
2019 IJARCCE  
So, data-driven Clinical Decision Support Systems (CDSS) are designated to assist physicians or other health professionals during clinical decision-making.  ...  This system provides data-driven therapy recommendation for the patients.  ...  For evaluating recommendation quality, decision support accuracy metrics are commonly utilized.  ... 
doi:10.17148/ijarcce.2019.8118 fatcat:r2thundp6jhjtoedx3w2w24v6q

PDPM: A Patient-Defined Data Privacy Management with Nudge Theory in Decentralized E-Health Environments

Seolah JANG, Sandi RAHMADIKA, Sang Uk SHIN, Kyung-Hyune RHEE
2021 IEICE transactions on information and systems  
Therefore, we design, implement, and evaluate user-defined data privacy utilizing nudge theory for decentralized e-health systems named PDPM to tackle these issues.  ...  In terms of privacy, the e-health system preserves a default privacy option as an initial state for every patient since the patients may frequently customize their medical data over time for several purposes  ...  Collaborative filtering protocol in the PDPM system can be defined into two steps.  ... 
doi:10.1587/transinf.2021ngp0015 fatcat:e3nuvgyvdbhgvah75biiuamhku

Filtering data from the collaborative initial glaucoma treatment study for improved identification of glaucoma progression

Greggory J Schell, Mariel S Lavieri, Joshua D Stein, David C Musch
2013 BMC Medical Informatics and Decision Making  
Conclusion: A Kalman filter approach for estimating the true value of OAG biomarkers resulted in data input which improved the accuracy of a logistic regression classification model compared to a model  ...  The mean AUC for the Kalman filter-based model is 0.961 while the mean AUC for the raw measurements model is 0.889.  ...  Filtering techniques are important for true measurement estimation for medical decision making and have been shown to result in improved significant disease progression classification when utilizing GEE  ... 
doi:10.1186/1472-6947-13-137 pmid:24359562 pmcid:PMC3878032 fatcat:ibi2sv5abbbynjmaejwzlzhkiy

Determining Health Utilities through Data Mining of Social Media [article]

Christopher Thompson, Josh Introne, Clint Young
2016 arXiv   pre-print
In this work, we filter a dataset that originally contained 2 billion tweets for relevant content on 60 diseases.  ...  'Health utilities' measure patient preferences for perfect health compared to specific unhealthy states, such as asthma, a fractured hip, or colon cancer.  ...  Selection of Tweets: We filtered our dataset of 11 million health-related tweets for content related to our research.  ... 
arXiv:1608.03938v1 fatcat:6ibbsfpvbbetvarwn45nbufk2y

Health Recommender System Using Big Data Analytics

J.Archenaa, E.A.Mary Anita
2017 Zenodo  
This paper gives an insight on how to use big data analytics for developing effective health recommendation engine by analyzing multi structured healthcare data.  ...  Apache Spark plays an effective role in making meaningful analysis on the large amount of healthcare data generated with the help of machine learning components and in-memory computations supported by  ...  This form of case-related information enrichment might support a physician with the process of clinical diagnostics as latest research results can be used for treatment decision support.  ... 
doi:10.5281/zenodo.833884 fatcat:f536knjc3fb3fexzjrcgca47oe

Using Negotiation for Dynamic Composition of Services in Multi-organizational Environmental Management [chapter]

Costin Bădică, Sorin Ilie, Michiel Kamermans, Gregor Pavlin, Mihnea Scafeş
2011 IFIP Advances in Information and Communication Technology  
We show how this framework can be used for dynamic composition of workflows spanning multiple organizations in a disaster management information system.  ...  We assume a collaborative solution based on the Dynamic Process Integration Framework, which supports systematic encapsulation of heterogeneous processing services, including human experts.  ...  Acknowledgement The work reported in this paper was carried out as part of the Diadem project: http:// www.ist-diadem.eu.  ... 
doi:10.1007/978-3-642-22285-6_20 fatcat:oicpond5xrhgxpourppbfsuo2a

Paying for performance to improve the delivery of health interventions in low- and middle-income countries

Karin Diaconu, Jennifer Falconer, Adrian Verbel, Atle Fretheim, Sophie Witter, Cochrane Effective Practice and Organisation of Care Group
2021 The Cochrane library  
to pro-poor effects (estimated at under 10% in comparison to least poor); similar in relation to utilization of ANC in first trimester.  ...  In relation to service utilization and delivery a mixed picture emerges.  ...  Study does not include a) Health workers/Providers of health care services, b) Public health facilities, c) Private for profit/not for profit health facilities, d) Non-governmental organizations, e) Sub-national  ... 
doi:10.1002/14651858.cd007899.pub3 fatcat:6iggkzdhrvemdize5fw4druo6e

Automatically recommending healthy living programs to patients with chronic diseases through hybrid content-based and collaborative filtering

Yizhou Zang, Yuan An, Xiaohua Tony Hu
2014 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)  
In this research, we develop a hybrid recommendation system recommendation system for healthy living programs to patients with chronic diseases.  ...  Our experiments indicate that our model compared favorably against other real-world recommendation applications in terms of accuracy.  ...  ACKNOWLEDGMENT This work is supported in part by a Drexel Jumpstart grant on Health Informatics and the NSF grant IIP 1160960 for the center for visual and decision informatics (CVDI).  ... 
doi:10.1109/bibm.2014.6999224 dblp:conf/bibm/ZangAH14 fatcat:hhpnltew4ffz3iy7bvo4zexdky

DeepReco: Deep Learning Based Health Recommender System Using Collaborative Filtering

Abhaya Kumar Sahoo, Chittaranjan Pradhan, Rabindra Kumar Barik, Harishchandra Dubey
2019 Computation  
In this context, health intelligent systems have become indispensable tools in decision making processes in the healthcare sector.  ...  In the healthcare sector, big data analytics using recommender systems have an important role in terms of decision-making processes with respect to a patient's health.  ...  a content-based approach, and utilizing some content-based filtering in the collaborative approach.  ... 
doi:10.3390/computation7020025 fatcat:vrz4jc55h5dexf4gy3yumzfkxy

Indexing and Optimization Techniques in Biomedical Industry

2019 International Journal of Engineering and Advanced Technology  
In order to achieve efficiency in providing highest quality health care information, an optimized index scheme is needed for big data which is based on accuracy and timelines.  ...  The unpredictable amount of data generated everyday by smart phones, social networks, health care systems etc. is really mind blowing.  ...  The examined methods result in improving the utilization, performance efficiency and data retrieval in big data analytics. to develop a collaborative filtering based medical knowledge recommendation system  ... 
doi:10.35940/ijeat.f9524.088619 fatcat:cbw2zhzipfdyrinfpf5ovto4xy

Do recommender systems function in the health domain: a system review [article]

Jia Su, Yi Guan, Yuge Li, Weile Chen, He Lv, Yageng Yan
2020 arXiv   pre-print
Health domains contain similar decision-making problems such as what to eat, how to exercise, and what is the proper medicine for a patient.  ...  such as content-based and collaborative filtering methods can hardly handle health constraints, but knowledge-based methods function more than ever; 3) evaluating a health recommendation is more complicated  ...  played a role in this study design; data collection, analysis; paper publishing.  ... 
arXiv:2007.13058v1 fatcat:4ku6jhiqjrhk7ksjqrj7wvlnju

The University Recommendation System for Higher Education

2020 International journal of recent technology and engineering  
Recommendation system has become a requirement in today's world. This recommendation system is spread over large sectors right from education, entertainment, health, business etc.  ...  The University Recommendation System is a system which recommend right university for the students based on their GRE, TOFEL score.  ...  RELATED WORK There have been various studies concerning the method of entry, but very few of them utilize Machine Learning domain for assisting the process of making decisions on university admissions.  ... 
doi:10.35940/ijrte.f7632.038620 fatcat:hlzxm2r3ijbszh3e5ppol3nsfu
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