849 research outputs found
Development of an Automated Physician Review Classification System: A hybrid Machine Learning Approach
Patients are increasingly turning to physician rating websites to help them make important healthcare decisions, such as selecting primary care doctors, specialists, and supplementary medical care providers. Previous research has identified a variety of topics and themes that emerge on these review platforms. However, there is little or no work that has been done to create an automated classifier that automatically categorizes these reviews into distinct topics after they have been explored in this context. Building such an automated classifier could assist IS developers and other stakeholders in automatically classifying patient reviews and understanding patient needs. Furthermore, using design science research we strategize how such machine learning systems can be built using design guidelines in turn having the potential to be generalized to other specific contextual problem spaces. Our work focuses on laying the foundation to design guidelines that need to be followed while building automated systems in specific contexts
Defining architectures for recommended systems for medical treatment. A Systematic Literature Review
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. To do so, a SLR researchmethodology obey. As a result, 12 intelligent recommendersystems related to prescribing medication were classed dependingto specific criteria. We assessed and analyze these medicinerecommender systems and enumerate the challenges. After studyingselected papers, our study concentrated on two researchquestions concerning the availability of medicine recommendersystems for physicians and the features these systems should have.Further research is encouraged in order to build an intelligentrecommender system based on the features analyzed in this work
Intelligent doctor patient matching: how José Mello saude experiments towards data-driven and patient-centric decision making
While data-driven decision-making is generally accepted as a fundamental capability of a
competitive firm, many firms are facing difficulties in developing this capability. This case
demonstrates how a private healthcare organization, José de Mello Saúde, engages in
collaboration with a global university-led program for such capability building, in a pilot project
of intelligent doctor-patient matching. The case walks the reader through the entire data science
pipeline, from project scoping to data curation, modelling, prototype testing, until
implementation. It enables discussions on how to overcome managerial challenges and build the
needed capabilities to successfully integrate advanced analytics into the organization’s operations
Asynchronous Remote Medical Consultation for Ghana
Computer-mediated communication systems can be used to bridge the gap between
doctors in underserved regions with local shortages of medical expertise and
medical specialists worldwide. To this end, we describe the design of a
prototype remote consultation system intended to provide the social,
institutional and infrastructural context for sustained, self-organizing growth
of a globally-distributed Ghanaian medical community. The design is grounded in
an iterative design process that included two rounds of extended design
fieldwork throughout Ghana and draws on three key design principles (social
networks as a framework on which to build incentives within a self-organizing
network; optional and incremental integration with existing referral
mechanisms; and a weakly-connected, distributed architecture that allows for a
highly interactive, responsive system despite failures in connectivity). We
discuss initial experiences from an ongoing trial deployment in southern Ghana.Comment: 10 page
Multidisciplinary perspectives on Artificial Intelligence and the law
This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio
NCBO Ontology Recommender 2.0: An Enhanced Approach for Biomedical Ontology Recommendation
Biomedical researchers use ontologies to annotate their data with ontology
terms, enabling better data integration and interoperability. However, the
number, variety and complexity of current biomedical ontologies make it
cumbersome for researchers to determine which ones to reuse for their specific
needs. To overcome this problem, in 2010 the National Center for Biomedical
Ontology (NCBO) released the Ontology Recommender, which is a service that
receives a biomedical text corpus or a list of keywords and suggests ontologies
appropriate for referencing the indicated terms. We developed a new version of
the NCBO Ontology Recommender. Called Ontology Recommender 2.0, it uses a new
recommendation approach that evaluates the relevance of an ontology to
biomedical text data according to four criteria: (1) the extent to which the
ontology covers the input data; (2) the acceptance of the ontology in the
biomedical community; (3) the level of detail of the ontology classes that
cover the input data; and (4) the specialization of the ontology to the domain
of the input data. Our evaluation shows that the enhanced recommender provides
higher quality suggestions than the original approach, providing better
coverage of the input data, more detailed information about their concepts,
increased specialization for the domain of the input data, and greater
acceptance and use in the community. In addition, it provides users with more
explanatory information, along with suggestions of not only individual
ontologies but also groups of ontologies. It also can be customized to fit the
needs of different scenarios. Ontology Recommender 2.0 combines the strengths
of its predecessor with a range of adjustments and new features that improve
its reliability and usefulness. Ontology Recommender 2.0 recommends over 500
biomedical ontologies from the NCBO BioPortal platform, where it is openly
available.Comment: 29 pages, 8 figures, 11 table
RECOMED: A Comprehensive Pharmaceutical Recommendation System
A comprehensive pharmaceutical recommendation system was designed based on
the patients and drugs features extracted from Drugs.com and Druglib.com.
First, data from these databases were combined, and a dataset of patients and
drug information was built. Secondly, the patients and drugs were clustered,
and then the recommendation was performed using different ratings provided by
patients, and importantly by the knowledge obtained from patients and drug
specifications, and considering drug interactions. To the best of our
knowledge, we are the first group to consider patients conditions and history
in the proposed approach for selecting a specific medicine appropriate for that
particular user. Our approach applies artificial intelligence (AI) models for
the implementation. Sentiment analysis using natural language processing
approaches is employed in pre-processing along with neural network-based
methods and recommender system algorithms for modeling the system. In our work,
patients conditions and drugs features are used for making two models based on
matrix factorization. Then we used drug interaction to filter drugs with severe
or mild interactions with other drugs. We developed a deep learning model for
recommending drugs by using data from 2304 patients as a training set, and then
we used data from 660 patients as our validation set. After that, we used
knowledge from critical information about drugs and combined the outcome of the
model into a knowledge-based system with the rules obtained from constraints on
taking medicine.Comment: 39 pages, 14 figures, 13 table
Why Should I Choose You? AutoXAI: A Framework for Selecting and Tuning eXplainable AI Solutions
In recent years, a large number of XAI (eXplainable Artificial Intelligence)
solutions have been proposed to explain existing ML (Machine Learning) models
or to create interpretable ML models. Evaluation measures have recently been
proposed and it is now possible to compare these XAI solutions. However,
selecting the most relevant XAI solution among all this diversity is still a
tedious task, especially when meeting specific needs and constraints. In this
paper, we propose AutoXAI, a framework that recommends the best XAI solution
and its hyperparameters according to specific XAI evaluation metrics while
considering the user's context (dataset, ML model, XAI needs and constraints).
It adapts approaches from context-aware recommender systems and strategies of
optimization and evaluation from AutoML (Automated Machine Learning). We apply
AutoXAI to two use cases, and show that it recommends XAI solutions adapted to
the user's needs with the best hyperparameters matching the user's constraints.Comment: 16 pages, 7 figures, to be published in CIKM202
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