Abstract：
:Quality of life of patients with Parkinson's disease degrades significantly with disease progression. This paper presents a step towards personalized management of Parkinson's disease patients, based on discovering groups of similar patients. Similarity is based on patients' medical conditions and changes in the prescribed therapy when the medical conditions change. We present two novel approaches. The first algorithm discovers symptoms' impact on Parkinson's disease progression. Experiments on the Parkinson Progression Markers Initiative (PPMI) data reveal a subset of symptoms influencing disease progression which are already established in Parkinson's disease literature, as well as symptoms that are considered only recently as possible indicators of disease progression by clinicians. The second novelty is a methodology for detecting patterns of medications dosage changes based on the patient status. The methodology combines multitask learning using predictive clustering trees and short time series analysis to better understand when a change in medications is required. The experiments on PPMI data demonstrate that, using the proposed methodology, we can identify some clinically confirmed patients' symptoms suggesting medications change. In terms of predictive performance, our multitask predictive clustering tree approach is mostly comparable to the random forest multitask model, but has the advantage of model interpretability.
journal_name
Artif Intell Medjournal_title
Artificial intelligence in medicineauthors
Valmarska A,Miljkovic D,Konitsiotis S,Gatsios D,Lavrač N,Robnik-Šikonja Mdoi
10.1016/j.artmed.2018.04.010subject
Has Abstractpub_date
2018-09-01 00:00:00pages
82-95eissn
0933-3657issn
1873-2860pii
S0933-3657(17)30587-0journal_volume
91pub_type
杂志文章abstract：:Problems involved in the specification of large expert systems are discussed. In the specification of causal probabilistic networks conditional probability tables for all nodes have to be provided. These conditional probability tables can often be described by models that specify the nature of interaction between node...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
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abstract：OBJECTIVES:The objective of this study is to devise a modelling strategy for attaining in-silico models replicating human physiology and, in particular, the activity of the autonomic nervous system. METHOD:Discrete Multiphysics (a multiphysics modelling technique) and Reinforcement Learning (a Machine Learning algorit...
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abstract：OBJECTIVE:Two major problems related the unsupervised analysis of gene expression data are represented by the accuracy and reliability of the discovered clusters, and by the biological fact that the boundaries between classes of patients or classes of functionally related genes are sometimes not clearly defined. The ma...
journal_title：Artificial intelligence in medicine
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doi：10.1016/j.artmed.2008.07.014
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abstract：OBJECTIVE:In this paper a new nonlinear system identification approach is developed for dynamical quantification of cardiovascular regulation. This approach is specifically focused on the identification of the heart rate (HR) baroreflex mechanism. The principal objective of this paper is to improve the model accuracy i...
journal_title：Artificial intelligence in medicine
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doi：10.1016/j.artmed.2011.01.002
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abstract：:Computer-aided detection (CADe) systems play a crucial role in pulmonary nodule detection via chest radiographs (CXRs). A two-stage CADe scheme usually includes nodule candidate detection and false positive reduction. A pure deep learning model, such as faster region convolutional neural network (faster R-CNN), has be...
journal_title：Artificial intelligence in medicine
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doi：10.1016/j.artmed.2020.101881
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abstract：BACKGROUND:Patients who are readmitted to an intensive care unit (ICU) usually have a high risk of mortality and an increased length of stay. ICU readmission risk prediction may help physicians to re-evaluate the patient's physical conditions before patients are discharged and avoid preventable readmissions. ICU readmi...
journal_title：Artificial intelligence in medicine
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journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2012.12.003
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journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2020.101852
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abstract：OBJECTIVES:The existence of proper non-invasive temperature estimators is an essential aspect when thermal therapy applications are envisaged. These estimators must be good predictors to enable temperature estimation at different operational situations, providing better control of the therapeutic instrumentation. In th...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2008.03.008
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journal_title：Artificial intelligence in medicine
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doi：10.1016/s0933-3657(03)00057-5
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journal_title：Artificial intelligence in medicine
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journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2004.07.015
更新日期：2005-02-01 00:00:00
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journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2008.07.013
更新日期：2009-05-01 00:00:00
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journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/s0933-3657(00)00090-7
更新日期：2001-01-01 00:00:00
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journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2004.07.001
更新日期：2005-03-01 00:00:00
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journal_title：Artificial intelligence in medicine
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doi：10.1016/j.artmed.2019.101727
更新日期：2019-11-01 00:00:00
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journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2017.06.007
更新日期：2017-11-01 00:00:00
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journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2014.06.003
更新日期：2014-09-01 00:00:00
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journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2006.04.003
更新日期：2006-07-01 00:00:00
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journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
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journal_title：Artificial intelligence in medicine
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doi：10.1016/j.artmed.2020.101821
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journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
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journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
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journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2020.101816
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journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2007.04.003
更新日期：2007-07-01 00:00:00
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journal_title：Artificial intelligence in medicine
pub_type： 杂志文章,评审
doi：10.1016/0933-3657(93)90013-s
更新日期：1993-04-01 00:00:00
abstract：OBJECTIVE:This paper identifies and reviews ethical issues associated with artificial intelligent care providers (AICPs) in mental health care and other helping professions. Specific recommendations are made for the development of ethical codes, guidelines, and the design of AICPs. METHODS:Current developments in the ...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2014.06.004
更新日期：2014-09-01 00:00:00
abstract：OBJECTIVE:To assess whether a user-centred prototype clinical decision support system (CDSS) providing patient-specific advice better supports healthcare practitioners in terms of (a) types of usability problems detected and (b) effective and efficient retrieval of childhood cancer survivor's follow-up screening proced...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2013.04.004
更新日期：2013-09-01 00:00:00
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journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
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journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2014.03.001
更新日期：2014-06-01 00:00:00
