Decision Support for Medication Change of Parkinson's Disease Patients.

BACKGROUND AND OBJECTIVE: Parkinson's disease (PD) is a degenerative disorder of the central nervous system for which currently there is no cure. Its treatment requires long-term, interdisciplinary disease management, and usage of typical medications, including levodopa, dopamine agonists, and enzymes, such as MAO-B inhibitors. The key goal of disease management is to prolong patients' independence and keep their quality of life. Due to the different combinations of motor and non-motor symptoms from which PD patients suffer, in addition to existing comorbidities, the change of medications and their combinations is difficult and patient-specific. To help physicians, we developed two decision support models for PD management, which suggest how to change the medication treatment. METHODS: The models were developed using DEX methodology, which integrates the qualitative multi-criteria decision modelling with rule-based expert systems. The two DEX models differ in the way the decision rules were defined. In the first model, the decision rules are based on the interviews with neurologists (DEX expert model), and in the second model, they are formed from a database of past medication change decisions (DEX data model). We assessed both models on the Parkinson's Progression Markers Initiative (PPMI) and on a questionnaire answered by 17 neurologists from 4 European countries using accuracy measure and the Jaccard index. RESULTS: Both models include 15 sub-models that address possible medication treatment changes based on the given patients' current state. In particular, the models incorporate current state changes in patients' motor symptoms (dyskinesia intensity, dyskinesia duration, OFF duration), mental problems (impulsivity, cognition, hallucinations and paranoia), epidemiologic data (patient's age, activity level) and comorbidities (cardiovascular problems, hypertension and low blood pressure). The highest accuracy of the developed sub-models for 15 medication treatment changes ranges from 69.31 to 99.06 %. CONCLUSIONS: Results show that the DEX expert model is superior to the DEX data model. The results indicate that the constructed models are sufficiently adequate and thus fit for the purpose of making "second-opinion" suggestions to decision support users.

Disease progression in Parkinson subtypes: the PPMI dataset.

INTRODUCTION: Discrete patterns of progression have been suggested for patients with Parkinson disease and presenting tremor dominant (TD) or postural instability gait disorders (PIGD). However, longitudinal prospective assessments need to take into consideration the variability in clinical manifestations and the evidence that only 40% of initially classified PIGD remain in this subtype at subsequent visits. METHODS: We analyzed clinical progression of PIGD compared to TD using longitudinal clinical data from the PPMI. Given the reported instability of such clinical classification, we only included patients who were reported as PIGD/TD at each visit during the 4-year observation. We used linear mixed-effects models to test differences in progression in these subgroups in 51 dependent variables. RESULTS: There were 254 patients with yearly assessment. The number of PIGD was 36/254 vs 144/254 TD. PIGD had more severe motor disease at baseline but progressed faster than TD only in three non-motor items of the MDS-UPDRS: cognitive impairment, hallucinations, and psychosis plus features of DDS. Our analysis also showed in PIGD faster increase in the average time with dyskinesia. CONCLUSIONS: PIGD are characterized by more severe disease manifestations at diagnosis and greater cognitive progression, more frequent hallucinations, psychosis as well as features of DDS than TD patients. We interpret these findings as expression of greater cortical and subcortical involvement in PIGD already at onset. Since PIGD/TD classification is very unstable at onset, our analysis based on stricter definition criteria provides important insight for clinical trial stratification and definition of related outcome measures.

Symptoms and medications change patterns for Parkinson's disease patients stratification.

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.

PD_Manager: an mHealth platform for Parkinson's disease patient management.

PD_Manager is a mobile health platform designed to cover most of the aspects regarding the management of Parkinson's disease (PD) in a holistic approach. Patients are unobtrusively monitored using commercial wrist and insole sensors paired with a smartphone, to automatically estimate the severity of most of the PD motor symptoms. Besides motor symptoms monitoring, the patient's mobile application also provides various non-motor self-evaluation tests for assessing cognition, mood and nutrition to motivate them in becoming more active in managing their disease. All data from the mobile application and the sensors is transferred to a cloud infrastructure to allow easy access for clinicians and further processing. Clinicians can access this information using a separate mobile application that is specifically designed for their respective needs to provide faster and more accurate assessment of PD symptoms that facilitate patient evaluation. Machine learning techniques are used to estimate symptoms and disease progression trends to further enhance the provided information. The platform is also complemented with a decision support system (DSS) that notifies clinicians for the detection of new symptoms or the worsening of existing ones. As patient's symptoms are progressing, the DSS can also provide specific suggestions regarding appropriate medication changes.

Plant defence model revisions through iterative minimisation of constraint violations.

Biologists have been investigating plant defence response to virus infections; however, a comprehensive mathematical model of this complex process has not been developed. One obstacle in developing a dynamic model, useful for simulation, is the lack of kinetic data from which the model parameters could be determined. We address this problem by proposing a methodology for iterative improvement of the model parameters until the simulation results come close to the expectation of biology experts. These expectations are formalised in the form of constraints to be satisfied by the model simulations. In three iterative steps the model converged to satisfy the biology experts. There are two results of our approach: individual simulations and optimised model parameters, which provide a deeper insight into the biological system. Our constraint-driven optimisation approach allows for an efficient exploration of the dynamic behaviour of biological models and, at the same time, increases their reliability.

Signalling network construction for modelling plant defence response.

Plant defence signalling response against various pathogens, including viruses, is a complex phenomenon. In resistant interaction a plant cell perceives the pathogen signal, transduces it within the cell and performs a reprogramming of the cell metabolism leading to the pathogen replication arrest. This work focuses on signalling pathways crucial for the plant defence response, i.e., the salicylic acid, jasmonic acid and ethylene signal transduction pathways, in the Arabidopsis thaliana model plant. The initial signalling network topology was constructed manually by defining the representation formalism, encoding the information from public databases and literature, and composing a pathway diagram. The manually constructed network structure consists of 175 components and 387 reactions. In order to complement the network topology with possibly missing relations, a new approach to automated information extraction from biological literature was developed. This approach, named Bio3graph, allows for automated extraction of biological relations from the literature, resulting in a set of (component1, reaction, component2) triplets and composing a graph structure which can be visualised, compared to the manually constructed topology and examined by the experts. Using a plant defence response vocabulary of components and reaction types, Bio3graph was applied to a set of 9,586 relevant full text articles, resulting in 137 newly detected reactions between the components. Finally, the manually constructed topology and the new reactions were merged to form a network structure consisting of 175 components and 524 reactions. The resulting pathway diagram of plant defence signalling represents a valuable source for further computational modelling and interpretation of omics data. The developed Bio3graph approach, implemented as an executable language processing and graph visualisation workflow, is publically available at http://ropot.ijs.si/bio3graph/and can be utilised for modelling other biological systems, given that an adequate vocabulary is provided.

On-site electronic observational assessment tool for discomfort and pain.

Over the recent years pen-paper observational assessment scales have proven to be useful to monitor behaviour and responses of humans and animals. Observational assessment tools are typically applied for subjects who are not able to communicate directly. For on-site observational assessment however it is hard to record and evaluate timing patterns of observed events using pen-paper scales. Although timing information is in many cases assumed highly valuable, only (videotaped) laboratory scales are able to benefit from this knowledge. In the work described in this paper we digitize pen-paper assessment scales resulting in new functionalities capable to improve assessment scores. A study of on-site pain and discomfort assessment of severely demented elderly is presented. The resulting system is a mobile electronic device with a graphical user interface (GUI) on a touch screen. Moreover digital information is stored in a database improving administration, providing immediate feedback and allowing applications like: visualisation, statistical analysis and scientific research like data mining. The device allows easily registering and automatically interpreting complex timing patterns of behaviours and responses, on-site. This feature could be employed in the development of new more accurate observational assessment instruments.

Acquiring a dataset of labeled video images showing discomfort in demented elderly.

One of the effects of late-stage dementia is the loss of the ability to communicate verbally. Patients become unable to call for help if they feel uncomfortable. The first objective of this article was to record facial expressions of bedridden demented elderly. For this purpose, we developed a video acquisition system (ViAS) that records synchronized video coming from two cameras. Each camera delivers uncompressed color images of 1,024 x 768 pixels, up to 30 frames per second. It is the first time that such a system has been placed in a patient's room. The second objective was to simultaneously label these video recordings with respect to discomfort expressions of the patients. Therefore, we developed a Digital Discomfort Labeling Tool (DDLT). This tool provides an easy-to-use software representation on a tablet PC of validated "paper" discomfort scales. With ViAS and DDLT, 80 different datasets were obtained of about 15 minutes of recordings. Approximately 80% of the recorded datasets delivered the labeled video recordings. The remainder were not usable due to under- or overexposed images and due to the patients being out of view as the system was not properly replaced after care. In one of 6 observed patients, nurses recognized a higher discomfort level that would not have been observed without the DDLT.