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A Survey of Personalized Medicine Recommendation
Abstract
Mining potential and valuable medical knowledge from massive medical data to support clinical decision-making has become an important research field. Personalized medicine recommendation is an important research direction in this field, aiming to recommend the most suitable medicines for each patient according to the health status of the patient. Personalized medicine recommendation can assist clinicians to make clinical decisions and avoid the occurrence of medical abnormalities, so it has been widely concerned by many researchers. Based on this, this paper makes a comprehensive review of personalized medicine recommendation. Specifically, we first make clear the definition of personalized medicine recommendation problem; then, starting from the key theories and technologies, the personalized medicine recommendation algorithms proposed in recent years are systematically classified (medicine recommendation based on multi-disease, medicine recommendation with combination pattern, medicine recommendation with additional knowledge, and medicine recommendation based on feedback) and in-depth analyzed; and this paper also introduces how to evaluate personalized medicine recommendation algorithms and some common evaluation indicators; finally, the challenges of personalized medicine recommendation problem are put forward, and the future research direction and development trends are prospected.
... As the guest editor for the "Special Issue on AI for Health and Ageless Aging" in International Journal of Crowd Science, it is my privilege to present a collection of six pioneering research papers. "Multi-Bands Joint Graph Convolution EEG Functional Connectivity Network for Predicting Mental Disorders" (Paper 1) [5] and "Enhancing the Well-Being of Seniors: A Teachable Agent for Ikigai" (Paper 2) [6] focus on the theme of aging and mental health. The former introduces a novel framework for predicting mental disorders, showcasing the potential of EEG functional connectivity networks in the individual predictions and monitoring of mental health issues. ...
Artificial Intelligence (AI) is increasingly being applied in the health and aging domain in recent decades. These interdisciplinary research efforts and the application of AI technologies offer potential solutions for improving healthcare, ageless aging, medicinal development, etc. These applications simplify the lives of seniors, patients, doctors, caregivers, etc., significantly by performing tasks that are typically done by humans, but in less time and at a fraction of the cost
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Background & Objective
High medicine diversity has always been a significant challenge for prescription, causing confusion or doubt in physicians’ decision-making process. This paper aims to develop a medicine recommender system called RecoMed to aid the physician in the prescription process of hypertension by providing information about what medications have been prescribed by other doctors and figuring out what other medicines can be recommended in addition to the one in question.
Methods
There are two steps to the developed method: First, association rule mining algorithms are employed to find medicine association rules. The second step entails graph mining and clustering to present an enriched recommendation via ATC code, which itself comprises several steps. First, the initial graph is constructed from historical prescription data. Then, data pruning is performed in the second step, after which the medicines with a high repetition rate are removed at the discretion of a general medical practitioner. Next, the medicines are matched to a well-known medicine classification system called the ATC code to provide an enriched recommendation. And finally, the DBSCAN and Louvain algorithms cluster medicines in the final step.
Results
A list of recommended medicines is provided as the system's output, and physicians can choose one or more of the medicines based on the patient's clinical symptoms. Only the medicines of class #2, related to high blood pressure medications, are used to assess the system's performance. The results obtained from this system have been reviewed and confirmed by an expert in this field.
Background
Personalized medicine tailors care based on the patient’s or pathogen’s genotypic and phenotypic characteristics. An automated Clinical Decision Support System (CDSS) could help translate the genotypic and phenotypic characteristics into optimal treatment and thus facilitate implementation of individualized treatment by less experienced physicians.
Methods
We developed a hybrid knowledge- and data-driven treatment recommender CDSS. Stakeholders and experts first define the knowledge base by identifying and quantifying drug and regimen features for the prototype model input. In an iterative manner, feedback from experts is harvested to generate model training datasets, machine learning methods are applied to identify complex relations and patterns in the data, and model performance is assessed by estimating the precision at one, mean reciprocal rank and mean average precision. Once the model performance no longer iteratively increases, a validation dataset is used to assess model overfitting.
Results
We applied the novel methodology to develop a treatment recommender CDSS for individualized treatment of drug resistant tuberculosis as a proof of concept. Using input from stakeholders and three rounds of expert feedback on a dataset of 355 patients with 129 unique drug resistance profiles, the model had a 95% precision at 1 indicating that the highest ranked treatment regimen was considered appropriate by the experts in 95% of cases. Use of a validation data set however suggested substantial model overfitting, with a reduction in precision at 1 to 78%.
Conclusion
Our novel and flexible hybrid knowledge- and data-driven treatment recommender CDSS is a first step towards the automation of individualized treatment for personalized medicine. Further research should assess its value in fields other than drug resistant tuberculosis, develop solid statistical approaches to assess model performance, and evaluate their accuracy in real-life clinical settings.
Since coronavirus has shown up, inaccessibility of legitimate clinical resources is at its peak, like the shortage of specialists and healthcare workers, lack of proper equipment and medicines etc. The entire medical fraternity is in distress, which results in numerous individual's demise. Due to unavailability, individuals started taking medication independently without appropriate consultation, making the health condition worse than usual. As of late, machine learning has been valuable in numerous applications, and there is an increase in innovative work for automation. This paper intends to present a drug recommender system that can drastically reduce specialists heap. In this research, we build a medicine recommendation system that uses patient reviews to predict the sentiment using various vectorization processes like Bow, TF-IDF, Word2Vec, and Manual Feature Analysis, which can help recommend the top drug for a given disease by different classification algorithms. The predicted sentiments were evaluated by precision, recall, f1score, accuracy, and AUC score. The results show that classifier LinearSVC using TF-IDF vectorization outperforms all other models with 93% accuracy.
This paper presents the first deep reinforcement learning (DRL) framework to estimate the optimal Dynamic Treatment Regimes from observational medical data. This framework is more flexible and adaptive for high dimensional action and state spaces than existing reinforcement learning methods to model real-life complexity in heterogeneous disease progression and treatment choices, with the goal of providing doctor and patients the data-driven personalized decision recommendations. The proposed DRL framework comprises (i) a supervised learning step to predict the most possible expert actions, and (ii) a deep reinforcement learning step to estimate the long-term value function of Dynamic Treatment Regimes. Both steps depend on deep neural networks. As a key motivational example, we have implemented the proposed framework on a data set from the Center for International Bone Marrow Transplant Research (CIBMTR) registry database, focusing on the sequence of prevention and treatments for acute and chronic graft versus host disease after transplantation. In the experimental results, we have demonstrated promising accuracy in predicting human experts' decisions, as well as the high expected reward function in the DRL-based dynamic treatment regimes.
The World Health Organization estimates that almost one-third of the world’s adult population are suffering from hypertension which has gradually become a “silent killer”. Due to the varieties of anti-hypertensive drugs, patients are interested in how these drugs can be selected to match their respective conditions. This study provides a personalized recommendation service system of anti-hypertensive drugs based on context-awareness and designs a context ontology framework of the service. In addition, this paper introduces a Semantic Web Rule Language (SWRL)-based rule to provide high-level context reasoning and information recommendation and to overcome the limitation of ontology reasoning. To make the information recommendation of the drugs more personalized, this study also devises three categories of information recommendation rules that match different priority levels and uses a ranking algorithm to optimize the recommendation. The experiment conducted shows that combining the anti-hypertensive drugs personalized recommendation service context ontology (HyRCO) with the optimized rule reasoning can achieve a higher-quality personalized drug recommendation service. Accordingly this exploratory study of the personalized recommendation service for hypertensive drugs and its method can be easily adopted for other diseases.
With the development of e-commerce, a growing number of people prefer to purchase medicine online for the sake of convenience. However, it is a serious issue to purchase medicine blindly without necessary medication guidance. In this paper, we propose a novel cloud-assisted drug recommendation (CADRE), which can recommend users with top-N related medicines according to symptoms. In CADRE, we first cluster the drugs into several groups according to the functional description information, and design a basic personalized drug recommendation based on user collaborative filtering. Then, considering the shortcomings of collaborative filtering algorithm, such as computing expensive, cold start, and data sparsity, we propose a cloud-assisted approach for enriching end-user Quality of Experience (QoE) of drug recommendation, by modeling and representing the relationship of the user, symptom and medicine via tensor decomposition. Finally, the proposed approach is evaluated with experimental study based on a real dataset crawled from Internet.
Drug-drug interactions (DDIs) are one of the commonest causes of medication error in developed countries, particularly in the elderly due to poly-therapy, with a prevalence of 20-40%. In particular, poly-therapy increases the complexity of therapeutic management and thereby the risk of clinically important DDIs, which can both induce the development of adverse drug reactions or reduce the clinical efficacy. DDIs can be classify into two main groups: pharmacokinetic and pharmacodynamic. In this review, using Medline, PubMed, Embase, Cochrane library and Reference lists we searched articles published until June 30 2012, and we described the mechanism of pharmacokinetic DDIs focusing the interest on their clinical implications.
Predicting the response of cancer patients to a particular treatment is a major goal of modern oncology and an important step toward personalized treatment. In the practical clinics, the clinicians prefer to obtain the most-suited drugs for a particular patient instead of knowing the exact values of drug sensitivity. Instead of predicting the exact value of drug response, we proposed a deep learning-based method, named Siamese Response Deep Factorization Machines (SRDFM) Network, for personalized anti-cancer drug recommendation, which directly ranks the drugs and provides the most effective drugs. A Siamese network (SN), a type of deep learning network that is composed of identical subnetworks that share the same architecture, parameters and weights, was used to measure the relative position (RP) between drugs for each cell line. Through minimizing the difference between the real RP and the predicted RP, an optimal SN model was established to provide the rank for all the candidate drugs. Specifically, the subnetwork in each side of the SN consists of a feature generation level and a predictor construction level. On the feature generation level, both drug property and gene expression, were adopted to build a concatenated feature vector, which even enables the recommendation for newly designed drugs with only chemical property known. Particularly, we developed a response unit here to generate weighted genetic feature vector to simulate the biological interaction mechanism between a specific drug and the genes. For the predictor construction level, we built this level integrating a factorization machine (FM) component with a deep neural network component. The FM can well handle the discrete chemical information and both low-order and high-order feature interactions could be sufficiently learned. Impressively, the SRDFM works well on both single-drug recommendation and synergic drug combination. Experiment result on both single-drug and synergetic drug data sets have shown the efficiency of the SRDFM. The Python implementation for the proposed SRDFM is available at at https://github.com/RanSuLab/SRDFM Contact: [email protected], [email protected] and [email protected].
Deep learning is revolutionizing predictive healthcare, including recommending medications to patients with complex health conditions. Existing approaches focus on predicting all medications for the current visit, which often overlaps with medications from previous visits. A more clinically relevant task is to identify medication changes. In this paper, we propose a new recurrent residual networks, named MICRON, for medication change prediction. MICRON takes the changes in patient health records as input and learns to update a hid- den medication vector and the medication set recurrently with a reconstruction design. The medication vector is like the memory cell that encodes longitudinal information of medications. Unlike traditional methods that require the entire patient history for prediction, MICRON has a residual-based inference that allows for sequential updating based only on new patient features (e.g., new diagnoses in the recent visit), which is efficient. We evaluated MICRON on real inpatient and outpatient datasets. MICRON achieves 3.5% and 7.8% relative improvements over the best baseline in F1 score, respectively. MICRON also requires fewer parameters, which significantly reduces the training time to 38.3s per epoch with 1.5× speed-up.
Medication recommendation is an essential task of AI for healthcare. Existing works focused on recommending drug combinations for patients with complex health conditions solely based on their electronic health records. Thus, they have the following limitations: (1) some important data such as drug molecule structures have not been utilized in the recommendation process. (2) drug-drug interactions (DDI) are modeled implicitly, which can lead to sub-optimal results. To address these limitations, we propose a DDI-controllable drug recommendation model named SafeDrug to leverage drugs’ molecule structures and model DDIs explicitly. SafeDrug is equipped with a global message passing neural network (MPNN) module and a local bipartite learning module to fully encode the connectivity and functionality of drug molecules. SafeDrug also has a controllable loss function to control DDI level in the recommended drug combinations effectively. On a benchmark dataset, our SafeDrug is relatively shown to reduce DDI by 19.43% and improves 2.88% on Jaccard similarity between recommended and actually prescribed drug combinations over previous approaches. Moreover, SafeDrug also requires much fewer parameters than previous deep learning based approaches, leading to faster training by about 14% and around 2× speed-up in inference.
Most of the existing medicine recommendation systems that are mainly based on electronic medical records (EMRs) are significantly assisting doctors to make better clinical decisions benefiting both patients and caregivers. Even though the growth of EMRs is at a lighting fast speed in the era of big data, content limitations in EMRs restrain the existed recommendation systems to reflect relevant medical facts, such as drug-drug interactions. Many medical knowledge graphs that contain drug-related information, such as DrugBank, may give hope for the recommendation systems. However, the direct use of these knowledge graphs in systems suffers from robustness caused by the incompleteness of the graphs. To address these challenges, we stand on recent advances in graph embedding learning techniques and propose a novel framework, called Safe Medicine Recommendation (SMR), in this paper. Specifically, SMR first constructs a high-quality heterogeneous graph by bridging EMRs (MIMIC-III) and medical knowledge graphs (ICD-9 ontology and DrugBank). Then, SMR jointly embeds diseases, medicines, patients, and their corresponding relations into a shared lower dimensional space. Finally, SMR uses the embeddings to decompose the medicine recommendation into a link prediction process while considering the patient's diagnoses and adverse drug reactions. Extensive experiments on real datasets are conducted to evaluate the effectiveness of proposed framework.
Geriatric people face health problems, mainly with chronic diseases such as hypertension, diabetes, osteoarthritis, among others, which require continuous treatment. The prescription of multiple medications is a common practice in that population, which increase the risk of unwanted or dangerous drug interactions. The quantity of drugs is constantly growing, as are they interactions. It is therefore desirable to have support systems for medical that digest all available data and warn for possible drug interactions. In this paper we proposed a drug recommendation system that takes into account pre-existing diseases of the geriatric patient, current symptoms and verification of drug interactions. A Bayesian network model of the patient was built to allow reasoning in situations of limited evidence of the patient. The system uses also a genetic algorithm, which seeks the best drug combination based on the available patient information. The system showed consistency in simulated settings, which were validated by a specialist.
Medicine Combination Prediction (MCP) based on Electronic Health Record (EHR) can assist doctors to prescribe medicines for complex patients. Previous studies on MCP either ignore the correlations between medicines (i.e., MCP is formulated as a binary classifcation task), or assume that there is a sequential correlation between medicines (i.e., MCP is formulated as a sequence prediction task). The latter is unreasonable because the correlations between medicines should be considered in an order-free way. Importantly, MCP must take additional medical knowledge (e.g., Drug-Drug Interaction (DDI)) into consideration to ensure the safety of medicine combinations. However, most previous methods for MCP incorporate DDI knowledge with a post-processing scheme, which might undermine the integrity of proposed medicine combinations. In this paper, we propose a graph convolutional reinforcement learning model for MCP, named Combined Order-free Medicine Prediction Network (CompNet), that addresses the issues listed above. CompNet casts the MCP task as an order-free Markov Decision Process (MDP) problem and designs a Deep Q Learning (DQL) mechanism to learn correlative and adverse interactions between medicines. Specifcally, we frst use a Dual Convolutional Neural Network (Dual-CNN) to obtain patient representations based on EHRs. Then, we introduce the medicine knowledge associated with predicted medicines to create a dynamic medicine knowledge graph, and use a Relational Graph Convolutional Network (R-GCN) to encode it. Finally, CompNet selects medicines by fusing the combination of patient information and the medicine knowledge graph. Experiments on a benchmark dataset, i.e., MIMIC-III, demonstrate that CompNet signifcantly outperforms state-of-the-art methods and improves a recently proposed model by 3.74%pt, 6.64%pt in terms of Jaccard and F1 metrics.
Recent progress in deep learning is revolutionizing the healthcare domain including providing solutions to medication recommendations, especially recommending medication combination for patients with complex health conditions. Existing approaches either do not customize based on patient health history, or ignore existing knowledge on drug-drug interactions (DDI) that might lead to adverse outcomes. To fill this gap, we propose the Graph Augmented Memory Networks (GAMENet), which integrates the drug-drug interactions knowledge graph by a memory module implemented as a graph convolutional networks, and models longitudinal patient records as the query. It is trained end-to-end to provide safe and personalized recommendation of medication combination. We demonstrate the effectiveness and safety of GAMENet by comparing with several state-of-the-art methods on real EHR data. GAMENet outperformed all baselines in all effectiveness measures, and also achieved 3.60% DDI rate reduction from existing EHR data.
Medication recommendation is an important healthcare application. It is commonly formulated as a temporal prediction task. Hence, most existing works only utilize longitudinal electronic health records (EHRs) from a small number of patients with multiple visits ignoring a large number of patients with a single visit (selection bias). Moreover, important hierarchical knowledge such as diagnosis hierarchy is not leveraged in the representation learning process. Despite the success of deep learning techniques in computational phenotyping, most previous approaches have two limitations: task-oriented representation and ignoring hierarchies of medical codes. To address these challenges, we propose G-BERT, a new model to combine the power of Graph Neural Networks (GNNs) and BERT (Bidirectional Encoder Representations from Transformers) for medical code representation and medication recommendation. We use GNNs to represent the internal hierarchical structures of medical codes. Then we integrate the GNN representation into a transformer-based visit encoder and pre-train it on EHR data from patients only with a single visit. The pre-trained visit encoder and representation are then fine-tuned for downstream predictive tasks on longitudinal EHRs from patients with multiple visits. G-BERT is the first to bring the language model pre-training schema into the healthcare domain and it achieved state-of-the-art performance on the medication recommendation task.
Dynamic treatment recommendation systems based on large-scale electronic health records (EHRs) become a key to successfully improve practical clinical outcomes. Prior relevant studies recommend treatments either use supervised learning (e.g. matching the indicator signal which denotes doctor prescriptions), or reinforcement learning (e.g. maximizing evaluation signal which indicates cumulative reward from survival rates). However, none of these studies have considered to combine the benefits of supervised learning and reinforcement learning. In this paper, we propose Supervised Reinforcement Learning with Recurrent Neural Network (SRL-RNN), which fuses them into a synergistic learning framework. Specifically, SRL-RNN applies an off-policy actor-critic framework to handle complex relations among multiple medications, diseases and individual characteristics. The "actor'' in the framework is adjusted by both the indicator signal and evaluation signal to ensure effective prescription and low mortality. RNN is further utilized to solve the Partially-Observed Markov Decision Process (POMDP) problem due to lack of fully observed states in real world applications. Experiments on the publicly real-world dataset, i.e., MIMIC-3, illustrate that our model can reduce the estimated mortality, while providing promising accuracy in matching doctors' prescriptions.
Personalized medicine (PM) aiming at tailoring medical treatment to individual patient is critical in guiding precision prescription. An important challenge for PM is comorbidity due to the complex interrelation of diseases, medications and individual characteristics of the patient. To address this, we study the problem of PM for comorbidity and propose a neural network framework Deep Personalized Prescription for Comorbidity (PPC). PPC exploits multi-source information from massive electronic medical records (EMRs), such as demographic information and laboratory indicators, to support personalized prescription. Patient-level, disease-level and drug-level representations are simultaneously learned and fused with a trilinear method to achieve personalized prescription for comorbidity. Experiments on a publicly real world EMRs dataset demonstrate PPC outperforms state-of-the-art works.
Building on the success of the 1998 edition, Clinical Decision Support Systems: Theory and Practice, Second Edition, once again brings together worldwide experts to illustrate the underlying science and day-to-day use of decision support systmes in clinical and educational settings.
Writes the editor, "If used properly, CDSS have the potential to change the way medicine has been taught and practiced." As clinical decision support systems (CDSS) gain an increasingly central role in the delivery of high quality health care, it becomes more important for the health care community to understand their use. This text is designed as a resource for practicing clinicians, informaticians, teachers and students alike, and provides the most current, comprehensive look a the development and evaluation of clinical decision support systems.
Topics discussed include:
-Mathematical Foundations of Decision Support Systems
-Design and Implementation Issues
-Ethical and Legal Issues in Decision Support
-Clinical Trials of Information Interventians
-Hospital-Based Decision Support
-Real World Case Studies
Eta S. Berner, EdD, is a Professor of Health Informatics at the University of Alabama at Birmingham.
In this paper, we propose the first deep reinforcement learning framework to estimate the optimal Dynamic Treatment Regimes from observational medical data. This framework is more flexible and adaptive for high dimensional action and state spaces than existing reinforcement learning methods to model real life complexity in heterogeneous disease progression and treatment choices, with the goal to provide doctor and patients the data-driven personalized decision recommendations. The proposed deep reinforcement learning framework contains a supervised learning step to predict the most possible expert actions; and a deep reinforcement learning step to estimate the long term value function of Dynamic Treatment Regimes. We motivated and implemented the proposed framework on a data set from the Center for International Bone Marrow Transplant Research (CIBMTR) registry database, focusing on the sequence of prevention and treatments for acute and chronic graft versus host disease. We showed results of the initial implementation that demonstrates promising accuracy in predicting human expert decisions and initial implementation for the reinforcement learning step.
Managing patients with complex multimorbidity has long been recognized as a difficult problem due to complex disease and medication dependencies and the potential risk of adverse drug interactions. Existing work either uses complicated rule-based protocols which are hard to implement and maintain, or simple statistical models that treat each disease independently, which may lead to sub-optimal or even harmful drug combinations. In this work, we propose the LEAP (LEArn to Prescribe) algorithm to decompose the treatment recommendation into a sequential decision-making process while automatically determining the appropriate number of medications. A recurrent decoder is used to model label dependencies and content-based attention is used to capture label instance mapping. We further leverage reinforcement learning to fine tune the model parameters to ensure accuracy and completeness. We incorporate external clinical knowledge into the design of the reinforcement reward to effectively prevent generating unfavorable drug combinations. Both quantitative experiments and qualitative case studies are conducted on two real world electronic health record datasets to verify the effectiveness of our solution. On both datasets, LEAP significantly outperforms baselines by up to 10-30% in terms of mean Jaccard coefficient and removes 99.8% adverse drug interactions in the recommended treatment sets.
Type 2 diabetes is a chronic and progressive disease with a large number of management strategies and treatment options. Doctors face a problem to concern many patients with different disease factors and many anti-diabetics drugs options to prescript treatment. Various researches have been considered the treatment of type 2 diabetes, but the individualization of the treatment that effectively control diabetes and avoids its complication has not been considered. In this paper, we present IRS-T2D, a recommendation system for individualizing the treatment of type 2 diabetes that utilized ontology and SWRL. The official documents management of type 2 diabetes were used as criteria and adapted to build ontologies. Thus, we built two OWL ontologies one for patient profiles and one for anti-diabetic drugs. The medication constraints were represented by Semantic Web Rule Language (SWRL). Finally, OWL/SWRL knowledge base is transformed to Jess reasoning engine format to build the IRS-T2D system.
During last few years we have witnessed a steady increase in medicine use for healthcare. The medicine experiences rated by other patients have huge potential to empower people to make more informed decisions. While the majority of previous research focused on rating prediction and recommendations on E-Commerce field, the area of healthcare or medical treatments has been rarely handled. Moreover, the geographical and temporal factors were not considered in their recommendation mechanisms. The rapid development of mobile devices, wireless networks, smart phones and ubiquitous wireless connections enable people to build and maintain mobile social interactions and relationships. In this paper, we identify and formalize the significant problem that exploits the over-the-counter medicine rating prediction and recommendation in mobile social networks. Then we devise the recommendation model and develop corresponding prototype of iDrug, reflecting a solution scheme of medicine rating prediction and recommendation in mobile social networks to increase the information accessibility for people’s decision support.
Diabetes mellitus is a common chronic disease in recent years. According to the World Health Organization, the estimated number of diabetic patients will increase 56% in Asia from the year 2010 to 2025, where the number of anti-diabetic drugs that doctors are able to utilize also increase as the development of pharmaceutical drugs. In this paper, we present a recommendation system for anti-diabetic drugs selection based on fuzzy reasoning and ontology techniques, where fuzzy rules are used to represent knowledge to infer the usability of the classes of anti-diabetic drugs based on fuzzy reasoning techniques. We adopt the "Medical Guidelines for Clinical Practice for the Management of Diabetes Mellitus" provided by the American Association of Clinical Endocrinologists to build the ontology knowledge base. The experimental results show that the proposed anti-diabetic drugs recommendation system gets the same accuracy rate as the one of Chen et al.'s method (R. C. Chen, Y. H. Huang, C. T. Bau and S. M. Chen, Expert Syst. Appl. 39(4) (2012) 3995-4006.) and it is better than Chen et al.'s method (R. C. Chen, Y. H. Huang, C. T. Bau and S. M. Chen, Expert Syst. Appl. 39(4) (2012) 3995-4006.) due to the fact that it can deal with the semantic degrees of patients' tests and can provide different recommend levels of anti-diabetic drugs. It provides us with a useful way for anti-diabetic drugs selection based on fuzzy reasoning and ontology techniques.
We demonstrate how data mining techniques can help recommend effective medications when physicians need to control the glucose level of patients with type 2 diabetes. We first identify the factors that may affect physicians' medication decisions and then develop a patient-similarity based approach to automatically recommend medications for a patient with the specific condition so that his blood glucose level (measured by HbA1C value) can be well controlled. The approach is validated through experiments on real data sets and compared with the recommendations by following a clinical guideline.
A recommendation system can help user to make requirement analysis and to recommend better decision from much complex information. This research is concentrated on medical prescription recommendation. According to the statistics of Department of Health, diabetes was the top ten of death in Taiwan. Therefore, developing an effective treatment for diabetes is very important. The purpose of this study is to develop a decision support system to assist a doctor to make more appropriate decision in selecting drugs. We first built the ontology of diabetic knowledge and compute medication by multiples criteria decision making method (MCDM). The entropy was used to compute data of patient's history and then take this result to integrate medicine knowledge ontology to list appropriate medications. Finally, more suitable medications are recommended to doctors. Primary experiments proof our method is useful.
Clinical decision support systems (CDSSs) have the potential to improve kidney-related drug prescribing by supporting the appropriate initiation, modification, monitoring, or discontinuation of drug therapy.
Systematic review. We identified studies by searching multiple bibliographic databases (eg, MEDLINE and EMBASE), conference proceedings, and reference lists of all included studies.
CDSSs used in hospital or outpatient settings for acute kidney injury and chronic kidney disease, including end-stage renal disease (chronic dialysis patients or transplant recipients).
Studies prospectively using CDSSs to aid in kidney-related drug prescribing.
Computerized or manual CDSSs.
Clinician prescribing and patient-important outcomes as reported by primary study investigators. CDSS characteristics, such as whether the system was computerized, and system setting.
We identified 32 studies. In 17 studies, CDSSs were computerized, and in 15 studies, they were manual pharmacist-based systems. Systems intervened by prompting for drug dosing adjustments in relation to the level of decreased kidney function (25 studies) or in response to serum drug concentrations or a clinical parameter (7 studies). They were used most in academic hospital settings. For computerized CDSSs, clinician prescribing outcomes (eg, frequency of appropriate dosing) were considered in 11 studies, with all 11 reporting statistically significant improvements. Similarly, manual CDSSs that incorporated clinician prescribing outcomes showed statistically significant improvements in 6 of 8 studies. Patient-important outcomes (eg, adverse drug events) were considered in 7 studies of computerized CDSSs, with statistically significant improvements in 2 studies. For manual CDSSs, 6 studies measured patient-important outcomes and 5 reported statistically significant improvements. Cost-savings also were reported, mostly for manual CDSSs.
Studies were heterogeneous in design and often limited by the evaluation method used. Benefits of CDSSs may be reported selectively in this literature.
CDSSs are available for many dimensions of kidney-related drug prescribing, and results are promising. Additional high-quality evaluations will guide their optimal use.
The recommendation of medicines based on multiple criteria decision making and domain ontology-an example of anti-diabetic medicines
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