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We provide a reappraisal of Tal and Wansink's study “Blinded with Science”, where seemingly trivial charts were shown to increase belief in drug efficacy, presumably because charts are associated with science. Through a series of four replications conducted on two crowdsourcing platforms, we investigate an alternative explanation, namely, that the charts allowed participants to better assess the drug's...
Observational data resources based on the capture of clinical data in the electronic health record (EHR) have produced significant learning opportunities in many areas of medicine. These large data resources can span multiple hospital systems and employ common semantics, ontologies, and data models. They have uncovered critical safety issues for patients, and spurred observational research and clinical...
Microarray platforms such as Gene expression, oligonucleotide (GeneChip), and cDNA (complementary DNA) can play a critical role in the understanding of genome sequencing, by providing information for hundreds or thousands genes in a single assay, information not available by using other methodologies of investigation. Microarray experiments aim to survey patterns of gene expression by assaying the...
This paper presents a temporal pattern mining method for medical data. It modifies the mining algorithms proposed by Batal et al. to incorporate with ranged relations. Experimental results demonstrate that the proposed method could generate frequent patterns with abstracted time ranges embedded in their temporal relations.
Opioid (e.g., heroin and morphine) addiction has become one of the largest and deadliest epidemics in the United States. To combat such deadly epidemic, there is an urgent need for novel tools and methodologies to gain new insights into the behavioral processes of opioid addiction and treatment. In this paper, we design and develop an intelligent system named iOPU to automate the detection of opioid...
Electronic medical record (EMR) system has become increasingly more important in developed countries due to its convenience and efficiency in medical information storage, management and analysis. However, one of the main limitations of EMR lies in that the clinical data for patients cannot be exchanged among different medical institutions. Recently, the cloud-based clinic system, featuring in lower...
Medical institutes use Electronic Medical Record (EMR) to record a series of medical events, including diagnostic information (diagnosis codes), procedures performed (procedure codes) and admission details. Plenty of data mining technologies are applied in the EMR data set for knowledge discovery, which is precious to medical practice. The knowledge found is conducive to develop treatment plans, improve...
A symptom is the physical indication of an unstable state or the beginning of diseases. Symptom analysis is an essential factor in the medical area, where it is used for disease diagnosis, drug prescription, and the development of new pharmaceuticals. Commensurate with its importance, symptom analysis has been the subject of various studies in recent years. However, prior literature on this topic...
A drug can be used to deal more than one diseases and to deal an illness often need a combination of more than one drugs. This paper present how to discover a pattern of a combination of medicines related to a diagnosis of diseases using FP-Growth one of frequent pattern mining algorithm. We use FP-Growth because it has better performance than Apriori and Eclat. Data is collected from outpatients...
The amount of data being collected and stored is huge and is expanding at a vivid pace at both the national and international level. Health care organizations correspondingly generate a large volume of information every day. The health care industry is rich in information but it needs to discover hidden relationships and patterns in this data. This paper intends to use data mining techniques to discover...
Migraine is a common disease throughout the world. Not only does it affect the life of people tremendously, but it also leads to high costs, e.g. due to inability to work or various required drug-taking cycles for finding the best drug for a patient. Solving the latter aspect could help to improve the life of patients and decrease the impact of the other consequences. Therefore, in this paper, we...
High-order Drug-Drug Interactions (DDI) are common particularly for elderly people. It is highly non-trivial to detect such interactions via in vivo/in vitro experiments. In this paper, we present SVM-based classification methods to predict whether a high-order directional drug-drug interaction (HoDDDI) instance is associated with adverse drug reactions (ADRs) and induced side effects. Specifically,...
Off-label drug use refers to prescribing marketed medications for the indications that are not included in their FDA-approved labeling information. Off-label drug use is quite common in clinical practice and inevitable to some extent. Considering the increasing discussions in online health communities (OHCs) among the health consumers, we proposed to harness the large volume of timely information...
The widespread adoption of Electronic Health Records (EHRs) has enabled data-driven approaches to clinical care and research. However, the performance and generalizability of those approaches are severely hampered by the lack of syntactic and semantic interoperability of EHR data across institutions. Towards resolving this problem, Common Data Models (CDMs) can be used to standardize the clinical...
Approximate functional dependencies, even with suitable temporal extensions, have been recently proposed as a methodological tool for mining clinical data. It allows healthcare stakeholders to derive new knowledge from overwhelming amount of healthcare and clinical data. Some examples of the kind of knowledge derivable from data through dependencies may be "month by month, patients with the same...
As a kind of popular problem in machine learning, multi-instance task has been researched by means of many classical methods, such as kNN, SVM, etc. For kNN classification, its performance on traditional task can be boosted by metric learning, which seeks for a data-dependent metric to make similar examples closer and separate dissimilar examples by a margin. It is a challenge to define distance between...
Chemical characterization of illicit drugs, like cocaine, is important to provide chemical and physical information to assist police agencies to understand drug trafficking and identify drug origin. In this context, the present work shows the use of an electrochemical sensor containing two working electrodes (pt and GC) to extract voltammetric information about cutting agents added to cocaine (procaine,...
We don't have a choice on whether we DO social media, the question is how well we DO it. To track the mood of people about any particular product by review we use opinion mining which is a natural language processing technique. Customer review analysis is most important only by which product is rated and it is a major problem today. Reviews from social media are collected manually and then pre-processed...
Many anticancer drugs currently used have an origin from natural sources. It is reported that in between 1981 and 2006, 47.1% of the 155 clinically approved anti-cancer drugs, were unmodified natural products or their semi-synthetic derivatives or even synthesized molecules based on natural models [4]. Aloe vera is one of the oldest known medicinal plants. Studies show that the Aloe vera leaf extract...
We are developing TextDB, an open-source datamanagement system that supports text-centric operations in a declarative and efficient way using an algebraic approach as in relational DBMS. In this demonstration, we show scenarios where we can use TextDB to perform powerful information extraction easily and efficiently on text documents. Video: https://github.com/TextDB/textdb/wiki/Video.
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