Abu Saleh Mohammad Mosa

A Systematic Review of Healthcare Applications for Smartphones

BackgroundAdvanced mobile communications and portable computation are now combined in handheld devices called “smartphones”, which are also capable of running third-party software. The number of smartphone users is growing rapidly, including among healthcare professionals. The purpose of this study was to classify smartphone-based healthcare technologies as discussed in academic literature according to their functionalities, and summarize articles in each category.MethodsIn April 2011, MEDLINE was searched to identify articles that discussed the design, development, evaluation, or use of smartphone-based software for healthcare professionals, medical or nursing students, or patients. A total of 55 articles discussing 83 applications were selected for this study from 2,894 articles initially obtained from the MEDLINE searches.ResultsA total of 83 applications were documented: 57 applications for healthcare professionals focusing on disease diagnosis (21), drug reference (6), medical calculators (8), literature search (6), clinical communication (3), Hospital Information System (HIS) client applications (4), medical training (2) and general healthcare applications (7); 11 applications for medical or nursing students focusing on medical education; and 15 applications for patients focusing on disease management with chronic illness (6), ENT-related (4), fall-related (3), and two other conditions (2). The disease diagnosis, drug reference, and medical calculator applications were reported as most useful by healthcare professionals and medical or nursing students.ConclusionsMany medical applications for smartphones have been developed and widely used by health professionals and patients. The use of smartphones is getting more attention in healthcare day by day. Medical applications make smartphones useful tools in the practice of evidence-based medicine at the point of care, in addition to their use in mobile clinical communication. Also, smartphones can play a very important role in patient education, disease self-management, and remote monitoring of patients.

Data-Mining Technologies for Diabetes: A Systematic Review

Background: The objective of this study is to conduct a systematic review of applications of data-mining techniques in the field of diabetes research. Method: We searched the MEDLINE database through PubMed. We initially identified 31 articles by the search, and selected 17 articles representing various data-mining methods used for diabetes research. Our main interest was to identify research goals, diabetes types, data sets, data-mining methods, data-mining software and technologies, and outcomes. Results: The applications of data-mining techniques in the selected articles were useful for extracting valuable knowledge and generating new hypothesis for further scientific research/experimentation and improving health care for diabetes patients. The results could be used for both scientific research and real-life practice to improve the quality of health care diabetes patients. Conclusions: Data mining has played an important role in diabetes research. Data mining would be a valuable asset for diabetes researchers because it can unearth hidden knowledge from a huge amount of diabetes-related data. We believe that data mining can significantly help diabetes research and ultimately improve the quality of health care for diabetes patients.

A Study on Pubmed Search Tag Usage Pattern: Association Rule Mining of a Full-day Pubmed Query Log

BackgroundThe practice of evidence-based medicine requires efficient biomedical literature search such as PubMed/MEDLINE. Retrieval performance relies highly on the efficient use of search field tags. The purpose of this study was to analyze PubMed log data in order to understand the usage pattern of search tags by the end user in PubMed/MEDLINE search.MethodsA PubMed query log file was obtained from the National Library of Medicine containing anonymous user identification, timestamp, and query text. Inconsistent records were removed from the dataset and the search tags were extracted from the query texts. A total of 2,917,159 queries were selected for this study issued by a total of 613,061 users. The analysis of frequent co-occurrences and usage patterns of the search tags was conducted using an association mining algorithm.ResultsThe percentage of search tag usage was low (11.38% of the total queries) and only 2.95% of queries contained two or more tags. Three out of four users used no search tag and about two-third of them issued less than four queries. Among the queries containing at least one tagged search term, the average number of search tags was almost half of the number of total search terms. Navigational search tags are more frequently used than informational search tags. While no strong association was observed between informational and navigational tags, six (out of 19) informational tags and six (out of 29) navigational tags showed strong associations in PubMed searches.ConclusionsThe low percentage of search tag usage implies that PubMed/MEDLINE users do not utilize the features of PubMed/MEDLINE widely or they are not aware of such features or solely depend on the high recall focused query translation by the PubMed’s Automatic Term Mapping. The users need further education and interactive search application for effective use of the search tags in order to fulfill their biomedical information needs from PubMed/MEDLINE.

Analysis of PubMed User Sessions Using a Full-Day PubMed Query Log: A Comparison of Experienced and Nonexperienced PubMed Users

Background PubMed is the largest biomedical bibliographic information source on the Internet. PubMed has been considered one of the most important and reliable sources of up-to-date health care evidence. Previous studies examined the effects of domain expertise/knowledge on search performance using PubMed. However, very little is known about PubMed users’ knowledge of information retrieval (IR) functions and their usage in query formulation. Objective The purpose of this study was to shed light on how experienced/nonexperienced PubMed users perform their search queries by analyzing a full-day query log. Our hypotheses were that (1) experienced PubMed users who use system functions quickly retrieve relevant documents and (2) nonexperienced PubMed users who do not use them have longer search sessions than experienced users. Methods To test these hypotheses, we analyzed PubMed query log data containing nearly 3 million queries. User sessions were divided into two categories: experienced and nonexperienced. We compared experienced and nonexperienced users per number of sessions, and experienced and nonexperienced user sessions per session length, with a focus on how fast they completed their sessions. Results To test our hypotheses, we measured how successful information retrieval was (at retrieving relevant documents), represented as the decrease rates of experienced and nonexperienced users from a session length of 1 to 2, 3, 4, and 5. The decrease rate (from a session length of 1 to 2) of the experienced users was significantly larger than that of the nonexperienced groups. Conclusions Experienced PubMed users retrieve relevant documents more quickly than nonexperienced PubMed users in terms of session length.

Evaluating the Benefits of Octree-based Indexing for LiDAR Data

6 In recent years the geospatial domain has seen a significant increase in the availability of very large three7 dimensional (3D) point datasets. These datasets originate from a variety of sources, such as for example 8 Light Detection and Ranging (LiDAR) or meteorological weather recordings. Increasingly, a desire 9 within the geospatial community has been expressed to exploit these types of 3D point data in a 10 meaningful engineering context that goes beyond mere visualization. However, current Spatial 11 Information Systems (SISs) provide only limited support for vast 3D point datasets. Even those systems 12 that advertise their support for in-built 3D data types provide very limited functionality to manipulate 13 such data types. In particular, an effective means of indexing large 3D point datasets is yet missing, 14 however it is crucial for effective analysis. Next to the large size of 3D point datasets they may also be 15 information rich, for example they may contain color information or some other associated semantic. This 16 paper presents an alternative spatial indexing technique, which is based on an octree data structure. We 17 show that it outperforms R-tree index, while being able to group 3D points based on their attribute values 18 at the same time. This paper presents an evaluation employing this octree spatial indexing technique and 19 successfully highlights its advantages for sparse as well as uniformly distributed data on the basis of an 20 extensive LiDAR dataset. 21

Association mining of search tags in PubMed search sessions

Background: Previous studies have shown that use of search tags in PubMed can significantly improve the performance of information retrieval. The objective of this study was to discover associations among search tags in typical PubMed search sessions. Methods: We performed session segmentation on a full-day PubMed query log, identified the search tags within those sessions, and applied association mining to identify strong associations of search tags. Results: A total of eight maximal frequent-itemsets (i.e. search tags) and 34 strong association rules from these itemsets were discovered. We also estimated that the query refinement occurs frequently (i.e. one query per minute on average) for any session length. Conclusions: The association rules consisting of PubMed search tags can be used to develop an interactive and intelligent PubMed search interface so that the users can build the search query using proper search tags and reduce the frequency of query refinement.