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Currently, the exponential growth of biomedical data along with the complexities of managing high dimensionality, imbalanced distribution, sparse attributes instigates a difficult challenge of effectively applying functional networks as a new large-scale predictive modeling in healthcare and biomedicine. This article proposes functional networks based on propensity score and Newton Raphson-maximum-likelihood optimizations as a new large-scale machine learning classifier to enhance its performance in addressing these challenges within big biomedical data. Different use-cases scenarios based on integrated phenotypic and genomics big biomedical data were proposed: real-life biomedical data, (i) optimal design of cancer chemotherapy; (ii) identify inpatient-admission of individuals with primary diagnosis of cancer; (iii) identify severe asthma exacerbation children using integrated phenotypic and SNP repository data; and (iv) mixture models simulation studies. Comparative studies were carried to compare the performance of the new paradigm versus the common state-of-the-art of machine learning, data mining, and statistics schemes. The results of performance of the new classifier with the most common classifiers on the four benchmark databases have been recorded in tables and graphs. The obtained results of the new classifier outperform most of existing state-of-the art statistical machine learning schemes with reliable and efficient performance. The new predictive modeling classifier is saving the computational time and having reliable performances along with future avenue for extension to deal with next generation sequencing data on high performance computing platforms.

A dire need of an affordable and accessible technology in order to maintain horses' health can be seen in the animals' care sector. Technology integration at this level will not only enable users to have access to a reliable tool to measure the heartbeat of the horses but also helps to eliminate the need of costly sensors that are expensive and seldom portable. The main objective of the proposed solution is to monitor vital signs of the horses in the form of maintaining their health by an effective instrument that would help any kind of user with a little knowledge of technology to be able to take care of their horses. The sensors of the proposed system allow transmitting horses' heartbeat to a Mobile App through Bluetooth. Then, the Mobile App generates instantly statistics and required information about the horse's heartbeat rate. In addition, the Mobile App sends the horse's heartbeat to the client's servers for more analysis to give the owner of the horses a genuine competitive advantage. The results show that any kind of training program that needs to be conducted for the horse can be customized according to the horse's heart beat and the vital signs that the heartbeat of the horse denotes.

We compared the performance of several prediction techniques for breast cancer prognosis, based on AU-ROC performance (Area Under ROC) for different prognosis periods. The analyzed dataset contained 1,981 patients and from an initial 25 variables, the 11 most common clinical predictors were retained. We compared eight models from a wide spectrum of predictive models, namely; Generalized Linear Model (GLM), GLM-Net, Partial Least Square (PLS), Support Vector Machines (SVM), Random Forests (RF), Neural Networks, k-Nearest Neighbors (k-NN) and Boosted Trees. In order to compare these models, paired t-test was applied on the model performance differences obtained from data resampling. Random Forests, Boosted Trees, Partial Least Square and GLMNet have superior overall performance, however they are only slightly higher than the other models. The comparative analysis also allowed us to define a relative variable importance as the average of variable importance from the different models. Two sets of variables are identified from this analysis. The first includes number of positive lymph nodes, tumor size, cancer grade and estrogen receptor, all has an important influence on model predictability. The second set incudes variables related to histological parameters and treatment types. The short term vs long term contribution of the clinical variables are also analyzed from the comparative models. From the various cancer treatment plans, the combination of Chemo/Radio therapy leads to the largest impact on cancer prognosis.

Background: During the past decade, the IT industry has introduced several new concepts within the health domain including e-health, electronic health record, digital hospital, and many more. Although each of these terms has brought its own unique definition and perspectives, they were all based on the foundation that healthcare and wellness management are dependent on effectively using technology to access accurate data in a timely fashion; ensuring enhanced patient care and medical error reduction. The Electronic Health Record (EHR) is an integrated system that collects data from different healthcare providers to create a unified electronic record for each patient among the population. Today, the patient's health information is scattered across different healthcare facilities causing significant inefficiencies within the healthcare system. A national EHR system will tackle these challenges by producing a personal health record for each patient, integrating information from all healthcare providers, and additionally giving access to patients themselves allowing their contribution. Motivation: There are many health IT implementations for EHR programs around the world that serve as great learning experiences for Qatar, offering it a great opportunity to leverage the best national EHR implementation strategies and practices. National EHR initiatives in Qatar emphasize the need to have secure electronic management of health data in structured and standardized formats, which can be communicated across its hospitals, primary healthcare centres, and other healthcare facilities. Personalized medicine initiatives share and extend these goals, with additional precision provided by genetic/genomic-based improved diagnostic, prognostic, and preventive information; thereby demanding a coordinated extension for the adoption and implementation requirements of an integrated national EHR system. It is for this purpose and understanding that the State of Qatar has taken the first concrete steps towards a promising EHR journey that will promote significant changes on how healthcare services are delivered, and more importantly, how each individual in Qatar can be empowered to become an active contributor to the management of their own health. In moving towards the widespread adoption and implementation of a national EHR system in Qatar, it is important to study the different challenges and trends used for the adoption of EHR systems, under national strategies, in other countries. This is essential for health informatics researchers, clinicians, and policy makers, to gain greater insight into the issues concerning the transformation of healthcare using a national EHR system. The results of this review study shall complement, explain, and extend the conclusions of earlier studies commissioned to explore the health information technology ecosystem in the State of Qatar. Objectives: The purpose of this study is to review EHR programs from various countries with regard to the issues documented in the studies commissioned in these countries. Our analysis will derive the most common critical aspects and lessons learned from international experiences during the implementation of national EHR programs. Additionally, it will explore opportunities, constraints, and characteristics present in Qatar, necessary for tailoring the strategies and approaches to fully realize a national EHR system in the country. This review study presents two important contributions: 1) it will significantly support promoting health IT solutions that are right for Qatar's need, recognizing the size and capabilities of the country, leveraging existing healthcare organizations and solutions, and respecting the unique cultural characteristics of its population. 2) it will serve as a baseline from which comparisons, performance against target measures, and forward thinking can be scoped; allowing significant contribution towards productive future development of health information technology and personalized medicine initiatives in Qatar. Methods: The data collection techniques included: (a) literature review for articles about EHR adoption under national strategies in several countries, (b) review of reports regarding national e-health strategy and government policies in Qatar, and (c) interviews of people participating in the policy making for national EHR system in Qatar (health and academic professionals involved in health IT research in Qatar). The reviewed EHR programs were selected according to the following criteria: (a) program for the implementation of national EHR system has been initiated since at least 5 years, (b) pilot projects have already been conducted, and (c) the planned EHR systems encompass various approaches of implementation. In line with these criteria, the EHR programs that have been studied were those of the following five countries: United States, England, Estonia, Japan, and Australia. Results: The analysis performed on the selected international EHR programs revealed many lessons learned, including: 1) To achieve a successful EHR implementation, it is critical to increase the awareness of the Qatari population about the upcoming changes in their healthcare experiences, paving the way to a smoother transition while having people's trust and confidence in the new system. 2) It is essential to legally define the legislation of privacy protection of personal medical data to support new e-health concepts and eliminate the risk of violating the privacy of patient data. 3) It is important to allow appropriate time for procurement, utilization, benefit realization and the complete project, otherwise you may risk having stakeholders and the public lose confidence in the EHR project. 4) Financial incentives for healthcare providers proved to be an effective method towards raising the EHR adoption rate. 5) To expedite EHR program acceptance, it is imperative to recruit knowledgeable and experienced technical staff and healthcare leaders, who encourage others to play a critical role during the transition process, and view this change as a dominantly positive one. 6) In order to make EHR an everyday tool for doctors, nurses, patients and public authorities, it is necessary to implement services based on the interests of the healthcare providers and society. 7) Continuous adjustment and enhancement is needed in order to sustain a successful and efficient system. Conclusion: Experiences from other countries suggest that a clear focus needs to be carefully placed on technical, clinical, organizational, financial, social, and patient perspectives to ensure that the full benefits of a national EHR system in Qatar can be realized. In addition, it demonstrates that strategic and human challenges are more difficult to master than technical aspects. The results of this review study can be used as a baseline to provide recommendations on how to tackle potential barriers towards successful adoption of a national EHR system in Qatar.

BackgroundElectronic Medical Records (EMRs) hold diverse clinical information about large populations. When this information is coupled with genetic data, it has the potential to make unprecedented associations between genes and diseases. The incorporation of these discoveries into healthcare practice offers the hope to improve healthcare through personalized treatments. The Qatar National Genome project aims to achieve this vision by building a warehouse of genome sequencing information linked to de-identified EMR data. The warehouse should facilitate accessibility to research data, but also protect patients’ privacy and confidentiality by employing responsible data de-identification and data sharing mechanisms.This abstract discusses the privacy and governance challenges encountered during the construction and deployment of the data warehouse. To simplify the presentation, we divide the data management lifecycle into four stages and discuss the challenges at each stage separately: 1) Initial data collection, 2) data storage, 3) data sharing (utilization) and 4) Dissemination of research findings to the community.Data collectionThe data for the Qatari genome project is sought from the community. Thus it is important to consult with the population to establish the basic principles for data collection and research oversight. To achieve that, a community engagement model should be defined. The model should establish:1. An advocating technique for advertising the project to the community and raising the number of individuals who are aware of the project. The technique should strive to reach different elements within the society, provide clear dissemination of risks and benefits and establish methods for recurrent evaluation of the community attitudes and understanding of the Project.2. A recruitment strategy for establishing the enrollment criteria and enrollment process: a. The enrollment criteria defines the basis for enrollment (should it be disease based or volunteer based) and the acceptable age for volunteers, and b. The enrollment process defines the scope of subjects’ consent (opt in/out or informed consent) and warrants a clear boundary between research and clinical practice.3. The extent of institutional review board (IRB) and community oversight, given the potential impact of the project on the community, an oversight for the program by the community and the IRB should be discussed and established. The scope includes oversight on data repositories, oversight on research studies as well as oversight on any changes to the protocol (data use agreements, communications, etc.)Data storageFoundational documents in modern research ethics stress the importance of reducing harm to participants and maximizing benefits to the society. Re-identification of participants’ identity is one form of harm that can be involuntarily or deliberately inflicted. Personal information derived from EMR records and/or genomic data can be used against the participants to limit insurance coverage, to guide employment decisions, or to apply social stigma. To minimize the risk of harm, the research platform should store de-identified clinical and biobank data while retaining the link between both data sources (the de-identified EMR data and the biobank data). This can be achieved by applying the following two operations:1. The first operation (known as pseudonymization) identifies a stable and unique identifier(s) (such as Qatari IDs) that is included in both data sources and replaces it with a unique random ID (or pseudonym).2. The second removes all uniquely identifying information (such as names, record number, and emails) from the structured data and masks all unique identifiers from the unstructured data (such as doctors’ notes). To perform this step properly, we need to determine the uniquely identifying information proper to the Qatari setting. Due to the relatively small population size in Qatar, some regular attributes might prove to be very informative. For example an age of 87 or above and certain professions, such as lawyer, might uniquely identify a participant.Multiple aspects need to be considered when designing the pseudonymization operation, these include:1. Ensuring that each subject is assigned the same random ID (pseudonym) across the different data sources. This consistency will ensure that data belonging to a particular subject will be mapped to one record.2. The pseudonymization process could be reversible or not. Reversible systems allow reverting back to the identity of the subjects through a process called de-pseudonymization. They are used when communication with patients is a foreseen possibility.3. In case communication with participants is forecasted, then a secure de-pseudonymization mechanism should be specified. The mechanism should define (i) the cases for which re-identification can occur, (ii) the bodies that can initiate re-identification requests, (iii) those that rule and regulate these requests, and (iv) the actual re-identification mechanism.Data sharingAfter the removal of uniquely identifying information, the resulting data is said to be de-identified -but not anonymized. Access to (non-anonymized) biomedical data collected in Qatar is governed by the QSCH “guidelines, regulations and policies for research involving human subjects”. So a critical part in defining a data access protocol is to:1. Identify and understand data access procedures and requirements set by QSCH, and2. Identify and understand data access desires of the Qatari community (through surveys, meetings with community representatives, etc.)3. And finally deploy the gathered policies and requirements along with the collected consents into the design of the data-access platform.Note that access to the research data platform has to be provided to all research institutes within Qatar. Such as researchers from Hamad Medical Corporation, Qatar Biomedical Research Institute, Qatar Computational Research Institute, Weil Cornell Medical College in Qatar, Qatar University, Hamad bin Khalifa University, Sidra Medical and Research Center and other research institutions. Moreover, the data warehouse is viewed as a platform for worldwide collaborative research projects. With such massive mandate, a principal feature is to have the capacity to foster timely research and discoveries. Data application processes and approvals should be smooth and should not delay project initiation significantly. This cannot be realized using traditional “IRB-based” data-sharing systems. Thus, there will, eventually be a need to (fully or partially) automate the data access process. In other words, we need to design a system to automatically match data access requests with access decisions. In general, access decisions could be provided at multiple access levels. For example in some cases, the requested data could be exported to the investigator premises while in other cases, secure remote access can be imposed. In general, the granted access levels should counter the risk posed by data requests. For example, a request for highly sensitive data (such as HIV data) from an investigator affiliated with a well-established Qatari research institute is inherently less risky than a request for the same dataset by an investigator affiliated with an institution outside Qatar, thus, the second request should receive more access limitations than the first.Dissemination of findingsPrior work demonstrated that in order to affirm the value of research participation and contribute to public education, it is important to have a mechanism for disseminating research findings to the public. This will keep the community aware of how their participation is facilitating research and improving knowledge in the biomedical field.The mechanism should also tackle the issue of disseminating specific research findings to specific participants. One of the main challenges in that regard is to define when a finding is considered scientifically valid and when it is considered valuable information for the recipient.

Purpose The Arabic language uses notations called diacritical marks around characters to change which letter a symbol represents, changing the word in which that letter is contained. We explore the potential for error of these marks when faxed in a critical case such as prescriptions. A large number of patients are hurt by medical errors each year [1]. Extensive literature already documents the risks from handwritten prescriptions, while little work has been done with risks from reproduced printed prescriptions. No literature exists to examine risks of prescriptions in non-Roman character alphabets being reproduced. Reproduction via fax transmission is a common practice and often produces damaged copies which can lead to medication errors [2–4]. Languages with diacritical marks, particularly small ones, used for critical contextual meaning would more likely be at risk for misreading due to fax damage. Methods We generated text in English and Arabic reproducing common prescribing instructions, such as "every day" at various font sizes. This was placed on commonly used prescription paper, and reproduced via fax between 2 medical facility fax machines. Results We demonstrate meaningful change of prescribing instructions in the Arabic text by both the fax compression algorithm changing the appearance of diacritical marks along with a large amount of stochastic noise and dropouts being present. This change produced a potentially dangerous change in the instructions in the example we present. Conclusions Prescriptions that are faxed in languages that use diacritical marks to denote contextual meaning, are at high risk for misreading when reproduced via fax. We suggest mitigating strategies, including minimal font size and use of alliteration text in other languages.

Recently, big data is becoming the key to improve the future healthcare. The era of big biomedical data comes with significant challenges in querying, storage, visualize, and analyze the available petabytes of biomedical data, which makes healthcare industry a data-driven field. Currently, the available Concurrent Risk Model (CRM) is limited to the availability of patient episodes that are sensitivity to its cost. Herein, we propose a novel hierarchical data mining based on functional networks to develop a new CRM. This new risk score evaluates the last twelve-month period of patients’ expected risk/cost/severity/illness burden/disease intervention using both medical and drugs claim-based predictors: diagnoses, medications (yes/no), and demographics. Our novel CRM predicts $50,000 permember- per month (PMPM) tracks risk trends over time for any particular group, especially severe chronic diseases. Our CRM model has R2=0.57 in comparison with the best results of Society of Actuaries.

Currently, due to the availability of massive biomedical data on each individual, both healthcare and life Science is becoming data-driven. The input-attributes are structured/un-structured data with many challenges, including sparse-binary attributes with imbalanced outcomes, non-unique distributed structure and high-dimensional data, which hamper efforts to make a clinical decision in clinical practice. In recent decades, considerable effort has been made toward overcoming most of these challenges, but still there is an essential need for significant improvements in this field, especially after integrating both omics and phenotype data for future personalized medicine. These challenges motivate us to use the state-of-the-art of big data analytics and large-scale machine learning frameworks to confront most of the challenges and provide proper clinical solutions to assess physicians in clinical practice at the bedside and subsequently provide high quality care while reducing its cost. This research proposes a new recursive screening incremental ranking machine learning paradigm to empower the desired classifiers, especially for imbalanced training data, to create suitable data-driven clusters without prior information and later reduce the dimensionality of large biomedical data sets. The new framework combines many binary-attributes based on two criteria: (i) the minimum power value for each combination and (ii) the classification power of such a combination. Next, these sets of combined attributes are investigated by physicians to select the proper set of rules that make clinical sense and subsequently to use the result to empower the desired healthcare event (binary or multinomial target) at the bedside. After empowering the target class categories, we select the k-significant risk drivers with a suitable volume of data and high correlation to the desire outcome, and next, we establish the proper segmentation using AND-OR associative relationships. Finally, we use the propensity score to handle the imbalanced data, and next, we build break-through machine learning/data mining predictive models based on functional networks’ maximum-likelihood and Newton-Raphson iterative matrix computation mechanism to expedite the implementations within high performance computing platforms, such as scalable MapReduce HDFS, Spark MLlib, and Google Sibyl. Comparative studies with both simulated and real-life biomedical databases are carried out for identifying specific biomedical and healthcare outcomes, such as asthma, breast cancer, gene mutations selection and genomic association studies for specific complex diseases. Results have shown that the proposed incremental learning scheme empower the new classifier with reliable and stable performance. The new classifier outperforms the current existing predictive models in both high quality outcome and less expensive in execution time, especially, with imbalanced and sparse with high-dimensional big biomedical data. We recommend future work to be conducted using real-life integrated clinic-genomic big data with genome-wide association studies for future personalized medicine.