Chihwen Cheng

Omic and Electronic Health Record Big Data Analytics for Precision Medicine

<italic>Objective:</italic> Rapid advances of high-throughput technologies and wide adoption of electronic health records (EHRs) have led to fast accumulation of –omic and EHR data. These voluminous complex data contain abundant information for precision medicine, and big data analytics can extract such knowledge to improve the quality of healthcare. <italic>Methods:</italic> In this paper, we present –omic and EHR data characteristics, associated challenges, and data analytics including data preprocessing, mining, and modeling. <italic>Results:</italic> To demonstrate how big data analytics enables precision medicine, we provide two case studies, including identifying disease biomarkers from multi-omic data and incorporating –omic information into EHR. <italic>Conclusion: </italic> Big data analytics is able to address –omic and EHR data challenges for paradigm shift toward precision medicine. <italic>Significance:</italic> Big data analytics makes sense of –omic and EHR data to improve healthcare outcome. It has long lasting societal impact.OBJECTIVE Rapid advances of high-throughput technologies and wide adoption of electronic health records (EHRs) have led to fast accumulation of -omic and EHR data. These voluminous complex data contain abundant information for precision medicine, and big data analytics can extract such knowledge to improve the quality of health care. METHODS In this article, we present -omic and EHR data characteristics, associated challenges, and data analytics including data pre-processing, mining, and modeling. RESULTS To demonstrate how big data analytics enables precision medicine, we provide two case studies, including identifying disease biomarkers from multi-omic data and incorporating -omic information into EHR. CONCLUSION Big data analytics is able to address -omic and EHR data challenges for paradigm shift towards precision medicine. SIGNIFICANCE Big data analytics makes sense of -omic and EHR data to improve healthcare outcome. It has long lasting societal impact.

Towards a magnetic localization system for 3-D tracking of tongue movements in speech-language therapy

This paper presents a new magnetic localization system based on a compact triangular sensor setup and three different optimization algorithms, intended for tracking tongue motion in the 3-D oral space. A small permanent magnet, secured on the tongue by tissue adhesives, will be used as a tracer. The magnetic field variations due to tongue motion are detected by a 3-D magneto-inductive sensor array outside the mouth and wirelessly transmitted to a computer. The position and rotation angles of the tracer are reconstructed based on sensor outputs and magnetic dipole equation using DIRECT, Powell, and Nelder-Mead optimization algorithms. Localization accuracy and processing time of the three algorithms are compared using one data set collected in which source-sensor distance was changed from 40 to 150 mm. Powell algorithm showed the best performance with 0.92 mm accuracy in position and 0.7o in orientation. The average processing time was 43.9 ms/sample, which can satisfy real time tracking up to ~20 Hz.

SickleREMOTE: A two-way text messaging system for pediatric sickle cell disease patients

Sickle cell disease, the most common hemo-globinopathy in the world, affects patient lives from early childhood. Effective care of sickle cell disease requires frequent medical monitoring, such as tracking the frequency, severity, and duration of painful events. Conventional monitoring includes paper- or web-based reporting diaries. These systems require that patients carry forms, which are easily lost, or laptop computers, which are impractical to scale to large populations. Both are prone to sporadic use by older adolescents due to lack of reminders. In this paper, we design and prototype a Sickle cell disease REporting and MOnitoring TElemedicine system (SickleREMOTE), aiming to resolve limitations of conventional monitoring diaries. This monitoring system is configured as automated short message service text (SMS-text) messages that arrive at a mobile phone anywhere on a cellular network. The messages may be reminders to encourage treatment adherence or questionnaires to collect self-assessed clinical data relating to treatment adjustments. Patients respond to the messages using pre-determined templates and a cloud database parses and stores messages automatically. Providers use a web-based interface to view, analyze, and download collected data. SickleREMOTE is developed by Georgia Institute of Technology in conjunction with Childrens Healthcare of Atlanta (CHOA). System effectiveness will be evaluated using a trial of 30 adolescents with sickle cell disease and measured by response rate, time to response, error rate, and correspondence with data collected by telephone calls.

iACT - An interactive mHealth monitoring system to enhance psychotherapy for adolescents with sickle cell disease

Sickle cell disease (SCD) is the most common inherited disease, and SCD symptoms impact functioning and well-being. For example, adolescents with SCD have a higher tendency of psychological problems than the general population. Acceptance and Commitment Therapy (ACT), a cognitive-behavioral therapy, is an effective intervention to promote quality of life and functioning in adolescents with chronic illness. However, traditional visit-based therapy sessions are restrained by challenges, such as limited follow-up, insufficient data collection, low treatment adherence, and delayed intervention. In this paper, we present Instant Acceptance and Commitment Therapy (iACT), a system designed to enhance the quality of pediatric ACT. iACT utilizes text messaging technology, which is the most popular cell phone activity among adolescents, to conduct real-time psychotherapy interventions. The system is built on cloud computing technologies, which provides a convenient and cost-effective monitoring environment. To evaluate iACT, a trial with 60 adolescents with SCD is being conducted in conjunction with the Georgia Institute of Technology, Childrens Healthcare of Atlanta, and Georgia State University.

Molecular analysis of congenital adrenal hyperplasia due to 21-hydroxylase deficiency in Hong Kong Chinese patients

BACKGROUND Congenital adrenal hyperplasia (CAH) caused by 21-hydroxylase deficiency (21OHD) is an autosomal recessive disorder due to mutation in the CYP21A2 gene. OBJECTIVE To elucidate the genetic basis of 21-hydroxylase-deficient CAH in Hong Kong Chinese patients. PATIENTS AND METHODS Mutational analysis of the CYP21A2 gene was performed on 35 Hong Kong Chinese patients with 21OHD using direct DNA sequencing and multiplex ligation-dependent probe amplification (MLPA). RESULTS The genetic findings of 21 male and 14 female patients are the following: c.293-13A/C>G (intron 2 splice site; 20 alleles), p.I172N (13), p.R356W (7), p.Q318X (4). A total of 20 mutant alleles contained gross deletion/conversion of all or part of the CYP21A2 gene. A novel mutation, c.1367delA (p.D456fs), was detected in one patient. One patient had only a heterozygous mutation detected. Out of 35 patients, 16 would have been incorrectly genotyped if either DNA sequencing or MLPA alone was used for molecular analysis. CONCLUSIONS The frequency of various mutations in the studied patients differs from those reported in other Asian populations. Gross deletion/conversion accounts for nearly one-third of the genetic defects. Therefore, laboratories must include methods for detecting point mutations as well as gross deletions/conversions to avoid misinterpretation of genotype. Genotyping has increasingly been proven to be a useful tool for supplementing, if not replacing, hormonal profiling for the diagnosis of 21OHD.

Activity and school attendance monitoring system for adolescents with Sickle cell disease

Sickle cell disease, the most common hemoglobin disorder, affects major organ systems with symptoms of pain, anemia and a multitude of chronic conditions. For adolescents, the disease adversely affects school attendance, academic progress and social activity. To effectively study the relationship among school attendance and other factors like demographics and academic performance, studies have relied on self-reporting and school records, all of which have some bias. In this study we design and prototype a system, called SickleSAM (Sickle cell School attendance and Activity Monitoring system), for automatically monitoring school attendance and daily activity of adolescents with sickle cell disease. SickleSAM intends to remove human bias and inaccuracies. The system uses built-in GPS to collect data which will be recorded into a cloud database using Short Messaging Service technology. SickleSAM is developed by Georgia Institute of Technology in conjunction with Childrens Healthcare of Atlanta (CHOA). System effectiveness is being evaluated using a trial of 10 adolescents with the disease.

PHARM - Association Rule Mining for Predictive Health

Predictive health is a new and innovative healthcare model that focuses on maintaining health rather than treating diseases. Such a model may benefit from computer-based decision support systems, which provide more quantitative health assessment, enabling more objective advice and action plans from predictive health providers. However, data mining for predictive health is more challenging compared to that for diseases. This is a reason why there are relatively fewer predictive health decision support systems embedded with data mining. The purpose of this study is to research and develop an interactive decision support system, called PHARM, in conjunction with Emory Center for Health Discovery and Well Being (CHDWB®). PHARM adopts association rule mining to generate quantitative and objective rules for health assessment and prediction. A case study results in 12 rules that predict mental illness based on five psychological factors. This study shows the value and usability of the decision support system to prevent the development of potential illness and to prioritize advice and action plans for reducing disease risks.

Mining Association Rules for Neurobehavioral and Motor Disorders in Children Diagnosed with Cerebral Palsy

Children diagnosed with cerebral palsy (CP) appear to be at high risk for developing neurobehavioral and motor disorders. The most common disorders for these children are impaired visual-perception skills and motor planning. Besides, they often have impaired executive functions, which can contribute to problematic emotional adjustment such as depression. Additionally, literature suggests that the tendency to develop these cognitive impairments and emotional abnormalities in pediatric CP is influenced by age and IQ. Because there are many other medical co-morbidities that can occur with CP (e.g., seizures and shunt placement), prediction of what percentages of patients will incur cognitive impairment and emotional abnormality is a difficult task. The purpose of this study was to investigate the associations between possible factors mentioned above, and neurobehavioral and motor disorders from a clinical database of pediatric subjects diagnosed with CP. The study resulted in 22 rules that can predict negative outcomes. These rules reinforced the growing body of literature supporting a link between CP, executive dysfunction, and subsequent neurobehavioral problems. The antecedents and consequents of some association rules were single factors, while other statistical associations were interactions of factor combinations. Further research is needed to include childrens comprehensive treatment and medication history in order to determine additional impacts on their neurobehavioral and motor disorders.

MotionTalk: personalized home rehabilitation system for assisting patients with impaired mobility

Physical injury, stroke, trauma, traumatic brain injury and spinal cord injury rank among the top causes of disability. There are a total of 54 million people in the US requiring rehabilitative assistance of which 15.3 million people are in the age groups of 18-44. However, the compliance rate for patients performing rehabilitation exercises in the home environment is poor. In this paper, we design and prototype a personalized home rehabilitation system, MotionTalk, for the real time quantitative assessment of mobility. Performance of rehabilitation is designed to be assessed using the changes in mobility, reflected in the exercises performed by patients at home with respect to the same exercises performed in the clinic. Our system is capable of capturing motion using Microsoft Kinect and analyzing the position and rotation information to give scores for assessing rehabilitation progress. In comparison to conventional rehabilitation systems, MotionTalk is an inexpensive (<

icuARM-II: improving the reliability of personalized risk prediction in pediatric intensive care units

150 compared to conventional systems costing >