Clinical Review of Smartphone Applications in Parkinson's Disease.

BACKGROUND: Parkinson's disease (PD) is the second leading neurodegenerative disease worldwide. Important advances in monitoring and treatment have been made in recent years. This article reviews literature on utility of smartphone applications in monitoring PD symptoms that may ultimately facilitate improved patient care, and on movement modulation as a potential therapeutic. REVIEW SUMMARY: Novel mobile phone applications can provide one-time and/or continuous data to monitor PD motor symptoms in person or remotely, that may support precise therapeutic adjustments and management decisions. Apps have also been developed for medication management and treatment. CONCLUSIONS: Smartphone applications provide a wide array of platforms allowing for meaningful short-term and long-term data collection and are also being tested for intervention. However, the variability of the applications and the need to translate complicated sensor data may hinder immediate clinical applicability. Future studies should involve stake-holders early in the design process to promote usability and streamline the interface between patients, clinicians, and PD apps.

Identifying sub-phenotypes of acute kidney injury using structured and unstructured electronic health record data with memory networks.

Acute Kidney Injury (AKI) is a common clinical syndrome characterized by the rapid loss of kidney excretory function, which aggravates the clinical severity of other diseases in a large number of hospitalized patients. Accurate early prediction of AKI can enable in-time interventions and treatments. However, AKI is highly heterogeneous, thus identification of AKI sub-phenotypes can lead to an improved understanding of the disease pathophysiology and development of more targeted clinical interventions. This study used a memory network-based deep learning approach to discover AKI sub-phenotypes using structured and unstructured electronic health record (EHR) data of patients before AKI diagnosis. We leveraged a real world critical care EHR corpus including 37,486 ICU stays. Our approach identified three distinct sub-phenotypes: sub-phenotype I is with an average age of 63.03±17.25 years, and is characterized by mild loss of kidney excretory function (Serum Creatinine (SCr) 1.55±0.34 mg/dL, estimated Glomerular Filtration Rate Test (eGFR) 107.65±54.98 mL/min/1.73 m2). These patients are more likely to develop stage I AKI. Sub-phenotype II is with average age 66.81±10.43 years, and was characterized by severe loss of kidney excretory function (SCr 1.96±0.49 mg/dL, eGFR 82.19±55.92 mL/min/1.73 m2). These patients are more likely to develop stage III AKI. Sub-phenotype III is with average age 65.07±11.32 years, and was characterized moderate loss of kidney excretory function and thus more likely to develop stage II AKI (SCr 1.69±0.32 mg/dL, eGFR 93.97±56.53 mL/min/1.73 m2). Both SCr and eGFR are significantly different across the three sub-phenotypes with statistical testing plus postdoc analysis, and the conclusion still holds after age adjustment.

Feature Selection in Predictive Modeling: A Systematic Study on Drug Response Heterogeneity for Type II Diabetic Patients.

With the rapid development of computer hardware and software technologies, more and more electronic health data from insurance claims, clinical trials and hospitals are becoming readily available. These data provide a rich resource for developing various healthcare analytics algorithms, among which predictive modeling is of key importance in many real health problems. One important issue for data-driven predictive modeling is high dimensionality, and feature selection is one effective strategy to reduce the number of independent variables and control the confounding factors. However, most of the existing studies just pick one feature selection approach without comprehensive investigations. In this paper, we investigate the issue of drug response heterogeneity for type II diabetes mellitus (T2DM) patients using a large scale clinical trial data. Our goal is to find out the important factors that may lead to the response heterogeneity for three popular T2DM drugs, Metformin, Rosiglitazone and Glimepiride. We implemented 8 different feature selection approaches and compared their performances with various measures including prediction error and the consistency of the identified important factors. Finally, we ensemble all factor lists picked by different algorithms and obtain a final set of factors that contribute to the drug response heterogeneities and verified them through existing literature.

Current state of educational compensation in academic neurology: Results of a US national survey.

In the current medical climate, medical education is at risk of being de-emphasized, leading to less financial support and compensation for faculty. A rise in compensation plans that reward clinical or research productivity fails to incentivize and threatens to erode the educational missions of our academic institutions. Aligning compensation with the all-encompassing mission of academic centers can lead to increased faculty well-being, clinical productivity, and scholarship. An anonymous survey developed by members of the A.B. Baker Section on Neurologic Education was sent to the 133 chairs of neurology to assess the type of compensation faculty receive for teaching efforts. Seventy responses were received, with 59 being from chairs. Key results include the following: 36% of departments offered direct compensation; 36% did not; residency program directors received the most salary support at 36.5% full-time equivalent; and administrative roles had greatest weight in determining academic compensation. We believe a more effective, transparent system of recording and rewarding faculty for their educational efforts would encourage faculty to teach, streamline promotions for clinical educators, and strengthen undergraduate and graduate education in neurology.

Data-Driven Subtyping of Parkinson's Disease Using Longitudinal Clinical Records: A Cohort Study.

Parkinson's disease (PD) is associated with diverse clinical manifestations including motor and non-motor signs and symptoms, and emerging biomarkers. We aimed to reveal the heterogeneity of PD to define subtypes and their progression rates using an automated deep learning algorithm on the top of longitudinal clinical records. This study utilizes the data collected from the Parkinson's Progression Markers Initiative (PPMI), which is a longitudinal cohort study of patients with newly diagnosed Parkinson's disease. Clinical information including motor and non-motor assessments, biospecimen examinations, and neuroimaging results were used for identification of PD subtypes. A deep learning algorithm, Long-Short Term Memory (LSTM), was used to represent each patient as a multi-dimensional time series for subtype identification. Both visualization and statistical analysis were performed for analyzing the obtained PD subtypes. As a result, 466 patients with idiopathic PD were investigated and three subtypes were identified. Subtype I (Mild Baseline, Moderate Motor Progression) is comprised of 43.1% of the participants, with average age 58.79 ± 9.53 years, and was characterized by moderate functional decay in motor ability but stable cognitive ability. Subtype II (Moderate Baseline, Mild Progression) is comprised of 22.9% of the participants, with average age 61.93 ± 6.56 years, and was characterized by mild functional decay in both motor and non-motor symptoms. Subtype III (Severe Baseline, Rapid Progression) is comprised 33.9% of the patients, with average age 65.32 ± 8.86 years, and was characterized by rapid progression of both motor and non-motor symptoms. These subtypes suggest that when comprehensive clinical and biomarker data are incorporated into a deep learning algorithm, the disease progression rates do not necessarily associate with baseline severities, and the progression rate of non-motor symptoms is not necessarily correlated with the progression rate of motor symptoms.