Towards AI-assisted Healthcare: System Design and Deployment for Machine Learning based Clinical Decision Support

Over the last decade, American hospitals have adopted electronic health records (EHRs) widely. In the next decade, incorporating EHRs with clinical decision support (CDS) together into the process of medicine has the potential to change the way medicine has been practiced and advance the quality of patient care. It is a unique opportunity for machine learning (ML), with its ability to process massive datasets beyond the scope of human capability, to provide new clinical insights that aid physicians in planning and delivering care, ultimately leading to better outcomes, lower costs of care, and increased patient satisfaction. However, applying ML-based CDS has to face steep system and application challenges. No open platform is there to support ML and domain experts to develop, deploy, and monitor ML-based CDS; and no end-to-end solution is available for machine learning algorithms to consume heterogenous EHRs and deliver CDS in real-time. Build ML-based CDS from scratch can be expensive and time-consuming. In this dissertation, CDS-Stack, an open cloud-based platform, is introduced to help ML practitioners to deploy ML-based CDS into healthcare practice. The CDS-Stack integrates various components into the infrastructure for the development, deployment, and monitoring of the ML-based CDS. It provides an ETL engine to transform heterogenous EHRs, either historical or online, into a common data model (CDM) in parallel so that ML algorithms can directly consume health data for training or prediction. It introduces both pull and push-based online CDS pipelines to deliver CDS in real-time. The CDS-Stack has been adopted by Johns Hopkins Medical Institute (JHMI) to deliver a sepsis early warning score since November 2017 and begins to show promising results. Furthermore, we believe CDS-Stack can be extended to outpatients too. A case study of outpatient CDS has been conducted which utilizes smartphones and machine learning to quantify the severity of Parkinson disease. In this study, a mobile Parkinson disease severity score (mPDS) is generated using a novel machine learning approach. The results show it can detect response to dopaminergic therapy, correlate strongly with traditional rating scales, and capture intraday symptom fluctuation

Protection Efficacy of the Extract of Ginkgo biloba

Repeated high sustained positive Gz (+Gz) exposures are known for the harmful pathophysiological impact on the brain of rats, which is reflected as the interruption of normal performance of learning and memory. Interestingly, extract of Ginkgo biloba (EGb) has been reported to have neuroprotective effects and cognition-enhancing effects. In this study, we are interested in evaluating the protective effects of EGb toward the learning and memory abilities. Morris Water Maze Test (MWM) was used to evaluate the cognitive function, and the physiological status of the key components in central cholinergic system was also investigated. Our animal behavioral tests indicated that EGb can release the learning and memory impairment caused by repeated high sustained +Gz. Administration of EGb to rats can diminish some of the harmful physiological effects caused by repeated +Gz exposures. Moreover, EGb administration can increase the biological activities of superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) but reduce the production of malondialdehyde (MDA). Taken together, our study showed that EGb can ameliorate the impairment of learning and memory abilities of rats induced by repeated high sustained +Gz exposure; the underlying mechanisms appeared to be related to the signal regulation on the cholinergic system and antioxidant enzymes system

Automated Quality Control for Sensor Based Symptom Measurement Performed Outside the Lab

The use of wearable sensing technology for objective, non-invasive and remote clinimetric testing of symptoms has considerable potential. However, the accuracy achievable with such technology is highly reliant on separating the useful from irrelevant sensor data. Monitoring patient symptoms using digital sensors outside of controlled, clinical lab settings creates a variety of practical challenges, such as recording unexpected user behaviors. These behaviors often violate the assumptions of clinimetric testing protocols, where these protocols are designed to probe for specific symptoms. Such violations are frequent outside the lab and affect the accuracy of the subsequent data analysis and scientific conclusions. To address these problems, we report on a unified algorithmic framework for automated sensor data quality control, which can identify those parts of the sensor data that are sufficiently reliable for further analysis. Combining both parametric and nonparametric signal processing and machine learning techniques, we demonstrate that across 100 subjects and 300 clinimetric tests from three different types of behavioral clinimetric protocols, the system shows an average segmentation accuracy of around 90%. By extracting reliable sensor data, it is possible to strip the data of confounding factors in the environment that may threaten reproducibility and replicability

Using Smartphones and Machine Learning to Quantify Parkinson Disease Severity:The Mobile Parkinson Disease Score

IMPORTANCE: Current Parkinson disease (PD) measures are subjective, rater-dependent, and assessed in clinic. Smartphones can measure PD features, yet no smartphone-derived rating score exists to assess motor symptom severity in real-world settings. OBJECTIVES: To develop an objective measure of PD severity and test construct validity by evaluating the ability of the measure to capture intraday symptom fluctuations, correlate with current standard PD outcome measures, and respond to dopaminergic therapy. DESIGN, SETTING, AND PARTICIPANTS: This observational study assessed individuals with PD who remotely completed 5 tasks (voice, finger tapping, gait, balance, and reaction time) on the smartphone application. We used a novel machine-learning-based approach to generate a mobile Parkinson disease score (mPDS) that objectively weighs features derived from each smartphone activity (eg, stride length from the gait activity) and is scaled from 0 to 100 (where higher scores indicate greater severity). Individuals with and without PD additionally completed standard in-person assessments of PD with smartphone assessments during a period of 6 months. MAIN OUTCOMES AND MEASURES: Ability of the mPDS to detect intraday symptom fluctuations, the correlation between the mPDS and standard measures, and the ability of the mPDS to respond to dopaminergic medication. RESULTS: The mPDS was derived from 6148 smartphone activity assessments from 129 individuals (mean [SD] age, 58.7 [8.6] years; 56 [43.4%] women). Gait features contributed most to the total mPDS (33.4%). In addition, 23 individuals with PD (mean [SD] age, 64.6 [11.5] years; 11 [48%] women) and 17 without PD (mean [SD] age 54.2 [16.5] years; 12 [71%] women) completed in-clinic assessments. The mPDS detected symptom fluctuations with a mean (SD) intraday change of 13.9 (10.3) points on a scale of 0 to 100. The measure correlated well with the Movement Disorder Society Unified Parkinson Disease's Rating Scale total (r = 0.81; P < .001) and part III only (r = 0.88; P < .001), the Timed Up and Go assessment (r = 0.72; P = .002), and the Hoehn and Yahr stage (r = 0.91; P < .001). The mPDS improved by a mean (SD) of 16.3 (5.6) points in response to dopaminergic therapy. CONCLUSIONS AND RELEVANCE: Using a novel machine-learning approach, we created and demonstrated construct validity of an objective PD severity score derived from smartphone assessments. This score complements standard PD measures by providing frequent, objective, real-world assessments that could enhance clinical care and evaluation of novel therapeutics

Smartphone motor testing to distinguish idiopathic REM sleep behavior disorder, controls, and PD

OBJECTIVE: We sought to identify motor features that would allow the delineation of individuals with sleep study-confirmed idiopathic REM sleep behavior disorder (iRBD) from controls and Parkinson disease (PD) using a customized smartphone application. METHODS: A total of 334 PD, 104 iRBD, and 84 control participants performed 7 tasks to evaluate voice, balance, gait, finger tapping, reaction time, rest tremor, and postural tremor. Smartphone recordings were collected both in clinic and at home under noncontrolled conditions over several days. All participants underwent detailed parallel in-clinic assessments. Using only the smartphone sensor recordings, we sought to (1) discriminate whether the participant had iRBD or PD and (2) identify which of the above 7 motor tasks were most salient in distinguishing groups. RESULTS: Statistically significant differences based on these 7 tasks were observed between the 3 groups. For the 3 pairwise discriminatory comparisons, (1) controls vs iRBD, (2) controls vs PD, and (3) iRBD vs PD, the mean sensitivity and specificity values ranged from 84.6% to 91.9%. Postural tremor, rest tremor, and voice were the most discriminatory tasks overall, whereas the reaction time was least discriminatory. CONCLUSIONS: Prodromal forms of PD include the sleep disorder iRBD, where subtle motor impairment can be detected using clinician-based rating scales (e.g., Unified Parkinson's Disease Rating Scale), which may lack the sensitivity to detect and track granular change. Consumer grade smartphones can be used to accurately separate not only iRBD from controls but also iRBD from PD participants, providing a growing consensus for the utility of digital biomarkers in early and prodromal PD

Accurate Caloric Expenditure of Bicyclists using Cellphones

Biking is one of the most efficient and environmentally friendly ways to control weight and commute. To precisely estimate caloric expenditure, bikers have to install a bike computer or use a smartphone connected to additional sensors such as heart rate monitors worn on their chest, or cadence sensors mounted on their bikes. However, these peripherals are still expensive and inconvenient for daily use. This work poses the following question: is it possible to use just a smartphone to reliably estimate cycling activity? We answer this question positively through a pocket sensing approach that can reliably measure cadence using the phone’s on-board accelerometer with less than 2 % error. Our method estimates caloric expenditure through a model that takes as inputs GPS traces, the USGS elevation service, and the detailed road database from OpenStreetMap. The overall caloric estimation error is 60 % smaller than other smartphone-based approaches. Finally, the smartphone can aggressively duty-cycle its GPS receiver, reducing energy consumption by 57%, without any degradation in the accuracy of caloric expenditure estimates. This is possible because we can recover the bike’s route, even with fewer GPS location samples, using map information from the USGS and OpenStreetMap databases.

ERN: Emergence Rescue Navigation with Wireless Sensor Networks

Navigation with wireless sensor networks (WSNs) can help people escape safely from an emergency. Previous navigation algorithms attempt to find safe and efficient escape paths for individuals under various environmental dynamics but ignore possible congestion caused by the individuals rushing for the exits. Moreover, all the previous works have overlooked the fact that the emergency rescue force can take actions strategically in order to save people out of danger. We propose ERN, Emergence Rescue Navigation algorithm by treating WSNs as navigation infrastructure. ERN takes both pedestrian congestion and rescue force flexibility into account. A directed graph is used to model the emergency regions. Human’s movements are regarded as network flows on the graph. By calculating the maximum flow and minimum cut on the graph, the system can provide firemen rescue commands to eliminate key dangerous areas, which may significantly reduce congestion and save trapped people. We have performed extensive simulations under dynamic environments to evaluate the effectiveness and response time of ERN. Simulation results show that with ERN people in emergency are evacuated much faster and less congestion is observed

Efficient Emergency Rescue Navigation with Wireless Sensor Networks *

Recently, many applications in wireless sensor networks (WSNs) have been discussed. Navigation with WSNs is among the most heated debated ones. Previous navigation algorithms attempt to find safe and efficient escape paths for individuals under various environmental dynamics but ignore possible congestion caused by the individuals rushing for the exits. Moreover, most previous works have overlooked the fact that the emergency rescue force can take actions strategically in order to save people out of danger. We propose an efficient Emergency Rescue Navigation strategy (ERN) by treating WSNs as navigation infrastructure. Our approach takes both pedestrian congestion and rescue force flexibility into account. A directed graph is used to model the emergency regions. Human’s movements are regarded as network flows on the graph. By calculating the maximum flow and minimum cut on the graph, the system can provide firemen rescue commands to eliminate key dangerous areas, which may significantly reduce congestion and save trapped people. We have performed extensive simulations under dynamic environments to evaluate the effectiveness and response time of our work. Simulation results show that, with our strategy, people in emergency are evacuated much faster and less congestion is observed