Smart Data enabling Personalized Digital Health
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Talk at PARC, Oct. 30, 2013. Abstract at: http://j.mp/PARCabs [To see animations, you may need to download the file and use powerpoint.] Also see related talks on Smart Data for Smart Energy and other applications: http://wiki.knoesis.org/index.php/Smart_Data The proliferation of smartphones and sensors, the continuous monitoring of physiology and environment (personal health signals), notifications from public health sources (public health signals), and more digital access to clinical data, are resulting in massive multisensory and multimodal observational data. The technology has significant potential to improve health and well-being, through early detection, better diagnosis, effective prevention and treatment of a disease; and improved the quality of life. However, to make this personalized digital medicine a reality, it is crucial to derive actionable insights from data including heterogeneous and fine-grained observations. At Kno.e.sis, we have collaborations with clinicians in growing number of specializations (Cardiovascular, Pulmonology, Gastroenterology) to study personalized health decision making that involve the use of real-world patient data, deep background knowledge and well targeted clinical applications. For example: * For a patient discharged from hospital with Acute Decompensated Heart Failure, can we compute post hospital discharge risk factor to reduce 30-day readmissions? * For children with Asthma, can we predict an impending attack to enable actions that prevent an attack reducing the need for post-attack symptomatic relief? * For Parkinson’s Disease, can we characterize the progression to adjust medication and therapeutic changes? The above provides the context for a research agenda around what I call Smart Data, which (a) provides value from harnessing the challenges posed by volume, velocity, variety and veracity of Big Data, in-turn providing actionable information and improve decision making, and/or (b) is focused on the actionable value achieved by human involvement in data creation, processing and consumption phases for improving the Human experience. In describing Smart Data approach to above heath applications, I will cover the following technical capabilities that adds semantics to enhance or complement traditional NLP and ML centric solutions: * Semantic Sensor Web- including semantic computation infrastructure, ability to semi-automatically create domain specific background knowledge (ontology) from unstructured data (e.g., EMR), and automatically do semantic annotation of multimodal and multisensory data * Semantic perception – convert low level signals into higher level abstractions using IntellegO framework that utilizes domain knowledge and hybrid abductive/deductive reasoning * Intelligence at Edge - perform scalable and efficient semantic computation on resource constrained devices
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Smart Data enabling Personalized Digital Health
- 1. Put Knoesis Banner Smart Data enabling Personalized Digital Health : Deriving Value via harnessing Volume, Variety and Velocity using semantics and Semantic Web Amit P. Sheth Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis) Wright State University, USA Contributions by many, but Special Thanks to: Pramod Anantharam Cory Henson Dr. T.K. Prasad Sujan Perera Delroy Cameron
- 2. A Historical Perspective on Collecting Health Observations Doctors relied only on external observations Stethoscope was the first instrument to go beyond just external observations Though the stethoscope has survived, it is only one among many observations in modern medicine Laennec’s stethoscope Imhotep Image Credit: British Museum 2600 BC Diseases treated only by external observations ~1815 First peek beyond just external observations http://en.wikipedia.org/wiki/Timeline_of_medicine_and_medical_technology Today Information overload! 2
- 3. The Patient of the Future MIT Technology Review, 2012 http://www.technologyreview.com/featuredstory/426968/the-patient-of-the-future/ 3
- 4. Big Data in Medicine: Implications “We should not make the mistake of seeing data as a technical issue. It’s a synthesis problem. That’s because information is not the scarce resource. Attention is.” -- Conrad Wai, The data addiction | The Ideas Economy http://www.davidscaduto.com/post/9048831674/we-should-not-make-the-mistake-of-seeing-data-as 4
- 5. Sources of Big Data in Digital Health Variety Veracity Velocity Volume Image: http://www.dr4ward.com/dr4ward/2013/04/what-is-the-power-of-the-big-data-in-healthcare-infographic.html 5
- 6. Future Interoperability Challenges: 360 degree health 6
- 7. Big Data in Digital Health: Can alerts work? "According to multiple recent studies, doctors ignore between 49–96% of all CDS alerts that EMRs give them.”1 "Clinical Decision Support systems link health observations with health knowledge to influence health choices by clinicians for improved health care". -- Robert Hayward, Centre for Health Evidence 1http://www.fastcodesign.com/1664763/badly-designed-electronic-medical-records-can-kill-you 7
- 8. Information Overload leading to Alert Fatigue Ignoring alerts is not limited to Emergency Rooms but has also crept into EMR alerts commonly referred to as “alert fatigue” http://health.embs.org/editorial-blog/noise-in-hospital-intensive-care-units-icus/ 8
- 9. Questions typically asked on Big Data • What if your data volume gets so large and varied you don't know how to deal with it? • Do you store all your data? • Do you analyze it all? • How can you find out which data points are really important? • How can you use it to your best advantage? http://www.sas.com/big-data/ 9
- 10. Variety of Data Analytics Enablers http://techcrunch.com/2012/10/27/big-data-right-now-five-trendy-open-source-technologies/ 10
- 11. Illustrative Big Data Applications • Prediction of the spread of flu in real time during H1N1 2009 – Google tested a mammoth of 450 million different mathematical models to test the search terms, comparing their predictions against the actual flu cases; 45 important parameters were founds – Model was tested when H1N1 crisis struck in 2009 and gave more meaningful and valuable real time information than any public health official system [Big Data, Viktor Mayer-Schonberger and Kenneth Cukier, 2013] • FareCast: predict the direction of air fares over different routes [Big Data, Viktor Mayer-Schonberger and Kenneth Cukier, 2013] • NY city manholes problem [ICML Discussion, 2012] 11
- 12. What is missing? • Current focus mainly to serve business intelligence and targeted analytics needs, not to serve complex individual and collective human needs (e.g., empower human in health, fitness and well-being; better disaster coordination, smart energy consumption) that is highly personalized/individualized/contextualized – Incorporate real-world complexity: multi-modal and multi-sensory nature of realworld and human perception – Need deeper understanding of data and its role to information (e.g., skew, coverage) – Beyond correlation -> causation :: actionable info, decisions grounded on insights • Human involvement and guidance: Leading to actionable information, understanding and insight right in the context of human activities – Bottom-up & Top-down processing: Infusion of models and background knowledge (data + knowledge + reasoning) 12
- 13. Makes Sense Actionable or help decision support/making 13
- 14. Human Centric Computing EXPERIENCE & DECISION MAKING Descriptive Exploratory Inferential Predictive Causal Improved Analytics CREATION PROCESSING 14
- 15. Smart Data Smart data makes sense out of Big data It provides value from harnessing the challenges posed by volume, velocity, variety and veracity of big data, in-turn providing actionable information and improve decision making. 15
- 16. Another perspective on Smart Data “OF human, BY human and FOR human” Smart data is focused on the actionable value achieved by human involvement in data creation, processing and consumption phases for improving the human experience. 16
- 17. Current Focus on Big Data • Focus on verticals: advertising‚ social media‚ retail‚ financial services‚ telecom‚ and healthcare – Aggregate data, focused on transactions, limited integration (limited complexity), analytics to find (simple) patterns – Emphasis on technologies to handle volume/scale, and to lesser extent velocity: Hadoop, NoSQL,MPP warehouse …. – Full faith in the power of data (no hypothesis), bottom up analysis 17
- 18. Another perspective on Smart Data “OF human, BY human and FOR human” 18
- 19. „OF human‟ : Relevant Real-time Data Streams for Human Experience Petabytes of Physical(sensory)-Cyber-Social Data everyday! More on PCS Computing: http://wiki.knoesis.org/index.php/PCS 19
- 20. Another perspective on Smart Data “OF human, BY human and FOR human” 20
- 21. „BY human‟: Involving Crowd Intelligence in data processing workflows Use of Prior Human-created Knowledge Models Crowdsourcing and Domain-expert guided Machine Learning Modeling 21
- 22. Another perspective on Smart Data “OF human, BY human and FOR human” 22
- 23. „FOR human‟ : Improving Human Experience Weather Application Weather Application Asthma Healthcare Application Action in the Physical World Personal Public Health Population Level Detection of events, such as wheezing sound, indoor temperature, humidity, dust, and CO2 level Close the window at home during day to avoid CO2 inflow, to avoid asthma attacks at night 23
- 24. Why do we care about Smart Data rather than Big Data? 24
- 25. 25
- 26. April 6, 2011 Mr. Michael Yocabet suffering from type 1 diabetes is recommended a kidney transplant at the University of Pittsburgh Medical Center. The organ donor is his life partner Ms. Christina Mecannic http://www.nbcnews.com/id/44599555/#.UmMHMWRDszQ 26
- 27. May 6, 2011 The couple leaned about the botched kidney transplant making the situation of Mr. Yocabet much worse! The kidney he got from his wife has infected him with Hepatitis C aggravating his health issues. http://www.nbcnews.com/id/44599555/#.UmMHMWRDszQ 27
- 28. Life Threatening Implications! Mr. Yocabet was a disabled former truck driver and he has diabetes type 1. Treatment for the liver may harm his kidney even cause organ failure and death! “Because he’s on anti-rejection drugs, the hepatitis C will be a lot worse in him,” - Ms. Christina Mecannic http://www.scientificamerican.com/article.cfm?id=2003-blackout-five-years-later 28
- 29. Cause of the Problem: Official Investigation • Jan 26: Ms. Mecannic gets her blood work positive for Hepatitis C virus. • March 29: Second attempt to test for Hepatitis C virus in Ms. Mecannic. • Several meetings of the transplant team -they fail to notice the problem. (alert fatigue?) • April 6: Transplant day! • May 6: Couple learned about botched transplant. http://www.post-gazette.com/stories/local/breaking/upmc-sued-over-botched-kidney-transplant-315580/ http://www.nbcnews.com/id/44599555/#.UmMHMWRDszQ 29
- 30. Can we Prevent such life threatening incidents? Over 28,000 organs of all types are transplanted every year in United States alone "Between 2007 and 2010, the CDC conducted 200 investigations into potential transmission of HIV and hepatitis B and C due to organ transplants.” http://www.nbcnews.com/id/44599555/#.UmMHMWRDszQ 30
- 31. How could Smart Data help? Value: Healthcare Provider Context 31
- 32. Clinical Decision Making is Complex! “Health professionals are required to make decisions with multiple foci (e.g. diagnosis, intervention, interaction and evaluation), in dynamic contexts, using a diverse knowledge base (including an increasing body of evidence-based literature), with multiple variables and individuals involved.” http://researchoutput.csu.edu.au/R/?func=dbin-jump-full&object_id=9063&local_base=GEN01-CSU01 32
- 33. Stakes are high for both doctors and patients! http://researchoutput.csu.edu.au/R/?func=dbin-jump-full&object_id=9063&local_base=GEN01-CSU01 33
- 34. Multimodal, Multisensory, and Multi-organizational Observations Expert opinion Clinical research Population health record Personal health record Clinical decision support What is the overall health of the person? What are the vulnerabilities for organ transplant? http://www.rugeleypower.com/electricity-generation/producing-electricity.php 34
- 35. Patient Health Score (diagnostic) Semantic Perception and risk assessment algorithms can transform raw data (hard to comprehend) to abstractions (e.g., Patient Health is 3 on a scale of 5) that is intuitively understandable and valuable for decision makers. Having health score for various patients will allow efficient utilization of a decision maker’s precious attention Expert opinion Clinical research Population health record Personal health record Clinical decision support Semantic Perception Risk assessment model 35
- 36. Patient Vulnerability Score (prognostic) The Clinical Decision Support systems such as EMR alert system in its current state follows the high recall philosophy by reporting every possible alert! Doctors need actionable information and not a deluge of alerts to make timely and important decisions. Providing a vulnerability score would facilitate right use of Doctor’s time to investigate further on vulnerabilities. Expert opinion Clinical research Population health record Personal health record Clinical decision support Semantic Perception Risk assessment model 36
- 37. How could Smart Data help? Value: Patient Context 37
- 38. “Intelligence at the Edges” of Digital Health 3.4 billion people will have smartphones or tablets by 2017 -- Research2Guidance m-health app market is predicted to reach $26 billion in 2017 -- Research2Guidance http://www.digikey.com/us/en/techzone/energy-harvesting/resources/articles/zigbees-smart-energy-20-profile.html 38
- 39. Data Overload for Patients/health aficionados Providing actionable information in a timely manner is crucial to avoid information overload or fatigue Personal Schedule Sleep data Activity data Personal health records Community data 39
- 40. Optimizing Cost, Benefit, and Preferences Algorithms on the patient side should consider all the health signals and provide actionable and timely information for informed decision making What are the reasons for my increasing weight? What should I consider before I get a kidney transplant? Personal Schedule Sleep data Activity data Personal health records Community data Semantic Perception Personalized optimization Img: http://marloncarvallovillae.blogspot.com/2011_02_01_archive.html http://www.1800timeclocks.com/icon-time-systems/icon-time-upgrades/icon-time-advanced-pack-upgrade-sb100-pro/ Personalized recommendation 40
- 41. 3 Primary Issues to be addressed 1 Annotation of sensor data Semantic Sensor Web 2 Interpretation of sensor data Semantic Perception 3 Efficient execution on resource-constrained devices Intelligence at the Edge 41
- 42. How are machines supposed to integrate and interpret sensor data? RDF OWL Semantic Sensor Networks (SSN) 42
- 43. W3C Semantic Sensor Network Ontology Lefort, L., Henson, C., Taylor, K., Barnaghi, P., Compton, M., Corcho, O., Garcia-Castro, R., Graybeal, J., Herzog, A., Janowicz, K., Neuhaus, H., Nikolov, A., and Page, K.: Semantic Sensor Network XG Final Report, W3C Incubator Group Report (2011). 43
- 44. W3C Semantic Sensor Network Ontology Lefort, L., Henson, C., Taylor, K., Barnaghi, P., Compton, M., Corcho, O., Garcia-Castro, R., Graybeal, J., Herzog, A., Janowicz, K., Neuhaus, H., Nikolov, A., and Page, K.: Semantic Sensor Network XG Final Report, W3C Incubator Group Report (2011). 44
- 45. W3C Semantic Sensor Network Ontology Lefort, L., Henson, C., Taylor, K., Barnaghi, P., Compton, M., Corcho, O., Garcia-Castro, R., Graybeal, J., Herzog, A., Janowicz, K., Neuhaus, H., Nikolov, A., and Page, K.: Semantic Sensor Network XG Final Report, W3C Incubator Group Report (2011). 45
- 46. Semantic Annotation of SWE Lefort, L., Henson, C., Taylor, K., Barnaghi, P., Compton, M., Corcho, O., Garcia-Castro, R., Graybeal, J., Herzog, A., Janowicz, K., Neuhaus, H., Nikolov, A., and Page, K.: Semantic Sensor Network XG Final Report, W3C Incubator Group Report (2011). 46
- 47. Smart Data in Healthcare To gain new insight in patient care & early indications of disease 47
- 48. What if we could automate this sense making ability? … and do it efficiently and at scale 49
- 49. Making sense of sensor data with 50
- 50. People are good at making sense of sensory input What can we learn from cognitive models of perception? • The key ingredient is prior knowledge 51
- 51. Perception Cycle* Translating low-level signals into high-level knowledge 1 Explanation Perceive Feature Observe Property Prior Knowledge Discrimination 2 * based on Neisser’s cognitive model of perception Focusing attention on those aspects of the environment that provide useful information 52
- 52. To enable machine perception, Semantic Web technology is used to integrate sensor data with prior knowledge on the Web 53
- 53. Prior knowledge on the Web W3C Semantic Sensor Network (SSN) Ontology Bi-partite Graph 54
- 54. Prior knowledge on the Web W3C Semantic Sensor Network (SSN) Ontology Bi-partite Graph 55
- 55. Explanation Explanation is the act of choosing the objects or events that best account for a set of observations; often referred to as hypothesis building Translating low-level signals into high-level knowledge Observe Property 1 Explanation Perceive Feature 56
- 56. Explanation Explanation is the act of choosing the objects or events that best account for a set of observations; often referred to as hypothesis building Inference to the best explanation • In general, explanation is an abductive problem; and hard to compute Finding the sweet spot between abduction and OWL • Simulation of Parsimonious Covering Theory in OWLDL (using the single-feature assumption*) * An explanation must be a single feature which accounts for all observed properties 57
- 57. Explanation Explanatory Feature: a feature that explains the set of observed properties ExplanatoryFeature ≡ ∃ssn:isPropertyOf—.{p1} ⊓ … ⊓ ∃ssn:isPropertyOf—.{pn} Observed Property elevated blood pressure clammy skin palpitations Explanatory Feature Hypertension Hyperthyroidism Pulmonary Edema 58
- 58. Discrimination Discrimination is the act of finding those properties that, if observed, would help distinguish between multiple explanatory features Explanation Perceive Feature Observe Property Discrimination 2 Focusing attention on those aspects of the environment that provide useful information 59
- 59. Discrimination To determine which possible observations are most informative, find those observable properties that can discriminate between the set of hypotheses. Expected Properties Not-applicable Properties Discriminating Properties Universe of observable properties 60
- 60. Discrimination Expected Property: would be explained by every explanatory feature ExpectedProperty ≡ ∃ssn:isPropertyOf.{f1} ⊓ … ⊓ ∃ssn:isPropertyOf.{fn} Expected Property elevated blood pressure clammy skin palpitations Explanatory Feature Hypertension Hyperthyroidism Pulmonary Edema 61
- 61. Discrimination Not Applicable Property: would not be explained by any explanatory feature NotApplicableProperty ≡ ¬∃ssn:isPropertyOf.{f1} ⊓ … ⊓ ¬∃ssn:isPropertyOf.{fn} Not Applicable Property elevated blood pressure clammy skin palpitations Explanatory Feature Hypertension Hyperthyroidism Pulmonary Edema 62
- 62. Discrimination Discriminating Property: is neither expected nor not-applicable DiscriminatingProperty ≡ ¬ExpectedProperty ⊓ ¬NotApplicableProperty Discriminating Property elevated blood pressure clammy skin palpitations Explanatory Feature Hypertension Hyperthyroidism Pulmonary Edema 63
- 63. Resource savings of abstracting sensor data Orders of magnitude resource savings for generating and storing relevant abstractions vs. raw observations. Raw observations Relevant abstractions 64
- 64. The Decisions are as Good as the Underlying Coded Knowledge • How do we know whether we have all possible relationships? • How do we know which relationships are missing? • How can we efficiently fill the missing relationships? 65
- 65. Semantics Driven Approach for Knowledge Acquisition from EMRs Knowledge is built by abstracting real world facts, once built it should be able to explain the real world Sujan Perera, Cory Henson, Krishnaprasad Thirunarayan, Amit Sheth, Suhas Nair, 'Semantics Driven Approach for Knowledge Acquisition from EMRs', Special Issue on Data Mining in Bioinformatics, Biomedicine and Healthcare Informatics, Journal of Biomedical and Health Informatics (To Appear) 66
- 66. Semantics Driven Approach for Knowledge Acquisition from EMRs D D D D Patient Notes D UMLS Explanation Module Explained? No D Hypothesis Generation Hypothesis Filtering Yes Hypothesis with High Confidence
- 67. The Algorithm 1. Annotate the EMR documents with given knowledgebase 2. Find unexplained symptoms 3. Generate hypothesis for unexplained symptoms 1. All disorders in document becomes candidates 4. Filter out candidate disorder with high confidence 1. Get disorders which has relationship with unexplained symptom in given knowledgebase 2. Collect the “neighborhood” of the disorders 3. Get the intersection of “neighborhood” and candidate disorders 68
- 68. Candidate Filtering Step Intuition: “similar disorders manifest similar symptoms” D6 D7 D1 D10 D9 D2 D2 D8 S1 D3 D11 D4 D5 D12 Candidate Disease Is symptom of rdfs:subClassOf D5 D13
- 69. Evaluation Precision = number of suggested correct relationships Total number of suggested = 73.09% Recall = correct relationships found all correct relationships – known correct relationships = 66.67% If we do not perform the semantic filtering step, the precision would be 30%. High precision is important since it is hard to find domain experts to validate the generated hypothesis. 70
- 70. kHealth knowledge-enabled healthcare Through physical monitoring and analysis, our cellphones could act as an early warning system to detect serious health conditions, and provide actionable information canary in a coal mine 71
- 71. kHealth to Manage ADHF (Acute Decompensated Heart Failure) 72
- 72. Risk Score: from Data to Abstraction and Actionable Information Machine Sensors Qualities -High BP -Increased Weight Validate correlations Personal Input kHealth Entities -Hypertension -Hypothyroidism Comorbidity risk score e.g., Charlson Index - Find correlations - Validation - domain knowledge - domain expert Model Creation Parameterize the model EMR/PHR Historical observations of each patient Risk Assessment Model Longitudinal studies of cardiovascular risks Risk Score (Actionable Information) Current Observations -Physical -Physiological -History 73
- 73. Asthma 25 million People in the U.S. are diagnosed with asthma (7 million are children)1. 300 million People suffering from asthma worldwide2. $50 billion Spent on asthma alone in a year2 155,000 Hospital admissions in 20063 593,000 Emergency department visits in 20063 1http://www.nhlbi.nih.gov/health/health-topics/topics/asthma/ 2http://www.lung.org/lung-disease/asthma/resources/facts-and-figures/asthma-in-adults.html 3Akinbami et al. (2009). Status of childhood asthma in the United States, 1980–2007. Pediatrics,123(Supplement 3), S131-S145. 77
- 74. WHY Big Data to Smart Data: Healthcare example Asthma is a multifactorial disease with health signals spanning personal, public health, and population levels. Velocity Variety semantics Value Can we detect the asthma severity level? Can we characterize asthma control level? What risk factors influence asthma control? What is the contribution of each risk factor? Veracity Understanding relationships between health signals and asthma attacks for providing actionable information Volume Real-time health signals from personal level (e.g., Wheezometer, NO in breath, accelerometer, microphone), public health (e.g., CDC, Hospital EMR), and population level (e.g., pollen level, CO2) arriving continuously in fine grained samples potentially with missing information and uneven sampling frequencies. 78
- 75. Asthma: Demonstration of Value Personal Variety: Health signals span heterogeneous sources Volume: Health signals are fine grained Velocity: Real-time change in situations Veracity: Reliability of health signals may be compromised Value: Can I reduce my asthma attacks at night? Population Level Public Health Decision support to doctors by providing them with deeper insights into patient asthma care 79
- 76. Asthma: Actionable Information for Asthma Patients Can I reduce my asthma attacks at night? Actionable Information Closing the window at home in the morning and taking an alternate route to office may lead to reduced asthma attacks Population Level What is the air quality indoors? Personal What are the triggers? Sensordrone – for monitoring environmental air quality What is the propensity toward asthma? What is the wheezing level? Wheezometer – for monitoring wheezing sounds Commute to Work What is the exposure level over a day? Public Health 80
- 77. Personal, Public Health, and Population Level Signals for Monitoring Asthma Sensors and their observations for understanding asthma Asthma Control => Asthma Control and Actionable Information Daily Medication Choices for starting therapy Not Well Controlled Poor Controlled Severity Level of Asthma (Recommended Action) (Recommended Action) (Recommended Action) Intermittent Asthma SABA prn - - Mild Persistent Asthma Low dose ICS Medium ICS Medium ICS Moderate Persistent Asthma Medium dose ICS alone Or with LABA/montelukast Medium ICS + LABA/Montelukast Or High dose ICS Severe Persistent Asthma High dose ICS with LABA/montelukast Needs specialist care Medium ICS + LABA/Montelukast Or High dose ICS* Needs specialist care ICS= inhaled corticosteroid, LABA = inhaled long-acting beta2-agonist, SABA= inhaled short-acting beta2-agonist ; *consider referral to specialist 81
- 78. Asthma Early Warning Model (EventShop*) (kHealth**) Personal Level Sensors Personal Level Signals Societal Level Signals (Personalized Societal Level Signal) Storage Recommended Action Action Justification Societal Level Sensors (Personal Level Signals) Societal Level Signals Relevant to the Personal Level (Societal Level Signals) Asthma Early Warning Model (AEWM) Qualify Action Recommendation Quantify Verify & augment domain knowledge Query AEWM What are the features influencing my asthma? What is the contribution of each of these features? How controlled is my asthma? (risk score) What will be my action plan to manage asthma? *http://www.slideshare.net/jain49/eventshop-120721, ** http://www.youtube.com/watch?v=btnRi64hJp4 82
- 79. Health Signal Extraction to Understanding Physical-Cyber-Social System Personal Public Health Observations Health Signal Extraction Health Signal Understanding PollenLevel Qualify <PollenLevel, ChectTightness, Pollution, Activity, Wheezing, RiskCategory> Pollution ChectTightness <2, 1, 1,3, 1, RiskCategory> <2, 1, 1,3, 1, RiskCategory> Wheezing Acceleration readings from <2, 1, 1,3, 1, RiskCategory> Activity on-phone sensors <2, 1, 1,3, 1, RiskCategory> Enrich . Risk Category assigned by . doctors Wheeze – Yes PollenLevel Do you have tightness of chest? –Yes Quantify . Sensor and personal Signals from personal, personal Pollution observations ChectTightness spaces, and community spaces Population Level Background Knowledge Expert Knowledge <Wheezing=Yes, time, location> <ChectTightness=Yes, time, location> Activity Wheezing Outdoor pollen and pollution <PollenLevel=Medium, time, location> <Pollution=Yes, time, location> tweet reporting pollution level and asthma attacks <Activity=High, time, location> RiskCategory Well Controlled - continue Not Well Controlled – contact nurse Poor Controlled – contact doctor 83
- 80. Personal Health Score and Vulnerability Score At Discharge Health Score Non-compliance Poor economic status No living assistance Vulnerability Score Well Controlled Low Well Controlled Very low Not Well Controlled High Not Well Controlled Medium Poor Controlled Very High Poor Controlled High Estimation of readmission vulnerability based on the personal health score 84
- 81. Health Signal Extraction Challenges Social streams has been used to extract many near real-time events Twitter provides access to rich signals but is noisy, informal, uncontrolled capitalization, redundant, and lacks context We formalize the event extraction from tweets as a sequence labeling problem Now you know why you’re miserable! Very High Alert for B-ALLERGEN Ragweed I-ALLERGEN pollen. B-FACILITY Oklahoma I-FACILITY Allergy I-FACILITY Clinic says it’s an extreme exposure situation How do we know the event phrases and who creates the training set? (manual creation is ruled out) Idea: Background knowledge used to create the training set e.g., typing information becomes the label for a concept 85
- 82. Health Signal Understanding Challenges Formalized as a problem of structure extraction of a Bayesian Network Find the structure that maximize the scoring function Huge exponential search space with n Where Xi represents each observation Where k indexes over all possible graph structures Where n is the number of nodes in the network Different structures may result in the same structure score (I-Map) We use declarative knowledge to choose between Gi and Gj , and to guide the search Ehsan Nazerfard, Bayesian Networks: Structure Learning, Topics in Machine Learning, 2011. 86
- 83. How do we implement machine perception efficiently on a resource-constrained device? Use of OWL reasoner is resource intensive (especially on resource-constrained devices), in terms of both memory and time • Runs out of resources with prior knowledge >> 15 nodes • Asymptotic complexity: O(n3) 87
- 84. Approach 1: Send all sensor observations to the cloud for processing Approach 2: downscale semantic processing so that each device is capable of machine perception intelligence at the edge Henson et al. 'An Efficient Bit Vector Approach to Semantics-based Machine Perception in Resource-Constrained Devices, ISWC 2012. 88
- 85. Efficient execution of machine perception Use bit vector encodings and their operations to encode prior knowledge and execute semantic reasoning 010110001101 0011110010101 1000110110110 101100011010 0111100101011 000110101100 0110100111 89
- 86. Evaluation on a mobile device Efficiency Improvement • Problem size increased from 10’s to 1000’s of nodes • Time reduced from minutes to milliseconds • Complexity growth reduced from polynomial to linear O(n3) < x < O(n4) O(n) 90
- 87. Semantic Perception for smarter analytics: 3 ideas to takeaway 1 Translate low-level data to high-level knowledge Machine perception can be used to convert low-level sensory signals into high-level knowledge useful for decision making 2 Prior knowledge is the key to perception Using SW technologies, machine perception can be formalized and integrated with prior knowledge on the Web 3 Intelligence at the edge By downscaling semantic inference, machine perception can execute efficiently on resource-constrained devices 91
- 88. PREDOSE: Prescription Drug abuse Online-Surveillance and Epidemiology Bridging the gap between researcher and policy makers Early identification of emerging patterns and trends in abuse Kno.e.sis - Ohio Center of Excellence in Knowledge-enabled Computing CITAR - Center for Interventions Treatment and Addictions Research http://wiki.knoesis.org/index.php/PREDOSE D. Cameron, G. A. Smith, R. Daniulaityte, A. P. Sheth, D. Dave, L. Chen, G. Anand, R. Carlson, K. Z. Watkins, R. Falck. PREDOSE: A Semantic Web Platform for Drug Abuse Epidemiology using Social Media. Journal of Biomedical Informatics. July 2013 (in press) 92
- 89. PREDOSE: Prescription Drug abuse Online-Surveillance and Epidemiology • Drug Overdose Problem in US • 100 people die everyday from drug overdoses • 36,000 drug overdose deaths in 2008 • Close to half were due to prescription drugs Gil Kerlikowske Director, ONDCP Launched May 2011 In 2008, there were 14,800 prescription painkiller deaths* *http://www.cdc.gov/homeandrecreationalsafety/rxbrief/
- 90. PREDOSE: Bringing Epidemiologists and Computer Scientists together Epidemiologist Computer Scientist Interviews Automatic Data Collection Problems Large Data Sample Sizes Sample Biases Automate Information Extraction & Content Analysis Online Surveys Manual Effort Not Scalable Qualitative Coding Group Therapy: http://www.thefix.com/content/treatment-options-prison90683 Access hard-to-reach Populations Early Identification and Detection of Trends
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
- 92. Entity Identification Drug Abuse Ontology (DAO) subClassOf subClassOf +ve 83 Classes 37 Properties Suboxone Subutex Sentiment Extraction experience sucked has_slang_term bupey feel great -ve Buprenorphine has_slang_term feel pretty damn good 33:1 Buprenorphine 24:1 Loperamide bupe didn’t do shit bad headache I was sent home with 5 x 2 mg Suboxones. I also got a bunch of phenobarbital (I took all 180 mg and it didn't do shit except make me a walking zombie for 2 days). I waited 24 hours after my last 2 mg dose of Suboxone and tried injecting 4 mg of the bupe. It gave me a bad headache, for hours, and I almost vomited. I could feel the bupe working but overall the experience sucked. Of course, junkie that I am, I decided to repeat the experiment. Today, after waiting 48 hours after my last bunk 4 mg injection, I injected 2 mg. There wasn't really any rush to speak of, but after 5 minutes I started to feel pretty damn good. So I injected another 1 mg. That was about half an hour ago. I feel great now. Triples DIVERSE DATA TYPES ENTITIES Codes Triples (subject-predicate-object) DOSAGE PRONOUN Suboxone used by injection, negative experience Suboxone injection-causes-Cephalalgia INTERVAL Route of Admin. Suboxone used by injection, amount Suboxone injection-dosage amount-2mg RELATIONSHIPS SENTIMENTS Suboxone used by injection, positive experience Suboxone injection-has_side_effect-Euphoria
- 93. PREDOSE: Smarter Data through Shared Context and Data Integration Ontology Lexicon Lexico-ontology Rule-based Grammar ENTITIES TRIPLES EMOTION INTENSITY PRONOUN SENTIMENT DRUG-FORM ROUTE OF ADM SIDEEFFECT DOSAGE FREQUENCY INTERVAL Suboxone, Kratom, Herion, Suboxone-CAUSE-Cephalalgia disgusted, amazed, irritated more than, a, few of I, me, mine, my Im glad, turn out bad, weird ointment, tablet, pill, film smoke, inject, snort, sniff Itching, blisters, flushing, shaking hands, difficulty breathing DOSAGE: <AMT><UNIT> (e.g. 5mg, 2-3 tabs) FREQ: <AMT><FREQ_IND><PERIOD> (e.g. 5 times a week) INTERVAL: <PERIOD_IND><PERIOD> (e.g. several years)
- 94. PREDOSE: Role of Semantic Web & Ontologies Data Type Semantic Web Technique Entity Ontology-driven Identification & Normalization Triple Schema-driven Sentiment Ontology-assisted Target Entity Resolution Limitations of Other Approaches ML/NLP IR Requires Labeled Unpredictable Data term frequencies Difficult to develop language model Requires entity disambiguation Inconsistent data Diverse simple & for Parse Trees or complex slang rules terms & phrases
- 95. with it, SOME of it has to make it through? Not sure.” “Normally around 100 milligrams of loperamide will get me out of withdrawals.” “Loperamide alone is enough to keep me well without being miserable, IF I megadose.” Loperamide-Withdrawal Discovery “This loperamide has saved my life during w/ds.... and made me even more careless Loperamide is used with my monthly meds.”to self-medicate to from Opioid Withdrawal symptoms “But I just wanted to tell you that loperamide WILL WORK. I take 105 mg of methadone/day, and recently have been running out early due to a renewed interest in IVing that shit. 200mg of lope 100 pills will make me almost 100 again. It brings the sickness down to the level of, say, a minor flu. Sleep returns, restlessness dissipates. dose of 16 mg per day. For example, web forum participants shared the following opinions: Sometimes a mild opiation is felt.” “Back in the day when I would run out of pills early I would take 8-10 Lopermide tabs and “So you just stick with it. Don’t go and score big with your next paycheck. Overcome the get some pretty good relief from w/d.” need to make everything numb. Learn to live with normality for a while. It’ll all seem worthwhile soon enough. Go for a like Get out of the house. Go grab some loperamide “If you take a shitload of loperamidewalk.10-20 pills at once in withdrawal, you’ll get relief from the store, the desperate junky’s methadone.” from some of the physical symptoms. Im not sure exactly how it works, but it’s definitely MORE than just relieving the GI symptoms. Im guessing if you just bombard your blood The most commonly of it has to side effects of loperamide use were constipation, dehydration with it, SOME discussed make it through? Not sure.” and other types of gastrointestinal discomforts. Some also reported mild withdrawal symptoms from using loperamide for anmilligrams period of time.will get me out of withdrawals.” “Normally around 100 extended of loperamide “Loperamide is good for a day keep but the without is on loperamide I I megadose.” “Loperamide alone is enough toor twome well problembeing miserable, IF lose all desire to eat OR drink, or do anything really.” “I used to sing the praises my life during w/ds.... and made short term standby until “This loperamide has savedof loperamide....and still do, as a me even more careless you can score. Long term maintenance, it really wears you out. Starts to “feel” toxic though I with my monthly meds.” 99
- 96. Big Data vs. Smart Data in Digital Health (Healthcare provider) Big Data from Healthcare Expert opinion Clinical research Population health record Personal health record Clinical decision support Smart Data for Healthcare What is the overall health of the person? What are the vulnerabilities for organ transplant? Red, yellow, and green indicate high, medium, and low risk allowing decision makers to focus on red & yellow variables Ms. Mecannic’s blood test not yet complete 100
- 97. Big Data vs. Smart Data in Digital Health (Healthcare consumer) Big Data from Healthcare Smart Data for Healthcare What are the reasons for my increasing weight? What should I consider before I get a kidney transplant? Personal Schedule Recommendation algorithms will analyze data deluge with domain knowledge Sleep data Activity data Red, yellow, and green indicating high, medium, and low risk factors Personal health records Community data Ms. Mecannic: Your blood work is incomplete. Please finish this before organ donation! 101
- 98. Demos • Real Time Feature Streams: http://www.youtube.com/watch?v=_ews4w_eCpg • kHealth: http://www.youtube.com/watch?v=btnRi64hJp4 • PREDOSE: https://www.youtube.com/watch?v=gCFPzMgEPQM 102
- 99. Take Away • Data processing for personalized healthcare is lot more than a Big Data processing problem • It is all about the human – not computing, not device: help them make better decisions, give actionable information – Computing for human experience • Whatever we do in Smart Data, focus on human-in-the-loop (empowering machine computing!): – Of Human, By Human, For Human – But in serving human needs, there is a lot more than what current big data analytics handle – variety, contextual, personalized, subjective, spanning data and knowledge across PC-S dimensions 103
- 100. Acknowledgements • Kno.e.sis team • Funds: NSF, NIH, AFRL, Industry… • • • Note: For images and sources, if not on slides, please see slide notes Some images were taken from the Web Search results and all such images belong to their respective owners, we are grateful to the owners for usefulness of these images in our context. 104
- 101. References and Further Readings • • • • OpenSource: http://knoesis.org/opensource Showcase: http://knoesis.org/showcase Vision: http://knoesis.org/vision Publications: http://knoesis.org/library 105
- 102. Alan Smith Wenbo Wang Vinh Nguyen Sujan Perera Hemant Purohit Cory Henson Pramod Koneru Amit Sheth’s PHD students Maryam Panahiazar Kalpa Gunaratna Ashutosh Jadhav Sanjaya Wijeratne Sarasi Lalithsena Pramod Anantharam Pavan Kapanipathi Lu Chen Delroy Cameron Kno.e.sis in 2012 = ~100 researchers (15 faculty, ~50 PhD students)
- 103. Smart Data thank you, and please visit us at http://knoesis.org/vision http://knoesis.org/amit/hcls Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled Computing Wright State University, Dayton, Ohio, USA 107
Editor's Notes
- Starting slide Various Big data problems – Traditional examples vs what we are doing examples. Variety and Velocity than Volume. kHealth problem. People will be interested in Smart Data.Traditional ML techniques, High Performance Computing, Statistics. Human level of Abstraction is Smart data.
- "2600 BC – Imhotep wrote texts on ancient Egyptian medicine describing diagnosis and treatment of 200 diseases in 3rd dynasty Egypt.”Sir William Osler, 1st Baronet, was a Canadian physician and one of the four founding professors of Johns Hopkins Hospital. He was called the father of modern medicine. Sir William Osler called Imhotep as the true father of medicine.
- Larry Smarr is a professor at the University of California, San DiegoAnd he was diagnosed with Crones DiseaseWhat’s interesting about this case is that Larry diagnosed himselfHe is a pioneer in the area of Quantified-Self, which uses sensors to monitor physiological symptomsThrough this process he discovered inflammation, which led him to discovery of Crones DiseaseThis type of self-tracking is becoming more and more common
- AMI – Advanced Metering Infrastructure
- Source: http://techcrunch.com/2012/10/27/big-data-right-now-five-trendy-open-source-technologies
- http://radhakrishna.typepad.com/rks_musings/2013/04/big-data-review.htmlGoogle predicted the spread of flu in real time - after analyzing two datasets, a.) 50 million most common terms that Americans type, b.) data on the spread of seasonal flu from public health agency- tested a mammoth of 450 million different mathematical models to test the search terms, comparing their predictions against the actual flu cases- model was tested when H1N1 crisis struck in 2009 and gave more meaningful and valuable real time information than any public health official system (Big Data, Viktor Mayer-Schonberger and Kenneth Cukier, 2013)
- Better Algorithms Beat More Data — And Here’s Whyhttp://allthingsd.com/20121128/better-algorithms-beat-more-data-and-heres-why/Big Data Cannot Replace Human Judgmenthttp://www.matchcite.com/blog/blog/2012/july/big-data-cannot-replace-human-judgment.aspx**Comments about the articles
- Smart data makes sense out of big data – it provides value from harnessing the challenges posed by volume, velocity, variety and veracity of big data, to provide actionable information and improve decision making.
- Information is CREATED by human with the Machinery available – Wikipedia tool, sensors and social networksInformation is STORED in Man+Machine readable format, LODInformation is PROCESSED using the LOD and Human assisted Knowledge-basedHigher level abstraction on info is now consumed in many mechanistic ways (including GIS) to provide EXPERIENCE for humans
- - HUMAN CENTRIC!!
- All the data related to human activity, existence and experiencesMore on PCS Computing: http://wiki.knoesis.org/index.php/PCS
- Information is CREATED by human with the Machinery available – Wikipedia tool, sensors and social networksInformation is STORED in Man+Machine readable format, LODInformation is PROCESSED using the LOD and Human assisted Knowledge-basedHigher level abstraction on info is now consumed in many mechanistic ways (including GIS) to provide EXPERIENCE for humans Example of a human guided modeling and improved performancehttp://research.microsoft.com/en-us/um/people/akapoor/papers/IJCAI%202011a.pdf
- Also, we have weather application which performs abstraction on weather sensory observations to identify blizzard conditions (food for actions!!) :--20,000 weather stations (with ~5 sensors per station)-- Real-Time Feature Streams - live demo: http://knoesis1.wright.edu/EventStreams/ - video demo: https://skydrive.live.com/?cid=77950e284187e848&sc=photos&id=77950E284187E848%21276
- Lets find it..
- http://www.nytimes.com/2003/09/13/national/13POWE.html?scp=2&sq=midwest%20iso&st=cse&pagewanted=1
- Such a blackout would cause billions of dollars in lost revenue. This particular blackout resulted in 6 billion dollar loss.Not only the consumers lose revenue even the power utility companies are fined almost a million dollars a day.
- Two of the problems are problems in “understanding” the system and the available data/observations. Lack of experience/training in deciphering this data had serious implications.The items 1 and 2 related to understanding real-world complexity by incorporating multi-modal and multi-sensory observations. Algorithms that can provide abstractions to decision makers for better comprehension of the situation.Providing abstractions in the context of the grid state continuously would lead to actionable information to decision makers.
- Doctors – error in judgment leads to legal complications and psychological stress Patients – life changing (social, psychological, economical changes) and even losing life at extreme situations!
- Core concepts of all sensing systems
- - what if we could automate this sense making ability?- and what if we could do this at scale?
- sense making based on human cognitive models
- perception cycle contains two primary phasesexplanationtranslating low-level signals into high-level abstractions inference to the best explanationdiscriminationfocusing attention on those properties that will help distinguish between multiple possible explanationsused to intelligently task sensors and collect additional observations (rather than brute force approach of blindly collecting all observations)
- perception cycle contains two primary phasesexplanationtranslating low-level signals into high-level abstractions inference to the best explanationdiscriminationfocusing attention on those properties that will help distinguish between multiple possible explanationsused to intelligently task sensors and collect additional observations (rather than brute force approach of blindly collecting all observations)
- A single-feature (disease) assumption means that all the observed properties (symptoms) must be explained by a single feature.i.e., this framework is not expressive enough to model comorbidity where there may be more than one feature (disease) co-existing For example, if there are two diseases causing disjoint symptoms, and all the symptoms of both the diseases are observed, then this framework will not be able to find the coverage and returns no diseases.Parsimony criteria is single feature assumption to choose from among multiple explanationsNot true: if multiple disease account for single property…Rewrite with more relaxed parcimony criteria (complex, cannot be modeled in OWL)Make KB more intelligent: create an individual that represents the two disease which together explain a symptom
- perception cycle contains two primary phasesexplanationtranslating low-level signals into high-level abstractions inference to the best explanationdiscriminationfocusing attention on those properties that will help distinguish between multiple possible explanationsused to intelligently task sensors and collect additional observations (rather than brute force approach of blindly collecting all observations)
- So check galvanic skin response sensor
- The problem’ addressed by the JBHI paper
- Background knowledge is used to explain the patient notes.The explain means each symptom should be explained by at least one disorder in the documentsIf there is at least one symptom which is not explained, then we generate hypothesis based on this observation.Initially all the disorder in the document becomes candidatesBy we developed a filtering mechanism to filter out hypothesis with low confidenceWe generate hypothesis with high confidence
- S1 is the unexplaned symptomD1 to D5 are the disorders in the documentSo initially D1 to D5 are candidates to have relationship with S1Within our background knowledge S1 linked to D8 and D12We collect the neighborhood of D8 and D12Then check the intersection with D1 to D5 and collected neighborhoodIf there are common disorders, they become the candidates with high confidenceSo D5 and D2 are the best candidates to have relationship with S1 from initial set (D1 to D5)
- Precision = 125/171Recall = 44/ (109-44) = 66/44
- - With this ability,many problems could be solved- For example: we could help solve health problems (before they become serious health problems) through monitoring symptoms and real-time sense making, acting as an early warning system to detect problematic health conditions
- Massive amount of data will be collected by sensors and mobile devices yet patients and doctors care about “actionable” information.This data has all the four Vs of big data and we used knowledge enabled techniques to transform it into valueIn the context of PD, we analyzed massive amount of sensor data collected by sensors on a smartphones to understand detection and characterization of PD severity.
- Main idea: Prior knowledge of PD was used to facilitate its detection from massive sensor data by reducing the search spaceDetails:Declarative knowledge of PD includes PD severity and their symptoms as shown in the logical rule aboveEach PD severity level is a conjunction of a set of PD symptomsEach symptom was mapped to its manifestation in sensor observationsThe availability of declarative knowledge significantly improved the analytics by aiding feature selection processThe graphs above contrasts the physical movements and voice of two control group members and two PD patients
- [WM-13] Wheezometer by iSonea, Available online at: http://www.isoneamed.com/wheezometer.html (Accessed May 13, 2013).[NOS-13] Nitric Oxide Sensor, Available online at: http://nodesensors.com/product/oxa-gas-sensor-nitric-oxide-no/ (Accessed May 13, 2013).[SD-13] Sensordrone, a bluetooth enabled low-cost sensor for monitoring the environment, Available online at: http://www.kickstarter.com/projects/453951341/sensordrone-the-6th-sense-of-your-smartphoneand-be/ (Accessed May 31, 2013).[ODS-13] Optical Dust Sensor, Available online at: https://www.sparkfun.com/products/9689 (Accessed May 13, 2013).[ESP-13] Everyaware, Sensing Air Pollution, Available online at: http://www.everyaware.eu/activities/case-studies/air-quality/ (Accessed May 31, 2013).[AQ-13] Community-led sensing of AirQuality, Available online at: http://airqualityegg.com/ (Accessed May 13, 2013).[NLAF-13] National and Local Allergy Forecast, Available online at: http://www.pollen.com/allergy-weather-forecast.asp (Accessed May 13, 2013).[NABA-13] National Allergy Bureau Alerts, Available online at: http://www.aaaai.org/global/nab-pollen-counts.aspx (Accessed May 13, 2013).[AQI-13] Air Quality Index from United States Environmental Protection Agency, Available online at : http://www.epa.gov/(Accessed May 23, 2013).[CDC-13] Centers for Disease Control and Prevention, Available online at: http://www.cdc.gov/ (Accessed May 23, 2013).
- kHealth:http://www.youtube.com/watch?v=btnRi64hJp4EventShop:*http://www.slideshare.net/jain49/eventshop-120721, http://dl.acm.org/citation.cfm?id=2488175
- Non-compliance, Poor economic status and No living assistance are good predictors for readmission
- Only score based structure extraction is presented here. Other popular structure extraction techniques include constraint based approaches which finds independences between random variables X1, …, XnI-Map => different structures result in the same loglikelihood score. Thus recovering the original structure of the graph generating data using data alone is considered impossible! We go the the rescue of declarative knowledge to: (1) choose promising structures and (2) to break ties when two structure results in the same score
- Intelligence distributed at the edge of the networkRequires resource-constrained devices (mobile phones, gateway notes, etc.) to be able to utilize SW technologies
- Intelligence distributed at the edge of the networkRequires resource-constrained devices (mobile phones, gateway notes, etc.) to be able to utilize SW technologiesHenson et al. 'An Efficient Bit Vector Approach to Semantics-based Machine Perception in Resource-Constrained Devices, ISWC 2012.
- compute machine perception inferences -- i.e., explanation and discrimination -- of high-complexity on a resource-constrained devices in milisecondsDifference between the other systems and what this system provides
- Intelligence at the age. Shipping computation and domain models to the edge (Distributed)
- For every 1 death from prescription drug overdose there are:10 users admitted for treatment 32 users admitted to the emergency department 130 people who are users/dependent 825 non-medical users of prescription drugsWhite House Office of National Drug Control Policy (ONDCP) launched Epidemic (May 24, 2011)
- Epidemiologist’s ApproachData collection from interviews, surveysContent Analysis using CodingComputer Scientists’ Approach Automate Data Collection Multiple sources of rich data Automate Content Analysis Information Extraction Trend Analysis
- Sample post from a user that was just discharged from rehab facility. Sent home with Suboxone and Phenobarbital treatment drugsPhenobarbital - an anti-anxiety and anticonvulsant barbiturate, used to treat anxiety and seizures This post contains entities, which require structured representations to resolve.We created the Drug Abuse Ontology (DAO) first ontology for prescription drug abuse.The ontology is very important because of the pervasive use of slang.In a manually created gold standard set of 601 posts the following was observed: 33:1 Buprenorphine 24:1 Loperamide
- INTENSITY – more than, abnormal, in excess of, too muchDRUG-FORM – ointment, tablet, pill, filmINTERVAL – for several years
- Loperamide is sold over the counter (OTC) in ImodiumYellow – positive sentimentsPink – EntitiesGreen – curious finding - indication of getting high in the processMention the practice of Megadosing!!
- More at: http://wiki.knoesis.org/index.php/PCSAnd http://knoesis.org/projects/ssw/