Medical Big Data Workshop 12:30-5pm Star Conference Room. #MedBigData15

Transcription

1 Medical Big Data Workshop 12:30-5pm Star Conference Room #MedBigData15

2 Welcome! Today s Goals: Introduce you to the Big CSAIL Introduce you to the popular MIMIC II Dataset Overview of Database Technologies Network and meet new people! Come up with some cool ideas

3 The Team Sam Madden Mornin Feng Ikaro Silva Tristan Naumann Jeremy Kepner Alex Poliakov Lauren Edwards Vijay Gadepally

4 Agenda IntroducWon and Welcome About the ISTC Program About the MIMIC II Dataset Break Database Technologies Project Ideas Group Discussion (more ideas!) Closing

5 Agenda IntroducWon and Welcome About the ISTC Program About the MIMIC II Dataset Break Database Technologies Project Ideas Group Discussion (more ideas!) Closing

6 Agenda IntroducWon and Welcome About the ISTC Program About the MIMIC II Dataset Break Database Technologies Project Ideas Group Discussion (more ideas!) Closing

7 Agenda IntroducWon and Welcome About the ISTC Program About the MIMIC II Dataset Break Database Technologies Project Ideas Group Discussion (more ideas!) Closing

8 Database Technologies

9 Database Fundamentals Database: CollecWon of data and supporwng data structures Database Management Systems: SoYware that provides interface between user and database Common User- DBMS interacwons: Defining new data, new schema, etc. UpdaWng data Retrieving (Querying) data DB administrawon, security, permissions, etc.

10 A Brief History of Open- Source Big Data NoSQL DATABASES Cluster BigTable Dremel NewSQL PARALLEL PROCESSING MapReduce Hadoop Pregel D4M Giraph

11 RelaQonal Databases What it is: Database that stores informawon about data and how it is related. Table based databases, and tables contain n rows when you have n data entries Predefined schema/organizawon of data VerWcally scalable (Depends on hardware power. Scales with beier hardware) Use SQL as query interface Typically provide full consistency (only one version of stored data in the whole cluster) RelaQonal Databases Use Cases: Strong need to have consistent results (for example dealing with $$) Willing to trade performance for accuracy Need for ACID guarantees Examples: mysql, postgresql, Oracle

12 Non- RelaQonal Databases What it is: Database based on documents, key- value pairs, graphs, or wide- column stores No standard schema definiwons necessary to adhere to. Dynamic schema Horizontal scalability (Usually run on COTS, scales with more systems) Typically provide eventual (nosql) consistency there may be different valid versions of the same data in the cluster with different values. Non RelaQonal/Distributed Databases Use Cases: OK with BASE (Basically available, soy state, eventual consistency) guarantees Examples: Accumulo, Cassandra, MongoDB, Google Big Table

13 Comparing RelaQonal and Non RelaQonal Databases RelaQonal Databases MySQL, PostgreSQL, Oracle NoSQL HBase, Cassandra, Accumulo Typed columns with relawonal keys Single- node or sharded Quick Reference Schema- less RDBMS vs. NoSQL Distributed, scalable ACID transacwons SQL, indexing, joins, and query planning Eventually consistent Low- level API (scans and filtering)

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21 Agenda IntroducWon and Welcome About the ISTC Program About the MIMIC II Dataset Break Database Technologies Project Ideas Group Discussion (more ideas!) Closing

22 Possible Projects

23 The MIMIC II Dataset The MulWparameter Intelligent Monitoring in Intensive Care (MIMIC II) dataset provides a realiswc and challenging corpus of data Made up of 2 parts: Clinical Dataset Waveform Dataset More informawon: hip://physionet.org/mimic2

24 Clinical Dataset Contents: General - PaWent demographics, hospital admissions & discharge dates, room tracking, death dates (in or out of the hospital), ICD- 9 codes, unique code for health care provider and type (RN, MD, RT, etc). All dates are surrogate dates due to privacy issues but Wme intervals (even those between mulwple admissions of the same pawent) are preserved. Physiological - Hourly vital sign metrics, SAPS, SOFA, venwlator seqngs, etc. MedicaWons - IV meds, provider order entry data, etc. Lab Tests - Chemistry, hematology, ABGs, imaging, etc. Fluid Balance - Intake (soluwons, blood, etc) and output (urine, eswmated blood loss, etc). Notes & Reports - Discharge summary, nursing progress notes, etc; cardiac catheterizawon, ECG, radiology, and echo reports. Currently stored in relawonal database

25 Waveform Dataset The waveform database contains thousands of recordings of mulwple physiologic signals ("waveforms") and Wme series of vital signs ("numerics") collected from bedside pawent monitors in adult and neonatal intensive care units (ICUs). Examples: ECG Signals Arterial Blood Pressure RespiraWon

26 MIMIC II Dataset Very useful, but, many major challenges: Messy Erroneous Unstructured Components Heterogeneous data types MIMIC II dataset provides insight into the challenges associated with real datasets

27 Project Ideas Common Themes: Cleaning AnalyWcs Viz 2015 Challenge

28 Project Ideas Meant to be interacwve! Please jump in with your thoughts or queswons We ve thought of a few projects along the following themes: AutomaWc pre processing/cleaning of data AnalyWcs VisualizaWon Discussion for people of different backgrounds and experwse

29 Theme: AutomaQc Pre Processing of Medical Big Data Big data means big problems in working with data collected over Wme. Challenges: Volume Velocity Variety Veracity (privacy) Big can be a relawve term. Depends your hardware, analywcs and types of data. Big can be anywhere from gigabytes to terabytes

30 Clean Data Look for possibly erroneous regions and extract points of interest based on physical or clinical informawon Project will perform literature review to find characteriswcs of correct signal, develop codebase that can read in waveforms and apply tests or comparisons against ideal data, extract regions that do not conform.

31 Outlier DetecQon/SubsQtuQon Look for signals or parts of signals that are outliers based on the stawswcs of the signal and biological limits (for example, having a heart beat above a threshold or 100 standard deviawons above the mean) Some useful tools: SCORPION/dbwipes PotenWal projects may perform literature review of current outlier detecwon algorithms and possible biological/ physical limits Reference: hip://web.mit.edu/mfeng/www/papers/arwfact_cr.pdf Reference: hip://web.mit.edu/mfeng/www/papers/ ICASSP13_HanMumaFengZoubir_draY.pdf

32 Outlier DetecQon/SubsQtuQon (2) Look for anomalies in the rate of change of signals, which may indicate errors in data collecwon. Signals are non stawonary and it may be necessary use enwre signal and not just pieces Project may be to develop filters that can look for regions of stawswcally or biologically anomalous rates of change

33 DetecQon of Human Bias OYen, there are errors in a dataset when human intervenwon is required. For example, someone may enter 100 KG instead of 100 lbs. Project will go through entries where human bias may exist and look for possible errors.

34 DetecQng Incorrect Signal Leads A common problem is when signal leads are mixed up (for example ECG lead V with IV). Project will look for signal characteriswcs associated with different leads, and go through dataset to extract erroneous connecwons. Reference: hips://github.com/ikarosilva/paweniracking

35 Theme: MIMIC AnalyQcs Developing a set of medically relevant analywcs that leverage the relawonal and Wme series porwons of the dataset. Will be great to have physical/medical pracwwoners involved!

36 Market Basket MedicaQon Use paiern analysis and text mining to predict the next medicawon for a parwcular pawent. Will involve looking at paierns of how medicawons were prescribe or taken by pawents. For example, pawents who take X medicawon have a tendency to take Y medicawon Reference: hip:// pii/s

37 RelaQng waveform and structured data Use Wming data, paiern matching and io_events to look at the relawonship between different measurements. For example, find the relawonship between blood pressure and urine. May need to control for possible intervenwons, age range, gender, etc.

38 PaQent Cohorts Determine how pawents are clustered. This can be very useful for some of the other analywcs. Possible approaches: cluster pawents based on waveform stawswcs, cluster on physical characteriswcs (age, gender, etc.), cluster based on medicawon/intervenwon.

39 Theme: VisualizaQon An important aspect of big data is big visualizawon. Data visualizawons can aid in the explorawon of knowledge and especially in complex datasets such as MIMIC, can help with the generawon of insight.

40 MIMIC Explorer Design a visualizawon framework (web or otherwise) to allow visualizawon of the relawonal and non- relawonal components of the MIMIC dataset Ideally, explorer should be able to perform some basic analywcs to get eswmates of data For example, group pawents by gender, age, Wme of admission, etc.

41 Visualize AnalyQcs Work with other groups to develop visualizawon for their analywcs/data pre- processing tasks Visualize data on new hardware such as Google Glass, Occulus RiY, etc. For example, port the MIMIC II explorer to different visualizawon hardware

42 Open Floor Anyone in the audience working on something intereswng?

43 How to get started? us! Get an account on MIT SuperCloud to get access to a compuwng cluster MIT SuperCloud has the MIMIC II data readily available.

44 Agenda IntroducWon and Welcome About the ISTC Program About the MIMIC II Dataset Break Database Technologies Project Ideas Group Discussion (more ideas!) Closing

45 Agenda IntroducWon and Welcome About the ISTC Program About the MIMIC II Dataset Break Database Technologies Project Ideas Group Discussion (more ideas!) Closing

46 Contact us! Sam Madden: Vijay Gadepally Get an account on MIT Systems.