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USING WORD EMBEDDINGS TO REPRESENT DIFFERENT TYPES OF CLINICAL DATA - PowerPoint PPT Presentation

Jan 03, 2024 •1.1k likes •1.33k views

USING WORD EMBEDDINGS TO REPRESENT DIFFERENT TYPES OF CLINICAL DATA MERIJN BEEKSMA (MERIJNBEEKSMA@GMAIL.COM) I MEDICAL RECORDS E L E C T R O N I C M E D I C A L R E C O R D S E L E C T R O N I C M E D I C A L R E C O R D S MORE OR LESS

  1. USING WORD EMBEDDINGS TO REPRESENT DIFFERENT TYPES OF CLINICAL DATA MERIJN BEEKSMA (MERIJNBEEKSMA@GMAIL.COM)

  2. I MEDICAL RECORDS

  3. E L E C T R O N I C M E D I C A L R E C O R D S

  4. E L E C T R O N I C M E D I C A L R E C O R D S MORE OR LESS… SIMILAR PROPERTIES MANY FEATURES SPARSE FEATURES ZIPFIAN DISTRIBUTIONS SIMILAR INFORMATION ICD-10 TEXT 1M PATIENTS: 5862 UNIQUE CODES, 50% FREQ ≤5

  5. E L E C T R O N I C M E D I C A L R E C O R D S GENERIC SOLUTIONS LANGUAGE-INDEPENDENT APPLICABLE TO MULTIPLE DATA TYPES ABLE TO HANDLE UNSEEN INPUT ROBUST TO NEW DEVELOPMENTS SIMPLE SOLUTIONS MINIMAL PREPROCESSING RETAIN IDIOSYNCRACIES SHAREABLE SOLUTIONS ”DATA CANNOT LEAVE THE BUILDING” HANDLE DISTRIBUTED DATA SOURCES

  6. W O R D E M B E D D I N G S : T E X T

  7. P R O S A N D C O N S PROS MINIMAL PREPROCESSING RETAIN/DETECT IDIOSYNCRACIES CAPTURE SIMILARITY DENSE REPRESENTATION SMALL AMOUNT OF FEATURES CONS EVALUATION OTHER DATA TYPES REDUNDANCY WITH OTHER DATA FREQUENCY IMPACTS STABILITY

  8. E M B E D D E D I C P C - 1 C O D E S

  9. T I M E L I N E T O S E N T E N C E

  10. ‘ P I L E ’ O F D A T A

  11. ‘ P I L E ’ O F D A T A HOWEVER… ● CAN’T INCLUDE TEXT DATA ● SOME DATA TYPES CLUSTER TOGETHER ● COMPARED TO DATA-TYPE SPECIFIC SPACES, SIMILARITIES WITHIN DATA TYPES ARE DISTURBED ● MANY UNSTABLE DATA POINTS

  12. II STABILITY

  13. I N T R I N S I C M E A S U R E M E N T S O F S T A B I L I T Y WHY MEASURE STABILITY? OPTIMIZE PARAMETER SETTINGS DETERMINE IMPACT FREQUENCY LEVERAGE STABLE POINTS TO STABILIZE UNSTABLE POINTS INTRINSIC MEASUREMENT OF QUALITY WHY NOT JUST DOWNSTREAM TASK? OVERFITTING

  14. H O W T O M E A S U R E S T A B I L I T Y ? I EMBED SAME DATA MULTIPLE TIMES WITH DIFFERENT INITIALIZATION* II FOR EACH ITEM: DETERMINE SIMILARITY BETWEEN THE VECTORS OF THIS ITEM IN DIFFERENT SPACES III DO SOMETHING USEFUL WITH IT, SUCH AS: CALCULATE AVERAGE STABILITY RANK THE ITEMS BY STABILITY *NB: WANT TO MAKE A FULLY REPRODUCIBLE RUN? (YES!) - FIX ALGORITHM PARAMETER “SEED” - USE 1 CPU - FIX ENVIRONMENTAL VARIABLE “PYTHONHASHSEED” WHEN WORKING WITH PYTHON AND GENSIM

  15. II MAPPING SPACES

  16. M A P P I N G B E T W E E N C O D E B O O K S ICD-10 ICPC-1 ICPC-2

  17. M A P P I N G B E T W E E N C O D E B O O K S

  18. M A P P I N G B E T W E E N C O D E B O O K S

  19. P R O J E C T O N T O S A M E S P A C E HOW? I DETERMINE ANCHOR POINTS II RANK BY STABILITY III ROTATE SPACE A ONTO SPACE B (E.G. WITH LEAST SQUARED ERROR METRIC) WHY? AUTOMATIC MAPPING SIMILAR DATA, SIMILAR REPRESENTATION → MINIMIZES AMOUNT OF FEATURES ORIGINAL SPACES ARE NOT ALTERED HMM… WILL IT WORK FOR MORE DIVERSE DATA TYPES TOO?

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