Detecting Patient Complexity from Free Text Notes Using a Hybrid AI Approach

Transcription

1 Detecting Patient Complexity from Free Text Notes Using a Hybrid AI Approach Malcolm Pradhan, CMO MBBS, PhD, FACHI Daniel Padilla, ML Engineer BEng,, PhD Alcidion Corporation

2 Overview Alcidion s Natural Language Processing (NLP) efforts Uses of NLP in healthcare Basics of clinical NLP Context and knowledge

3 Why? Detect patient complexity and complications in clinical notes in real-time Clinical benefits Safety, flow, automation Understanding patient complexity, comorbid conditions Identify changes in patient conditions Early warning for complications Complexity implicated in errors, LOS, resource utilization, planning Business benefits Clinical coding & ABF Hospitals get reimbursed for patient complexity Reduce overhead in manually searching through notes for coding

4 How? Extract clinical notes from EMR, other sources Admission, discharge Progress Handover notes Problem lists Run NLP algorithms to detect clinical complexity Highlight clinical risks Generate real-time coding

5 Miya Precision Conceptual Overview HL7, ESB & Other Data Data to FHIR Event Bus Clinical Event Generation Decision Support Engine Web & Mobile Clients OntoServer Store & Cache Rules, AI, Predictive

6 Precision Dashboards Specialty specific views Problems

7 Concept Detection Concepts detected and mapped to Snomed, and then to ICD-10 AM

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9 Complexity Detection Recent analysis 600,000+ clinical notes 300,000+ pathology results 24,000+ handover notes Detected ICD code One or more new ICD codes in 65% of admissions 25% of multi-day episodes with complexity B and C were upgraded to A and B

10 Different Uses of NLP Detect particular phrases/concepts Specific concepts to predict NYHA grade As part of broad-based prediction Mortality, LOS from clinical notes, labs, meds, etc. Identify which concepts are present Phenotyping Lack of maintained problem lists Dynamic conditions Rajkomar, et.al. Scalable and accurate deep learning with electronic health records. npj Digital Medicine (2018)1:18

11 NLP & Terminology Basics Concept a clinical idea Terms one or more descriptions that describe a concept Bills on ports and immigration were submitted by Senator Brownback, Republican of Kansas Ambiguous term A single term can describe multiple concepts MS : Multiple Sclerosis, Mitral Stenosis, Mental State, Morphine Sulphate, etc. Traditional NLP Parts of Speech (POS), Named Entity Recognition (NER) Deep Learning NLP, word embeddings

12 Word Embeddings You shall know a word by the company it keeps R. Firth 1957 Vector based approaches define numeric values to words based on their context Popular because they allow text to be encoded for use in deep neural networks

13 Requirements for Complexity NLP Identify terms Detect negation Disambiguate to map terms to concepts Real world issues Clinical notes do not contain sentences ( Unnatural language processing ) Spelling errors Local abbreviations and idioms Aortic Valve Stenosis (C ) Aortic Valve Stenosis Aortic Valve Stenoses Stenoses Aortic Valve Valve Stenoses Aortic Valve Stenosis Aortic Aortic Valve Steis Steis Aortic Valve Stenosis Aortic Valve Stenosed Aortic Valve Stenosis of Aortic Valve Aortic Stenosis Stenoses Aortic Aortic Steis Stenosis Aortic Stenose Aortic Stenosis AS Stenosis Of Aorta Aorta Stenosis Stenosis Aorta Valvular Aortic Stenosis Aortic Valvular Steis Aortic Valvular Stenosis Narrowing Of Aortic Valve Aorta Stenosis Valve Aortic Valve Stricture

14 Negation Detection Negex Rule-based negation detection Fast Fails to pick up complex negation e.g. Signs of fever not seen Alcidion approach Deep learning parser with customized negation rules Slow compared to regex Sophisticated negation detection

15 Example While on the surface, this woman has suffered a CVA, the CT brain imaging as well as the clinical picture are not congruent with one another. One would always have to exclude the possibility of a brain tumour. Pos/ Neg Indicator CUI Concept Term Cat egory Original St ring pos C X-Ray Comput ed Tomography Diagnostic Procedure CT pos C Imaging Techniques Diagnostic Procedure imaging neg C Cerebrovascular accident Disease or Syndrome CVA neg C Brain Neoplasms Neoplastic Process brain tumour

16 Disambiguation & Context Examples RA Rheumatic Arthritis, but commonly Room Air Nurse accidently typed ALS instead of ALT Local word context %, sat, O2 Note context PMH, observations Sometimes poorly structured AI model to detect note context Other patient information Previous admissions Medications User context Nurse, physician, specialist

17 Making EMR Notes NLP Friendly Structured notes are ideal, but hard to enter and lack expressiveness Semi-structured notes Different sections to make context easier to identify e.g. SOAP Real-time NLP to identify misspellings, terms that cannot be disambiguated Confirmation of key conditions Automation is key to engaging clinicians

18 Conclusions Effective clinical NLP for concept detection requires a hybrid approach Traditional NLP Deep learning Topic models Probabilistic models Rules There is no general solution to NLP Significant improvements if EMRs were designed for NLP

19 Thank you Copyright 2018 Alcidion Corporation