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Apply Deep Learning Technology to Implement AI Doctor Kan Deng,Ph.D [email protected] 139-1122-6337 11/09/2016 1 / 14 Computer can play go, but how about practicing medicine? In 1997, IBM DeepBlue outperformed Kasparov. In 2011, IBM Watson won the human champion of Jeopardy! In 2016, Google AlphaGo outperformed Lee Sedol 2 / 14 What is the correct objective? Not to replace human doctor, but assist? 8 Dan - 10 Dan: Recommend personalized precision medicine, Covers rare diseases. 4 Dan – 7 Dan: Following clinical guideline, • Intermediate level, Recommend the most likely diagnosis results and therapies • No surgery, Of common diseases. • Focus on 400 common diseases, 1 Dan – 3 Dan: Looks up the medical documents smartly, • Recommendation only. Enhances human doctor’s efficiency. 3 / 14 How big is the market? 全国每日约 1.7 亿 人次, 在网上咨询医疗,基本是两类问题。 1. 知情权: 确认医院的诊断是否准确, 了解医院的诊断依据。 2. 行动方案: 找性价比好的药品, 找临床经验好的医生, 找治疗质量可靠的医院, 找性价比好的医疗保险。 四种行动方案都直接是赢利点。 4 / 14 Where to use AI Doctor? Health-care Community • Help patients to enjoy quick and good medical consulting service. • Help hospitals to find target patients. • Help government to construct public healthcare records. • Help insurance company to investigate credit and audit claims. 5 / 14 How to use AI Doctor? Diagnosis and pathway • Given (limited) illness status description, output likely diseases. • Recommend the further lab tests and examination, to diagnose the disease more accurately. • Instantly respond to the patients. • Reduce hospital labor cost. 修改病情输 入 6 / 14 To-do list to implement AI doctor • Construct a private cloud inside hospital intra-network. • Collect data from all sources, to assemble the EHR data warehouse. • Construct a medical synonym dictionary. Using WordVector algorithm to cluster the synonym candidates automatically, and then verify by human doctors. • Transform the free-text EHRs into structured vectors, using NER / LSTM algorithm. • Construct the medical knowledge graph, by approximating the correlations among the variables. • Train the models to predict the diagnosis results given symptoms, lab tests and other disease status. 7 / 14 Construct a private cloud HIS Synonym Dictionary Medical Knowledge-Base EMR Data Cleaning LIS EHR Structuration Data Indexing Data Mining Model Training PACS Gene Scheduler Original EHRs Structured EHRs EHR Indices Medical Knowledge Graph 8 / 14 So far we have processed 160 million EHRs Similar to conventional CDR, but our system is capable of data mining and clinical decision supporting. 3 weeks to launch the private cloud, 0 on-site engineers, No need to collaborate with the original HIS, EMR, LIS, PACS system vendors. 9 / 14 Construct the medical synonym dictionary Using WordVector algorithm to find the synonym candidates. Disease standard name number: 25,856 Disease synonym name number: 43,476 Symptom term number: 1591 Lab testing indicator standard name number: 4,495 Lab testing indicator synonym name number: 4,495 Drug standard name number: 18,189 Drug synonym name number: 16,443 10 / 14 Transform free-text into structured entities Using Long Short Term Memory algorithm to recognize the entities from free-text. 11 / 14 Construct the medical knowledge graph Do statistics on the co-occurrence among symptoms, lab testing indicators, radiology marks, diseases, and drugs. Each symptom, lab testing indicator, radiology mark, disease, and drug is regarded as a node, the co-occurrence frequency between two nodes is an edge. 12 / 14 Clinical decision support Given a collection of symptoms, lab test and radiology exam results, remind doctors of the likely diseases and recommend the guideline of further tests and exams. 13 / 14 http://www.iyoudoctor.com 14 / 14