Research

An Incremental Learning Method for Preserving World Coffee Aromas by Using an Electronic Nose and Accumulated Specialty Coffee Datasets

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The development of thermal error compensation on CNC machine tools by combining ridge parameter selection and backward elimination procedure

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Unlocking the Secrets Behind Advanced Artificial Intelligence Language Models in De-Identifying Chinese-English Mixed Clinical Text

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Investigating Cross-Institutional Recognition of Cancer Registration Items: A Case Study on Catastrophic Forgetting

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AI CUP-隱私保護與醫學數據標準化競賽：解碼臨床病例、讓數據說故事

與澳洲新南威爾斯大學合作開發之去識別化與時間分析語料庫。

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• Client: MOU
• Date: April 2023
• Service: Clinical Natural Language Processing

Automatic Extraction of Medication Mentions from Tweets—Overview of the BioCreative VII Shared Task 3 Competition

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Augmenting DSM-5 diagnostic criteria with self- attention-based BiLSTM models for psychiatric diagnosis

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Robust fault recognition and correction scheme for induction motors using an effective IoT with deep learning approach

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Vickers Hardness Value Test via Multi-Task Learning Convolutional Neural Networks and Image Augmentation

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Principle-Based Approach for the De-Identification of Code-Mixed Electronic Health Records

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Protected Health Information Recognition of Unstructured Code-Mixed Electronic Health Records in Taiwan

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Cancer Registry Coding via Hybrid Neural Symbolic Systems in the Cross-Hospital Setting

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去識別化工具

去識別化檢視與分析工具開發，保障患者相關隱私資訊

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• Client: KMUH/CMUH
• Date: September 2021
• Service: Clinical Natural Language Processing

癌症研究跨機構合作平台及其整合應用

自然語言處理輔助之癌症登記自動化平台，可分析大腸癌等多種癌別的病患電子病歷資料。進行自 動化的非結構化資料擷取與整理，以供後續轉譯醫學使用，研發之系統已嫁接於合作醫院的資訊室內，提供自動化的分 析服務，並輔助降低癌症登記的錯誤率。

Fund# MOHW110-TDU-B-212-144026

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• Client: NHRI
• Date: August 2021
• Service: Clinical Natural Language Processing

精準廣告行銷

與 iLinker 合作開發之廣告圖像人工智慧模型訓練資料集。

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• Client: iLinker
• Date: August 2021
• Service: Computer Vision, Natural Language Processing

智慧晶片應用與聯網技術課程推廣計畫

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Cohort selection for construction of a clinical natural language processing corpus

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人工智慧導入肺癌早期偵測與癌症登記之研發

本計畫執行「110年人工智慧導入肺癌早期偵測與癌症登記之研發 」之「方案一：人工智慧 導入於肺癌癌症登記報告」，藉由導入人工智慧輔助癌症登記人員進行肺癌症登錄報告的自動產出，現階段以兩家醫院試辦執行中。

Fund# A1100302

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• Client: NHRI
• Date: April 2021
• Service: Clinical Natural Language Processing

Deep Learning-Based Natural Language Processing for Screening Psychiatric Patients

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Family History Information Extraction With Neural Attention and an Enhanced Relation-Side Scheme: Algorithm Development and Validation

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Cohort selection for clinical trials using multiple instance learning

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Using text mining to extract depressive symptoms and to validate the diagnosis of major depressive disorder from electronic health records

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Adverse drug event and medication extraction in electronic health records via a cascading architecture with different sequence labeling models and word embeddings

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Family member information extraction via neural sequence labeling models with different tag schemes

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Classifying adverse drug reactions from imbalanced twitter data

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智慧聯網技術課程推廣計畫

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Statistical principle-based approach for recognizing and normalizing microRNAs described in scientific literature

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Overview of the BioCreative VI Precision Medicine Track: mining protein interactions and mutations for precision medicine

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MRI - MicroRNA Identification

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An Ensemble Neural Network Model for Benefiting Pregnancy Health Stats from Mining Social Media

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NTTMUNSW System for n2c2 Track1: Cohort Selection for Clinical Trails

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Ensemble of Different Sequential Labeling Algorithms for Medication and Adverse Drug Event Extraction

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Assessing the severity of positive valence symptoms in initial psychiatric evaluation records: Should we use convolutional neural networks?

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癌症研究跨機構合作平台及其整合應用

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Family History Information Extraction with Neural Sequence Labeling Model

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非結構化病歷資料之去識別化研究與應用

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SPRENO: a BioC module for identifying organism terms in figure captions

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Identification of Adverse Drug Reactions and Medication Intakes on Twitter Using Various Combinations of Language Features

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SPRENO

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Biomarker identification of hepatocellular carcinoma using a methodical literature mining strategy

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NTTMU System in the 2nd Social Media Mining for Health Applications Shared Task

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Pregnant Women Recognition from Social Media for Health-related Information Exploration

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Principle base Approach for Classifying Tweets with Flu-related Information in NTCIR-13 MedWeb Task

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Using a Recurrent Neural Network Model for Classification of Tweets Conveyed Influenza-related Information

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SPRENO: A BioC Module for Recognizing and Normalizing Species and Their Model Organisms

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Identifying Mutation-induced Protein-Protein Interactions in Scientific Literature

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A Study on Identification of Organism and micro-RNA Mentions in Figure Captions

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Exploring Associations of Clinical and Social Parameters with Violent Behaviors among Psychiatric Patients

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運用文字探勘技術於電子病歷-評估精神疾病的診斷、治療、與預後

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中英文社群媒體之不良藥物反應事件探勘與應用

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NTTMU-SCHEMA BeCalm API in BioCreative V.5

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An Ensemble Algorithm for Sequential Labelling: A Case Study in Chemical Named Entity Recognition

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Performance and interoperability assessment of Disease Extract Annotation Server

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巨量資料分析技術與應用課程模組-巨量資料分析平台與應用

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運用文字探勘技術於電子病歷-評估精神疾病的診斷、治療、與預後

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MET network in PubMed

METastasisWay annotation tool (MET) is an interactive curation system that provides you a powerful and easy-to-use way to access the contents of MetastasisWay and to contribute your reading about the metastatic pathway. MET is developed as browser extensions to make it easy to install and update. With MET, you can constribute your exciting observations while reading or searching on PubMed.

Fund# MOST 105-2221-E-143-003

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• Client: NHRI
• Date: August 2016
• Service: Biomedical Text Mining

整合式全球癌症轉移巨量文獻知識庫及視覺化分析系統之建構

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NTTMUNSW BioC modules for recognizing and normalizing species and gene/protein mentions

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MET network in PubMed: a text-mined network visualization and curation system

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Feature Engineering for Recognizing Adverse Drug Reactions from Twitter Posts

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A Context-Aware Approach for Progression Tracking of Medical Concepts in Electronic Medical Records

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國人十大癌症生物標靶擷取之研究

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Text Mining for Translational Bioinformatics

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Recognition and Evaluation of Clinical Section Headings in Clinical Documents Using Token-Based Formulation with Conditional Random Fields

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nttmuClinical.NET

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Enhancing of chemical compound and drug name recognition using representative tag scheme and fine-grained tokenization

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Azure Machine Learning Research for Mining Biomarkers from Literature

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LiverCancerMarkerRIF: a liver cancer biomarker interactive curation system combining text mining and expert annotations

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LiverCancerMarkerRIF

LiverCancerMarkerRIF is a liver cancer biomarker database along with a text-mining-based curation system, which allows users to retrieve biomarker-related narrations and curate supporting evidence on liver cancer biomarkers directly while browsing PubMed.

Fund# MOST 104-2221-E-143-005

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• Client: NHRI/BCT
• Date: April 2014
• Service: Biomedical Text Mining

以生醫命名實體為導向之次世代搜尋系統之開發與研究-II

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Joint Learning of Entity Linking Constraints Using a Markov-Logic Network

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Collective Instance-Level Gene Normalization on the IGN Corpus

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TEMPTING system: a hybrid method of rule and machine learning for temporal relation extraction in patient discharge summaries

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以生醫命名實體為導向之次世代搜尋系統之開發與研究

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巨量資料之疾病共病性分析

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T-HOD

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T-HOD: a literature-based candidate gene database for hypertension, obesity and diabetes

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Coreference resolution of medical concepts in discharge summaries by exploiting contextual information

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Integration of gene normalization stages and co-reference resolution using a Markov logic network

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PubMed-EX

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BIOSMILE Web Search

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