Predicting the Reasons of Customer Complaints: A First Step Toward Anticipating Quality Issues of In Vitro Diagnostics Assays with Machine Learning.

about

Predicting the Reasons of Customer Complaints: A First Step Toward Anticipating Quality Issues of In Vitro Diagnostics Assays with Machine Learning.

Item

http://www.wikidata.org/entity/Q55413558

description

2018 nî lūn-bûn

@nan

2018年の論文

@ja

2018年学术文章

@wuu

2018年学术文章

@zh-cn

2018年学术文章

@zh-hans

2018年学术文章

@zh-my

2018年学术文章

@zh-sg

2018年學術文章

@yue

2018年學術文章

@zh

2018年學術文章

@zh-hant

name

Predicting the Reasons of Cust ...... Assays with Machine Learning.

@en

Predicting the Reasons of Cust ...... Assays with Machine Learning.

@nl

type

Item

label

Predicting the Reasons of Cust ...... Assays with Machine Learning.

@en

Predicting the Reasons of Cust ...... Assays with Machine Learning.

@nl

prefLabel

Predicting the Reasons of Cust ...... Assays with Machine Learning.

@en

Predicting the Reasons of Cust ...... Assays with Machine Learning.

@nl

P1476

Predicting the Reasons of Cust ...... s Assays with Machine Learning

@en

P2093

James Kim

http://www.w3.org/2001/XMLSchema#string

P2860

Causes for the underreporting of adverse drug events by health professionals: a systematic review

Mining electronic health records: towards better research applications and clinical care

Dermatologist-level classification of skin cancer with deep neural networks

Monitoring laboratory data across manufacturers and laboratories--A prerequisite to make "Big Data" work.

Predictive data mining in clinical medicine: current issues and guidelines.

A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model.

Patient complaints in healthcare systems: a systematic review and coding taxonomy.

Analysis of the US Food and Drug Administration Manufacturer and User Facility Device Experience database for adverse events involving Amplatzer septal occluder devices and comparison with the Society of Thoracic Surgery congenital cardiac surgery d

Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records.

Underreporting of patient safety incidents reduces health care's ability to quantify and accurately measure harm reduction.

P304

e34

http://www.w3.org/2001/XMLSchema#string

P31

scholarly article

P356

10.2196/MEDINFORM.9960

http://www.w3.org/2001/XMLSchema#string

P433

http://www.w3.org/2001/XMLSchema#string

P478

http://www.w3.org/2001/XMLSchema#string

P50

P577

2018-05-15T00:00:00Z

http://www.w3.org/2001/XMLSchema#dateTime

P698

29764796

http://www.w3.org/2001/XMLSchema#string

P921

machine learning

P932

5974458

http://www.w3.org/2001/XMLSchema#string