Predicting Health Care Utilization After Behavioral Health Referral Using Natural Language Processing and Machine Learning

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Predicting Health Care Utilization After Behavioral Health Referral Using Natural Language Processing and Machine Learning

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2015 nî lūn-bûn

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2015年の論文

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2015年論文

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2015年論文

@zh-hant

2015年論文

@zh-hk

2015年論文

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2015年論文

@zh-tw

2015年论文

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2015年论文

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2015年论文

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Predicting Health Care Utiliza ...... rocessing and Machine Learning

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Predicting Health Care Utiliza ...... rocessing and Machine Learning

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Predicting Health Care Utiliza ...... rocessing and Machine Learning

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Predicting Health Care Utiliza ...... rocessing and Machine Learning

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Predicting Health Care Utiliza ...... rocessing and Machine Learning

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Predicting Health Care Utiliza ...... rocessing and Machine Learning

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P1476

Predicting Health Care Utiliza ...... rocessing and Machine Learning

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P2093

Adam Wright

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Nathaniel Roysden

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P2860

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Unlimited access to health care--impact of psychosomatic co-morbidity on utilisation in German general practices.

Somatic presentation of psychiatric morbidity in general practice.

Predicting suicides after psychiatric hospitalization in US Army soldiers: the Army Study To Assess Risk and rEsilience in Servicemembers (Army STARRS).

Prediction of cardiac arrest in critically ill patients presenting to the emergency department using a machine learning score incorporating heart rate variability compared with the modified early warning score

Somatic presentation of depressive illness in primary care.

Use of a support vector machine for categorizing free-text notes: assessment of accuracy across two institutions.

Using electronic medical records to enable large-scale studies in psychiatry: treatment resistant depression as a model.

P304

2063-2072

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scholarly article

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2015

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2015-11-05T00:00:00Z

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26958306

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machine learning

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4765610

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