A computer system for generating a diagnostic tool by applying artificial intelligence to an instrument for diagnosis of a disorder, such as autism. For autism, the instrument can be a caregiver-directed set of questions designed for an autism classification tool or an observation of the subject in a video, video conference, or in person and associated set of questions about behavior that are designed for use in a separate autism classification tool. The computer system can have one or more processors and memory to store one or more computer programs having instructions for generating a highly statistically accurate set of diagnostic items selected from the instrument, which are tested against a first test using a technique using artificial intelligence and a second test against an independent source. Also, a computer implemented method and a non-transitory computer-readable storage medium are disclosed.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer implemented method of generating a diagnostic tool by applying artificial intelligence to an instrument for diagnosis of a disorder, wherein the instrument comprises a full set of diagnostic items, the computer implemented method comprising: on a computer system having one or more processors and a memory storing one or more computer programs for execution by the one or more processors, the one or more computer programs including instructions for: testing diagnostic items from the instrument using a technique using artificial intelligence; determining from the testing the most statistically accurate set of diagnostic items from the instrument; selecting a set of the most statistically accurate diagnostic items from the instrument; determining the accuracy of the set of the most statistically accurate diagnostic items from the instrument by testing the set of the most statistically accurate diagnostic items from the instrument against an independent source; and generating the diagnostic tool for diagnosis of the disorder. 2-132. (canceled) 133. The computer implemented method of claim 1, wherein the instrument is the Autism Diagnostic Interview-Revised and wherein the disorder is autism, wherein the full set of diagnostic items consists of 153 diagnostic items, and wherein the diagnostic tool consists of 7 diagnostic items. 134. The computer implemented method of claim 133, wherein a time for administering the full set of diagnostic items is about 2.5 hours, and wherein a time for administering the diagnostic tool is less than about an hour. 135. The computer implemented method of claim 133, wherein subjects of the 7 diagnostic items are comprehension of simple language, reciprocal conversation, imaginative play, imaginative play with peers, direct gaze, group play with peers and age when abnormality first evident. 136. The computer implemented method of claim 133, wherein the technique using artificial intelligence is a machine learning technique. 137. The computer implemented method of claim 136, wherein the machine learning technique is one from the group consisting of: ADTree, BFTree, ConjunctiveRule, DecisionStump, Filtered Classifier, J48, J48graft, JRip, LADTree, NNge, OneR, OrdinalClassClassifier, PART, Ridor and SimpleCart. 138. The computer implemented method of claim 133, wherein the independent source comprises completed Autism Diagnostic Interview-Revised score sheets from Simons Foundation, Boston Autism Consortium, National Database for Autism Research or The Autism Genetic Research Exchange. 139. The computer implemented method of claim 133, wherein the following types of diagnostic items are removed from the 153 diagnostic items: diagnostic items containing a majority of exception codes indicating that the diagnostic item could not be answered in a desired format, diagnostic items involving special isolated skills and diagnostic items with hand-written answers. 140. The computer implemented method of claim 1, wherein the instrument is the Autism Diagnostic Observation Schedule-Generic and wherein the disorder is autism, wherein the full set of diagnostic items consists of four modules, wherein the first of the four modules consists of 29 diagnostic items, and wherein the diagnostic tool consists of 8 diagnostic items from the first module. 141. The computer implemented method of claim 140, wherein a time for administering the full set of diagnostic items is about 2-4 hours, and wherein a time for administering the diagnostic tool is less than about an hour. 142. The computer implemented method of claim 140, wherein subjects of the 8 diagnostic items are frequency of vocalization directed to others, unusual eye contact, responsive social smile, shared enjoyment in interaction, showing, spontaneous initiation of joint attention, functional play with objects and imagination or creativity. 143. The computer implemented method of claim 140, wherein the technique using artificial intelligence is a machine learning technique. 144. The computer implemented method of claim 143, wherein the machine learning technique is one from the group consisting of: ADTree, BFTree, DecisionStump, FT, J48, J48graft, kip, LADTree, LMT, Nnge, OneR, PART, RandomTree, REPTree, Ridor and SimpleCart. 145. The computer implemented method of claim 140, wherein the independent source comprises score sheets for the first of the four modules from Boston Autism Consortium or Simons Simplex Collection. 146. The computer implemented method of claim 1, wherein the one or more computer programs further comprise instructions for: training an analyst to review a video of a test subject; and scoring the video using the diagnostic tool. 147. The computer implemented method of claim 1, wherein the one or more computer programs further comprise instructions for: generating a report based on the diagnostic tool, wherein the report comprises a suggested clinical action. 148. The computer implemented method of claim 147, wherein the report further comprises at least one of the following: a link to a video of a test subject; at least one chart depicting results of the diagnostic tool; a list of facilities or clinicians, wherein the facilities or clinicians are capable of performing the suggested clinical action; and a map depicting locations of facilities or clinicians, wherein the facilities or clinicians are capable of performing the suggested clinical action. 149. The computer implemented method of claim 1, wherein the one or more computer programs further comprise instructions for: testing a test subject with the diagnostic tool; and testing the test subject with the full set of diagnostic items if the test subject demonstrates a need for the full set of diagnostic items based on the results of the diagnostic tool. 150. The computer implemented method of claim 1, wherein the one or more computer programs further comprise instructions for: treating a test subject for the disorder. 151. A computer system for generating a diagnostic tool by applying artificial intelligence to an instrument for diagnosis of a disorder, wherein the instrument comprises a full set of diagnostic items, the computer system comprising: one or more processors; and memory to store: one or more computer programs, the one or more computer programs comprising instructions for: generating a highly statistically accurate set of diagnostic items selected from the instrument, wherein the highly statistically accurate set of diagnostic items from the instrument pass a first test using a technique using artificial intelligence and a second test against an independent source. 152. The computer system of claim 151, wherein the instrument is the Autism Diagnostic Interview-Revised and wherein the disorder is autism, wherein a full set of diagnostic items from the Autism Diagnostic Interview-Revised consists of 153 diagnostic items, and wherein the diagnostic tool consists of 7 diagnostic items. 153. The computer system of claim 152, wherein a time for administering the full set of diagnostic items is about 2.5 hours, and wherein a time for administering the diagnostic tool is less than about an hour. 154. The computer system of claim 152, wherein subjects of the 7 diagnostic items are comprehension of simple language, reciprocal conversation, imaginative play, imaginative play with peers, direct gaze, group play with peers and age when abnormality first evident. 155. The computer system of claim 152, wherein the technique using artificial intelligence is a machine learning technique. 156. The computer system of claim 155, wherein the machine learning technique is one from the group consisting of: ADTree, BFTree, ConjunctiveRule, DecisionStump, Filtered Classifier, J48, J48graft, JRip, LADTree, NNge, OneR, OrdinalClassClassifier, PART, Ridor and SimpleCart. 157. The computer system of claim 152, wherein the independent source comprises completed Autism Diagnostic Interview-Revised score sheets from Simons Foundation, Boston Autism Consortium, National Database for Autism Research or The Autism Genetic Research Exchange. 158. The computer system of claim 152, wherein the following types of diagnostic items are removed from the 153 diagnostic items: diagnostic items containing a majority of exception codes indicating that the diagnostic item could not be answered in a desired format, diagnostic items involving special isolated skills and diagnostic items with hand-written answers. 159. The computer system of claim 151, wherein the instrument is the Autism Diagnostic Observation Schedule-Generic and wherein the disorder is autism, wherein a full set of diagnostic items consists of four modules, wherein the first of the four modules consists of 29 diagnostic items, and wherein the diagnostic tool consists of 8 diagnostic items from the first module. 160. The computer system of claim 159, wherein a time for administering the full set of diagnostic items is about 2-4 hours, and wherein a time for administering the diagnostic tool is less than about an hour. 161. The computer system of claim 159, wherein subjects of the 8 diagnostic items are frequency of vocalization directed to others, unusual eye contact, responsive social smile, shared enjoyment in interaction, showing, spontaneous initiation of joint attention, functional play with objects and imagination or creativity. 162. The computer system of claim 159, wherein the technique using artificial intelligence is a machine learning technique. 163. The computer system of claim 162, wherein the machine learning technique is one from the group consisting of: ADTree, BFTree, DecisionStump, FT, J48, J48graft, kip, LADTree, LMT, Nnge, OneR, PART, RandomTree, REPTree, Ridor and SimpleCart. 164. The computer system of claim 159, wherein the independent source comprises score sheets for the first of the four modules from Boston Autism Consortium or Simons Simplex Collection. 165. The computer system of claim 151, wherein the one or more computer programs further comprise instructions for: training an analyst to review a video of a test subject; and scoring the video using the diagnostic tool. 166. The computer system of claim 151, wherein the one or more computer programs further comprise instructions for: generating a report based on the diagnostic tool, wherein the report comprises a suggested clinical action. 167. The computer system of claim 166, wherein the report further comprises at least one of the following: a link to a video of a test subject; at least one chart depicting results of the diagnostic tool; a list of facilities or clinicians, wherein the facilities or clinicians are capable of performing the suggested clinical action; and a map depicting locations of facilities or clinicians, wherein the facilities or clinicians are capable of performing the suggested clinical action. 168. A non-transitory computer-readable storage medium storing one or more computer programs configured to be executed by one or more processing units at a computer comprising instructions for: testing diagnostic items from the instrument using a technique using artificial intelligence; determining from the testing the most statistically accurate set of diagnostic items from the instrument; selecting a set of the most statistically accurate diagnostic items from the instrument; determining the accuracy of the set of the most statistically accurate diagnostic items from the instrument by testing the set of the most statistically accurate diagnostic items from the instrument against an independent source; and generating the diagnostic tool for diagnosis of the disorder. 169. The non-transitory computer-readable storage medium of claim 168, wherein the instrument is the Autism Diagnostic Interview-Revised and wherein the disorder is autism, wherein the full set of diagnostic items consists of 153 diagnostic items, and wherein the diagnostic tool consists of 7 diagnostic items. 170. The non-transitory computer-readable storage medium of claim 169, wherein a time for administering the full set of diagnostic items is about 2.5 hours, and wherein a time for administering the diagnostic tool is less than about an hour. 171. The non-transitory computer-readable storage medium of claim 169, wherein subjects of the 7 diagnostic items are comprehension of simple language, reciprocal conversation, imaginative play, imaginative play with peers, direct gaze, group play with peers and age when abnormality first evident. 172. The non-transitory computer-readable storage medium of claim 169, wherein the technique using artificial intelligence is a machine learning technique. 173. The non-transitory computer-readable storage medium of claim 172, wherein the machine learning technique is one from the group consisting of: ADTree, BFTree, ConjunctiveRule, DecisionStump, Filtered Classifier, J48, J48graft, JRip, LADTree, NNge, OneR, OrdinalClassClassifier, PART, Ridor and SimpleCart. 174. The non-transitory computer-readable storage medium of claim 169, wherein the independent source comprises completed Autism Diagnostic Interview-Revised score sheets from Simons Foundation, Boston Autism Consortium, National Database for Autism Research or The Autism Genetic Research Exchange. 175. The non-transitory computer-readable storage medium of claim 169, wherein the following types of diagnostic items are removed from the 153 diagnostic items: diagnostic items containing a majority of exception codes indicating that the diagnostic item could not be answered in a desired format, diagnostic items involving special isolated skills and diagnostic items with hand-written answers. 176. The non-transitory computer-readable storage medium of claim 168, wherein the instrument is the Autism Diagnostic Observation Schedule-Generic and wherein the disorder is autism, wherein the full set of diagnostic items consists of four modules, wherein the first of the four modules consists of 29 diagnostic items, and wherein the diagnostic tool consists of 8 diagnostic items from the first module. 177. The non-transitory computer-readable storage medium of claim 176, wherein a time for administering the full set of diagnostic items is about 2-4 hours, and wherein a time for administering the diagnostic tool is less than about an hour. 178. The non-transitory computer-readable storage medium of claim 176, wherein subjects of the 8 diagnostic items are frequency of vocalization directed to others, unusual eye contact, responsive social smile, shared enjoyment in interaction, showing, spontaneous initiation of joint attention, functional play with objects and imagination or creativity. 179. The non-transitory computer-readable storage medium of claim 176, wherein the technique using artificial intelligence is a machine learning technique. 180. The non-transitory computer-readable storage medium of claim 179, wherein the machine learning technique is one from the group consisting of: ADTree, BFTree, DecisionStump, FT, J48, J48graft, kip, LADTree, LMT, Nnge, OneR, PART, RandomTree, REPTree, Ridor and SimpleCart. 181. The non-transitory computer-readable storage medium of claim 176, wherein the independent source comprises score sheets for the first of the four modules from Boston Autism Consortium or Simons Simplex Collection. 182. The non-transitory computer-readable storage medium of claim 168, wherein the one or more computer programs further comprise instructions for: training an analyst to review a video of a test subject; and scoring the video using the diagnostic tool. 183. The non-transitory computer-readable storage medium of claim 168, wherein the one or more computer programs further comprise instructions for: generating a report based on the diagnostic tool, wherein the report comprises a suggested clinical action. 184. The non-transitory computer-readable storage medium of claim 183, wherein the report further comprises at least one of the following: a link to a video of a test subject; at least one chart depicting results of the diagnostic tool; a list of facilities or clinicians, wherein the facilities or clinicians are capable of performing the suggested clinical action; and a map depicting locations of facilities or clinicians, wherein the facilities or clinicians are capable of performing the suggested clinical action. 185. The non-transitory computer-readable storage medium of claim 168, wherein the one or more computer programs further comprise instructions for: testing a test subject with the diagnostic tool; and testing the test subject with the full set of diagnostic items if the test subject demonstrates a need for the full set of diagnostic items based on the results of the diagnostic tool. 186. The non-transitory computer-readable storage medium of claim 168, wherein the one or more computer programs further comprise instructions for: treating a test subject for the disorder. 187. A non-transitory computer-readable storage medium storing one or more computer programs configured to be executed by one or more processing units at a computer comprising instructions for: generating a highly statistically accurate set of diagnostic items selected from the instrument, wherein the highly statistically accurate set of diagnostic items from the instrument pass a first test using a technique using artificial intelligence and a second test against an independent source. 188. The non-transitory computer-readable storage medium of claim 187, wherein the instrument is the Autism Diagnostic Interview-Revised and wherein the disorder is autism, wherein a full set of diagnostic items from the Autism Diagnostic Interview-Revised consists of 153 diagnostic items, and wherein the diagnostic tool consists of 7 diagnostic items. 189. The non-transitory computer-readable storage medium of claim 188, wherein a time for administering the full set of diagnostic items is about 2.5 hours, and wherein a time for administering the diagnostic tool is less than about an hour. 190. The non-transitory computer-readable storage medium of claim 188, wherein subjects of the 7 diagnostic items are comprehension of simple language, reciprocal conversation, imaginative play, imaginative play with peers, direct gaze, group play with peers and age when abnormality first evident. 191. The non-transitory computer-readable storage medium of claim 188, wherein the technique using artificial intelligence is a machine learning technique. 192. The non-transitory computer-readable storage medium of claim 191, wherein the machine learning technique is one from the group consisting of: ADTree, BFTree, ConjunctiveRule, DecisionStump, Filtered Classifier, J48, J48graft, JRip, LADTree, NNge, OneR, OrdinalClassClassifier, PART, Ridor and SimpleCart. 193. The non-transitory computer-readable storage medium of claim 188, wherein the independent source comprises completed Autism Diagnostic Interview-Revised score sheets from Simons Foundation, Boston Autism Consortium, National Database for Autism Research or The Autism Genetic Research Exchange. 194. The non-transitory computer-readable storage medium of claim 188, wherein the following types of diagnostic items are removed from the 153 diagnostic items: diagnostic items containing a majority of exception codes indicating that the diagnostic item could not be answered in a desired format, diagnostic items involving special isolated skills and diagnostic items with hand-written answers. 195. The non-transitory computer-readable storage medium of claim 187, wherein the instrument is the Autism Diagnostic Observation Schedule-Generic and wherein the disorder is autism, wherein a full set of diagnostic items consists of four modules, wherein the first of the four modules consists of 29 diagnostic items, and wherein the diagnostic tool consists of 8 diagnostic items from the first module. 196. The non-transitory computer-readable storage medium of claim 195, wherein a time for administering the full set of diagnostic items is about 2-4 hours, and wherein a time for administering the diagnostic tool is less than about an hour. 197. The non-transitory computer-readable storage medium of claim 195, wherein subjects of the 8 diagnostic items are frequency of vocalization directed to others, unusual eye contact, responsive social smile, shared enjoyment in interaction, showing, spontaneous initiation of joint attention, functional play with objects and imagination or creativity. 198. The non-transitory computer-readable storage medium of claim 195, wherein the technique using artificial intelligence is a machine learning technique. 199. The non-transitory computer-readable storage medium of claim 198, wherein the machine learning technique is one from the group consisting of: ADTree, BFTree, DecisionStump, FT, J48, J48graft, kip, LADTree, LMT, Nnge, OneR, PART, RandomTree, REPTree, Ridor and SimpleCart. 200. The non-transitory computer-readable storage medium of claim 195, wherein the independent source comprises score sheets for the first of the four modules from Boston Autism Consortium or Simons Simplex Collection. 201. The non-transitory computer-readable storage medium of claim 187, wherein the one or more computer programs further comprise instructions for: training an analyst to review a video of a test subject; and scoring the video using the diagnostic tool. 202. The non-transitory computer-readable storage medium of claim 187, wherein the one or more computer programs further comprise instructions for: generating a report based on the diagnostic tool, wherein the report comprises a suggested clinical action. 203. The non-transitory computer-readable storage medium of claim 202, wherein the report further comprises at least one of the following: a link to a video of a test subject; at least one chart depicting results of the diagnostic tool; a list of facilities or clinicians, wherein the facilities or clinicians are capable of performing the suggested clinical action; and a map depicting locations of facilities or clinicians, wherein the facilities or clinicians are capable of performing the suggested clinical action.